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CoverSeCTIoN
A
INDIA
National
Multidimensional
Poverty Index
Baseline Report
Based on NFHS-4 (2015-16)
NITI Aayog, 2021
Based on the National Family Health Survey-4 (2015-16)
INDIA
BASELINE REPORT
NATIONAL
MULTIDIMENSIONAL
POVERTY INDEX
Copyright@ NITI Aayog, 2021
NITI Aayog,
Government of India,
Sansad Marg, New Delhi - 110001, India
Visit the dashboard:
sdgindiaindex.niti.gov.in/MPI
Contact us at:
e-mail: sanyukta.samaddar@nic.in
Source of Maps: Census of India 2011 &
Political Map of India 10
th
Edition (Survey of India)
Cover &
Report Design by Sourav Das,
Data Analytics Officer (MPI-SDG), NITI Aayog sourav.das.3195@gmail.com
INDIA MPI BASELINE REPORT II IIIFOREWORD
Vice Chairperson
National Institution for Transforming India
Government of India
DR. RAJIV KUMAR
MESSAGE
The Sustainable Development Goals framework, adopted by 193 countries in 2015, has redefined de-
velopment policies, government priorities, and metrics for measuring development progress across
the world. The SDG framework, with seventeen Global Goals and 169 targets, is significantly wider
in scope and scale relative to the Millennium Development Goals, its predecessor. The expansion
of scope includes the significant development of recognising the need to address poverty in all its
forms and dimensions. This has been articulated in the SDG framework through target 1.2 –which
is aimed at reducing “at least by half the proportion of men, women and children of all ages living
in poverty in all its dimensions according to national definitions”. The development of the National
Multidimensional Poverty Index of India is an important contribution towards instituting a public
policy tool which monitors multidimensional poverty, informs evidence-based and focused inter-
ventions, thereby ensuring that no one is left behind.
India’s national MPI measure uses the globally accepted and robust methodology developed by the
Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development
Programme (UNDP), who have been key partners in ensuring the public policy utility and technical
rigour of the Index. Importantly, as a measure of multidimensional poverty, it captures multiple and
simultaneous deprivation faced by households. This report presents an in-depth analysis of the
headcount ratio and intensity of multidimensional poverty at the national, State/UT, and district
levels. I am certain that the results and findings of this baseline edition of the index will be of great
relevance and interest to policy makers and administrators in States and districts, researchers and
scholars, and the wider public.
This baseline report of India’s first ever national MPI measure is based on the reference period of
2015-16 of the National Family Health Survey (NFHS). The national MPI measure has been construct-
ed by utilising twelve key components which cover areas such as health and nutrition, education
and standard of living.
It has been envisaged as a comprehensive tool to expedite goal-oriented action to measure multi-
dimensional poverty and steer its systematic eradication. Since the data reference period of NFHS
2015-16, India has made remarkable strides through flagship programmatic interventions in housing,
electricity, sanitation, drinking water, and cooking fuel, among others. Apart from building infra-
structure and ensuring basic services, focused initiatives to improve health and nutritional outcomes
have been undertaken expeditiously during this period. The baseline report will be updated upon
the release of the NFHS-5 (2019-20) dataset. I am confident that India’s multisectoral approach to
address poverty and the impact of the flagship programmes and schemes will be reflected in that
edition, thereby improving the national MPI score further.
I congratulate Shri Amitabh Kant, CEO, NITI Aayog, for encouraging the SDG team at NITI Aayog,
led by Ms. Sanyukta Samaddar, Adviser, to develop India’s inaugural Multidimensional Poverty Index.
My compliments and sincere thanks to the officials of State Governments, UTs, Union ministries,
OPHI and UNDP, who have contributed towards this report.
DR. RAJIV KUMAR
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT IV VFOREWORD
The year 2021 marks the sixth anniversary of the adoption of the Sustainable Development Goals
which envisages to end poverty in all its forms everywhere. Steered by the Government of India’s
Global Indices for Reforms and Growth (GIRG) initiative, the National Multidimensional Poverty
Index (MPI) for India is aimed at leveraging the monitoring mechanism and methodology of the
globally recognised MPI to rigorously benchmark national and subnational performance and drive
programmatic actions and reforms.
India’s national MPI captures multiple and simultaneous deprivations faced by households across
the three macro dimensions of health, education and living standards. It highlights the need for a
whole-of-government approach towards addressing poverty and its multidimensionality. This mul-
tisectoral approach must be horizontally and vertically integrated across all levels of governance.
Estimates of national MPI headcount ratio and intensity have been furnished not only for the coun-
try and States but also for all the districts, which is a unique feature of this report. This will not only
enable the analysis of comparative and relative performance among States and UTs but also among
the districts of a State. This assumes salience given the federal structure of the country and the im-
portance of the involvement of district administrations for effective implementation of interventions
and schemes.
As a multidimensional poverty measure and a policy tool, this inaugural baseline Report provides us
with important insights into the degree of success of multi-sectoral interventions to address various
aspects of poverty. With the release of the National Family Health Survey 2019-20, the subsequent
update of the Report will capture the remarkable strides the country has taken to improve the lives
of households across these multifaceted parameters.
Simultaneously, under the GIRG initiative, reform areas and actions are being formulated to utilise
the insights made available through the Index to ramp up our efforts further to aggressively and
systematically eradicate poverty. My appreciation goes to the Union Ministries and State/UT Gov-
ernments, and all officials who have been engaging with us in this endeavour. I also compliment
our technical partners, Oxford Poverty and Human Development Initiative (OPHI) and the United
Nations Development Programme (UNDP), for their support.
I congratulate the SDG team at NITI Aayog led by Ms. Sanyukta Samaddar, Adviser, who has been
steering our overall national MPI efforts in bringing out our first national Multidimensional Poverty
Index & Dashboard and our continuous engagement with key stakeholders, the Governments of
States and UTs and Union Ministries through her commendable leadership.
Chief Executive Officer
National Institution for Transforming India
Government of India
AMITABH KANT
MESSAGE
AMITABH KANT
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT VI VIIFOREWORD
Director
Human Development Report Office
United Nations Development Programme
PEDRO CONCEIÇÃO
MESSAGE
I congratulate the Government of India, NITI Aayog, and my UNDP colleagues on the launch of In-
dia's first National Multidimensional Poverty Index: Baseline Report & Dashboard.
Since its development in 2010, the Multidimensional Poverty Index has served as a valuable analyti-
cal tool to identify the most vulnerable people – the poorest among the poor and revealing poverty
patterns within countries and over time, thereby enabling policy makers to target resources and
design policies more effectively.
This new Indian national version of the index complements the 10 indicators of the global MPI with
metrics on maternal health and bank account under the dimensions of health and standard of liv-
ing, respectively. The resulting work offers powerful insights that can enhance India’s capability to
reduce poverty as the country builds back stronger from the effects of the COVID-19 pandemic.
National and state averages can mask important distinctions. The Indian national MPI’s granular data
– which cover not only all the States and Union Territories, but also all the districts – can empower
policymakers and local officials to manage complexity and scale, inform evidence-led policy, design
programs, and track progress. It can also help fine-tune the policies and programmes of State gov-
ernments who are at the forefront of public service delivery, social protection, and multidimensional
poverty reduction in India’s federal structure. It will also be a tool to inform public dialogue across a
range of stakeholders, including civil society, the research community, and businesses.
We at UNDP reaffirm our commitment to our partners Government of India and NITI Aayog in the
collective journey to eradicate poverty in all its forms and accelerate the achievement of the Sus-
tainable Development Goals.
PEDRO CONCEIÇÃO
20 September 2021 New York United States of America
INDIA MPI BASELINE REPORT VIII IXFOREWORD
Resident Representative
United Nations Development Programme
India
SHOKO NODA
MESSAGE
To end poverty in all its forms everywhere is at the core of the comprehensive, expansive, and in-
terdependent framework of the 2030 Agenda for Sustainable Development adopted by the world
in 2015. The Multidimensional Poverty Index (MPI) is a systematic, robust, and nuanced measure to
estimate our progress towards achieving this goal. MPI as a measure was first developed by Oxford
Poverty and Human Development Initiative (OPHI) and United Nations Development Programme
(UNDP) for inclusion in UNDP’s flagship Human Development Report in 2010, where it has been
since published.
India’s National MPI: Baseline Report & Dashboard is a milestone in its journey towards realising the
SDGs, in particular –target 1.2 of the 2030 Agenda which specifically focuses on addressing poverty
in all its dimensions. The national MPI is being launched at an important juncture where I am sure
it will provide key insights into the development scenario at the national, State and district levels,
especially for India’s sustainable recovery from the COVID-19 pandemic. On this important national
endeavour, it has been a genuine pleasure to collaborate with NITI Aayog –Government of India’s
premier thinktank, and with our partner OPHI.
India’s national MPI will provide crucial insights into multiple cross-sectoral indicators for decision
makers at the national, State and district levels. From the perspective of planning and policy design,
it will complement existing metrices in tracking progress, informing high-impact interventions and
simultaneously engendering evidence-based policy making.
I would like to express my heartfelt gratitude to Dr Rajiv Kumar, Vice Chairperson, NITI Aayog, for
his visionary leadership and unwavering commitment in steering the process of instituting India’s
national MPI. I also extend my sincere appreciation to Shri Amitabh Kant, CEO, NITI Aayog, for his
continuous engagement and encouragement in this endeavour. UNDP is committed to continue and
strengthen this important partnership. Lastly, I commend Dr Sabina Alkire, Director, OPHI, and her
team for the technical support in this exercise.
SHOKO NODA
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT X XIFOREWORD
It has been an honour to collaborate on India’s National MPI: Baseline Report & Dashboard under
the leadership of NITI Aayog, Government of India. This baseline report is a contribution towards
measuring and monitoring progress on achieving target 1.2 of the Sustainable Development Goals
on multidimensional poverty. It not only presents the MPI results – plus headcount ratio and inten-
sity – at the national level, but also at the levels of States and all the districts of the country. The
high-resolution disaggregation by indicators makes it a powerful policy tool to benchmark progress
and inform policy making.
Similar to the interlinked nature of the goals and targets of the 2030 Agenda, as a measure, India’s
national MPI reflects the interlinkages across the indicators at the level of households. This is salient
to designing and implementing coordinated multisectoral policies and focused interventions. As
a policy tool, it can be utilised by decision makers to focus on reducing overall multidimensional
poverty by targeting to reduce deprivations in each of its dimensions and indicators across the
country at the sub-national and district levels. This disaggregation is crucial, especially in a country
as diverse as India, for not only reflecting information on inter-State variations in outcomes, but also
intra-State variations. The National MPI: Baseline Report & Dashboard is based on the rich dataset
of the National Family Health Survey (NFHS) 2015-16. The follow-up to this baseline report, with the
release of the NFHS 2019-20, will make it possible to estimate multidimensional poverty reduction
trends as envisaged under SDG target 1.2.
I would like to express my sincere gratitude to Dr Rajiv Kumar, Vice Chairperson, NITI Aayog for his
leadership, guidance and constant encouragement extended to this important project. Our techni-
cal assistance in this regard is a product of our strengthened partnership with UNDP India.
I would also like to congratulate Shri Amitabh Kant, CEO, NITI Aayog and his energetic, dedicated,
and professional SDG team led by Ms Sanyukta Samaddar, Adviser, NITI Aayog, for producing this
technically rigorous measure.
Director
Oxford Poverty and Human Development Initiative
Department of International Development,
University of Oxford
DR. SABINA ALKIRE
MESSAGE
DR. SABINA ALKIRE
20 September 2021 Oxford United Kingdom
INDIA MPI BASELINE REPORT XII XIIIFOREWORD
Our relentless efforts at NITI Aayog in adopting, implementing, and monitoring the Sustainable
Development Goals at the national and subnational levels have been anchored on the core principle
of “Leaving No One Behind”. Instituting a robust SDG monitoring framework, promoting healthy
competition among the States, strengthening the statistical systems, and building partnerships with
a range of stakeholders have primarily aimed at reaching the farthest behind first. Given India’s de-
velopment context, the most effective way to ensure development of the most disadvantaged sec-
tions is through faster poverty reduction. To implement this strategy, the first step is to estimate and
monitor poverty levels, using a framework which defines poverty in all its forms, which is relevant to
the present and aligns with the SDGs and our national context. This is precisely what our efforts at
NITI Aayog regarding Multidimensional Poverty Index (MPI) aim to achieve.
The national MPI, an aggregate measure which defines poverty, in simple terms, as the deprivation
in crucial and basic parameters of health, education, and living standards, is a significant departure
from the way poverty has been understood and conceptualised historically. This shift in focus from
income or consumption expenditure as the basis of poverty estimation is founded on the policy
narrative at the national level that human and capability development along with access to basic
infrastructure is at the centre of India’s development policy. The national MPI is an outcome of NITI
Aayog’s focussed strategy to execute this policy directive and the mandate given by the Cabinet
Secretariat to improve India’s position in global rankings of important and globally accepted indices.
This baseline report on the national MPI estimates at the national, State/ UT, and district levels
based on NFHS-4 (2015-16) is a pivotal first step in bringing multidimensional poverty as a tool to
the policy table. The national MPI will complement monetary poverty statistics, throw light on “how
many are poor” and “how poor are the poor”, track poverty over time, offer disaggregated esti-
mates by States, districts, and population groups, and support priority-based resource allocation
and targeted reforms, all at a granular level. We expect that the Report will play an instrumental role
in sensitizing governments, researchers, civil society, the public, and other stakeholders on the need
for and importance of MPI as a powerful policy instrument.
Adviser (SDG)
National Institution for Transforming India
Government of India
SANYUKTA SAMADDAR
FOREWORD
In this nationally important endeavour, we owe our deepest gratitude to the Central Ministries and the State Governments, which have strongly supported the national MPI initiative. The insightful discussions we have had during the MPI Coordination Committee meetings, and the detailed delib- erations held with the States as part of our SDG-MPI workshops in more than 20 States in the past four months, have contributed immensely to improving the framework. We hope that the State- specific reform action plan will aid faster reduction of multidimensional poverty and will result in better outcomes as measured by subsequent NFH Surveys.
To fully realise the potential of the MPI as a policy tool for focused interventions towards addressing
multidimensional poverty, utilising it at the level of States and districts is of paramount importance.
In this regard we thank all the State Governments and especially the ones where we organised over
20 workshops in the State capitals with Chief Secretaries and senior Government officials from the
departments and districts. These workshops facilitated building interest and engendering under-
standing and acceptability of this important tool for governance. The positive, enthusiastic and
constructive feedback from our State governments has played a very important role in the process
of developing the national MPI.
We are unequivocally thankful to Dr Sabina Alkire, Director, Oxford Poverty and Human Develop-
ment Initiative, and her team for offering technical advice from time to time. As the designer of the
global MPI, Sabina’s deep knowledge on the subject based on her experience of working with coun-
tries across the globe, has contributed significantly to the robustness of the national MPI.
We would like to place on record our gratitude to Ms Shoko Noda, Resident Representative, UNDP
India, for her support to the project, as a key partner. We are certain that the MPI initiative will offer
crucial inputs to UNDP’s SDG efforts across the country. We are also grateful to the United Nations
Resident Coordinator and his office for their support to the project.
Our deepest thanks goes to Prof KS James, Director and Senior Professor, International Institute for
Population Sciences (IIPS), and his team, for their unswerving support to the project. IIPS has been
kind enough to share its expertise in Demographic and Health Surveys which has supported the
project technically to a great extent.
Finally, sincere appreciation to my team in the SDG Vertical in NITI Aayog: Alen John, Farha Anis,
Soumya Guha, Sourav Das, Sundar Mishra and Vidya Warrier. Special acknowledgments are due
to the core team, comprising Soumya Guha, SDG Officer, and Sourav Das, Data Analytics Officer,
who have remarkably shouldered the responsibility of this inordinately vast range of computations,
estimations and visualisations at all levels, and the development of the reform action plan and the
MPI Dashboard.
We are grateful to Shri Amitabh Kant, CEO, NITI Aayog, for his consistent support to the idea of mul-
tidimensional poverty, and the project and its initiatives. The project would not have been a success
without the relentless support of our Hon’ble Vice Chairman, Dr Rajiv Kumar who has thrown his
weight behind all efforts, with passion, to pursue the execution of the national MPI project.
We sincerely hope that the national MPI project and its outcomes will act as a strong force which
will further accelerate SDG achievements for our country.
SANYUKTA SAMADDAR (IAS)
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT XIV XV
CONTENTS
Message from the Vice Chairperson, NITI Aayog II
Message from the CEO, NITI Aayog IV
Message from the Director, Human Development
Report Office, United Nations Development Programme
VI
Message from the Resident Representative,
United Nations Development Programme, India
VIII
Message from the Director, Oxford Poverty and Human Development Initiative
X
Foreword by Adviser, NITI Aayog XII
contents
1. Introduction 2
2.Developing India’s MPI 6
PAGE 1
Context & Introduction
3. Methodology 14
4.Way forward 28
5. India 32
States
Andhra Pradesh 50
Arunachal Pradesh 56
Assam 62
Bihar 68
Chhattisgarh 74
Goa 80
Gujarat 84
Haryana 90
Himachal Pradesh 96
Jharkhand 102
Karnataka 108
Kerala 114
Madhya Pradesh 120
Maharashtra 126
Manipur 132
Meghalaya 138
Mizoram 144
Nagaland 150
Odisha 156
Punjab 162
Rajasthan 168
Sikkim 174
Tamil Nadu 178
Telangana 184
Tripura 190
Uttar Pradesh 194
Uttarakhand 202
West Bengal 208
PAGE 31
III
National & State/UT Results
6. Technical Notes 246
7.References 250
8. Index of tables 251
9.Data Tables 250-414
PAGE 245
IV
Technical Notes & Data Tables
Union Territories
Andaman & Nicobar Islands 214
Chandigarh 218
Dadra & Nagar Haveli 220
Daman & Diu 222
Delhi 226
Jammu & Kashmir, & Ladakh 232
Lakshadweep 238
Puducherry 240
National & State/UT
Results
PAGE 31
I
PAGE 13
Methodology & Way Forward
II
Chapter 1SeCTIoN
SECTION
I
CONTEXT
&
INTRODUCTION
INDIA MPI 2 3
IntroductionCHAPTER Chapter 1
INDIA’S NATIONAL MPI
The Resolution of the United Nations General Assem-
bly on 25 September 2015 established the 17 Sustain-
able Development Goals (SDG). SDG 1 in its entirety
(“End poverty in all its forms everywhere”) is multi-
dimensional in nature and definition. While target 1.1
seeks to eradicate extreme poverty –measured as
people living on less than $1.25 a day (subsequently
increased to $1.90/day), target 1.2 aims at reducing
multidimensional poverty, as defined by national defi-
nitions, by half.
The Multidimensional Poverty Index (MPI) has been
used by the United Nations Development Programme
in its flagship Human Development Report since 2010
and is the most widely employed non-monetary pov -
erty index in the world (Godinot & Walker, 2020). It
captures overlapping deprivations in health, education
and living standards (UNDP, 2010). It complements
income poverty measurements because it measures
and compares deprivations directly. In this context, a
national Multidimensional Poverty Index for India will
enable estimation of poverty not only at the level of
the States but also for all the 700 plus districts (600
plus in 2015-16, 700 plus in 2019-20) across twelve
indicators, capture simultaneous deprivations and in-
dicator-wise contribution to poverty, and most impor-
tantly, will facilitate formulation of sectoral policies
and targeted interventions which contribute towards
ensuring that “no one is left behind”. The district-wise
estimation of the national MPI will also ensure reach-
ing out to the furthest behind first through focused
efforts on specific indicators and dimensions.
This chapter provides a brief overview of the national and international context of measuring poverty across multiple dimensions, the conceptual framework be- hind multidimensional measures and how they con- tribute towards ending poverty in all its forms. The latter half of this chapter discusses the salience and features of the national MPI and the purpose of such a measure
1.1 India - Multidimensional Poverty Index:
National context and Global Goals
The history of poverty estimation in India dates back
to as early as 1901 when Dadabhai Naoroji estimated
poverty in the country based on the cost of a subsis-
tence diet. In 1938, the National Planning Committee
suggested a poverty line estimation based on living
standards followed by the authors of the Bombay Plan
in 1944. Addressing and ending poverty has been part
of the national agenda since independence. Various
committees, working groups and scholars including
the working group of 1962, Dandekar and Rath in 1971
and the Y.K. Alagh taskforce in 1979 were engaged
in the estimating the headline statistic of poverty to
inform public policy. Similarly, the Expert Groups un-
der Lakdawala (1993) and Tendulkar (2009) and the
Rangarajan Committee (2014) undertook the exercise
of estimating monetary poverty. Globally, the focus
on reducing monetary poverty was mirrored in the
Millennium Development Goals target of halving the
proportion of people living on less than $ 1.25 a day
between 1990 and 2015.
The adoption of Transforming our world: the 2030 Agenda for Sustainable Development by 193 countries of the United National General Assembly, including In- dia, brought institutionalised focus in measuring, and addressing poverty in “all its forms” enshrined in its preamble and explicated under Goal 1. The impact of this was also reflected in the work of the World Bank, the custodian of the International Poverty Line (IPL) statistic, which convened a high-level Commission led by Sir Anthony Atkinson and supported by an advi- sory board of global poverty measurement experts.
One of the specific tasks of this commission was to
go “beyond Goal 1.1” and engage with “complemen-
tary indicators and multidimensionality” (World Bank,
2017). The Commission recommended the inclusion
of a multidimensional poverty measure based on the
counting approach, seen in the Alkire-Foster method
of the MPI. The Global Coalition of Partners to End
Child Poverty in its submission to this Commission
emphasised on the importance of an aggregate mul-
tidimensional poverty measure and its potential to
powerfully summarize and communicate global and
national figures to a wider audience, therefore mobil-
ising public support to end poverty in all its dimen-
sions.
In India, the Cabinet Secretariat’s mandate on the MPI
is aimed at utilising the monitoring mechanism of the
Index to gauge and benchmark poverty reduction to
improve the country’s performance and therefore,
reach out to the furthest behind by identifying reform
areas and formulating reform actions on each of its
components. The development of the India MPI is in-
tended to drive competition among the States and
Union Territories and provide insights on multidimen-
sional poverty at the subnational and district levels.
1.2 Conceptual framework of poverty and
its multidimensionality
Early 20th century examples of attempts at capturing
poverty and its multidimensionality include studies
by Booth (1903) and Rowntree (1901) in the United
Kingdom. For a measure of wellbeing, application
of multidimensional approaches dates back to the
Level of Living Survey conducted in Sweden in 1968
(World Bank, 2017). Townsend’s landmark study in
1979 serves as an early example of a multidimension-
al approach towards relative deprivation across 60
indicators which covered dimensions like diet, cloth-
ing, fuel and light, housing, conditions and security
of work, recreation, health, and education. Similarly,
the European Union’s portfolio of social indicators to
monitor social protection and inclusion is inherently
multidimensional in nature. The need for complemen-
tary non-monetary statistics also stems from the rec-
ognition that income is one of the many dimensions and not the only attribute of wellbeing (Chakravarty, 2009).
The theoretical underpinnings of a non-monetary
approach towards poverty and instead, as a multidi-
mensional phenomenon is drawn from the capability
approach (Sen 1979, 1987, 1999). The capability ap-
proach suggests that functionings and capabilities are
two integral parts of a person’s quality of life and well
being where functionings are the “beings and doings”
that they value and have reason to value –such as be-
ing healthy and nourished; and capabilities reflect the
freedom that they have in achieving valuable func-
tionings. Therefore, to arrive at the conclusion that a
household or individual is deprived in basic capabili-
ties, it is pertinent to examine and consider multiple
dimensions of well being (Dotter & Klasen, 2020).
Multidimensional measures complement monetary
measures by capturing information they lack –such as
broader qualitative aspects of life such as child mor-
tality, housing conditions, and other basic services
such as water and sanitation (Greve, 2020). This is of
significance to policy formulation and targeted inter-
ventions in the context of intra-country or intra-re-
gion heterogeniety in development.
Simple headcount related measures do not provide
information on the depth of poverty, as the Atkin-
son Commission observes, and therefore, potentially,
while the extent of poverty captured by the head-
count ratio can reduce, the poorest may fall even
further behind. To address this, the Multidimensional
Poverty Index, based on the Alkire-Foster method,
employs an adjusted headcount ratio (MPI score)
which is arrived at by multiplying the headcount ra-
tio with the average deprivation among the MPI poor
(Alkire & Foster, Counting and Multidimensional Pov-
erty Measurement, 2011). In 2010, this measure of mul-
tidimensional poverty replaced the Human Poverty
Index (HPI) in UNDP’s Human Development Report. It
draws from the capability approach by including mul-
tiple dimensions of poverty across the dimensions of
health, education and living standards, and examines
the “fundamental objective features” which affect the
poor (Brando & Fragoso, 2020).
1.3 Ending poverty in all its forms: Multidi-
mensional Poverty Index
The multidimensionality of poverty is an integral part
of the Sustainable Development Goals. Target 1.2. re-
fers to reducing “at least by half the proportion of
men, women and children of all ages living in poverty
in all its dimensions according to national definitions”.
The interlinked nature of the SDGs is reflected in mul-
1
INTRODUCTION
12
INDIA MPI BASELINE REPORT 4 5
IntroductionChapter 1
tidimensional poverty measures as well, since they
examine deprivations in areas such as nutrition (Goal
2), health (SDG 3), education (SDG 4) and living stan-
dards related indicators such as water and sanitation
(SDG 6), and electricity and clean cooking fuel (SDG
7), among others.
Development of multidimensional poverty measures
was motivated to not only capture multiple but also
overlapping deprivations faced by the poor, which
was not sufficiently reflected in monetary poverty
measures (Alkire, 2020). Measures such as the MPI,
based on the Alkire-Foster methodology, consider si-
multaneous deprivations and disadvantages articulat-
ed through indicators in the broad dimensions of ade-
quate health and nutrition, education, and standard of
living. MPI can be utilised as a complementary policy
tool to monetary poverty, to build and illuminate a
more comprehensive depiction of poverty. The infor-
mation and insights from this tool can drive cross-sec-
toral policies which address the interconnected and
simultaneous disadvantages and deprivations faced
by the poor. Two distinctive normative conditions are
fulfilled by the MPI –the recognition that non-mone-
tary deprivations are integral to what can be termed
as poverty, and that deprivations often simultane-
ously overlap. As discussed above, MPI not only con-
cerns itself with the headcount ratio of proportion
of people who are multidimensionally poor but also
includes important information on average depriva-
tions or “depth of poverty”. The Index also provides
information of policy relevance on each of the indica-
tors by way of deconstructing each of the indicators
for sectoral focus and elucidating cross-sectoral over-
laps exhibited through multiple deprivations. There-
fore, the MPI as a measure of multiple dimensions of
poverty complements monetary poverty statistics,
enables close monitoring of individual indicators and
dimensions which overlap with several SDGs, allows
for disaggregation at the levels of States and districts,
and urban and rural to better inform policy focus, and
engenders integrated cross-sectoral policy actions by
capturing multiple deprivations in its methodology.
Apart from its theoretical and statistical merits, the
MPI produces clear and easy insights to communicate
policy relevant information for a wider audience.
In 2010, the Multidimensional Poverty Index (MPI)
developed by Sabina Alkire and James Foster was
adopted by the United Nations Development Pro-
gramme (UNDP) in their Human Development Re-
port as a replacement for its Human Poverty Index.
The MPI serves as a complementary measure to the
more traditional measures that are based on income
or consumption. The rationale for multidimension-
al measurement of poverty is that wellbeing can be
adversely impacted in many forms that may only be
indirectly related to an individual’s income or level of consumption. Deprivations can include an over- lap related to poor health, undernourishment, and inadequate access to clean cooking fuel, electricity, water, sanitation, and housing. Importantly, the MPI also reflects some of our national priorities articulat- ed through focused interventions on housing, clean water, sanitation, cooking fuel, electricity along with our universal education goals, improved nutrition and good health for all.
1.4 National MPI: Salience and features
A national MPI is a headline statistic of multidimen- sional poverty that is used (OPHI, 2019):
• To compare poverty across subnational regions,
• To track poverty over time,
• To highlight “how” poor are the people in poverty,
using direct information from the set of MPI indi-
cators.
• National MPIs are always reported along with sev-
eral intuitive statistics that show the level and com- position of poverty by indicator. These are:
•
Incidence, ‘H’ which shows the percentage of peo- ple who are multidimensionally poor.
• Intensity, ‘A’ which shows the percentage of weighted deprivations the average multidimen- sionally poor person suffers from.
• The national MPI is constructed directly from each person’s profile of deprivations across each indica- tor, built from a single household survey that cap- tures the data on all the indicators. So, the national MPI is always reported together with its composi- tion by indicator. This is done in one of two ways: (1) analysing the percentage of people who are multidimensionally poor and deprived in each in- dicator one by one, and (2) analysing the weighted contributions of each indicator to the national MPI.
1.5 Purpose of National MPI as a measure
A national MPI statistic for a country is tailored to the
national priorities and therefore, countries choose
their own set of dimensions, indicators, weights, and
cut-offs, according to their plans and contexts (OPHI,
2019). National MPIs are disaggregated by subnation-
al regions, urban or rural areas, age, and other factors.
They are also always reported with the indicator-wise
deconstruction and breakdown. These details can
guide and monitor national policies such as budget
allocation, targeting specific interventions, and poli-
cy coordination across sectors. The purpose of con-
structing such a measure is discussed below:
•
Enhanced high-level view of poverty at the na-
tional le
vel: The international community, includ-
ing government officials, international agencies,
academia, and society, understand poverty today as a complex, multidimensional phenomenon. The national MPI provides a high-level view of the level of multidimensional poverty and its change with time. This presents an overall picture of poverty in the country, while also enabling closer and more in-depth analyses of areas of interest such as re- gions –State or districts, and specific sectors –un- der the dimensions of health and nutrition, educa- tion and living standards.
• Complements monetary poverty measures: The national MPI complements existing monetary pov-
erty statistics. The dimensions of the Index have proven to help identify and achieve targeted policy interventions. The ability to provide a better de- piction of poverty and inform more precise policy actions has been an incentive in every country that has developed a national MPI so far. Evidence has shown that people who are experiencing multiple deprivations in crucial areas of their lives, such as education, health, safety, or employment, may not be income poor (Bourguignon, et al., 2008), and policies to reduce income poverty may not affect other deprivations. Furthermore, public action in areas like education, infrastructure, and hous- ing, which might only impact income in the next generation, are not well captured by traditional monetary metrics. In contrast, a national MPI that includes such indicators can show rapid improve- ments in these areas, making visible the impact of social policies and interventions more directly.
•
Information to shape policy: A national MPI can guide coordinated actions by several ministries and departments, provide clear goals and targets for each indicator, and act as a monitoring and ac- countability tool within the government. One rea- son for this is that it allows robust disaggregation by groups (such as between urban and rural areas, subnational regions, gender, age groups,). One can also unpack the numbers to analyse the compo- sition of poverty by dimensions and indicators – nationally, and at the level of States and districts, which allows for more efficient policy design, pol- icy coordination and focus, and assignment of re- sources.
• The MPI is based on each person’s or household’s
pr
ofile of the overlapping or “joint” deprivations
they experience: This provides new information that is not available in many other measures of poverty estimation. For example, 20 percent of the population may not have access to adequate san- itation and 20 percent may have insufficient edu- cation, but these two indicators separately do not provide information on the degree of intersection
of the population without adequate sanitation and those without an adequate education. Elucidating such overlaps is a specific feature of the MPI. This additional information is extremely relevant for identifying the poorest of the poor, who experi- ence serious and multiple deprivations at the same time. It is also useful for guiding multisectoral and integrated policies, because it highlights the com- plexity of simultaneous deprivations.
• Provides incentives for leaving no one behind
and
reaching the furthest behind first: By reflect-
ing the intensity of poverty (detailing the multiple deprivations that a family has at the same time), the national MPI has an advantage over headcount poverty measures since efforts to reduce the pro- portion of simultaneous hardships faced by the poor will reduce the MPI even if they have not yet moved out of poverty. For example, if a poor per- son goes from being deprived in 90 percent of the indicators to being deprived in 50 percent of them, then the MPI goes down, even if they are still iden- tified as multidimensionally poor. This further bol- sters the incentive for focussing on the poorest of the poor, because if any deprivation of any multidi- mensionally poor person is removed, the MPI falls.
•
Adaptable to national context and transparent: The design of the national MPI is flexible, as the di- mensions, indicators and weights can be adapted to the national context. These are attributes that can be defined by policymakers to accurately char- acterize poverty in diverse contexts. The national MPI is also transparent and easy to implement. This provides legitimacy for official estimates. In addi- tion, it is intuitive and easy to communicate to the media, private sector and civil society as seen in countries which have implemented such a mea- sure.
• Robus It is crucial that an official poverty measure be robust. It means that the pol- icy conclusions are not overly sensitive to small changes in its own components, like indicators, cut-offs or weights. This is because in a pluralist societies people often agree on a broad range of priorities but disagree on details. A measure that is robust to a number of specifications has more legitimacy among a wider group of citizens and stakeholders. Furthermore, the national MPI can be rigorously applied, using standard errors and tests of statistical inference. This means that pol- icymakers can ensure that their statements –such as “poverty has reduced” refer to statistically sig- nificant changes.
INDIA MPI 6 7
Developing India’s MPICHAPTER Chapter 2
THE PROCESS
DEVELOPING INDIA’S MPI
2.1 The Global Indices for Reforms &
Growth (GIRG) mandate
In early 2020, the Cabinet Secretariat, Government
of India, identified 29 global indices to monitor, anal-
yse and evaluate with the aim of improving India’s
position in global rankings. Under this mandate, also
known as the Global Indices for Reforms and Growth
(GIRG) mandate, NITI Aayog was identified as the
nodal agency for the Multidimensional Poverty Index
(MPI).
The GIRG exercise is aimed at leveraging the moni-
toring mechanism of important social, economic and
other internationally recognised indices to drive re-
forms and growth. The primary goal of this exercise is
to gauge India’s performance - not only on the overall
country's result in the indices but on the parameters
and sub-parameters as well; and subsequently draft
an action plan to improve in those areas and sub-areas.
It was widely recognised that this exercise would re-
quire a whole-of-government approach in letter and
spirit. As one of the first steps in this direction, NITI
Aayog, as the nodal Ministry for the MPI, identified
all relevant Union Ministries and departments which
mapped not only to the broad dimensions of the in-
dex but also to the individual components, parame-
ters and sub-parameters. For example -improvement
in dimensions such as living standards required co-
ordination among Ministries concerned with cooking
gas, electricity, rural and urban development, minis-
tries which work on housing, sanitation and drinking
water, and so on. This exercise also enabled the iden- tification of existing government actions articulated through schemes and policies which were definition- ally impacting the parameters and sub-parameters of the index: for example, various nutrition, and child and maternal health related schemes which impact the MPI dimension of health. It underscored the im- portance of inter-ministerial coordination and part- nership for effective and focused action.
The emphasis of the GIRG initiative is not only to im-
prove country’s performance and ranking but to lever-
age them as tools for systemic reforms in the policies
and processes aimed at improving and enhancing the
ease of living. In this context, NITI Aayog has been co-
ordinating with the concerned Ministries and depart-
ments mapped to the indicators and sub-indicators
of the index to develop a roadmap and action plan.
Parameter and sub-parameter linked reform areas
and actions are being developed by the inter-ministe-
rial committee on the index. The exercise includes the
identification of reform actions, duration, sub-national
applicability, priority and target setting for implemen-
tation by Ministries and departments.
As the nodal agency, NITI Aayog is also responsible
for constructing an indigenised index for monitoring
the performance of States and Union Territories and
ranking them. The national MPI for India is a milestone
contribution towards this effort. It aims to become the
primary tool for monitoring progress on SDG 1 ‘end-
ing poverty in all its forms everywhere’ in the coun-
try and simultaneously foster competition among
the States and Union Territories to expeditiously take action towards this goal. In this endeavour, engage- ment with publishing agencies and relevant technical partners was impressed upon from the outset. Build- ing partnerships and engaging with- i) the publishing agencies— United Nations Development Programme (UNDP) and Oxford Poverty and Human Develop- ment Initiative (OPHI) and ii) other technical partners such as the survey implementors of the National Fam- ily Health Survey— International Institute for Popula- tion Sciences (IIPS) of Ministry of Health and Family Welfare, has been critical in developing the national MPI for States and districts and ensuring its technical rigour and robustness.
To institutionalise this inter-ministerial effort for de-
veloping the national MPI and formulating reform ar-
eas and actions, the Multidimensional Poverty Index
Coordination Committee (MPICC) was constituted by
NITI Aayog, which included twelve Ministries and de-
partments.
2.2 MPI Coordination Committee (MPICC)
The inter-ministerial coordination committee con-
stituted under NITI Aayog included Ministries and
departments pertaining to areas such as health, ed-
ucation, nutrition, rural development, drinking water,
sanitation, electricity, and urban development, among
others. It also included experts from Ministry of Statis-
tics and Programme Implementation and the publish-
ing agencies – OPHI and UNDP.
The composition of the MPICC drew from the multidi-
mensional nature of the indicators and sub-indicators
within the index. This brought forth a cross-sectoral
collection of perspectives on policies and interven- tions to improve achievements at the level of house- holds. It also highlighted various approaches required for improving outcomes of intrinsic significance such as nutrition and education; and material infrastructure and household services such as electricity, drinking water, cooking fuel and others.
As critical stakeholders in the process, the MPICC en-
gaged in extensive discussions on the index, its the-
oretical underpinnings, technical computations and
indicators. The inaugural MPICC round tables on dif-
ferent facets of the index enabled a technical exposi-
tion of the computation of the index, the Alkire-Foster
method, dimensions, indicators and cut-offs. Conse-
quently, deliberations on two facets of the GIRG ex-
ercise -i) developing an India index or national MPI
and ii) identification of reform areas and actions, took
place simultaneously.
NITI Aayog in collaboration with the publishing agen-
cies (OPHI and UNDP) led the discussion on develop-
ing the national iteration of the index, with the MPICC.
The guiding principle of this exercise was the objec-
tive of developing a national measure which can con-
tribute towards measuring the progress under SDG 1
-of halving poverty in its multidimensional form, and
subsequently supporting evidence-based policymak -
ing to formulate government action to address it.
Members from each Ministry of the MPICC explicat-
ed their experience in their domain in relation with
public service delivery and the macro challenges in
each sector in a demographically and geographically
diverse country such as India. Their rich experience in
identifying past, present and future challenges in their
respective sectors informed the discussion on indica-
2
22
MPI Coordination Committee
Inter-Ministerial Coodrination Committee on the MPI
MEMBER MINISTRIES
NITI Aayog
Ministry of Statistics and Programme Implementation
1
2
Ministry of Women and Child Development 3
Ministry of Petroleum and Natural Gas 4
Ministry of Power 5
Ministry of Housing and Urban Affairs 6
Department of Health and Family Welfare
Department of Rural Development
7
8
Department of Food and Public Distribution 9
Department of School Education and Literacy 10
Department of Drinking Water and Sanitation 11
Department of Financial Services 12
SUBJECT MATTER EXPERTS
United Nations Development Programme
Oxford Poverty and Human Development Initiative
1
2
INDIA MPI BASELINE REPORT 8 9
Developing India’s MPIChapter 2
tor selection and formulating the reform areas. This
was followed by technical feasibility assessment of
the indicators in the NFHS and selection of respective
weights. Cognizance was taken to enable the national
MPI to simultaneously track the performance of the
country, and all States and districts on the global indi-
cators of the Index. Drawing from the GIRG mandate,
the consensus was twofold: i) to adapt the MPI to the
national and local contexts while tracking the global
MPI indicators and ii) to estimate it not only at the
level of the States, but districts as well.
2.2.1
Engagement with States: Building
c
onsensus on M
PI at the subnational level
State and Union Governments are pivotal stakehold- ers which make up the institutional bulwark of the country. With 36 States and Union Territories and over 700 districts –subnational entities represent the myriad socio-political, geographical and econom- ic diversity in the country. For a public policy tool such as the national MPI to fully realise its potential, utilisation of its results and findings by State and UT governments is crucial. Simultaneously, the success of identification and implementation of reform areas and actions to improve the lives of households and indi- viduals, would significantly be influenced by the level of adoption at the level of States. Therefore, build- ing consensus on the need to create a national MPI and the model thereof, developing capacities, under- standing and appetite for this novel policy tool, with our primary stakeholders, ie., the State governments and policy makers and implementers at the sub-na- tional level, was felt to be imperative at the stage of MPI project design. Close collaboration and extensive engagement with subnational governments –through State MPI workshops with top policy makers and all line departments in the States, was recognised as a pivotal driver of outreach on the Index.
NITI Aayog, at the time of writing, had organised MPI
workshops in the capitals of over 20 States. These
workshops were chaired by the Chief Secretaries
and other senior officials of the State Governments,
district level officials, and in some cases by the Chief
Ministers. The meetings covered the basics of MPI,
mandate from Cabinet Secretariat, India’s position in
global rankings, index estimations, status of the State
on MPI and district-wise performance, MPI parameter
dashboard, national MPI, and reform action plan. The
technical sessions on computation were conducted in
the presence of the State Statistical Officers and dis-
trict officials to generate feedback and insights from
their experience on the ground. The feature of the in-
dex –to estimate multidimensional poverty for the dis-
tricts of a State, generated significant interest among
the stakeholders for the measure itself. Insights on
district performance including that of headcount ra- tio, intensity of poverty, factors causing poverty, cen- sored and uncensored deprivations and contribution to MPI, were of keen interest to the departments and district administrations.
Deliberations in these 20 State-level MPI workshops
largely focused on the State-specific developmental
specificities, experience in domains of public service
delivery, challenges in the related sectors contextu-
al to the developmental reality of the State, and in-
tra-State diversities. Issues related to convergence
of inter-departmental action, synergies across line
departments to achieve the broad outcomes aligned
with the parameters of the Index, challenges to the
efficacy of implementation of the aligned Centrally
Sponsored Schemes and State schemes, analysis of
sub-optimal effiencies in public service delivery, as-
sessment of the adequacy of financial, technical, and
human resources, level of capacities of the State sa-
tistical systems to generate high frequency data for
periodic monitoring, inter alia, were some of the most
recurring discussion points that emerged during the
MPI workshops in the States. The common outcome of
these numerous State meetings was consensus that it
is important to monitor and reduce multidimensional
poverty, MPI is a technically robust and contextually
relevant instrument, and progress monitoring at dis-
trict level is crucial.
The draft State Reform Action Action Plan with an
illustrative set of reform areas and actions for each
indicator to achieve progress as measured by the In-
dex was also presented to the Chief Secretaries and
Heads of Departments in these workshops. The re-
form action plan has to be further customised by the
State Governments to align it with its own context and
reality. Therefore, the workshops paved the way for i)
the introduction and deliberation on this important
measure of multidimensional poverty which goes as
granular as the district, ii) presentation on the causal
factors to the MPI estimates and the areas of improve-
ments and iii) discussions on formulation of reform ar-
eas and actions for improving the lives of households
in the sectors under the purview of the MPI.
The process of developing the national MPI, since its
beginning, has been a collaborative one. As the tool
has to have both policy utility and technical robust-
ness, no stone has been left unturned in ensuring
that expert opinions, ground realities, and practical
approaches have been accommodated. Both central
ministries and State governments contributed im-
mensely to this process. The road ahead too will be
collaborative, with active involvement of all relevant
stakeholders.
Assam
Andhra Pradesh
Arunachal Pradesh
Goa
Bihar
Chhattisgarh
Jammu & Kashmir
Gujarat
Himachal Pradesh
Kerala
Jharkhand
Karnataka
Uttar Pradesh
Tamil Nadu
Uttarakhand
Meghalaya
Madhya Pradesh
Maharashtra
Sikkim
Nagaland
Odisha
WORKSHOPS AND CONSULTATIONS HELD
WITH 21 STATES AND UNION TERRITORIES
States/UTs remaining States/UTs where workshops and consultations were held
INDIA MPI BASELINE REPORT 10 11
Developing India’s MPIChapter 2
ENGAGEMENT WITH STATES & UNION TERRITORIES ENGAGEMENT WITH STATES & UNION TERRITORIES
Developing India’s MPIChapter 2
SECTION
II
METHODOLOGY
&
WAY FORWARD
INDIA MPI 14 15
MethodologyCHAPTER Chapter 3
COMPUTING INDIA’S MPI
METHODOLOGY
3.1 The Alkire-Foster Methodology
At the core of the MPI is the Alkire-Foster (AF) meth-
odology. The AF methodology is a general framework
for measuring multidimensional poverty that identi-
fies people as poor or not poor based on a dual-cutoff
counting method. The first order cut-off within each
component indicator is applied to determine which
person is “deprived” in that indicator. The information
across all indicators is then aggregated to arrive at
a deprivation score for each individual. The second
order cut-off is then applied to identify the individ-
uals who are multidimensionally poor. The AF meth-
odology is an extension of the widely accepted Fos-
ter-Greer-Thorbecke (FGT) class of poverty measures
and has a range of technical and practical advantages
that make it favorable for use in non-monetary pover-
ty estimation.
Poised within a family of axiomatic measures, the AF
methodology achieves multiple technical milestones
associated with poverty measures including dimen-
sional monotonicity, subgroup decomposability, scale
and replication invariance, poverty and deprivation
focus and symmetry. This ability of the AF methodol-
ogy to provide an idea of not only the degree of pov-
erty but also its composition and distribution is what
makes it a powerful tool for decision-making.
The AF methodology’s intuitive counting approach
for poverty identification, explicit consideration of
joint distributions, consistent partial indices and most
importantly, its ability to utilize ordinal or binary data, make it adaptable to existing data systems without the need to introduce any specialized modules within surveys that relate only to the estimation of multidi- mensional poverty.
The dual-cutoff approach of the AF methodology also
mitigates a number of issues that arise from the union
and intersection approaches in the measurement of
multidimensional poverty with the former tending to-
wards overestimation and the latter tending towards
underestimation. The flexibility it provides (within
bounds of logic and reason) in terms of selection of
indicators, determination of first and second order
cutoffs and indicator weights adds a layer of custom-
ization essential for the construction of a multidimen-
sional poverty measure suited to the national context.
3.2 Steps in computing the MPI
The process of computing the MPI can be divided into
2 broad categories: i) Identification, and ii) Aggre-
gation.
3.2.1 Identification
i.
Detmine the set of indicators to be used in the
MPI and group thematically similar indicators into dimensions. For example, years of schooling and school attendance are indicators under the di- mension of education.
3
31
ii. Set the deprivation cut-offs for each indicator, i.e.,
the level of achievement considered normatively sufficient in order for an individual to be consid- ered not deprived in an indicator. E.g., the individ- ual has completed at least six years of schooling.
iii.
Apply the cutoff and determine whether the indi-
vidual is deprived in each indicator.
iv. Select weights to be applied to each indicator
such that the sum of the weights for all indicators adds up to 1. Optionally, the weights of the indica- tors could be such that the weight attributable to each dimension (i.e. the sum of the weights of the indicators in that dimension) is the same.
v.
Calculate the weighted sum of deprivations for
each individual. This is known as their deprivation score.
vi.
Apply the second order cutoff, i.e., the proportion
of weighted deprivations that an individual needs to experience to be identified as multidimension- ally poor. India’s national MPI follows the second order cutoff of 33.33 percent used in the global MPI measure.
3.2.2 Aggregation
i. Det
as multidimensionally poor in the population. This is known as the headcount ratio (H) of the MPI or the incidence of poverty. The headcount ratio broadly explains ‘how many are poor’.
ii.
Determine the average share of weighted indica-
tors in which multidimensionally poor individuals are deprived i.e., add the deprivation scores of the poor and divide it by the total number of poor in- dividuals. This is known as the intensity of poverty (A) in the MPI or the breadth of poverty, which broadly explains ‘how poor are the poor’.
iii.
Compute the MPI score (M
0
) as the product of the
partial indices of Headcount Ratio and Intensity.
3.3 Indicators in India’s National MPI
The national MPI model retains the ten indicators of the global MPI model, essentially to be closely aligned to the global methodology and rankings. India’s MPI has three equally weighted dimensions – health, edu- cation, and standard of living - which are represented by twelve indicators (Figure 1).
Figure 1: INDICATORS IN INDIA'S NATIONAL MPI
Education
Standard
of Living
1/3
1/3
1/3
1/6
1/6
1/6
1/12
1/12
1/21
1/21
1/21
1/21
1/21
1/21
1/21
A household is considered deprived if any child between the ages of 0 to 59 months, or woman
between the ages of 15 to 49 years, or man between the ages of 15 to 54 years -for whom nutri-
tional information is available - is found to be undernourished.
A child/adolescent under 18 years of age has died in the family in the five-year period preceding
the survey.
A household is deprived if any woman in the household who has given birth in the 5 years pre-
ceding the survey, has not received at least 4 antenatal care visits for the most recent birth, or has
not received assistance from trained skilled medical personnel during the most recent childbirth.
Not even one member of the household aged 10 years or older has completed six years of
schooling.
Any school-aged child is not attending school up to the age at which he/she would complete
class 8.
A household cooks with dung, agricultural crops, shrubs, wood, charcoal or coal.
The household has unimproved or no sanitation facility or it is improved but shared with other
households.
The household does not have access to improved drinking water or safe drinking water is at least
a 30-minute walk from home (as a round trip).
The household has no electricity.
The household has inadequate housing: the floor is made of natural materials, or the roof or wall
are made of rudimentary materials.
The household does not own more than one of these assets: radio, TV, telephone, computer,
animal cart, bicycle, motorbike, or refrigerator; and does not own a car or truck.
No household member has a bank account or a post office account.
Nutrition
Child & Adolescent
Mortality
Antenatal Care
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
Dimension Weights Indicator Deprived if
Health
INDIA MPI BASELINE REPORT 16 17
MethodologyChapter 3
3.3.1 Dimension: Health
The ‘health’ dimension comprises parameters repre-
senting nutrition, child mortality and maternal health.
The indicators for Nutrition and Child Mortality echo
the definitions and cutoffs followed by their coun-
terparts in the Global MPI. The indicator for Maternal
Health is unique to India’s national MPI. A point of note
is that in the national MPI, the Child Mortality indica-
tor has been renamed as Child-Adolescent Mortality.
According to the parlance of the Indian statistical sys-
tem, the use of the term “Child Mortality” is usually
associated with mortality under 5 years of age. Given
that the indicator in the MPI refers to deaths below 18
years of age, the indicator has been renamed so as
to mitigate confusion arising from the nomenclature.
Digressing from the precedent set by the Global MPI
measure, the indicators in the dimension for Health
are not equally weighted. Nutrition – with a weight of
1/6, carries half the dimension weight of 1/3. The re-
maining dimension weight is split between Child-Ad-
olescent Mortality and Maternal Health, with each in-
dicator having a weight of 1/12. The sharing of weights
between the Child-Mortality and Maternal Health Mor-
tality prevents the overall MPI measure from favoring
households with no children or households with no
births in the last 5 years, while allowing for the mon-
itoring of deprivations in the domains of childbirth,
and access to antenatal and maternal care. The shared
weights also allow for the indicator on Nutrition to re-
tain its weight in the Global MPI, thus enabling unifor-
mity in reporting across both.
3.3.1.i Nutrition
A household is considered deprived if any child be-
tween the ages of 0 to 59 months, or woman be -
tween the ages of 15 to 49 years, or man between the
ages of 15 t
o 54 years -for whom nutritional informa-
tion is available- is found to be undernourished.
A woman (15 to 49 years) or a man (15 to 54 years) is
considered undernourished if their Body Mass Index
(BMI) is below 18.5 kg/m
2
. Children under 5 years of
age are considered malnourished if their z-score of
height-for-age (stunting) or weight-for-age (under-
weight) is below minus two standard deviations from
the median of the reference population.
It is to be noted that even if a single member of the
household is identified as undernourished, the entire
household is treated as deprived in nutrition. This is
owing to two primary reasons: i) the unit of analysis is
the household, and ii) the indicator for nutrition oper- ates within the implicit principle of shared positive or negative externality, wherein the debilitating effects of undernourishment on one household member will have a direct or indirect effect on other members of the same household.
Contributing to nearly one-third of the multidimen-
sional poverty in India, nutrition is arguably one of the
most important indicators in India’s national MPI. Mal-
nutrition has significant consequences to early child-
hood development as well as to the health and overall
wellbeing of adults. The indicator for nutrition carries
a weight of 1/6 and its definition is aligned with the
Global MPI.
3.3.1.ii Child-Adolescent Mortality
A household is deprived if any child or adolescent
under 18 years of age has died in the household in
the five-year period preceding the survey.
The child-adolescent mortality indicator is based on
birth history data provided by mothers aged 15-49
years. However, if the data from the mother is missing,
and if the male in the household reported no child-ad-
olescent mortality, then the household is reported to
be not deprived. A household with no children would
also be treated as not deprived.
For the five-year period preceding NFHS 2015-16, the
neonatal mortality rate (i.e., the probability of death
within the first month of life) was 30 deaths per 1000
live births. This means that one newborn in 33 live
births died during the neonatal period. The under-five
mortality rate during this time period stood at 50
deaths per 1000 live births, indicating that one in 20
children in India, died before their fifth birthday. A sig-
nificant proportion of neonatal deaths are a result of
preventable diseases and, lack of access to pediatric
healthcare. Certain demographic risk factors can also
be identified with under five deaths being significant-
ly higher among vulnerable population groups (for in-
stance, Scheduled Tribes, and Scheduled Castes) and
among the lowest wealth quintiles (Ministry of Health
and Family Welfare, 2017).
The rationale behind the indicator on Child-Adoles-
cent Mortality and the age group it considers is that it
is indicative of the set of deprivations experienced by
the household which may have contributed to the un-
fortunate demise of a child in the household and the
effect of that incident on the set of deprivations that
the household may experience over time.
The death of a child or adolescent in a household is emblematic of a larger set of deprivations already experienced by the household. Factors such as, lack of access to healthcare, infectious diseases, malnutri- tion, iron-deficiency anemia, an unsafe environment are all contributors to child and adolescent mortal- ity (WHO, 2017). The death of a child or adolescent therefore indicates the deprivations experienced by a household in one or more of these factors. Further- more, it highlights the risks that other living children or adolescents in the household are being exposed to.
Child-Adolescent mortality also possesses multiple
negative externalities which directly affect all indi-
viduals and in extension the deprivation status of the
individuals in that household. These externalities can
manifest in a number of different ways over time.
For example, the death of a school-going child aged
10 years or older may deprive the household of their
only member who had attained more than six years
of schooling, thereby depriving them of the positive
externalities that are associated with having a house-
hold member with formal education. Another exam-
ple, where a more direct effect can be observed is
where a 17-year-old adolescent who was an earning
member of a household in the lowest wealth quintile
passes away resulting in their sibling having to drop
out of school.
It is therefore that the death of a child or adolescent
below the age of 18 years in a household is normative-
ly considered a tragedy for the household and is in-
cluded as a determinant for multidimensional poverty.
The indicator for Child-Adolescent Mortality carries a
weight of 1/12 and its definition remains aligned with
the Global MPI.
3.3.1.iii Maternal
Health
A household is deprived if any woman in the house-
hold who has given birth in the 5 years preceding the
survey has not received at least 4 antenatal care
visits for the most recent birth or has not received assistance from trained skilled medical personnel during the most recent childbirth.
Introduced as a new indicator to India’s national MPI, the indicator for Maternal Health is a union of two dis- tinct components – antenatal care and assisted deliv-
ery. The indicator captures if a woman in the house- hold who has given birth in the 5 years preceding the survey has received at least 4 antenatal care visits and has received assistance from skilled medical person-
nel during the most recent childbirth. Not fulfilling any one of the two criteria would cause the household to be considered deprived. If the household has not had any births in the 5 years preceding the survey, it would be considered to be not deprived in this indicator. The indicator carries a weight of 1/12.
Antenatal care (ANC) and assisted delivery, even
when taken in isolation, form a critical prerequisite to
positive healthcare outcomes for mothers and new-
borns alike. With a significant percentage of maternal
deaths occurring during the period of pregnancy, the
four-visit antenatal care model outlined in the WHO
clinical guidelines has been instrumental in the early
identification of complications in pregnancy, monitor-
ing of foetal growth and the management of compli-
cations through the referral of mothers to the appro-
priate facility for further treatment.
In India, as per NFHS-4, only 51 percent of women had
received at least 4 ANC visits during their most recent
pregnancy with the highest proportion of women be-
ing in Kerala (90 percent) and the lowest in Bihar (14
percent). There also exists a significant disparity of
ANC among income groups with women in the high-
est wealth quintile being almost twice as likely to have
received ANC from a skilled provider than women in
the lowest wealth quintile.
Of all globally reported child deaths, 2.5 million oc-
curred within the first month of life and 2 million were
stillbirths (UNICEF, WHO, World Bank, United Nations,
2019). According to NFHS-4, in India, approximately
60 percent of deaths below 18 years are neo-natal and
infant deaths occurring before the completion of the
first month since birth and before the completion of
one year since birth, respectively.
The causes of nearly 80 percent of new-born deaths
can be identified and there are solutions to address
them, preventing death or life-long disability (WHO,
UNICEF, 2014). These causes are - complications due
to prematurity, intrapartum deaths, and neonatal in-
fections. Thus, ANC cannot be looked at in isolation
as prevention of intrapartum deaths requires quality
care provided during childbirth. In India, 81 percent of
live births were assisted by a skilled provider. 93 per-
cent of women who had received four or more ANC
visits also received skilled assistance during delivery
as compared to only 60 percent of women who had
no ANC visits.
It is based on this premise that India’s national MPI
digresses from the precedence of Afghanistan, Gua-
INDIA MPI BASELINE REPORT 18 19
MethodologyChapter 3
temala, Panama, and Pakistan (which have indicators
for either one of the two, ANC or assisted delivery in
their respective national MPIs). India’s MPI seeks to
adopt a stricter union measure when determining the
deprivation status of an individual in Maternal Health -
ensuring that an expectant mother must receive both
4 or more antenatal care visits and be assisted by
skilled personnel during childbirth.
India being a signatory to the 2030 Agenda, the ma-
ternal health indicator in the national MPI aims to en-
force strict compliance to the SDG targets of reduc-
ing maternal mortality and end preventable deaths of
new-borns in the country.
3.3.2 Dimension:
Education
The ‘Education’ dimension is represented by param- eters pertaining to school attendance and years of schooling, with each indicator – weighted at 1/6 - car- rying half of the dimension weight (1/3) for Education. The definitions and cut-offs for the indicators remain unchanged and aligned with the Global MPI.
3.3.2.i Years of Schooling
A household is deprived if not even one member of the household aged 10 years or older has completed six years of schooling.
Years of schooling has a shared positive effect on the household, wherein even if one member has more than six years of schooling, the positive effect of that education, be in terms of increase in economic oppor- tunities such as the ability to enter high paying em- ployment or in terms of improvement in social stand- ing, is shared among all members of the household.
A point to be noted is that because of the nature of
the indicator, an individual living in a household where
there is at least one member with six years of school-
ing is considered to be non-deprived, even though
they themselves may not have attended school. The
indicator carries a weight of 1/6.
3.3.2.ii School Attendance
A household is deprived if any school-aged child is
not attending school up to the age at which he/she
would complete class 8.
The indicator for school attendance is the logical pre-
cursor to the indicator for years of schooling. A child
not attending school is indicative of both the present
set of deprivations experienced by the household as
well as the possible future deprivations that may un-
fold as a result of the child not attending school. A child not attending school is emblematic of a greater set of deprivations being experienced by the house- hold that acts as an impediment to the education of the child. Similarly, because the child is not attending school, the household members will be deprived of the positive externalities that arise from having a for- mally educated member in the household.
An individual living in a household where there is at
least one child not attending school, is treated as de-
prived in this indicator, even though they themselves
may have completed schooling. The indicator has a
weight of 1/6.
3.3.3 Dimension: Standard of Living
Lastly, the dimension for ‘Standard of Living’ compris- es parameters representing access of the household to basic services such as electricity, clean cooking fuel, improved and safe drinking water, improved sanitation, pucca housing (proper flooring, roof and walls), bank account, and household assets. All indi- cators with the exception of the indicator for bank ac- counts – which is unique to India’s national MPI – align with the global definitions and cutoffs. The dimension weight of 1/3 is split evenly across all the seven indica- tors therefore giving each a weight of 1/21.
3.3.3.i Cooking Fuel
A household is deprived if the primary source of cooking fuel is dung, agricultural crops, shrubs, wood, charcoal or coal.
Improved or safe sources of cooking fuel include elec- tricity, LPG/natural gas, biogas. A point of importance here is that simply the presence of an improved/safe source of cooking fuel in the household is not enough to warrant a “not deprived” status. The household must also be utilizing the improved/safe source of cooking fuel as their primary source of cooking fuel - i.e. a household may have a LPG connection and stove, but if wood/coal is the primary (most used) fuel for cooking, then the household will be considered to be deprived in the indicator.
3.3.3.ii Sanitation
The household has unimproved or no sanitation fa- cility or it is improved but shared with other house-
holds.
Improved sanitation includes any non-shared toilet of the following types: flush/pour flush toilets to piped sewer systems, septic tanks, and pit latrines; venti-
lated improved pit (VIP)/biogas latrines; pit latrines with slabs; and twin pit/composting toilets. It must be noted that exclusive access to an improved sanitation facility, which is not shared with members of another household, is required for a household to be consid- ered not deprived in this indicator.
3.3.3.iii Drinking Water
A household is deprived if it does not have access to improved drinking water or safe drinking water is more than a 30-minute walk from home (as a round trip).
Safe or improved sources of drinking water include piped water supply, public taps, standpipes, tube wells, boreholes, protected dug wells and springs, rainwater, and community reverse osmosis (RO) plants. Even if a household has access to an improved water source, it will be considered deprived in this in- dicator if the source is more than 30-minute roundtrip walk from home.
3.3.3.iv Electricity
A household is deprived if it has no electricity.
Access to household electricity has a multiplier effect on any household and deprivation in this basic and essential service is ground for treating any household as deprived.
3.3.3.v Housing
A household is deprived if it has inadequate hous-
ing: the floor is made of natural materials, or the roof or w
alls are made of rudimentary materials.
Mud/clay/earth, sand and dung are considered rudi- mentary/natural materials.
3.3.3.vi
Ownership of Assets
The household is deprived if it does not own more than one of these assets: radio, T
V, telephone, com-
puter, animal cart, bicycle, motorbike, or refrigera-
tor; and does not own a car or truck.
In the case of the indicator for assets, the criteria for the car or truck ownership acts as an exclusion cri- teria. Therefore, even if a household does not have a radio, television, telephone, computer, animal cart, bicycle, motorbike, or refrigerator, but has either a car or a truck, then the household will be treated as non-deprived.
3.3.3.vii Bank Account
No household member has a bank account or a post office account.
The indicator for bank accounts is an additional indi- cator in India’s national MPI. The ownership of a bank account or post office account is the key to financial inclusion of the hitherto unbanked households. The access of a household to a bank account is critical for availing the benefits of several flagship government programs aimed at reduction of poverty, increas- ing access to education, and creation of livelihoods – which often utilize direct benefit transfers. Bank accounts also play an important role in the delivery of public services, access to institutionalized credit and also act as long-term savings instruments - ei- ther through self-deposits or through institutionalized savings schemes.
Extensive evidence suggests that there exists a strong
and positive correlation between access to financial
services and improved capabilities and functionings.
Empirical studies that have analyzed spatial data have
cited significant correlation between areas with lower
banking access and higher or relatively severe inci-
dences of poverty (Iqbal, Roy, & Alam, 2020). Oth-
er studies which have probed demographic datasets
have concluded that financial inclusion plays an im-
portant role in preventing a household’s exposure to
future poverty while also aiding in sustained escapes
from poverty, especially female-headed households
(Koomson, Villano, & Hadley, 2020).
These factors necessitate the inclusion of an indica-
tor pertaining to financial inclusion in India’s national
MPI not only to identify the geographical regions and
population sub-groups where immediate intervention
is required but also to ensure that the efforts to fast-
track financial inclusion in India are sustained.
The SDG target 8.1 focuses on strengthening the ca-
pacity of domestic financial institutions to encourage
and expand access to banking and financial services
for all. The inclusion of the indicator for bank accounts
thus allows for the national MPI to have a larger cov-
erage of and increased cross-linkages with the SDGs.
At the global front, the national MPIs of Rwanda and
Sierra Leone also include indicator for bank accounts
with the former having included it in a new dimen-
sion titled as “Social Services and Economic Inequal-
ity” and the latter having kept in in the dimension for
“Standard of Living”.
INDIA MPI BASELINE REPORT 20 21
MethodologyChapter 3
3.4 Computing the MPI
As stated previously, the process of computing the
MPI is divided into two distinct stages – identification
and aggregation. Identification involves obtaining the
deprivation score for every individual followed by
censoring of deprivation scores to identify the multi-
dimensionally poor for a given cutoff. Aggregation in-
volves the estimation of two partial indices, headcount
ratio and intensity, the product of which provides us
with the MPI. Each of the aforementioned concepts
has been detailed in the following paragraphs.
3.4.1 Identifying the poor
Based on the AF methodology, identification of the
poor is dependent on a set of within-indicator depri-
vation cutoffs as well as an across-indicators depri-
vation cutoff (hence the term dual-cutoff approach).
The cutoffs within indicators (also known as the
first-order cutoff) are used to determine the depri-
vation score while the across-indicator cutoff (also
known as the second-order cutoff) is used to finally
determine who is multidimensionally poor. Both con-
cepts have been detailed in the following sections.
3.4.1.i Deprivation Score
Each individual (and in extension everyone in the
same household), is first marked as deprived (denot-
ed by 1) or not deprived (denoted by 0) in each of
the indicators based on their achievement (or lack
thereof) in the respective first order cutoffs for each
indicator.
For example, if an 18-year-old individual (referred to
as A for the sake of simplicity) has 3 years of school-
ing, she does not meet the first order cutoff for the
indicator on years of schooling (any individual aged
10 years or older must have at least 6 years of school-
ing). Therefore, A is considered deprived in the indica-
tor for years of schooling and assigned a score of 1 for
that indicator. Conversely, individual B has 7 years of
schooling and is 12 years old, therefore B is assigned a
score of 0 for the indicator on years of schooling. This
process is repeated for each indicator until individuals
A and B have been assigned a score for all indicators.
The next step is to determine the counting vector also
known as the deprivation score for the individual. The
deprivation score is the sum of the weighted status of
all the indicators for an individual.
Extending the previous example, individual A is de-
prived in the indicator for years of schooling. The
weighted status of the indicator for A would then be
1 (the number assigned to her denoting that she is
Deprivation Status
If the achievement of an individual �� in indicator �� is denoted by ��
����
,
the first order cutoff for indicator �� is denoted by ��
��
, and the status
of the individual is denoted as g
0
����
, then
g
0
����
=1 if ��
����
<��
��
& g
0
����
=0 otherwise for all ��=1,2⋯n & ��=1,2⋯d
Note to the Reader
In order to facilitate ease of reading, the section on the computa-
tion of the MPI has been divided into two columns, the left column
provides the descriptive overview of the process of arriving at In-
dia’s national MPI and its various partial indices while the right
column provides the associated mathematical notations and il-
lustrations for the concepts provided in the left column.
Example: Finding g
0
����
for Individual A
Steps in Computing the MPI
Identification 1
Build a deprivation profile by applying cutoffs within an indicator
Identify who is multidimensionally poor by applying a cutoff across
all indicators
Calculate the Intensity of Poverty
(A):
On average, how poor are the poor?
Calculate the Headcount Ratio
(H):
How many are poor?
Compute the MPI by taking the product of H and A (MPI=HxA)
Aggregation 2
Has 6 years of schooling
Does not have 6 years of schooling
Indicator Deprived? Status (g
0
����
)
Individual
A
1 Ye s
0 No
deprived) multiplied by 1/6 (which is the weight as-
signed to the indicator for years of schooling. Thus,
A’s weighted status for indicator on years of schooling
would be 1/6 or 0.166. Following this, the weighted
status for individual B would be 0. This is repeated for
all the indicators following which the weighted scores
are added, giving us the deprivation scores for A and
B. Because the weight structure follows the AF meth-
odology, the sum of the relative weights of all the in-
dicators equals to 1.
3.4.1.ii
Po
The second-order cutoff (��), defined in the AF meth- odology as the poverty cutoff is the deprivation score which is the identifier for multidimensional poverty. Individuals with a deprivation score greater than or equal to the second-order cutoff are identified as multidimensionally poor.
For example, if the second-order cutoff is 0.33 and
individual A has a deprivation score of 0.54, then she
is considered multidimensionally poor. Likewise, if in-
dividual B has a deprivation score of 0.28, she will not
be considered multidimensionally poor even though
she has a non-zero deprivation score.
India for its national MPI has adopted the second-or-
der cutoff of 0.33 which is also the standard cutoff
used globally. Thus, for an individual to be considered
as multidimensionally poor, she should be deprived in
at least 1/3rd of the weighted indicators.
It is at this juncture that the potential of the AF meth-
odology is realized. The union method of multidimen-
sional poverty identification considers an individual to
be poor if she is deprived in even one indicator – lead-
ing to overestimation, while the intersection method
only considers an individual as poor if she is deprived
in all indicators, leading to underestimation. Neither
of these provides any useful insights to a policy mak-
er. The AF methodology, with its dual cutoff approach
thus provides a realistic middle ground for poverty
estimation.
3.4.1.iii Censoring
Following the computation of the deprivation scores
for all individuals, those individuals for whom the
score is less than the second order cutoff, is replaced
with 0. This step in known as censoring in multidimen-
sional poverty estimations.
Following our example, the deprivation score of indi-
vidual A (0.54) will remain unaltered while the score
of individual B (0.28) will be replaced with 0.
Counting Vector and Deprivation Score
The counting vector for individual �� up to the ��
th
indicator (denoted
by c
��
), also known as deprivation score, is their status in each indi-
cator (g
0
����
) multiplied by the weight (w
��
) assigned to that indicator.
The deprivation score (or weighted deprivation) of individual ��
can thus be denoted as:
c
��
= w
1
g
0
��1
+
w
2
g
0
��2
+
...+ w
��
g
0
����
w
��
g
0
����
or
d
��=1
⅀c
��
= w
��
=1where
d
��=1
⅀
Example: Calculating the Deprivation Score for Individual A
Indicator WeightsDeprived?Status (g
0
����
) Score (w
��
g
0
����
)
1/6 Ye s 1 0.16X = Nutrition
Child & Adolescent Mortality 1/12 No 0 0X =
Maternal Health 1/12 Ye s 1 0.08X =
Years of Schooling 1/6 Ye s 1 0.16X =
School Attendance 1/6 No 0 0X =
Cooking Fuel 1/21 Ye s 1 0.04X =
Sanitation 1/21 No 0 0X =
Electricity 1/21 No 0 0X =
Drinking Water 1/21 No 0 0X =
Housing 1/21 Ye s 1 0.04X =
X = Assets 1/21 No 0 0
X = 1/21 No 0 0 Bank Account
Deprivation Score (c
��
) 0.48=
Applying the Poverty Cut-off
The identification function for multidimensional poverty is denot- ed by ρ. The function ρ is dependent on the deprivation status of
an individual (��
��
) given the cutoffs within an indicator (��) as well as
on the cutoffs across indicators (��) and is therefore represented by:
ρ
��
(��
��
;��)=
1 if c
��
≥�� and ρ
��
(��
��
;��)=0 otherwise
Therefore, the function ρ considers an individual �� as multidimen-
sionally poor when her deprivation score (c
��
) is greater than of
equal to the second-order cutoff (��).
Example: Applying the Poverty-Cutoff
Censored Deprivation Scores
Censored scores are denoted as c
��
(��) to differentiate them from
deprivation scores c��. Thus, if c
��
<��, then c
��
(��)=0 and if c
��
≥�� then
c
��
(��)=c
��
. Thus, c
��
(��) is the deprivation score of the multidimension-
ally poor.
Example: Censoring in MPI
Deprivation
Score (c
��
)
Higher than
0.33? (c
��
≥��)
Is MPI Poor?
Score
ρ
��
(��
��
;��)
Individual A
No No 0 Individual B 0.20
0.48 Ye s Ye s 1
Deprivation
Score (c
��
)
Higher than
0.33? (c
��
≥��)
Is MPI Poor?
Censored
Deprivation
Score (c
��
(��))
Individual A
No No 0 Individual B 0.20
0.48 Ye s Ye s 0.48
INDIA MPI BASELINE REPORT 22 23
MethodologyChapter 3
3.4.2 Headcount Ratio
Following the identification of multidimensionally
poor individuals, the next step is to determine the
proportion of multidimensionally poor individuals in
the total population. This is known as the headcount
ratio of multidimensional poverty or the incidence of
poverty and is the first of two partial indices used to
determine the MPI. The headcount ratio (denoted by
H) answers the question ‘how many are poor?’ India’s
national MPI identifies 25.01 percent of the population
as multidimensionally poor.
3.4.2.i Uncensored (
Raw) Headcount Ratio
While the headcount ratio (H) provides the proportion of multidimensionally poor individuals in the popula- tion, the uncensored headcount ratio (denoted by h
��
)
provides the proportion of individuals who are de- prived in an indicator �� irrespective of whether they are multidimensionally poor or not.
The uncensored headcount ratios of the indicators in
India’s MPI have been provided in Figure 2. Each bar
represents the percentage of India’s population who
are deprived in that indicator.
3.4.2.ii Censored
Headcount Ratio
Akin to its uncensored counterpart, the censored headcount ratio (denoted by h
��
(��)) provides the pro-
portion of individuals who are multidimensionally poor and deprived in an indicator ��.
The censored headcount ratios of the indicators in In-
dia’s MPI have been provided in
Figure 3. Each bar rep-
resents the percentage of individuals who are multi-
dimensionally poor and are deprived in that indicator.
3.4.3 Intensity of
Poverty
The intensity of poverty (denoted by A) is the average proportion of deprivations which is experienced by multidimensionally poor individuals. It is the average deprivation score of all multidimensionally poor indi- viduals. A is the second partial index used in the con-
struction of the MPI and answers the question how poor are the poor?
3.4.4
TPI
The Multidimensional Poverty Index reflects both the incidence and the intensity of multidimensional pov-
erty. The index (denoted by M
0
) is the product of the
two partial indices, the headcount ratio (H ) and inten-
sity (A) of multidimensional poverty. This can also be defined as the share of population that is multidimen- sionally poor adjusted by the intensity of deprivation.
Headcount Ratio
where q is the total number of multidimensionally poor individ-
uals identified (i.e., the total number of individuals for whom
ρ
��
(����;��)=1) and �� is the total population. In this report, the head-
count ratio has been reported as a percentage (H×100 ).
H=
q
��
Uncensored Headcount Ratio
where denotes the sum of the deprivation status up to the ��
th
individual for the indicator �� and �� is the total population. In this
report, the uncensored headcount ratios have been reported as
percentages (h
��
×100).
g
0
����
��
��=1
⅀h
��
=
1
��
g
0
����
��
��=1
⅀
Censored Headcount Ratio
where �� is the total population, and g
0
����
(��) is the censored depriva-
tion score of individual �� in indicator �� using a second-order cutoff (��) of 33.33 percent. In this report, the censored headcount ratios have been reported as percentages (h
��
(��)×100).
g
0
����
(��)
��
��=1
⅀h
��
(��)=
1
��
Intensity
Where c
��
(��) is the censored deprivation score (i.e. deprivation
score of multidimensionally poor individuals) up to the ��
th
individu-
al and q is the number of multidimensionally poor individuals.
c
��
(��)
q
��=1
⅀A=
1
q
Multidimensional Poverty Index
M
0
=H��A
q
��
c
��
(��)
q
��=1
⅀
1
q
H��A=�� = c
��
(��)
��
��=1
⅀
1
��
= w
��
g
0
����
(��)
��
��=1
⅀
1
��
d
��=1
⅀or
The MPI, therefore, is the share of weighted deprivations faced by
multidimensionally poor individuals divided by the total popula-
tion. The MPI is therefore known as the adjusted headcount ratio.
Percentage of the total population of India who are deprived in each indicator
37.6%
2.7%
22.6%
13.9%
6.4%
14.6%
45.6%
14.0%
9.7%
58.5%
41.4%
52.0%
29.8%
12.2%
3.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population deprived
19.90%
1.88%
14.71%
10.71%
5.23%
23.13%
21.32%
5.53%
8.29%
20.56%
8.87%
5.37%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population MPI poor & deprived
Figure 2: Uncensored Headcount Ratio
Percentage of total population of India who are multidimensionally poor and deprived in each indicator
Figure 3: Censored Headcount Ratio
Dimension Health Education Standard of Living NFHS-5: Standard of Living
Note on comparison: The striped bars denote the provisional estimates for the uncensored headcount ratio based on the data available
in the NFHS-5 India Factsheet (2019-20).
INDIA MPI BASELINE REPORT 24 25
MethodologyChapter 3
3.4.4.i Why is the adjustment important?
An understandable question at this point would be as
to why the adjustment (using the intensity of poverty)
is required when the headcount ratio already identi-
fies who is multidimensionally poor.
Traditionally, poverty measures (such as poverty lines)
would utilize a single threshold to determine if an in-
dividual was poor or not. However, this would only
convey the information regarding number of people
in poverty but not the extent of their poverty.
Therefore, any change in the level of deprivations (for
better or for worse) faced by an individual in pover-
ty would not affect the poverty measure unless the
change was substantial enough to make the individual
cross the determined poverty threshold.
To put it in simpler terms, traditional poverty mea-
sures would remain unaltered if an individual who is
already poor became poorer, or an individual who is
poor became less poor but not enough to cross the
poverty line. This means that these measures do not adhere to the axiom of dimensional monotonicity in poverty measurement i.e., if the number of depriva- tions faced by poor individuals decreases, then the overall poverty measure should also decrease and vice versa.
M
0
(or the MPI) estimated by the AF methodology is
dependent both on the headcount ratio as well as the
intensity of poverty, and therefore may change if the
headcount ratio decreases/ increases (i.e. the abso-
lute number of people in poverty decreases/ increas-
es) or if the deprivations faced by multidimensionally
poor individuals decrease/ increase (which may hap-
pen without changing the headcount ratio). There-
fore, the MPI adheres to the axiom of dimensional
monotonicity.
Thus, for policy makers the MPI presents a responsive
measure that improves not only when the absolute
number of individuals in poverty decreases, but also
when the severity of poverty experienced by a multi-
dimensionally poor individual decreases.
Headcount Ratio
The Headcount Ratio is computed by di-
viding the total number of multidimen-
sional poor (q) by the total population (��)
q=7+5 & ��=7+5+4
H=
q
��
7+5
7+5+4
12
16
0.75= = =
In this illustration, 75% of individuals are
multidimensionally poor
Intensity of Poverty
The Intensity of multidimensional poverty is computed by summing the weighted deprivation scores of all the MPI poor di- vided by the total number of MPI poor
On an average, an MPI poor individual is deprived in 59% of weighted indicators
0.68��7+0.48��5
7+5
= 0.596=c
��
(��)
q
��=1
⅀A=
1
q
Multidimensional Poverty Index
The MPI score is the product of the head- count ratio and intensity. It is known as the adjusted headcount ratio
MPI=H��A=0.75��0.596=0.447
Example: Calculating the Headcount Ratio, Intensity and MPI for 3 Households
Indicator
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Electricity
Drinking Water
Housing
Assets
Bank Account
Deprivation Score (c
��
) =
Censored Deprivation Score (c
��
(��))=
Members of HH1 and HH2 are
multidimensionally poor
HH2
5 members
Status (g
0
����
)
1
0
1
1
0
1
0
0
0
1
0
0
HH1
7 members
Status (g
0
����
)
1
0
1
1
1
1
1
0
0
1
0
0
=
=
=
=
=
=
=
=
=
=
=
=
1/6X
1/12X
1/12X
1/6X
1/6X
1/21X
1/21X
1/21X
1/21X
1/21X X
1/21
X 1/21
Weights
HH2
5 members
0.16
0
0.08
0.16
0
0.04
0
0
0
0.04
0
0
Score (w
��
g
0
����
)
HH3
4 members
0
0.08
0.08
0
0
0.04
0
0
0
0
0
0
Score (w
��
g
0
����
)
HH3
4 members
Status (g
0
����
)
0
1
1
0
0
1
0
0
0
0
0
0
0.68
0.68
0.48
0.48
0.20
0
HH1
7 members
0.16
0
0.08
0.16
0.16
0.04
0.04
0
0
0.04
0
0
Score (w
��
g
0
����
)
X =
3.5 Deconstruction of Estimates & Indicators
One of the defining characteristics of the AF meth-
odology is sub-group decomposability, i.e. breaking
down estimates by sub-groups such as geographical
region and population groups. The AF methodology
also allows for deconstruction by indicators that can
allow the determination of the contribution of each
indicator to the MPI. This contribution can be deter-
mined for the total population as well as for each
sub-group. This ability to “drill-down” through the
estimates lends importance to the MPI at every ad-
ministrative level in India, from the Union Government,
the State Government and even the district adminis-
tration.
3.5.1
Estimates by geographical level and
popula
tion sub-groups
In order to arrive at the estimates for the headcount ratio, intensity and the adjusted headcount ratio (and the sub-components under the same), each sub- group is treated as the total population over which the estimates are computed.
For example, when computing the estimates for Dis-
trict ��, we will take all households in District �� and com-
pute the MPI like we would do for the total population,
i.e., we will carry out the end to end process of as-
signing deprivation scores, applying the second-order
cutoff, determining who is multidimensionally poor
and compute the aggregate estimates for only the
population in District ��.
Similarly, if we would like to look even further and de-
termine the estimates for the rural areas within Dis-
trict ��, then we would carry out the identification and
aggregation process for only the population living in
the rural area within District ��.
It would be prudent to note that a simple average of
sub-group estimates will not provide the estimate for
the parent group. Thus, taking the average of district
MPIs for a state will not provide the state MPI, nor will
taking the average of state MPIs provide the national
MPI. Only the population weighted sum of the sub-
group MPIs will provide the MPI for the larger group
it is a part of.
3.5.2 Contribution of Indicators
The MPI can be deconstructed into its component
censored indicators. Therefore, we can not only look
at the MPI for a certain sub-group, but we can also
look at the factors (i.e., indicators) which are contrib-
uting to multidimensional poverty for that sub-group.
Estimates for a Region: Example, Headcount Ratio
where q
��
is the total number of multidimensionally poor individ-
uals in region ��, where �� can be any region of interest such as a
State, a District, and even urban or rural areas within a selected
State or District. Intensity of poverty will then include the average
deprivations experienced by the multidimensionally poor individ-
uals in region ��.
H
��
=
q
��
��
��
Dissaggregation by Urban and Rural Areas
Let us assume that the MPI for District �� is MPI
��
and the MPI for the
urban and rural areas within District �� is MPI
u��
and MPI
r��
, therefore,
Where ��
��
denotes the total population in District ��, ��
u��
is the pop-
ulation living in the urban areas of District ��, and ��
r��
is the popula-
tion living in the rural areas of District �� assuming that ��
��
= ��
u��
+ ��
r��
.
MPI
��
=
��
u��
��
��MPI
u��
+MPI
r��
��
r��
��
��
Dissaggregation by Districts
Taking the above example forward, if we wish to arrive at the MPI for India from the MPI of the 640 districts in India’s national MPI, then:
Where, MPIc and �� are India’s MPI and population respectively,
MPI
��
and ��
��
are the MPI and population for the ��
th
district with ��
taking a value up to 640, equivalent to the number of districts in
the country as of 2011.
MPI
c
=
��
1
��
��
�� ��
MPI
1
+ MPI
��
MPI
2
+...+
��
2
��
2
��
��
MPI
��
640
��=1
⅀MPI
c
=
1
��
or
Disaggregations in this Report
The Baseline Report on the MPI Provides:
National, State and District level estimates of Headcount Ratio, Intensity
and the MPI based on the NFHS-4.
Uncensored and Censored Headcounts for 12 indicators of the national MPI up to the level of the Districts
Contribution of each of the 12 indicators to India's MPI Score up to the level of the Districts
Headcount Ratio, Intensity and the MPI for Urban and Rural areas upto the level of the Districts
INDIA MPI BASELINE REPORT 26 27
MethodologyChapter 3
The contribution of indicators is determined by divid-
ing the weighted censored headcount ratio for each
indicator by the MPI. This is multiplied by 100 to arrive
at the percentage contribution.
Analogous to the process of disaggregation by geo-
graphical and population sub-groups, the contribu-
tion of each region (e.g. how much does a district
contribute to the national figure) or of each popula-
tion group (e.g. how much does female/male poverty
contribute to the national figure) can be computed
through the method illustrated, where the weighted
censored headcounts is replaced by the population
weighted MPI for the sub-group.
3.5.2.i Why is looking at contributions import-
ant?
The contribution of an indicator provides an insight
into the relative deprivation in a particular indicator
based on the weight attached to that indicator. When
looking at the censored or uncensored headcount
ratios, we can gauge, in absolute terms, how many
individuals in the total population are deprived in an
indicator (for uncensored) and how many multidi-
mensionally poor individuals are deprived in an indi-
cator (for censored).
However, a high percentage of absolute deprivation
in an indicator may not result in a high MPI. While the
number of individuals experiencing joint deprivations
Determining the Contribution of an Indicator
The process for determining the contribution of an indicator is a
derivative of the fact that the sum of weighted censored head-
count ratios for all indicators provides us with the MPI.
As shown earlier, the censored headcount ratio is represented as
h
��
(��) where �� is the indicator and �� is the second-order cutoff at
which the censoring was done. Therefore,
Where, MPI
c
is India’s MPI, w
��
is the weight of the ��
th
indicator with
�� taking a value up to 12 - equivalent to the number of indicators
in India’s national MPI. Thus, the contribution of each indicator �� is,
Contribution
��
=
w
��
h
��
(��)
MPI
c
��100
MPI
c
= w
1
h
1
(��)+w
2
h
2
(��)+...+w
��
h
��
(��)
w
��
h
��
(��)
12
��=1
⅀MPI
c
=or
Dimension Indicator
Uncensored
Headcount
Censored
Headcount (CH)
Weight (W) Contribution
Health
Nutrition 37.60% 19.90% 1/6 28.14%
Child-Adolescent Mortality2.69% 1.88% 1/12 1.33%
Maternal Health 22.59% 14.71% 1/12 10.40%
Education
Years of Schooling 13.88% 10.71% 1/6 15.14%
School Attendance 6.40% 5.23% 1/6 7.39%
Standard of
Living
Cooking Fuel 58.48% 23.13% 1/21 9.34%
Sanitation 51.97% 21.32% 1/21 8.61%
Drinking Water 14.60% 5.53% 1/21 2.23%
Electricity 12.16% 8.29% 1/21 3.35%
Housing 45.65% 20.56% 1/21 8.31%
Assets 13.97% 8.87% 1/21 3.58%
Bank Account 9.66% 5.37% 1/21 2.17%
MPI (M
0
) = Sum of (CH × W) = 0.118
Figure 4: CONTRIBUTION OF INDICATORS TO INDIA'S MPI SCORE
across multiple indicators is one of the determinant
factors of the MPI, the weights assigned to those in-
dicators also play an important role. In order to un-
derstand this with more clarity, we can look at Figure
4 that portrays the uncensored headcount, censored
headcount, and contribution for each indicator in In-
dia’s national MPI.
Taking the case of the indicator for maternal health,
one can see that the uncensored headcount (i.e.,
percentage of total population) deprived is 22.59
percent. Similarly, 14.71 percent of multidimensional-
ly poor individuals are deprived in maternal health.
However, the contribution of the indicator to the MPI
is 10.40 percent. Similarly, for the indicator for years
of schooling, the converse can be observed with both
censored and uncensored headcounts being lower
than the contribution to the MPI score. Therefore, in
order to arrive at an objective assessment of poverty
it is important to consider all three factors:
I.
The uncensored headcount gives us the absolute
number of individuals who are deprived in an indi-
cator; it gives us the status of deprivations among
the entire population.
II.
The censored headcount gives us the proportion
of individuals who are multidimensionally poor and deprived in an indicator; it gives us the composi- tion of deprivations among the multidimensionally poor.
III.
The contribution of an indicator gives us the per-
centage contribution of an indicator to the overall MPI considering the weights attached to each in- dicator.
From the point of view of a policy maker, the un- censored headcount outlines the broader priorities for intervention required for the benefit of the entire population, the censored headcount outlines the im- mediate priorities required for the benefit of the mul- tidimensionally poor population, and the contribution outlines where interventions would lead to the reduc- tion of the overall MPI of the population.
3.6 The Data Source & Unit of Analysis
The MPI captures the multiple deprivations faced by an individual and by extension, a household. These deprivations lie across a broad spectrum of domains such as health, education, access to basic infrastruc- ture, and ownership of assets, to name a few. The aim of the MPI is therefore to identify the various set of in- dicators in which an individual is deprived at the same time. Thus, the prerequisite for the construction of the MPI is that all the data required for it, must come
from the same single survey, otherwise the creation of household deprivation profiles will not be possible. Therefore, it is neither possible nor feasible to collate data on a single household from several different sur- veys i.e., health indicators from the different rounds of National Sample Surveys, education indicators from the National Achievement Surveys etc.
3.6.1 The National Family Health Survey
The globally established practice is to use the Demo- graphic and Health Surveys (DHS) in countries where it is available, for the computation of the MPI. This has several benefits, as the DHS allows for cross-country comparisons, can be disaggregated at multiple levels by geography or by population sub-groups, and most importantly, collects data across all the dimensions critical to the computation of the MPI. The DHS for India is the National Family Health Survey (NFHS), which is conducted by the International Institute for Population Sciences (IIPS) under the aegis of the Min- istry of Health and Family Welfare (MoHFW), Govern- ment of India. This is the baseline report for India’s national MPI, and has been computed using the data from the 4
th
round of the NFHS conducted in 2015-16.
The NFHS-4 captures the data for 28,69,043 individ- uals across 6,28,892 households. The data is repre- sentative at the national, state and district levels, and can be further disaggregated into urban and rural ar- eas to provide granular estimates. The NFHS covers all States and Union Territories and provides data for 640 administrative Districts defined in the 2011 cen- sus.
The national MPI will be updated upon the release of
the data for the 5
th
round of the NFHS conducted in
2019-20.
3.6.2 The Unit of Identification & Analysis
The unit of identification, i.e., the entity that is iden-
tified as poor or non-poor for India’s national MPI
is the household. The information for all members
in a household is considered altogether. Therefore,
all members in a household are assigned the same
deprivation scores. This also acknowledges the in-
tra-household positive or negative externalities in fac-
tors such as nutrition, maternal health and education.
The unit of analysis i.e., the unit for analyzing and
reporting of the results is the individual. Therefore,
the headcount ratio provides the percentage of indi-
viduals who are poor rather than the percentage of
households who are poor. This approach treats every
individual as equal in terms of reporting and differen-
tial treatment of the deprivations faced by individuals
within the same household.
INDIA MPI 28 29
Way forwardCHAPTER Chapter 4
REFORMS & PROGRESS
WAY FORWARD
This baseline National MPI Report and Dashboard is
a landmark first step in bringing multidimensional
poverty as a tool to the policy table at the nation-
al and subnational levels in India. It is expected that
the report will play an instrumental role in sensitizing
government, researchers, civil society, citizens, and
other stakeholders on the need for and importance
of MPI as a powerful policy instrument. At the higher
levels, MPI could be used as an input to the design
of development policies schemes, budget allocations,
and target setting. At the lower levels, for instance,
of that of district, MPI could decide priority of execu-
tion and delivery. With every revision of MPI based on
new survey data, actions could be redesigned to shift
focus to those who need it the most. NITI Aayog will
play a key role in charting this path and supporting
the stakeholders in their actions, through the follow-
ing approaches.
4.1 Trend analysis based on estimates from
NFHS-5
While this report is an indispensable first step in
mainstreaming MPI, it is based on a dataset which
is five years old. The success of numerous develop-
ment interventions in the recent past have resulted in
progress in key parameters on health, education, and
standard of living. For instance, saturation of village
electrification and toilets was achieved in 2018 and
2019, respectively. The NFHS-5, conducted during
2019-20, is expected to capture the progress achieved in these areas. The unit-level data of the survey re- quired for MPI estimations is likely to be published before the end of 2021, based on which the nation- al MPI figures will be revised, at the national, State/ UT, and district levels. A trend analysis, using the two datasets, will also be carried out. This will clearly point out areas of focus for the near future. The decision to conduct subsequent National Family Health Surveys once in every three years will increase the frequency of MPI revisions and reduce the lag in the reflection of development outcomes in poverty estimates. A high- er frequency of NFHS will also address the issue of stagnation of India’s global rankings in MPI and reflect the improvements adequately. Corresponding to the revision of MPI estimates, the MPI dashboard, which will present national, State, and district-level MPI and related figures, will be updated.
4.2 Reform action plan for the States/UTs
Between two consecutive NFH Surveys, focused gov- ernment action to fill the gaps and reduce depriva- tions will result in improved outcomes. The reform action plan is a tool designed to support the States in this crucial endeavor. The plan maps the government schemes and policies which have a direct bearing on the health, education, and living standards outcomes which MPI captures, to each national MPI indicator. Further, the plan also identifies indicators under these
4
44
schemes and programmes whose achievements are directly and positively correlated with MPI outcomes. This implies that progress, as measured by these indicators, will result in improvement of the corre- sponding national MPI indicators. To demonstrate, one of the indicators in the reform action plan, under the “nutrition” indicator of national MPI, is “number of Anganwadi Centres having weighing scales as a proportion of total number of Anganwadi Centres”, which is mapped to the ICDS scheme. Improvement as measured under this indicator will result in bet- ter monitoring of nutritional outcomes by the AWCs which will in turn trigger action to improve nutrition, whose success will consequently bring about reduc- tion in levels of malnutrition. This change will result in reduction of deprivation under the “nutrition” indica- tor of the national MPI, leading to a better MPI score, ceteris paribus.
While NITI Aayog has prepared a template for the re-
form action plan through consultations with central
ministries, it is important to note that the States are
being encouraged to suitably modify it, taking into
account their realities, development challenges, and
priorities. Though national MPI measures outcomes,
the reform action plan will invariably consist of admin-
istrative and input indicators. The idea is that these
input indicators will act as high- frequency proxies for
the outcome indicators. In the coming months, NITI
Aayog aims to support the States in developing effec-
tive reform action plans which will periodically cap-
ture progress under development programmes and
schemes, and contribute to reduction in deprivations
and multidimensional poverty.
4.3 Progress dashboard
While the periodic NFH Surveys will measure out-
comes and will be used for revising MPI estimates,
there is a need to strengthen implementation which
will eventually result in improved outcomes. To moni-
tor the progress of the implementation, the Develop-
ment Monitoring and Evaluation Office (DMEO), an
attached office under NITI Aayog, is in the process of
developing a progress dashboard.
This dashboard will track the progress of the reforms
implemented by the States to improve outcomes
which will eventually reflect in reduced multidimen-
sional poverty. Though this dashboard will not mon-
itor outcomes directly, nevertheless it plays a crucial
role, as the implementation of reform actions by the
States is the only way the country can achieve faster
poverty reduction and correspondingly a better posi-
tion in global MPI rankings.
4.4 Technical support to States
While NITI Aayog will continue to estimate and pub- lish MPI figures based on NFHS data periodically, the States are encouraged to pursue analysis at multiple levels. Household surveys could be designed and carried out to estimate MPI at block or district lev-
els, with higher frequency. This will offer insights into block-level estimates, which are not possible from NFHS owing to its sample design and size and deliver more frequent estimations at the district levels. The experience of the Government of Andhra Pradesh, which carried out a household survey in 2016 exclu- sively to estimate MPI at the State and district lev-
els is an example in this regard. NITI Aayog has in- house capabilities and will be willing to offer technical support to the States, should they be interested in surveys for this purpose. This support could include design of indicators, sampling design, questionnaire development, training of enumerators, data cleaning, processing, and analysis, report structuring, and ac- tion plan for improvement. On one hand, this support will result in disaggregated and more frequent MPI estimates and corresponding action plans for poverty reduction, while on the other hand, it will contribute significantly to improving state capacity in poverty estimation, monitoring, and reduction.
While the aforementioned are the clear actions
planned for the near future to further mainstream MPI
as a powerful policy tool at the national and subna-
tional levels, the long-term actions will depend on
how the project and its associated initiatives will un-
fold. Depending on the acceptance at various levels,
NITI Aayog will accordingly design and implement or
support further initiatives to fast track the adoption
of MPI. Some of this could include State-specific MPI
reports, focus on disaggregated MPI etc. The util-
ity, relevance and acceptability of a national MPI as
a powerful policy tool for fast tracking development
and leaving no one behind at the national and local
levels, will eventually shape the discourse on devel-
opmental policy in the coming days in the country as
much as in the global arena.
INDIA MpI BASelINe reporT
ResultsSeCTIoN
30 31
SECTION
III
NATIONAL
&
STATE/UT RESULTS
INDIA MPI BASELINE REPORT 32 33
IndiaResults
India: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
37.6%
2.7%
22.6%
13.9%
6.4%
14.6%
45.6%
14.0%
9.7%
58.5%
41.4%
52.0%
29.8%
12.2%
3.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population deprived
19.90%
1.88%
14.71%
10.71%
5.23%
23.13%
21.32%
5.53%
8.29%
20.56%
8.87%
5.37%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population MPI poor & deprivedIndia: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI DOWNLOAD DATA
A snapshot of multidimensional poverty in India
Overview
India Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
25.01%
Rural
Headcount Ratio Intensity MPI
32.75% 47.38% 0.155
India: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.13%
MPI (HxA)
0.118
India
Urban
Headcount Ratio Intensity MPI
8.81% 45.25% 0.04
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.1%
Child & Adolescent Mortality: 1.3%
Maternal Health: 10.4%
Years of Schooling: 15.1%
School Attendance: 7.4%
Cooking Fuel: 9.3%
Sanitation: 8.6%
Drinking Water: 2.2%
Electricity: 3.3%
Housing: 8.3%
Assets: 3.6%
Bank Account: 2.2%
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 India Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 34 35
IndiaResults
Percentage of population who are multidimensionally poor in each State/UT
India: Headcount Ratio
The size of the bar represents the percentage of population who are multidimensionally poor in each State/UT of India.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.052 0.053 to 0.105 0.106 to 0.158 0.159 to 0.211 0.212 to 0.265
States Union Territories
1.72%
1.82%
4.30%
4.79%
5.97%
6.82%
0.71%
3.76%
3.82%
4.89%
5.59%
7.62%
9.80%
12.58%
12.28%
12.31%
13.16%
13.74%
14.85%
16.65%
17.72%
17.89%
18.60%
21.43%
27.36%
24.27%
25.23%
29.35%
29.46%
29.91%
32.67%
32.67%
36.65%
37.79%
42.16%
51.91%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Puducherry
Lakshadweep
Andaman & Nicobar Islands
Delhi
Chandigarh
Daman & Diu
Jammu & Kashmir, & Ladakh
Dadra & Nagar Haveli
Kerala
Goa
Sikkim
Tamil Nadu
Punjab
Himachal Pradesh
Mizoram
Haryana
Andhra Pradesh
Karnataka
Telangana
Maharashtra
Tripura
Uttarakhand
Manipur
Gujarat
West Bengal
Arunachal Pradesh
N
agaland
Odisha
Rajasthan
Chhattisgarh
Assam
Meghalaya
Madhya Pradesh
Uttar Pradesh
Jharkhand
Bihar
Headcount Ratio (% of population who are multidimensionally poor)
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territories of Jammu & Kash-
mir, and Ladakh have been computed for their combined geographical region. Similarly, the estimates for the present Union Territory of Dadra
& Nagar Haveli & Daman & Diu have been computed separately for their erstwhile regions.
Multidimensional Poverty Index Score (State/UT-wise)
India: States & Union Territories
The colour represents the MPI score of a State/UT. The colour moves from green, through yellow, to red as the MPI score increases. Green rep- resents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.052 0.053 to 0.105 0.106 to 0.158 0.159 to 0.211 0.212 to 0.265
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 36 37
IndiaResults
0.000 to 0.0490.050 to 0.0990.100 to 0.149 0.150 to 0.199 0.200 to 0.249 0.250 to 0.299 0.300 to 0.349 0.350 to 0.399 0.400 to 0.450
Multidimensional Poverty Index Score (District-wise)
India: Districts
Districts of Jammu and Kashmir, and Ladakh are as per the Political Map of India 10
th
Edition (Survey of India). Other districts are as per the
Census of India, 2011. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score
increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the
range of MPI scores represented by a colour. Regions with no data are shown in grey.
Multidimensional Poverty Index Score (Rural and Urban)
India: States & Union Territories
The colour represents the MPI score of a State/UT. The colour moves from green, through yellow, to red as the MPI score increases. Green rep- resents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.056 0.057 to 0.114 0.115 to 0.171 0.172 to 0.228 0.229 to 0.286
MPI: Rural
MPI: Urban
INDIA MPI BASELINE REPORT 38 39
IndiaResults
State/UT-wise percentage of population deprived
Uncensored Headcount: Nutrition
Union TerritoriesStates
20.92%
13.32%
15.29%
21.04%
21.87%
22.05%
21.37%
22.11%
23.11%
23.40%
23.56%
25.9%
24.5%
24.6%
24.8%
26.38%
27.18%
28.02%
31.47%
31.10%
32.34%
32.85%
33.56%
33.62%
36.09%
37.05%
37.26%
39.67%
41.37%
42.62%
43.02%
44.47%
45.00%
45.49%
47.99%
51.88%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Daman & Diu
Puducherry
Andaman & Nicobar Islands
Chandigarh
Delhi
Jammu & Kashmir & Ladakh
Lakshadweep
Dadra & Nagar Haveli
Sikkim
Kerala
Arunachal Pradesh
Mizoram
Punjab
Manipur
N
agaland
Goa
Tamil Nadu
Andhra Pradesh
Himachal Pradesh
Tripura
Telangana
Haryana
Uttarakhand
Karnataka
West Bengal
Maharashtra
Meghalaya
Odisha
Assam
Gujarat
Rajasthan
Chhattisgarh
Uttar Pradesh
Madhya Pradesh
Jharkhand
Bihar
% of population deprived in nutrition
State/UT-wise percentage of population deprived
Uncensored Headcount: Child & Adolescent Mortality
Union Territories
0.66%
0.83%
0.19%
0.57%
0.90%
1.16%
1.00%
1.15%
1.28%
1.34%
1.38%
1.4%
1.4%
1.5%
1.7%
1.85%
1.80%
1.82%
1.91%
1.96%
2.01%
1.97%
2.07%
2.17%
2.21%
2.23%
2.30%
2.58%
2.90%
2.95%
3.10%
3.32%
3.32%
3.60%
4.58%
4.97%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
Puducherry
Andaman & Nicobar Islands
Daman & Diu
Chandigarh
Jammu & Kashmir & Ladakh
Delhi
Lakshadweep
Dadra & Nagar Haveli
Kerala
Goa
Sikkim
Tamil Nadu
Tripura
Karnataka
Telangana
Punjab
Maharashtra
West Bengal
Himachal Pradesh
Manipur
Andhra Pradesh
Arunachal Pradesh
N
agaland
Haryana
Gujarat
Odisha
Mizoram
Uttarakhand
Assam
Rajasthan
Meghalaya
Jharkhand
Chhattisgarh
Madhya Pradesh
Bihar
Uttar Pradesh
% of population deprived in child & adolescent mortality
States
Definition: A household is considered deprived if any child between the ages of 0 to 59 months, or woman between the ages of 15 to 49 years,
or man between the ages of 15 to 54 years -for whom nutritional information is available - is found to be undernourished.
Definition: A household is deprived if any child or adolescent under 18 years of age has died in the household in the five-year period preceding
the survey.
INDIA MPI BASELINE REPORT 40 41
IndiaResults
State/UT-wise percentage of population deprived
Uncensored Headcount: Maternal Health
Union TerritoriesStates
4.13%
5.11%
1.73%
5.42%
6.50%
6.70%
7.14%
9.66%
11.05%
10.87%
12.36%
12.7%
13.4%
12.7%
13.5%
14.69%
14.38%
14.77%
15.19%
15.95%
16.11%
17.42%
17.66%
19.50%
23.86%
24.70%
25.44%
26.33%
28.34%
28.56%
29.39%
31.70%
33.06%
33.07%
35.45%
45.62%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
Puducherry
Andaman & Nicobar Islands
Lakshadweep
Chandigarh
Jammu & Kashmir & Ladakh
Dadra & Nagar Haveli
Daman & Diu
Delhi
Kerala
Sikkim
Tamil Nadu
Goa
Andhra Pradesh
Telangana
Karnataka
Punjab
Tripura
West Bengal
Gujarat
Maharashtra
Mizoram
Himachal Pradesh
Manipur
O
disha
Haryana
Chhattisgarh
Assam
Rajasthan
Arunachal Pradesh
Uttarakhand
Madhya Pradesh
Meghalaya
Nagaland
Jharkhand
Uttar Pradesh
Bihar
% of population deprived in maternal health
State/UT-wise percentage of population deprived
Uncensored Headcount: Years of Schooling
Union TerritoriesStates
0.95%
3.29%
1.78%
3.78%
4.87%
5.83%
4.70%
5.36%
5.95%
6.54%
6.61%
6.8%
7.6%
7.1%
7.3%
7.76%
7.93%
8.20%
8.70%
9.79%
9.83%
10.79%
13.47%
13.63%
15.84%
15.86%
16.09%
16.19%
16.66%
16.90%
17.10%
17.52%
17.77%
18.32%
19.71%
26.27%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Lakshadweep
Puducherry
Andaman & Nicobar Islands
Chandigarh
Delhi
Jammu & Kashmir & Ladakh
Daman & Diu
Dadra & Nagar Haveli
Kerala
Himachal Pradesh
Goa
Manipur
Maharashtra
Tamil Nadu
Haryana
Punjab
Mizoram
Sikkim
Karnataka
Uttarakhand
Gujarat
Tripura
Chhattisgarh
N
agaland
Telangana
West Bengal
Madhya Pradesh
Assam
Odisha
Andhra Pradesh
Rajasthan
Uttar Pradesh
Arunachal Pradesh
Jharkhand
Meghalaya
Bihar
% of population deprived in years of schooling
Definition: A household is deprived if not even one member of the household aged 10 years or older has completed six years of schooling. Definition: A household is deprived if any woman in the household who has given birth in the 5 years preceding the survey has not received
at least 4 antenatal care visits for the most recent birth or has not received assistance from trained skilled medical personnel during the most
recent childbirth.
INDIA MPI BASELINE REPORT 42 43
IndiaResults
Definition: A household is deprived if the primary source of cooking fuel is dung, agricultural crops, shrubs, wood, charcoal or coal.
State/UT-wise percentage of population deprived
Uncensored Headcount: School Attendance
Union TerritoriesStates
2.80%
0.30%
1.10%
0.90%
1.80%
2.10%
2.70%
1.70%
2.50%
2.20%
2.50%
2.70%
2.40%
5.40%
5.40%
4.40%
5.20%
9.80%
0.92%
1.21%
1.43%
1.76%
2.65%
3.72%
4.72%
7.73%
0.54%
0.89%
0.96%
1.03%
1.42%
2.10%
2.19%
2.34%
2.36%
2.59%
3.54%
3.75%
3.82%
3.83%
4.20%
4.37%
4.81%
4.95%
5.38%
6.15%
6.55%
6.68%
8.15%
8.19%
8.39%
8.48%
11.91%
12.52%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
Jammu & Kashmir
Andaman & Nicobar Islands
Puducherry
Lakshadweep
Chandigarh
Delhi
Jammu & Kashmir & La
Daman & Diu
Dadara & Nagar Havelli
Kerala
Himachal Pradesh
Goa
Tamil Nadu
Sikkim
Telangana
Tripura
Andhra Pradesh
Manipur
Punjab
Karnataka
Mizoram
Haryana
West Bengal
Maharashtra
Uttarakhand
Nagaland
Odisha
Chhattisgarh
Meghalaya
Assam
Gujarat
Arunachal Pradesh
Jharkhand
Madhya Pradesh
Rajasthan
Uttar Pradesh
Bihar
% of population deprived in school attendance
State/UT-wise percentage of population deprived
Uncensored Headcount: Cooking Fuel
Union TerritoriesStates
7.90%
14.70%
23.70%
32.70%
1.10%
4.20%
7.70%
20.20%
25.70%
40.60%
3.10%
17.10%
7.80%
16.80%
23.30%
16.40%
20.80%
23.40%
28.20%
21.90%
35.10%
40.50%
40.80%
46.80%
28.70%
56.20%
53.10%
50.50%
56.90%
58.60%
59.90%
61.60%
69.20%
60.50%
67.00%
65.30%
68.10%
63.20%
2.21%
4.85%
9.24%
13.51%
24.53%
45.21%
46.62%
58.15%
14.91%
24.07%
31.66%
32.18%
36.40%
37.90%
39.49%
42.20%
43.89%
45.54%
48.79%
51.24%
52.07%
57.79%
58.93%
65.84%
67.90%
68.85%
69.28%
69.94%
71.25%
73.02%
77.08%
77.12%
78.04%
80.94%
82.14%
82.92%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
Daman
Diu
Ladakh
Jammu & Kashmir
Delhi
Chandigarh
Daman & Diu
Puducherry
Andaman & Nicobar Islands
Jammu & Kashmir & Ladakh
Dadara & Nagar Havelli
Lakshadweep
Goa
Tamil Nadu
Telangana
Mizoram
Punjab
Andhra Pradesh
Maharashtra
Sikkim
Kerala
Karnataka
Gujarat
Haryana
Uttarakhand
Arunachal Pradesh
Manipur
Tripura
Himachal Pradesh
Uttar Pradesh
Nagala
Rajasthan
Madhya Pradesh
West Bengal
Meghalaya
Assam
Chhattisgarh
Odisha
Jharkhand
Bihar
% of population deprived in cooking fuel
Definition: A household is deprived if any school-aged child is not attending school up to the age at which he/she would complete class 8.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of school attendance is based on the NFHS-5 State/
UT Reports. Final estimates based on the microdata may vary.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of cooking fuel is based on the NFHS-5 State/UT Reports and Factsheets. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 44 45
IndiaResults
Definition: A household is deprived if it does not have access to improved drinking water or safe drinking water is more than a 30-minute walk
from home (as a round trip).
Definition: The household has unimproved or no sanitation facility or it is improved but shared with other households.
State/UT-wise percentage of population deprived
Uncensored Headcount: Sanitation
Union TerritoriesStates
0.20%
15.00%
12.00%
18.90%
15.10%
36.90%
1.30%
12.70%
4.70%
13.40%
15.00%
12.10%
12.30%
18.30%
21.20%
26.40%
26.00%
17.10%
17.10%
25.20%
22.70%
27.40%
35.10%
28.00%
32.00%
23.80%
31.50%
28.90%
31.20%
34.90%
23.20%
39.50%
50.60%
43.30%
0.44%
19.04%
24.38%
26.70%
34.80%
35.06%
47.04%
68.11%
1.86%
10.42%
15.82%
17.30%
19.26%
21.43%
23.28%
27.75%
34.11%
36.39%
37.16%
38.61%
38.94%
43.11%
46.38%
47.57%
47.68%
47.97%
47.99%
49.27%
51.25%
53.95%
63.69%
65.25%
65.39%
70.43%
73.61%
75
.38%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
32.90%Daman
57.70%Ladakh
24.30%Jammu & Kashmir
10.30%Diu
Lakshadweep
Chandigarh
Andaman & Nicobar Islands
Delhi
Daman & Diu
Puducherry
Jammu & Kashmir & Ladakh
Dadara & Nagar Havelli
Kerala
Sikkim
Mizoram
Punjab
Haryana
Goa
Nagaland
Himachal Pradesh
Uttarakhand
Tripura
Gujarat
Meghalaya
Arunachal Pradesh
Karnataka
Andhra Pradesh
Tamil Nadu
Manipur
Maharashtra
West Bengal
Telangana
Assam
Rajasthan
Uttar Pradesh
Madhya Pradesh
Chhattisgarh
Odisha
Bihar
Jharkhand
% of population deprived in sanitation
State/UT-wise percentage of population deprived
Uncensored Headcount: Drinking Water
Union TerritoriesStates
10.30%
1.70%
7.40%
1.60%
5.40%
5.00%
4.70%
5.10%
5.40%
9.10%
6.90%
13.90%
14.90%
10.70%
3.20%
9.30%
23.60%
28.50%
2.35%
5.49%
6.20%
9.31%
11.67%
14.16%
23.75%
26.95%
1.93%
2.34%
2.34%
4.11%
5.38%
5.91%
7.92%
9.03%
9.48%
11.53%
12.51%
12.61%
12.67%
13.89%
14.51%
14.95%
16.31%
17.71%
18.30%
20.97%
21.23%
26.77%
27.81%
29.83%
31.06%
32.53%
33.52%
60.89%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Jammu & Kashmir
Chandigarh
Puducherry
Andaman & Nicobar Islands
Laksha
Daman & Diu
Jammu & Kashmir & Ladakh
Delhi
Dadara & Nagar Havelli
Punjab
Bihar
Sikkim
Goa
Uttar Pradesh
Kerala
Himachal Pradesh
Uttarakhand
Mizoram
West Bengal
Gujarat
Tamil Nadu
Haryana
Maharashtra
Karnataka
Arunachal Pradesh
Tripura
Assam
Chhattisgarh
Odisha
Nagaland
Rajasthan
Telangana
Madhya Pradesh
Jharkhand
Andhra Pradesh
Meghalaya
Manipur
% of population deprived in drinking water
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of drinking water is based on the NFHS-5 State/UT
Reports. Final estimates based on the microdata may vary.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of sanitation is based on the NFHS-5 State/UT Re- ports and Factsheets. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 46 47
IndiaResults
Definition: A household is deprived if it has inadequate housing: the floor is made of natural materials, or the roof or walls are made of rudi-
mentary materials.
Definition: A household is deprived if it has no electricity.
State/UT-wise percentage of population deprived
Uncensored Headcount: Electricity
Union TerritoriesStates
0.50%
0.70%
0.50%
0.20%
0.10%
0.10%
0.10%
2.40%
0.30%
0.30%
0.50%
0.70%
0.40%
0.50%
0.70%
0.40%
0.40%
0.90%
0.40%
1.50%
1.20%
2.40%
1.80%
2.50%
2.20%
1.80%
1.90%
8.10%
1.90%
1.60%
5.20%
3.00%
5.70%
7.40%
9.00%
3.70%
0.03%
0.05%
0.24%
0.28%
0.48%
2.72%
2.74%
2.78%
0.18%
0.39%
0.49%
0.65%
0.74%
0.77%
0.97%
1.06%
1.23%
1.71%
2.17%
3.25%
3.64%
3.75%
4.08%
5.75%
6.59%
7.18%
7.31%
8.18%
8.73%
8.96%
11.83%
13.37%
18.79%
21.78%
27.43%
39.86%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0
Diu
Daman
Jammu & Kashmir
Ladakh
Daman & Diu
Lakshadweep
Puducherry
Delhi
Chandigarh
Andaman & Nicobar Islands
Dadara & Nagar Havelli
Jammu & Kashmir & Ladakh
Goa
Punjab
Himachal Pradesh
Sikkim
Kerala
Andhra Pradesh
Tamil Nadu
Haryana
Telangana
Karnataka
Uttarakhand
Nagaland
Chhattisgarh
Gujarat
Mizoram
West Bengal
Maharashtra
Tripura
Manipur
Meghalaya
Rajasthan
Madhya Pradesh
Arunachal Pradesh
Odisha
Jharkhand
Assam
Uttar Pr
Bihar
% of population deprived in electricity
0.00%
0.00%
State/UT-wise percentage of population deprived
Uncensored Headcount: Housing
Union TerritoriesStates
25.40%
16.70%
9.50%
14.70%
23.30%
19.70%
26.20%
23.10%
24.00%
36.40%
55.90%
47.20%
66.40%
65.30%
67.00%
69.30%
76.80%
1.54%
6.40%
8.08%
10.79%
17.59%
29.62%
33.61%
57.06%
10.76%
16.16%
17.55%
19.30%
20.18%
24.17%
24.25%
24.27%
25.53%
26.71%
27.90%
29.30%
35.54%
35.59%
37.30%
50.40%
54.26%
55.81%
61.78%
63.31%
64.38%
67.52%
70.98%
73.73%
74.66%
75.90%
76.15%
81.49%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
Jammu & Kashmir
Lakshadweep
Chandigarh
Dama
Delhi
Puducherry
Jammu & Kashmir & Ladakh
Andaman & Nicobar Islands
Dadara & Nagar Havelli
Kerala
Goa
Andhra Pradesh
Punjab
Tamil Nadu
Mizoram
Gujarat
Haryana
Telangana
Sikkim
Maharashtra
Himachal Pradesh
Rajasthan
Uttarakhand
Karnataka
Meghalaya
West Bengal
Odisha
Jharkhand
Chhattisgarh
Madhya Pradesh
Uttar Pradesh
Nagaland
Bihar
Tripura
Assam
Arunachal Pradesh
Manipur
% of population deprived in housing
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of electricity is based on the NFHS-5 State/UT Re-
ports and Factsheets. Final estimates based on the microdata may vary.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of housing is based on the NFHS-5 State/UT Re- ports. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 48 49
IndiaResults
Definition: No household member has a bank account or a post office account.
State/UT-wise percentage of population deprived
Uncensored Headcount: Assets
Union TerritoriesStates
1.02%
1.65%
2.71%
1.72%
2.94%
2.97%
3.38%
4.65%
5.54%
7.10%
7.52%
9.5%
10.0%
11.0%
12.4%
12.79%
13.60%
13.84%
13.93%
13.95%
13.97%
14.11%
14.74%
16.12%
14.92%
18.76%
19.22%
19.31%
19.95%
20.81%
20.49%
21.37%
23.36%
24.32%
29.88%
33.91%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
Lakshadweep
Puducherry
Chandigarh
Delhi
Andaman & Nicobar Islands
Daman & Diu
Jammu & Kashmir & Ladakh
Dadra & Nagar Haveli
Punjab
Kerala
Goa
Tamil Nadu
Haryana
Himachal Pradesh
Sikkim
Karnataka
Andhra Pradesh
Uttar Pradesh
Telangana
Gujarat
Uttarakhand
Manipur
Mizoram
Maharashtra
West Bengal
Chhattisgarh
Tripura
O
disha
Madhya Pradesh
Assam
Rajasthan
Jharkhand
Arunachal Pradesh
Bihar
Meghalaya
Nagaland
% of population deprived in assets
State/UT-wise percentage of population deprived
Uncensored Headcount: Bank Accounts
Union TerritoriesStates
3.00%
2.10%
3.00%
2.60%
3.30%
3.60%
3.40%
2.90%
6.90%
5.10%
4.40%
5.00%
4.50%
3.70%
9.10%
4.20%
4.00%
7.10%
1.57%
3.95%
3.97%
5.35%
5.62%
8.36%
10.92%
12.52%
2.69%
3.63%
3.71%
4.02%
4.03%
4.32%
4.73%
4.88%
5.74%
5.81%
6.35%
6.90%
7.46%
8.17%
8.38%
8.83%
8.98%
9.41%
10.35%
10.94%
11.15%
13.81%
15.40%
15.40%
19.91%
21.54%
26.00%
28.66%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Jammu & Kashmir
Andaman & Nicobar Islands
Jammu & Kashmir & Ladakh
Chandigarh
Puducher
Lakshadweep
Delhi
Dadara & Nagar Havelli
Daman & Diu
Himachal Pradesh
Tripura
Punjab
Goa
Rajasthan
Kerala
Andhra Pradesh
Uttar Pradesh
Chhattisgarh
Mizoram
Tamil Nadu
Uttarakhand
Telangana
Haryana
Sikkim
Karnataka
Jharkhand
Gujarat
Maharashtra
Odisha
Madhya Pradesh
West Bengal
Arunachal Pradesh
Assam
Meghalaya
Manipur
Bihar
Nagaland
% of population deprived in bank accounts
Definition: The household is deprived if it does not own more than one of these assets: radio, TV, telephone, computer, animal cart, bicycle,
motorbike, or refrigerator; and does not own a car or truck.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of bank accounts is based on the NFHS-5 State/UT Reports and Factsheets. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 50 51
Andhra PradeshResults
Andhra Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
26.4%
1.8%
9.7%
16.9%
2.3%
11.0%
1.7%
37.9%
16.4%
46.4%
22.7%
32.5%
9.3%
0.8%0.5%
17.5%
14.7%
4.7%
3.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
9.22%
0.87%
4.76%
7.72%
1.47%
9.72%
10.50%
4.39%
0.60%
5.69%
4.82%
1.74%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Andhra Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Andhra Pradesh
Overview
Andhra Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
12.31%
Rural
Headcount Ratio Intensity MPI
15.37% 43.28% 0.067
Andhra Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.23%
MPI (HxA)
0.053
Andhra Pradesh
Urban
Headcount Ratio Intensity MPI
4.91% 42.83% 0.021
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Nutrition: 28.9%
Child & Adolescent Mortality: 1.4%
Maternal Health: 7.5%
Years of Schooling: 24.2%
School Attendance: 4.6%
Cooking Fuel: 8.7%
Sanitation: 9.4%
Drinking Water: 3.9%
Electricity: 0.5%
Housing: 5.1%
Assets: 4.3%
Bank Account: 1.6%
Health Education Standard of Living
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Andhra Pradesh State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 52 53
Andhra PradeshResults
0.034 to 0.042 0.043 to 0.050 0.051 to 0.059 0.060 to 0.068 0.069 to 0.076 0.077 to 0.085 0.086 to 0.094
8.31%
8.55%
8.98%
9.11%
9.96%
10.34%
11.67%
13.24%
14.01%
15.10%
15.63%
19.00%
20.69%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0%
Guntur
East Godavari
Krishna
West Godavari
Y.S.R. Kadapa
Chittoor
SPSR Nellore
Anantapur
Srikakulam
Visakhapatnam
Prakasam
Vizianagaram
Kurnool
Headcount Ratio (% of population who are multidimensionally poor)
Andhra Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Andhra Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Andhra Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Andhra Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.034 to 0.042 0.043 to 0.050 0.051 to 0.059 0.060 to 0.068 0.069 to 0.076 0.077 to 0.085 0.086 to 0.094
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 54 55
Andhra PradeshResults
Multidimensional Poverty in Andhra Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Andhra Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Andhra Pradesh are as per the 2011 Census of India Dis tricts of Andhra Pradesh are as per the 2011 Census of India
Districts of Andhra Pradesh Headcount Ratio Intensity MPI
Anantapur 13.24% 42.08% 0.056
Chittoor 10.34% 42.35% 0.044
East Godavari 8.55% 41.46% 0.035
Guntur 8.31% 41.21% 0.034
Krishna 8.98% 41.74% 0.037
Kurnool 20.69% 45.56% 0.094
Prakasam 15.63% 44.52% 0.070
SPSR Nellore 11.67% 44.41% 0.052
Srikakulam 14.01% 41.57% 0.058
Visakhapatnam 15.10% 47.03% 0.071
Vizianagaram 19.00% 42.51% 0.081
West Godavari 9.11% 39.90% 0.036
Y.S.R. Kadapa 9.96% 41.91% 0.042
Rural Urban
Districts of Andhra Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anantapur 17.02% 42.02% 0.072 5.05% 42.52% 0.021
Chittoor 12.24% 42.13% 0.052 4.90% 43.94% 0.022
East Godavari 10.34% 41.36% 0.043 3.05% 42.51% 0.013
Guntur 10.77% 41.64% 0.045 4.06% 39.23% 0.016
Krishna 12.08% 41.88% 0.051 3.09% 40.66% 0.013
Kurnool 25.67% 45.66% 0.117 9.58% 44.95% 0.043
Prakasam 17.04% 44.74% 0.076 9.68% 42.91% 0.042
SPSR Nellore 12.93% 43.97% 0.057 8.15% 46.34% 0.038
Srikakulam 16.89% 41.57% 0.070 0.00% - 0.000
Visakhapatnam 25.50% 47.21% 0.120 3.08% 45.29% 0.014
Vizianagaram 23.09% 42.49% 0.098 4.42% 42.85% 0.019
West Godavari 9.39% 39.93% 0.037 7.94% 39.76% 0.032
Y.S.R. Kadapa 13.68% 42.36% 0.058 3.55% 38.90% 0.014
INDIA MPI BASELINE REPORT 56 57
Arunachal PradeshResults
Arunachal Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
21.0%
2.0%
28.3%
17.8%
8.1%
15.0%
76.1%
23.4%
15.4%
57.8%
46.8%
38.9%
17.1%
11.8%
5.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
13.80%
1.19%
14.86%
13.48%
5.90%
21.29%
16.65%
6.19%
7.15%
23.29%
12.85%
9.23%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Arunachal Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Arunachal Pradesh
Overview
Arunachal Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
24.27%
Rural
Headcount Ratio Intensity MPI
29.23% 47.6% 0.139
Arunachal Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.26%
MPI (HxA)
0.115
Arunachal Pradesh
Urban
Headcount Ratio Intensity MPI
8.15% 43.18% 0.035
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 20.1%
Child & Adolescent Mortality: 0.9%
Maternal Health: 10.8%
Years of Schooling: 19.6%
School Attendance: 8.6%
Cooking Fuel: 8.8%
Sanitation: 6.9%
Drinking Water: 2.6%
Electricity: 3.0%
Housing: 9.7%
Assets: 5.3%
Bank Account: 3.8%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Arunachal Pradesh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 58 59
Arunachal PradeshResults
0.037 to 0.061 0.062 to 0.087 0.088 to 0.112 0.113 to 0.138 0.139 to 0.163 0.164 to 0.189 0.190 to 0.215
8.84%
12.85%
14.55%
15.95%
15.97%
17.13%
22.56%
22.86%
23.56%
26.5%
28.3%
29.8%
31.30%
31.97%
39.69%
44.08%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
East Siang
Papum Pare
West Siang
Upper Siang
Lower Subansiri
Dibang Valley
West Kameng
Anjaw
Lower Dibang Valley
Changlang
Tirap
Upper Subansiri
Tawang
Lohit
Kurung Kumey
East Kameng
Headcount Ratio (% of population who are multidimensionally poor)
Arunachal Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Arunachal Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Arunachal Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Arunachal Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.037 to 0.061 0.062 to 0.087 0.088 to 0.112 0.113 to 0.138 0.139 to 0.163 0.164 to 0.189 0.190 to 0.215
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 60 61
Arunachal PradeshResults
Multidimensional Poverty in Arunachal Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Arunachal Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Arunachal Pradesh are as per the 2011 Census of India Dis tricts of Arunachal Pradesh are as per the 2011 Census of India
Districts of Arunachal Pradesh Headcount Ratio Intensity MPI
Anjaw 22.86% 42.92% 0.098
Changlang 26.53% 48.04% 0.127
Dibang Valley 17.13% 41.40% 0.071
East Kameng 44.08% 48.84% 0.215
East Siang 8.84% 41.61% 0.037
Kurung Kumey 39.69% 47.46% 0.188
Lohit 31.97% 51.28% 0.164
Lower Dibang Valley 23.56% 45.96% 0.108
Lower Subansiri 15.97% 43.84% 0.070
Papum Pare 12.85% 45.95% 0.059
Tawang 31.30% 45.62% 0.143
Tirap 28.30% 48.12% 0.136
Upper Siang 15.95% 41.41% 0.066
Upper Subansiri 29.78% 45.02% 0.134
West Kameng 22.56% 43.86% 0.099
West Siang 14.55% 45.19% 0.066
Rural Urban
Districts of Arunachal Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anjaw 22.79% 43.04% 0.098 24.96% 39.73% 0.099
Changlang 28.65% 48.31% 0.138 7.65% 39.07% 0.030
Dibang Valley 20.54% 41.38% 0.085 3.18% 41.81% 0.013
East Kameng 56.81% 49.70% 0.282 15.64% 41.83% 0.065
East Siang 10.67% 41.94% 0.045 4.84% 40.02% 0.019
Kurung Kumey 39.82% 47.65% 0.190 34.25% 38.43% 0.132
Lohit 36.66% 51.15% 0.188 12.82% 52.84% 0.068
Lower Dibang Valley 26.75% 46.16% 0.123 11.89% 44.31% 0.053
Lower Subansiri 16.65% 44.36% 0.074 10.07% 36.31% 0.037
Papum Pare 20.25% 46.96% 0.095 6.85% 43.55% 0.030
Tawang 34.26% 45.61% 0.156 1.73% 45.87% 0.008
Tirap 33.16% 48.40% 0.160 6.13% 41.11% 0.025
Upper Siang 18.42% 41.38% 0.076 3.62% 42.31% 0.015
Upper Subansiri 35.30% 45.54% 0.161 8.98% 37.22% 0.033
West Kameng 26.47% 43.97% 0.116 5.43% 41.49% 0.023
West Siang 17.82% 45.94% 0.082 4.72% 36.65% 0.017
INDIA MPI BASELINE REPORT 62 63
AssamResults
Assam: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
39.7%
2.9%
25.4%
16.2%
19.9%
6.6%
4.4%
77.1%
60.5%
51.3%
31.5%
17.7%
14.9%
21.8%
7.4%
75.9%
69.3%
15.4%
3.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
25.46%
2.18%
17.78%
14.26%
5.64%
31.64%
24.43%
8.25%
14.71%
31.39%
13.90%
10.51%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Assam: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Assam
Overview
Assam Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
32.67%
Rural
Headcount Ratio Intensity MPI
36.16% 48.07% 0.174
Assam: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.89%
MPI (HxA)
0.156
Assam
Urban
Headcount Ratio Intensity MPI
9.97% 43.63% 0.044
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.1%
Child & Adolescent Mortality: 1.2%
Maternal Health: 9.5%
Years of Schooling: 15.2%
School Attendance: 6.0%
Cooking Fuel: 9.6%
Sanitation: 7.4%
Drinking Water: 2.5%
Electricity: 4.5%
Housing: 9.6%
Assets: 4.2%
Bank Account: 3.2%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Assam State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 64 65
AssamResults
0.052 to 0.081 0.082 to 0.110 0.111 to 0.140 0.141 to 0.170 0.171 to 0.199 0.200 to 0.229 0.230 to 0.260
11.04%
16.94%
20.24%
20.60%
23.59%
24.23%
25.32%
25.69%
26.22%
27.71%
28.97%
29.46%
30.5%
31.2%
32.1%
33.8%
36.2%
36.70%
36.75%
37.73%
38.22%
39.41%
40.15%
42.37%
46.02%
51.06%
51.07%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Kamrup Metropolitan
Nalbari
Jorhat
Golaghat
Baksa
Lakhimpur
Sonitpur
Sivasagar
Kamrup
Dhemaji
Dibrugarh
Udalguri
Nagaon
Dima Hasao
Kokrajhar
Bongaigaon
Chirang
Tinsukia
Marigaon
Karbi Anglong
Darrang
Barpeta
Goalpara
Cachar
Karimganj
Dhubri
Hailakandi
Headcount Ratio (% of population who are multidimensionally poor)
Assam: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Assam.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Assam
Multidimensional Poverty Index Score (District-wise)
Districts of Assam are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.052 to 0.081 0.082 to 0.110 0.111 to 0.140 0.141 to 0.170 0.171 to 0.199 0.200 to 0.229 0.230 to 0.260
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 66 67
AssamResults
Multidimensional Poverty in Assam
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Assam
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Assam are as per the 2011 Census of India Districts of Assam are as per the 2011 Census of India
Districts of Assam Headcount Ratio Intensity MPI
Baksa 23.59% 43.45% 0.102
Barpeta 39.41% 46.52% 0.183
Bongaigaon 33.77% 45.80% 0.155
Cachar 42.37% 49.54% 0.210
Chirang 36.20% 45.71% 0.165
Darrang 38.22% 49.49% 0.189
Dhemaji 27.71% 45.03% 0.125
Dhubri 51.06% 50.85% 0.260
Dibrugarh 28.97% 47.05% 0.136
Dima Hasao 31.24% 49.90% 0.156
Goalpara 40.15% 50.65% 0.203
Golaghat 20.60% 45.64% 0.094
Hailakandi 51.07% 49.21% 0.251
Jorhat 20.24% 43.62% 0.088
Kamrup 26.22% 45.03% 0.118
Kamrup Metropolitan 11.04% 47.03% 0.052
Karbi Anglong 37.73% 48.05% 0.181
Karimganj 46.02% 48.45% 0.223
Kokrajhar 32.14% 46.11% 0.148
Lakhimpur 24.23% 46.80% 0.113
Marigaon 36.75% 47.71% 0.175
Nagaon 30.51% 47.10% 0.144
Nalbari 16.94% 44.69% 0.076
Sivasagar 25.69% 49.16% 0.126
Sonitpur 25.32% 46.55% 0.118
Tinsukia 36.70% 52.07% 0.191
Udalguri 29.46% 44.74% 0.132
Rural Urban
Districts of Assam
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Baksa 23.76% 43.46% 0.103 8.57% 40.48% 0.035
Barpeta 41.34% 46.61% 0.193 18.78% 44.34% 0.083
Bongaigaon 36.86% 45.89% 0.169 16.12% 44.75% 0.072
Cachar 49.01% 49.84% 0.244 7.28% 38.55% 0.028
Chirang 37.00% 45.62% 0.169 25.80% 47.34% 0.122
Darrang 40.28% 49.60% 0.200 9.78% 43.64% 0.043
Dhemaji 29.23% 45.09% 0.132 9.02% 42.62% 0.038
Dhubri 54.88% 51.04% 0.280 12.52% 42.15% 0.053
Dibrugarh 33.94% 47.20% 0.160 9.30% 44.85% 0.042
Dima Hasao 41.55% 50.78% 0.211 8.08% 39.71% 0.032
Goalpara 45.38% 50.80% 0.231 6.65% 44.34% 0.029
Golaghat 21.81% 45.67% 0.100 9.78% 45.19% 0.044
Hailakandi 54.12% 49.28% 0.267 8.39% 42.47% 0.036
Jorhat 23.01% 43.82% 0.101 9.00% 41.49% 0.037
Kamrup 26.27% 45.07% 0.118 25.72% 44.64% 0.115
Kamrup Metropolitan 20.96% 48.15% 0.101 8.46% 46.31% 0.039
Karbi Anglong 39.63% 48.46% 0.192 20.01% 40.63% 0.081
Karimganj 49.72% 48.56% 0.241 6.53% 39.86% 0.026
Kokrajhar 33.60% 46.24% 0.155 8.53% 37.81% 0.032
Lakhimpur 26.06% 46.82% 0.122 8.14% 46.22% 0.038
Marigaon 39.00% 47.61% 0.186 13.43% 50.76% 0.068
Nagaon 33.16% 47.39% 0.157 11.37% 40.83% 0.046
Nalbari 18.32% 44.83% 0.082 4.03% 38.77% 0.016
Sivasagar 28.02% 49.22% 0.138 1.95% 40.75% 0.008
Sonitpur 27.27% 46.55% 0.127 1.37% 46.43% 0.006
Tinsukia 44.13% 52.47% 0.232 8.50% 44.16% 0.038
Udalguri 30.62% 44.88% 0.137 8.45% 35.37% 0.030
INDIA MPI BASELINE REPORT 68 69
BiharResults
Bihar: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
51.9%
4.6%
45.6%
26.3%
24.3%
12.5%
9.8%
82.9%
63.2%
73.6%
50.6%
2.3%
1.7%
39.9%
3.7%
73.7%
65.3%
26.0%
4.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
41.60%
3.92%
36.51%
24.70%
11.62%
50.20%
46.57%
1.64%
28.79%
47.10%
18.69%
19.60%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Bihar: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Bihar
Overview
Bihar Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
51.91%
Rural
Headcount Ratio Intensity MPI
56.01% 51.14% 0.286
Bihar: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
51.02%
MPI (HxA)
0.265
Bihar
Urban
Headcount Ratio Intensity MPI
23.91% 49% 0.117
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 26.2%
Child & Adolescent Mortality: 1.2%
Maternal Health: 11.5%
Years of Schooling: 15.5%
School Attendance: 7.3%
Cooking Fuel: 9.0%
Sanitation: 8.4%
Drinking Water: 0.3%
Electricity: 5.2%
Housing: 8.5%
Assets: 3.4%
Bank Account: 3.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Bihar State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 70 71
BiharResults
0.138 to 0.168 0.169 to 0.199 0.200 to 0.231 0.232 to 0.262 0.263 to 0.293 0.294 to 0.324 0.325 to 0.356
29.20%
40.50%
40.55%
40.74%
41.84%
40.99%
42.75%
42.80%
43.90%
43.94%
44.48%
45.41%
45.6%
46.6%
47.6%
48.0%
50.7%
51.7%
52.2%
52.7%
54.67%
55.47%
56.45%
57.83%
55.87%
57.50%
58.23%
60.03%
61.48%
62.38%
63.29%
63.46%
64.01%
64.10%
64.13%
64.43%
64.65%
64.75%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Patna
Bhojpur
Siwan
Rohtas
Munger
Buxar
Gopalganj
Saran
Lakhisarai
Aurangabad
Kaimur
Jehanabad
Bhagalpur
Nalanda
Vaishali
Muzaffarpur
Begusarai
Nawada
Arwal
Sheikhpura
Gaya
Madhubani
Samastipur
Darbhanga
Pashchim Champaran
Banka
Khagaria
Sheohar
Saharsa
Katihar
Purnia
Sitamarhi
Jamui
Supaul
Purba Champaran
Madhepura
A
raria
Kishanganj
Headcount Ratio (% of population who are multidimensionally poor)
Bihar: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Bihar.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Bihar
Multidimensional Poverty Index Score (District-wise)
Districts of Bihar are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.138 to 0.168 0.169 to 0.199 0.200 to 0.231 0.232 to 0.262 0.263 to 0.293 0.294 to 0.324 0.325 to 0.356
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 72 73
BiharResults
Multidimensional Poverty in Bihar
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Bihar
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Bihar are as per the 2011 Census of India Districts of Bihar are as per the 2011 Census of India
Districts of Bihar Headcount Ratio Intensity MPI
Araria 64.65% 55.12% 0.356
Arwal 52.18% 47.83% 0.250
Aurangabad 43.94% 46.91% 0.206
Banka 57.83% 50.39% 0.291
Begusarai 50.68% 51.17% 0.259
Bhagalpur 45.60% 51.97% 0.237
Bhojpur 40.50% 46.54% 0.188
Buxar 41.84% 45.48% 0.190
Darbhanga 56.45% 51.77% 0.292
Gaya 54.67% 49.67% 0.272
Gopalganj 42.75% 47.21% 0.202
Jamui 64.01% 50.71% 0.325
Jehanabad 45.41% 50.42% 0.229
Kaimur 44.48% 47.79% 0.213
Katihar 62.38% 53.82% 0.336
Khagaria 58.23% 54.38% 0.317
Kishanganj 64.75% 53.88% 0.349
Lakhisarai 43.90% 50.62% 0.222
Madhepura 64.43% 54.42% 0.351
Madhubani 55.47% 51.16% 0.284
Munger 40.99% 48.98% 0.201
Muzaffarpur 48.00% 49.82% 0.239
Nalanda 46.61% 50.65% 0.236
Nawada 51.72% 50.53% 0.261
Pashchim Champaran 57.50% 52.83% 0.304
Patna 29.20% 47.26% 0.138
Purba Champaran 64.13% 52.78% 0.339
Purnia 63.29% 54.52% 0.345
Rohtas 40.74% 44.38% 0.181
Saharsa 61.48% 54.84% 0.337
Samastipur 55.87% 52.58% 0.294
Saran 42.80% 48.35% 0.207
Sheikhpura 52.70% 49.41% 0.260
Sheohar 60.03% 51.84% 0.311
Sitamarhi 63.46% 52.67% 0.334
Siwan 40.55% 46.23% 0.187
Supaul 64.10% 51.78% 0.332
Vaishali 47.64% 48.72% 0.232
Rural Urban
Districts of Bihar
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Araria 67.70% 55.26% 0.374 25.32% 50.26% 0.127
Arwal 54.20% 47.91% 0.260 26.94% 45.76% 0.123
Aurangabad 46.46% 46.72% 0.217 20.42% 50.83% 0.104
Banka 58.29% 50.38% 0.294 45.09% 50.65% 0.228
Begusarai 54.00% 51.56% 0.278 36.84% 48.76% 0.180
Bhagalpur 52.69% 52.36% 0.276 21.64% 48.81% 0.106
Bhojpur 44.10% 46.48% 0.205 23.35% 47.06% 0.110
Buxar 46.15% 45.37% 0.209 9.91% 49.31% 0.049
Darbhanga 59.35% 51.91% 0.308 24.26% 48.01% 0.116
Gaya 59.48% 49.92% 0.297 23.84% 45.75% 0.109
Gopalganj 41.80% 46.56% 0.195 55.16% 53.64% 0.296
Jamui 66.42% 50.99% 0.339 32.44% 43.16% 0.140
Jehanabad 50.51% 50.47% 0.255 12.01% 48.83% 0.059
Kaimur 46.87% 47.54% 0.223 8.22% 69.51% 0.057
Katihar 66.36% 53.95% 0.358 22.90% 50.15% 0.115
Khagaria 60.58% 54.51% 0.330 11.05% 40.01% 0.044
Kishanganj 66.46% 53.82% 0.358 47.85% 54.58% 0.261
Lakhisarai 45.68% 50.96% 0.233 35.30% 48.49% 0.171
Madhepura 66.52% 54.54% 0.363 20.93% 46.73% 0.098
Madhubani 56.38% 51.27% 0.289 32.89% 46.67% 0.153
Munger 47.23% 49.56% 0.234 23.71% 45.74% 0.108
Muzaffarpur 50.42% 49.62% 0.250 22.40% 54.63% 0.122
Nalanda 51.95% 50.71% 0.263 24.32% 50.12% 0.122
Nawada 56.17% 50.80% 0.285 18.39% 44.41% 0.082
Pashchim Champaran 64.37% 52.92% 0.341 18.95% 51.08% 0.097
Patna 45.46% 47.66% 0.217 13.80% 46.02% 0.064
Purba Champaran 65.68% 52.54% 0.345 47.76% 56.29% 0.269
Purnia 68.08% 54.91% 0.374 29.73% 48.26% 0.143
Rohtas 43.97% 44.22% 0.194 19.13% 46.92% 0.090
Saharsa 64.08% 54.99% 0.352 25.54% 49.56% 0.127
Samastipur 56.98% 52.63% 0.300 29.72% 50.40% 0.150
Saran 45.34% 48.39% 0.219 19.50% 47.45% 0.093
Sheikhpura 58.17% 49.52% 0.288 28.23% 48.38% 0.137
Sheohar 60.65% 51.74% 0.314 46.82% 54.65% 0.256
Sitamarhi 65.50% 53.05% 0.347 42.30% 46.54% 0.197
Siwan 40.97% 46.32% 0.190 32.72% 44.14% 0.144
Supaul 63.54% 51.62% 0.328 76.33% 54.78% 0.418
Vaishali 50.89% 48.78% 0.248 20.39% 47.50% 0.097
INDIA MPI BASELINE REPORT 74 75
ChhattisgarhResults
Chhattisgarh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
43.0%
3.3%
24.7%
13.5%
5.4%
18.3%
63.3%
14.9%
5.7%
78.0%
67.0%
65.4%
23.2%
3.6%
1.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
24.04%
2.25%
16.96%
10.91%
4.31%
29.14%
26.64%
10.19%
2.78%
26.78%
10.43%
3.40%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Chhattisgarh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Chhattisgarh
Overview
Chhattisgarh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
29.91%
Rural
Headcount Ratio Intensity MPI
35.73% 44.83% 0.16
Chhattisgarh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.64%
MPI (HxA)
0.134
Chhattisgarh
Urban
Headcount Ratio Intensity MPI
10.2% 42.41% 0.043
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.0%
Child & Adolescent Mortality: 1.4%
Maternal Health: 10.6%
Years of Schooling: 13.6%
School Attendance: 5.4%
Cooking Fuel: 10.4%
Sanitation: 9.5%
Drinking Water: 3.6%
Electricity: 1.0%
Housing: 9.6%
Assets: 3.7%
Bank Account: 1.2%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Chhattisgarh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 76 77
ChhattisgarhResults
0.076 to 0.105 0.106 to 0.136 0.137 to 0.166 0.167 to 0.197 0.198 to 0.227 0.228 to 0.257 0.258 to 0.289
18.59%
20.00%
21.82%
23.14%
23.16%
25.66%
27.03%
29.85%
30.82%
31.86%
38.2%
39.6%
41.2%
45.85%
46.95%
47.37%
51.52%
54.59%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Dhamtari
Durg
Raipur
Rajnandgaon
Janjgir-Champa
Bilaspur
North Bastar Kanker
Mahasamund
Raigarh
Korba
Koriya
Kabeerdham
Bijapur
Jashpur
Bastar
Surguja
Narayanpur
Dantewada
Headcount Ratio (% of population who are multidimensionally poor)
Chhattisgarh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Chhattisgarh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Chhattisgarh
Multidimensional Poverty Index Score (District-wise)
Districts of Chhattisgarh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.076 to 0.105 0.106 to 0.136 0.137 to 0.166 0.167 to 0.197 0.198 to 0.227 0.228 to 0.257 0.258 to 0.289
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 78 79
ChhattisgarhResults
Multidimensional Poverty in Chhattisgarh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Chhattisgarh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Chhattisgarh are as per the 2011 Census of India Districts of Chhattisgarh are as per the 2011 Census of India
Districts of Chhattisgarh Headcount Ratio Intensity MPI
Bastar 46.95% 48.22% 0.226
Bijapur 41.20% 44.52% 0.183
Bilaspur 25.66% 43.25% 0.111
Dantewada 54.59% 52.92% 0.289
Dhamtari 18.59% 40.74% 0.076
Durg 20.00% 41.97% 0.084
Janjgir-Champa 23.16% 41.55% 0.096
Jashpur 45.85% 46.09% 0.211
Kabeerdham 39.56% 46.50% 0.184
Korba 31.86% 45.87% 0.146
Koriya 38.24% 44.89% 0.172
Mahasamund 29.85% 42.03% 0.125
Narayanpur 51.52% 49.42% 0.255
North Bastar Kanker 27.03% 43.47% 0.117
Raigarh 30.82% 43.48% 0.134
Raipur 21.82% 43.50% 0.095
Rajnandgaon 23.14% 40.37% 0.093
Surguja 47.37% 46.60% 0.221
Rural Urban
Districts of Chhattisgarh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bastar 51.19% 48.30% 0.247 16.97% 46.57% 0.079
Bijapur 44.90% 44.75% 0.201 12.81% 38.16% 0.049
Bilaspur 31.38% 43.44% 0.136 8.45% 41.18% 0.035
Dantewada 61.59% 53.31% 0.328 15.26% 44.11% 0.067
Dhamtari 20.83% 40.08% 0.083 10.22% 45.81% 0.047
Durg 27.45% 42.46% 0.117 8.11% 39.34% 0.032
Janjgir-Champa 24.89% 41.84% 0.104 12.81% 38.17% 0.049
Jashpur 48.56% 46.17% 0.224 9.92% 41.10% 0.041
Kabeerdham 42.28% 46.36% 0.196 15.58% 49.78% 0.078
Korba 44.08% 46.15% 0.203 9.75% 43.52% 0.042
Koriya 48.48% 45.13% 0.219 14.25% 42.97% 0.061
Mahasamund 30.90% 41.87% 0.129 20.07% 44.30% 0.089
Narayanpur 57.36% 49.95% 0.287 19.37% 40.77% 0.079
North Bastar Kanker 29.65% 43.62% 0.129 4.34% 34.64% 0.015
Raigarh 35.16% 43.47% 0.153 4.07% 44.10% 0.018
Raipur 28.74% 43.61% 0.125 9.94% 42.96% 0.043
Rajnandgaon 24.96% 40.25% 0.100 14.70% 41.31% 0.061
Surguja 51.63% 46.59% 0.241 10.23% 47.12% 0.048
INDIA MPI BASELINE REPORT 80 81
GoaResults
Goa: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
24.6%
0.6%
7.1%
4.7%
3.0%
1.0%0.9%
14.9%
3.1%
21.4%
12.1%
4.1%
1.6%
0.2%0.0%
16.2%
9.5%
4.0%
2.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
2.96%
0.20%
1.39%
2.24%
0.59%
2.06%
2.81%
0.30% 0.00%
1.83%
0.86% 0.79%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Goa: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Goa
Overview
Goa Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
3.76%
Rural
Headcount Ratio Intensity MPI
4.44% 39.3% 0.017
Goa: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
40.16%
MPI (HxA)
0.015
Goa
Urban
Headcount Ratio Intensity MPI
3.34% 40.84% 0.014
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 32.7%
Child & Adolescent Mortality: 1.1%
Maternal Health: 7.7%
Years of Schooling: 24.8%
School Attendance: 6.5%
Cooking Fuel: 6.5%
Sanitation: 8.9%
Drinking Water: 0.9%
Electricity: 0.0%
Housing: 5.8%
Assets: 2.7%
Bank Account: 2.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Goa State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 82 83
GoaResults
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
3.33%
4.37%
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%
North Goa
South Goa
Headcount Ratio (% of population who are multidimensionally poor)
Goa: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.014
0.017
Goa
Multidimensional Poverty Index Score (District-wise)
Districts of Goa are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States
of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow,
to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The
legend provides the range of MPI scores represented by a colour.
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Goa. The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Goa
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
North Goa 4.74% 39.27% 0.019 2.45% 42.90% 0.011
South Goa 3.99% 39.34% 0.016 4.58% 39.32% 0.018
Multidimensional Poverty in Goa
Districts of Goa Headcount Ratio Intensity MPI
North Goa 3.33% 40.92% 0.014
South Goa 4.37% 39.32% 0.017
Districts of Goa are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 84 85
GujaratResults
Gujarat: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
41.4%
2.2%
14.8%
9.8%
13.6%
6.7%
5.2%
48.8%
35.1%
37.2%
26.0%
12.5%
5.4%
3.7%
2.4%
24.2%
23.3%
9.4%
4.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
15.42%
1.11%
8.74%
6.68%
4.79%
17.26%
15.51%
4.74%
2.90%
11.41%
8.20%
4.36%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Gujarat: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Gujarat
Overview
Gujarat Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
18.6%
Rural
Headcount Ratio Intensity MPI
27.4% 45.12% 0.124
Gujarat: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
45%
MPI (HxA)
0.084
Gujarat
Urban
Headcount Ratio Intensity MPI
6.59% 44.34% 0.029
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.7%
Child & Adolescent Mortality: 1.1%
Maternal Health: 8.7%
Years of Schooling: 13.3%
School Attendance: 9.5%
Cooking Fuel: 9.8%
Sanitation: 8.8%
Drinking Water: 2.7%
Electricity: 1.6%
Housing: 6.5%
Assets: 4.7%
Bank Account: 2.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Gujarat State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 86 87
GujaratResults
0.024 to 0.059 0.060 to 0.095 0.096 to 0.132 0.133 to 0.168 0.169 to 0.205 0.206 to 0.241 0.242 to 0.278
5.87%
8.74%
8.94%
9.22%
10.28%
10.51%
11.68%
11.94%
13.18%
14.81%
16.57%
17.85%
17.90%
21.19%
20.16%
21.24%
24.85%
25.16%
25.50%
27.76%
28.6%
31.2%
37.1%
41.62%
55.05%
57.33%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Ahmadabad
Rajkot
Porbandar
Surat
Junagadh
Mahesana
Navsari
Amreli
Jamnagar
Anand
Gandhinagar
Bharuch
Bhavnagar
Valsad
Patan
Vadodara
Sabar Kantha
Surendranagar
Kheda
Tapi
Kachchh
Banas Kantha
Narmada
Panch Mahals
Dohad
Dang
Headcount Ratio (% of population who are multidimensionally poor)
Gujarat: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Gujarat.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Gujarat
Multidimensional Poverty Index Score (District-wise)
Districts of Gujarat are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.024 to 0.059 0.060 to 0.095 0.096 to 0.132 0.133 to 0.168 0.169 to 0.205 0.206 to 0.241 0.242 to 0.278
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 88 89
GujaratResults
Multidimensional Poverty in Gujarat
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Gujarat
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Gujarat are as per the 2011 Census of India Districts of Gujarat are as per the 2011 Census of India
Districts of Gujarat Headcount Ratio Intensity MPI
Ahmadabad 5.87% 40.50% 0.024
Amreli 11.94% 42.81% 0.051
Anand 14.81% 41.91% 0.062
Banas Kantha 31.24% 46.14% 0.144
Bharuch 17.85% 43.87% 0.078
Bhavnagar 17.90% 41.88% 0.075
Dang 57.33% 48.54% 0.278
Dohad 55.05% 46.89% 0.258
Gandhinagar 16.57% 47.19% 0.078
Jamnagar 13.18% 46.25% 0.061
Junagadh 10.28% 43.48% 0.045
Kachchh 28.60% 49.80% 0.142
Kheda 25.50% 42.50% 0.108
Mahesana 10.51% 43.68% 0.046
Narmada 37.11% 43.32% 0.161
Navsari 11.68% 41.43% 0.048
Panch Mahals 41.62% 45.56% 0.190
Patan 21.19% 43.95% 0.093
Porbandar 8.94% 42.31% 0.038
Rajkot 8.74% 43.61% 0.038
Sabar Kantha 24.85% 43.46% 0.108
Surat 9.22% 44.64% 0.041
Surendranagar 25.16% 48.23% 0.121
Tapi 27.76% 41.38% 0.115
Vadodara 21.24% 46.11% 0.098
Valsad 20.16% 48.08% 0.097
Rural Urban
Districts of Gujarat
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ahmadabad 22.84% 41.29% 0.094 2.10% 38.60% 0.008
Amreli 13.12% 42.87% 0.056 8.62% 42.53% 0.037
Anand 18.77% 41.95% 0.079 5.24% 41.63% 0.022
Banas Kantha 34.41% 46.08% 0.159 9.33% 47.60% 0.044
Bharuch 23.03% 44.71% 0.103 8.67% 39.90% 0.035
Bhavnagar 26.12% 41.57% 0.109 5.51% 44.15% 0.024
Dang 60.97% 48.69% 0.297 19.54% 43.62% 0.085
Dohad 59.50% 47.11% 0.280 14.92% 39.17% 0.058
Gandhinagar 15.85% 41.45% 0.066 17.35% 52.83% 0.092
Jamnagar 13.97% 43.61% 0.061 12.34% 49.45% 0.061
Junagadh 13.34% 44.36% 0.059 4.23% 37.98% 0.016
Kachchh 31.75% 49.78% 0.158 21.32% 49.85% 0.106
Kheda 26.02% 41.61% 0.108 23.88% 45.50% 0.109
Mahesana 13.64% 43.86% 0.060 1.95% 40.18% 0.008
Narmada 40.14% 43.37% 0.174 8.15% 40.95% 0.033
Navsari 15.07% 40.93% 0.062 4.16% 45.43% 0.019
Panch Mahals 48.39% 45.55% 0.220 2.27% 46.77% 0.011
Patan 23.22% 44.44% 0.103 13.77% 40.94% 0.056
Porbandar 12.59% 42.13% 0.053 4.86% 42.81% 0.021
Rajkot 10.69% 42.68% 0.046 7.45% 44.49% 0.033
Sabar Kantha 28.36% 43.55% 0.124 6.06% 41.44% 0.025
Surat 26.60% 46.65% 0.124 4.62% 41.57% 0.019
Surendranagar 34.64% 48.30% 0.167 0.75% 39.61% 0.003
Tapi 29.61% 41.52% 0.123 9.33% 36.87% 0.034
Vadodara 34.44% 47.05% 0.162 8.51% 42.41% 0.036
Valsad 27.42% 48.73% 0.134 7.82% 44.21% 0.035
INDIA MPI BASELINE REPORT 90 91
HaryanaResults
Haryana: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
32.3%
2.2%
23.9%
7.1%
3.8%
12.7%
24.3%
4.6%
8.2%
51.2%
40.5%
19.3%
15.0%
1.1%
0.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
10.41%
1.21%
9.42%
4.61%
2.83%
10.17%
6.20%
3.38%
0.75%
7.44%
2.57% 2.92%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Haryana: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Haryana
Overview
Haryana Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
12.28%
Rural
Headcount Ratio Intensity MPI
14.86% 44.38% 0.066
Haryana: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.4%
MPI (HxA)
0.055
Haryana
Urban
Headcount Ratio Intensity MPI
8.16% 44.48% 0.036
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.8%
Child & Adolescent Mortality: 1.8%
Maternal Health: 14.4%
Years of Schooling: 14.1%
School Attendance: 8.6%
Cooking Fuel: 8.9%
Sanitation: 5.4%
Drinking Water: 3.0%
Electricity: 0.7%
Housing: 6.5%
Assets: 2.2%
Bank Account: 2.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Haryana State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 92 93
HaryanaResults
0.008 to 0.054 0.055 to 0.100 0.101 to 0.147 0.148 to 0.194 0.195 to 0.241 0.242 to 0.287 0.288 to 0.335
1.99%
2.47%
4.47%
5.96%
6.40%
6.42%
6.76%
7.16%
7.92%
8.24%
9.27%
9.96%
10.68%
11.02%
11.59%
13.14%
13.09%
13.72%
14.58%
26.98%
63.18%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Ambala
Panchkula
Yamunanagar
Jhajjar
Karnal
Kurukshetra
Mahendragarh
Sonipat
Kaithal
Panipat
Jind
Hisar
Gurgaon
Fatehabad
Rewari
Faridabad
Bhiwani
Rohtak
Sirsa
Palwal
Mewat
Headcount Ratio (% of population who are multidimensionally poor)
Haryana: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Haryana.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Haryana
Multidimensional Poverty Index Score (District-wise)
Districts of Haryana are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.008 to 0.054 0.055 to 0.100 0.101 to 0.147 0.148 to 0.194 0.195 to 0.241 0.242 to 0.287 0.288 to 0.335
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 94 95
HaryanaResults
Multidimensional Poverty in Haryana
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Haryana
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Haryana are as per the 2011 Census of India Dis tricts of Haryana are as per the 2011 Census of India
Districts of Haryana Headcount Ratio Intensity MPI
Ambala 1.99% 39.52% 0.008
Bhiwani 13.14% 39.59% 0.052
Faridabad 13.09% 43.99% 0.058
Fatehabad 11.02% 41.37% 0.046
Gurgaon 10.68% 41.96% 0.045
Hisar 9.96% 39.85% 0.040
Jhajjar 5.96% 39.45% 0.023
Jind 9.27% 39.47% 0.037
Kaithal 7.92% 41.58% 0.033
Karnal 6.40% 42.92% 0.027
Kurukshetra 6.42% 42.22% 0.027
Mahendragarh 6.76% 38.15% 0.026
Mewat 63.18% 53.03% 0.335
Palwal 26.98% 46.68% 0.126
Panchkula 2.47% 40.83% 0.010
Panipat 8.24% 43.30% 0.036
Rewari 11.59% 39.31% 0.046
Rohtak 13.72% 41.93% 0.058
Sirsa 14.58% 41.10% 0.060
Sonipat 7.16% 39.49% 0.028
Yamunanagar 4.47% 43.11% 0.019
Rural Urban
Districts of Haryana
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ambala 2.72% 39.24% 0.011 1.10% 40.38% 0.004
Bhiwani 13.64% 39.61% 0.054 10.99% 39.51% 0.043
Faridabad 24.41% 47.72% 0.116 11.18% 42.62% 0.048
Fatehabad 12.38% 41.93% 0.052 5.66% 36.58% 0.021
Gurgaon 12.69% 38.74% 0.049 10.04% 43.26% 0.043
Hisar 13.40% 39.44% 0.053 2.11% 45.79% 0.010
Jhajjar 6.52% 38.82% 0.025 4.54% 41.73% 0.019
Jind 10.50% 38.66% 0.041 4.85% 45.80% 0.022
Kaithal 9.54% 41.22% 0.039 3.25% 44.61% 0.014
Karnal 7.27% 40.85% 0.030 4.41% 50.64% 0.022
Kurukshetra 7.00% 41.69% 0.029 5.01% 44.03% 0.022
Mahendragarh 7.54% 38.36% 0.029 2.62% 34.92% 0.009
Mewat 65.04% 52.78% 0.343 54.70% 54.40% 0.298
Palwal 30.72% 47.54% 0.146 17.31% 42.75% 0.074
Panchkula 5.61% 41.72% 0.023 0.45% 33.77% 0.002
Panipat 8.96% 44.47% 0.040 7.52% 41.94% 0.032
Rewari 12.06% 38.45% 0.046 10.62% 41.37% 0.044
Rohtak 16.83% 40.44% 0.068 9.18% 45.91% 0.042
Sirsa 18.29% 41.09% 0.075 4.40% 41.23% 0.018
Sonipat 8.79% 38.41% 0.034 3.83% 44.53% 0.017
Yamunanagar 5.89% 44.00% 0.026 2.27% 39.54% 0.009
INDIA MPI BASELINE REPORT 96 97
Himachal PradeshResults
Himachal Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
27.2%
1.7%
17.4%
3.8%
7.5%
0.9%1.1%
67.9%
53.1%
27.8%
18.3%
7.9%
5.0%
0.5%0.5%
29.3%
24.0%
2.7%
2.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
6.79%
0.59%
5.73%
1.47%
0.43%
7.14%
4.82%
1.39%
0.24%
5.24%
2.16%
0.65%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Himachal Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Himachal Pradesh
Overview
Himachal Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
7.62%
Rural
Headcount Ratio Intensity MPI
8.24% 39.28% 0.032
Himachal Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
39.43%
MPI (HxA)
0.03
Himachal Pradesh
Urban
Headcount Ratio Intensity MPI
1.46% 48.24% 0.007
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 37.7%
Child & Adolescent Mortality: 1.6%
Maternal Health: 15.9%
Years of Schooling: 8.2%
School Attendance: 2.4%
Cooking Fuel: 11.3%
Sanitation: 7.6%
Drinking Water: 2.2%
Electricity: 0.4%
Housing: 8.3%
Assets: 3.4%
Bank Account: 1.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Himachal Pradesh State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 98 99
Himachal PradeshResults
0.017 to 0.020 0.021 to 0.024 0.025 to 0.029 0.030 to 0.033 0.034 to 0.037 0.038 to 0.042 0.043 to 0.047
4.60%
5.10%
5.12%
5.88%
7.54%
7.5%
7.7%
8.3%
8.97%
9.24%
10.88%
11.27%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
Hamirpur
Kinnaur
Una
Kangra
Shimla
Bilaspur
Lahul & Spiti
Mandi
Kullu
Solan
Sirmaur
Chamba
Headcount Ratio (% of population who are multidimensionally poor)
Himachal Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Himachal Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Himachal Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Himachal Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.017 to 0.020 0.021 to 0.024 0.025 to 0.029 0.030 to 0.033 0.034 to 0.037 0.038 to 0.042 0.043 to 0.047
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 100 101
Himachal PradeshResults
Multidimensional Poverty in Himachal Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Himachal Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Himachal Pradesh are as per the 2011 Census of India Dis tricts of Himachal Pradesh are as per the 2011 Census of India
Districts of Himachal Pradesh Headcount Ratio Intensity MPI
Bilaspur 7.54% 36.62% 0.028
Chamba 11.27% 41.25% 0.046
Hamirpur 4.60% 36.36% 0.017
Kangra 5.88% 37.40% 0.022
Kinnaur 5.10% 38.60% 0.020
Kullu 8.97% 38.98% 0.035
Lahul & Spiti 7.72% 38.38% 0.030
Mandi 8.35% 39.09% 0.033
Shimla 7.46% 40.07% 0.030
Sirmaur 10.88% 43.14% 0.047
Solan 9.24% 40.41% 0.037
Una 5.12% 38.83% 0.020
Rural Urban
Districts of Himachal Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bilaspur 7.86% 36.34% 0.029 1.97% 55.95% 0.011
Chamba 11.93% 41.24% 0.049 2.17% 41.67% 0.009
Hamirpur 4.92% 36.36% 0.018 0.41% 35.71% 0.001
Kangra 6.09% 37.40% 0.023 0.00% 0.000
Kinnaur 5.10% 38.60% 0.020 - - -
Kullu 9.10% 38.76% 0.035 7.16% 42.60% 0.030
Lahul & Spiti 7.72% 38.38% 0.030 - - -
Mandi 8.86% 39.17% 0.035 1.98% 34.52% 0.007
Shimla 9.49% 40.07% 0.038 0.00% - 0.000
Sirmaur 12.31% 42.94% 0.053 0.84% 64.29% 0.005
Solan 10.61% 39.37% 0.042 3.21% 55.46% 0.018
Una 5.51% 38.83% 0.021 0.00% - 0.000
INDIA MPI BASELINE REPORT 102 103
JharkhandResults
Jharkhand: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
48.0%
3.3%
33.1%
18.3%
8.2%
31.1%
61.8%
21.4%
9.0%
82.1%
68.1%
75.4%
43.3%
18.8%
5.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
34.42%
2.74%
26.51%
16.46%
7.17%
41.25%
39.40%
17.47%
13.58%
35.93%
15.54%
6.63%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Jharkhand: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Jharkhand
Overview
Jharkhand Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
42.16%
Rural
Headcount Ratio Intensity MPI
50.93% 48.27% 0.246
Jharkhand: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.91%
MPI (HxA)
0.202
Jharkhand
Urban
Headcount Ratio Intensity MPI
15.26% 44.24% 0.067
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.4%
Child & Adolescent Mortality: 1.1%
Maternal Health: 10.9%
Years of Schooling: 13.6%
School Attendance: 5.9%
Cooking Fuel: 9.7%
Sanitation: 9.3%
Drinking Water: 4.1%
Electricity: 3.2%
Housing: 8.5%
Assets: 3.7%
Bank Account: 1.6%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Jharkhand State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 104 105
JharkhandResults
0.110 to 0.138 0.139 to 0.168 0.169 to 0.197 0.198 to 0.226 0.227 to 0.255 0.256 to 0.285 0.286 to 0.315
23.99%
27.70%
28.57%
29.49%
29.80%
32.68%
35.75%
41.79%
45.4%
46.7%
47.4%
45.54%
47.88%
48.65%
49.98%
50.56%
51.81%
52.71%
52.93%
53.26%
55.93%
57.60%
60.66%
60.74%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Purbi Singhbhum
Ranchi
Dhanbad
Bokaro
Ramgarh
Kodarma
Hazaribagh
Saraikela-Kharsawan
Lohardaga
Palamu
Gumla
Deoghar
Giridih
Khunti
Simdega
Jamtara
Godda
Latehar
Dumka
Garhwa
Sahibganj
Pashchimi Singhbhum
Pakur
Chatra
Headcount Ratio (% of population
Jharkhand: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Jharkhand.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Jharkhand
Multidimensional Poverty Index Score (District-wise)
Districts of Jharkhand are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.110 to 0.138 0.139 to 0.168 0.169 to 0.197 0.198 to 0.226 0.227 to 0.255 0.256 to 0.285 0.286 to 0.315
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 106 107
JharkhandResults
Multidimensional Poverty in Jharkhand
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Jharkhand
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Jharkhand are as per the 2011 Census of India Dis tricts of Jharkhand are as per the 2011 Census of India
Districts of Jharkhand Headcount Ratio Intensity MPI
Bokaro 29.49% 44.16% 0.130
Chatra 60.74% 50.40% 0.306
Deoghar 47.40% 46.71% 0.221
Dhanbad 28.57% 43.84% 0.125
Dumka 52.93% 48.33% 0.256
Garhwa 53.26% 48.44% 0.258
Giridih 47.88% 47.89% 0.229
Godda 51.81% 49.02% 0.254
Gumla 46.70% 47.10% 0.220
Hazaribagh 35.75% 43.70% 0.156
Jamtara 50.56% 47.54% 0.240
Khunti 48.65% 47.27% 0.230
Kodarma 32.68% 44.69% 0.146
Latehar 52.71% 50.55% 0.266
Lohardaga 45.37% 47.08% 0.214
Pakur 60.66% 51.90% 0.315
Palamu 45.54% 51.07% 0.233
Pashchimi Singhbhum 57.60% 53.90% 0.310
Purbi Singhbhum 23.99% 45.94% 0.110
Ramgarh 29.80% 44.23% 0.132
Ranchi 27.70% 43.72% 0.121
Sahibganj 55.93% 52.49% 0.294
Saraikela-Kharsawan 41.79% 46.33% 0.194
Simdega 49.98% 47.34% 0.237
Rural Urban
Districts of Jharkhand
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bokaro 43.46% 43.72% 0.190 15.47% 45.41% 0.070
Chatra 63.10% 50.75% 0.320 25.52% 37.28% 0.095
Deoghar 54.97% 46.95% 0.258 16.17% 43.27% 0.070
Dhanbad 40.45% 43.55% 0.176 20.13% 44.27% 0.089
Dumka 56.23% 48.35% 0.272 7.56% 45.58% 0.034
Garhwa 54.39% 48.65% 0.265 30.15% 40.57% 0.122
Giridih 51.21% 48.16% 0.247 16.20% 39.92% 0.065
Godda 54.15% 48.99% 0.265 11.58% 51.66% 0.060
Gumla 49.04% 47.28% 0.232 14.13% 38.42% 0.054
Hazaribagh 39.78% 43.88% 0.175 15.62% 41.45% 0.065
Jamtara 54.63% 47.65% 0.260 12.98% 43.33% 0.056
Khunti 50.98% 47.58% 0.243 22.11% 38.95% 0.086
Kodarma 37.13% 45.50% 0.169 17.13% 38.58% 0.066
Latehar 54.71% 50.63% 0.277 23.31% 47.76% 0.111
Lohardaga 50.29% 47.16% 0.237 6.57% 42.33% 0.028
Pakur 61.73% 52.05% 0.321 48.76% 49.88% 0.243
Palamu 51.26% 51.28% 0.263 6.33% 39.39% 0.025
Pashchimi Singhbhum 64.13% 54.18% 0.347 17.65% 47.67% 0.084
Purbi Singhbhum 42.86% 46.29% 0.198 8.51% 44.51% 0.038
Ramgarh 37.12% 43.24% 0.161 20.40% 46.55% 0.095
Ranchi 41.65% 44.01% 0.183 8.15% 41.60% 0.034
Sahibganj 59.62% 53.10% 0.317 32.15% 45.23% 0.145
Saraikela-Kharsawan 52.21% 46.17% 0.241 18.41% 47.33% 0.087
Simdega 52.38% 47.52% 0.249 27.14% 43.99% 0.119
INDIA MPI BASELINE REPORT 108 109
KarnatakaResults
Karnataka: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
33.6%
1.3%
12.4%
8.7%
10.0%
3.5%
2.2%
45.5%
21.9%
43.1%
25.2%
14.5%
6.9%
1.7%
0.9%
37.3%
36.4%
8.8%
5.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
10.08%
0.71%
5.35% 5.45%
2.36%
11.58%
11.04%
3.46%
0.97%
9.43%
4.99%
3.39%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Karnataka: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Karnataka
Overview
Karnataka Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
13.16%
Rural
Headcount Ratio Intensity MPI
19.01% 42.79% 0.081
Karnataka: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
42.7%
MPI (HxA)
0.056
Karnataka
Urban
Headcount Ratio Intensity MPI
5.07% 42.23% 0.021
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 29.9%
Child & Adolescent Mortality: 1.1%
Maternal Health: 7.9%
Years of Schooling: 16.1%
School Attendance: 7.0%
Cooking Fuel: 9.8%
Sanitation: 9.4%
Drinking Water: 2.9%
Electricity: 0.8%
Housing: 8.0%
Assets: 4.2%
Bank Account: 2.9%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Karnataka State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 110 111
KarnatakaResults
0.009 to 0.035 0.036 to 0.062 0.063 to 0.088 0.089 to 0.115 0.116 to 0.142 0.143 to 0.168 0.169 to 0.196
2.31%
6.62%
6.64%
6.69%
7.79%
8.39%
8.77%
8.74%
9.65%
10.30%
10.32%
11.19%
11.71%
12.26%
12.72%
13.21%
14.00%
15.16%
15.61%
15.79%
18.91%
19.42%
20.23%
20.27%
21.8%
22.4%
23.4%
24.6%
32.19%
41.67%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%
Bangalore
Mandya
Hassan
Dakshina Kannada
Mysore
Bangalore Rural
Kodagu
Ramanagara
Dharwad
Kolar
Udupi
Chikmagalur
Davanagere
Belgaum
Shimoga
Uttara Kannada
Tumkur
Chikkaballapura
Haveri
Chitradurga
Chamrajnagar
Bidar
Bagalkot
Gadag
Gulbarga
Bijapur
Bellary
Koppal
Raichur
Yadgir
Headcount Ratio (% of population who are multidimensionally poor)
Karnataka: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Karnataka.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Karnataka
Multidimensional Poverty Index Score (District-wise)
Districts of Karnataka are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.009 to 0.035 0.036 to 0.062 0.063 to 0.088 0.089 to 0.115 0.116 to 0.142 0.143 to 0.168 0.169 to 0.196
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 112 113
KarnatakaResults
Multidimensional Poverty in Karnataka
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Karnataka
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Karnataka are as per the 2011 Census of India Districts of Karnataka are as per the 2011 Census of India
Districts of Karnataka Headcount Ratio Intensity MPI
Bagalkot 20.23% 43.39% 0.088
Bangalore 2.31% 40.30% 0.009
Bangalore Rural 8.39% 39.58% 0.033
Belgaum 12.26% 39.96% 0.049
Bellary 23.44% 46.51% 0.109
Bidar 19.42% 41.63% 0.081
Bijapur 22.40% 42.62% 0.095
Chamrajnagar 18.91% 41.93% 0.079
Chikkaballapura 15.16% 41.88% 0.064
Chikmagalur 11.19% 41.38% 0.046
Chitradurga 15.79% 41.19% 0.065
Dakshina Kannada 6.69% 40.32% 0.027
Davanagere 11.71% 42.53% 0.050
Dharwad 9.65% 40.27% 0.039
Gadag 20.27% 43.12% 0.087
Gulbarga 21.75% 44.34% 0.096
Hassan 6.64% 40.22% 0.027
Haveri 15.61% 41.07% 0.064
Kodagu 8.74% 43.92% 0.038
Kolar 10.30% 40.56% 0.042
Koppal 24.56% 42.69% 0.105
Mandya 6.62% 43.36% 0.029
Mysore 7.79% 41.16% 0.032
Raichur 32.19% 45.44% 0.146
Ramanagara 8.77% 38.37% 0.034
Shimoga 12.72% 41.13% 0.052
Tumkur 14.00% 41.23% 0.058
Udupi 10.32% 41.24% 0.043
Uttara Kannada 13.21% 42.64% 0.056
Yadgir 41.67% 46.99% 0.196
Rural Urban
Districts of Karnataka
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bagalkot 24.37% 43.78% 0.107 12.33% 41.92% 0.052
Bangalore 5.89% 33.97% 0.020 1.98% 42.01% 0.008
Bangalore Rural 9.10% 39.54% 0.036 6.32% 39.72% 0.025
Belgaum 15.41% 40.16% 0.062 4.35% 38.17% 0.017
Bellary 28.15% 48.14% 0.136 16.67% 42.56% 0.071
Bidar 25.04% 42.10% 0.105 5.81% 36.62% 0.021
Bijapur 27.52% 42.99% 0.118 7.08% 38.33% 0.027
Chamrajnagar 19.91% 42.29% 0.084 14.97% 40.05% 0.060
Chikkaballapura 19.16% 41.31% 0.079 3.67% 50.49% 0.019
Chikmagalur 12.48% 41.02% 0.051 5.55% 44.87% 0.025
Chitradurga 19.61% 41.64% 0.082 4.88% 35.95% 0.018
Dakshina Kannada 9.79% 40.07% 0.039 3.10% 41.22% 0.013
Davanagere 16.26% 42.93% 0.070 2.90% 38.19% 0.011
Dharwad 17.92% 40.44% 0.072 3.72% 39.66% 0.015
Gadag 23.98% 42.53% 0.102 14.94% 44.49% 0.066
Gulbarga 28.33% 44.32% 0.126 11.58% 44.43% 0.051
Hassan 8.68% 40.39% 0.035 0.77% 34.52% 0.003
Haveri 17.37% 40.98% 0.071 9.06% 41.70% 0.038
Kodagu 9.97% 43.94% 0.044 0.20% 35.71% 0.001
Kolar 11.86% 39.59% 0.047 7.69% 43.07% 0.033
Koppal 27.91% 42.31% 0.118 11.36% 46.40% 0.053
Mandya 8.04% 43.26% 0.035 1.02% 46.43% 0.005
Mysore 12.98% 41.25% 0.054 0.62% 38.38% 0.002
Raichur 40.50% 45.34% 0.184 12.47% 46.18% 0.058
Ramanagara 10.60% 38.67% 0.041 3.50% 35.75% 0.013
Shimoga 16.00% 41.50% 0.066 6.16% 39.22% 0.024
Tumkur 16.31% 40.75% 0.066 6.48% 45.17% 0.029
Udupi 12.20% 40.92% 0.050 5.20% 43.31% 0.023
Uttara Kannada 16.43% 43.10% 0.071 5.82% 39.67% 0.023
Yadgir 48.37% 47.08% 0.228 20.24% 46.29% 0.094
INDIA MPI BASELINE REPORT 114 115
KeralaResults
Kerala: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
15.3%
0.2%
1.7% 1.8%
2.9%
0.5%0.3%
43.9%
28.2%
1.9%
1.3%
5.9%
5.4%
0.7%0.4%
10.8%
16.7%
4.3%
3.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
0.56%
0.00% 0.15% 0.19% 0.22% 0.58% 0.30% 0.14% 0.20% 0.40% 0.32% 0.17%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Kerala: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Kerala
Overview
Kerala Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
0.71%
Rural
Headcount Ratio Intensity MPI
0.95% 39.81% 0.004
Kerala: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
39.02%
MPI (HxA)
0.003
Kerala
Urban
Headcount Ratio Intensity MPI
0.43% 37.06% 0.002
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 34.0%
Child & Adolescent Mortality: 0.1%
Maternal Health: 4.5%
Years of Schooling: 11.3%
School Attendance: 13.6%
Cooking Fuel: 10.0%
Sanitation: 5.1%
Drinking Water: 2.4%
Electricity: 3.5%
Housing: 6.9%
Assets: 5.6%
Bank Account: 3.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Kerala State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 116 117
KeralaResults
0.000 to 0.001 0.002 to 0.003 0.004 to 0.005 0.006 to 0.007 0.008 to 0.009 0.010 to 0.0110.012 to 0.014
0.10%
0.26%
0.33%
0.44%
0.62%
0.71%
0.72%
0.83%
1.00%
1.08%
1.11%
1.6%
3.48%
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0%
Kottayam
Ernakulam
Kozhikode
Thrissur
Kannur
Palakkad
Alappuzha
Kollam
Pathanamthitta
Kasaragod
Thiruvananthapuram
Malappuram
Idukki
Wayanad
Headcount Ratio (% of population who are multidimensionally poor)
0%
Kerala: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Kerala.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Kerala
Multidimensional Poverty Index Score (District-wise)
Districts of Kerala are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.001 0.002 to 0.003 0.004 to 0.005 0.006 to 0.007 0.008 to 0.009 0.010 to 0.0110.012 to 0.014
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 118 119
KeralaResults
Multidimensional Poverty in Kerala
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Kerala
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Kerala are as per the 2011 Census of India Districts of Kerala are as per the 2011 Census of India
Districts of Kerala Headcount Ratio Intensity MPI
Alappuzha 0.71% 38.12% 0.003
Ernakulam 0.10% 38.10% 0.000
Idukki 1.65% 37.53% 0.006
Kannur 0.44% 36.04% 0.002
Kasaragod 1.00% 51.38% 0.005
Kollam 0.72% 42.76% 0.003
Kottayam 0.00% - 0.000
Kozhikode 0.26% 37.31% 0.001
Malappuram 1.11% 36.64% 0.004
Palakkad 0.62% 37.04% 0.002
Pathanamthitta 0.83% 42.48% 0.004
Thiruvananthapuram 1.08% 37.40% 0.004
Thrissur 0.33% 37.12% 0.001
Wayanad 3.48% 40.94% 0.014
Rural Urban
Districts of Kerala
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Alappuzha 0.93% 38.14% 0.004 0.52% 38.10% 0.002
Ernakulam 0.32% 38.10% 0.001 0.00% - 0.000
Idukki 1.72% 37.53% 0.006 0.00% - 0.000
Kannur 0.32% 40.48% 0.001 0.50% 34.46% 0.002
Kasaragod 1.47% 53.15% 0.008 0.29% 38.10% 0.001
Kollam 1.14% 42.98% 0.005 0.26% 41.67% 0.001
Kottayam 0.00% - 0.000 0.00% - 0.000
Kozhikode 0.52% 37.95% 0.002 0.12% 35.71% 0.000
Malappuram 1.02% 38.66% 0.004 1.20% 34.84% 0.004
Palakkad 0.75% 36.54% 0.003 0.16% 45.24% 0.001
Pathanamthitta 0.92% 42.48% 0.004 0.00% - 0.000
Thiruvananthapuram 1.58% 35.73% 0.006 0.65% 40.90% 0.003
Thrissur 0.07% 35.71% 0.000 0.47% 37.22% 0.002
Wayanad 3.62% 40.94% 0.015 0.00% - 0.000
INDIA MPI BASELINE REPORT 120 121
Madhya PradeshResults
Madhya Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
45.5%
3.6%
29.4%
16.1%
8.4%
29.8%
64.4%
19.3%
11.1%
71.2%
59.9%
65.3%
34.9%
9.0%
1.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
29.05%
2.72%
20.87%
14.02%
7.34%
34.90%
33.21%
17.56%
6.47%
32.73%
13.65%
7.36%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Madhya Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Madhya Pradesh
Overview
Madhya Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
36.65%
Rural
Headcount Ratio Intensity MPI
45.96% 47.57% 0.219
Madhya Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.25%
MPI (HxA)
0.173
Madhya Pradesh
Urban
Headcount Ratio Intensity MPI
13.82% 44.62% 0.062
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.0%
Child & Adolescent Mortality: 1.3%
Maternal Health: 10.0%
Years of Schooling: 13.5%
School Attendance: 7.1%
Cooking Fuel: 9.6%
Sanitation: 9.1%
Drinking Water: 4.8%
Electricity: 1.8%
Housing: 9.0%
Assets: 3.8%
Bank Account: 2.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Madhya Pradesh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 122 123
Madhya PradeshResults
0.049 to 0.099 0.100 to 0.150 0.151 to 0.201 0.202 to 0.253 0.254 to 0.304 0.305 to 0.355 0.356 to 0.407
10.86%
12.91%
19.50%
22.38%
24.73%
26.80%
29.67%
29.80%
30.14%
30.55%
31.87%
32.50%
33.18%
33.27%
34.12%
33.11%
33.59%
34.31%
34.50%
34.52%
35.80%
36.99%
37.04%
39.94%
40.14%
40.47%
40.51%
41.70%
41.99%
42.55%
41.7%
42.5%
42.6%
42.8%
43.5%
45.6%
45.7%
46.1%
46.3%
47.2%
47.5%
48.1%
49.0%
49.8%
51.92%
52.68%
56.23%
61.60%
68.86%
71.31%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
Indore
Bhopal
Jabalpur
Gwalior
Hoshangabad
Sehore
Dewas
Ujjain
Chhindwara
Narsimhapur
Neemuch
Morena
Harda
Bhind
Mandsaur
Shajapur
Satna
Datia
Betul
Raisen
West Nimar
Burhanpur
Rewa
Katni
Balaghat
Sagar
Dhar
Ratlam
Anuppur
Rajgarh
East Nimar
Seoni
Panna
Ashoknagar
Shahdol
Umaria
Guna
Shivpuri
Damoh
Vidisha
Tikamgarh
Mandla
Chhatarpur
Sheopur
Singrauli
Sidhi
Dindori
Barwani
Jhabua
Alirajpur
Headcount Ratio (% of population who are multidimensionally poor)
Madhya Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Madhya Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Madhya Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Madhya Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.049 to 0.099 0.100 to 0.150 0.151 to 0.201 0.202 to 0.253 0.254 to 0.304 0.305 to 0.355 0.356 to 0.407
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 124 125
Madhya PradeshResults
Multidimensional Poverty in Madhya Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Madhya Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Madhya Pradesh are as per the 2011 Census of India Dis tricts of Madhya Pradesh are as per the 2011 Census of India
Districts of Madhya Pradesh Headcount Ratio Intensity MPI
Alirajpur 71.31% 57.06% 0.407
Anuppur 41.70% 45.25% 0.189
Ashoknagar 42.78% 47.19% 0.202
Balaghat 40.14% 43.59% 0.175
Barwani 61.60% 57.27% 0.353
Betul 34.50% 46.90% 0.162
Bhind 33.18% 44.06% 0.146
Bhopal 12.91% 45.29% 0.058
Burhanpur 36.99% 52.20% 0.193
Chhatarpur 48.95% 48.35% 0.237
Chhindwara 30.14% 46.01% 0.139
Damoh 46.31% 46.31% 0.214
Datia 34.31% 44.23% 0.152
Dewas 29.67% 46.42% 0.138
Dhar 40.51% 49.29% 0.200
Dindori 56.23% 47.28% 0.266
East Nimar 42.53% 47.59% 0.202
Guna 45.67% 47.31% 0.216
Gwalior 22.38% 44.26% 0.099
Harda 33.11% 46.57% 0.154
Hoshangabad 24.73% 44.05% 0.109
Indore 10.86% 45.22% 0.049
Jabalpur 19.50% 45.39% 0.089
Jhabua 68.86% 55.97% 0.385
Katni 39.94% 45.25% 0.181
Mandla 48.09% 47.20% 0.227
Mandsaur 33.27% 45.15% 0.150
Morena 32.50% 44.83% 0.146
Narsimhapur 30.55% 44.63% 0.136
Neemuch 31.87% 44.94% 0.143
Panna 42.63% 47.93% 0.204
Raisen 34.52% 44.76% 0.155
Rajgarh 41.99% 45.91% 0.193
Ratlam 41.67% 48.50% 0.202
Rewa 37.04% 44.72% 0.166
Sagar 40.47% 44.53% 0.180
Satna 34.12% 43.96% 0.150
Sehore 26.80% 46.46% 0.125
Seoni 42.55% 44.50% 0.189
Shahdol 43.47% 46.41% 0.202
Shajapur 33.59% 45.72% 0.154
Sheopur 49.83% 49.56% 0.247
Shivpuri 46.09% 46.43% 0.214
Sidhi 52.68% 48.18% 0.254
Singrauli 51.92% 50.76% 0.264
Tikamgarh 47.52% 45.89% 0.218
Ujjain 29.80% 45.70% 0.136
Umaria 45.58% 46.06% 0.210
Vidisha 47.19% 48.64% 0.230
West Nimar 35.80% 49.16% 0.176
Rural Urban
Districts of Madhya Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Alirajpur 75.85% 57.26% 0.434 15.27% 44.80% 0.068
Anuppur 49.91% 45.49% 0.227 14.44% 42.46% 0.061
Ashoknagar 46.07% 47.48% 0.219 26.93% 44.80% 0.121
Balaghat 44.01% 43.71% 0.192 14.25% 41.12% 0.059
Barwani 70.72% 57.79% 0.409 14.24% 44.00% 0.063
Betul 41.59% 46.96% 0.195 3.89% 44.13% 0.017
Bhind 37.35% 44.46% 0.166 24.45% 42.78% 0.105
Bhopal 30.30% 45.66% 0.138 8.64% 44.97% 0.039
Burhanpur 49.48% 53.73% 0.266 16.29% 44.51% 0.073
Chhatarpur 57.12% 48.27% 0.276 19.05% 49.23% 0.094
Chhindwara 37.14% 46.31% 0.172 8.73% 42.21% 0.037
Damoh 54.26% 46.87% 0.254 20.29% 41.41% 0.084
Datia 41.62% 43.97% 0.183 8.60% 48.75% 0.042
Dewas 36.35% 45.98% 0.167 13.19% 49.47% 0.065
Dhar 48.00% 49.86% 0.239 14.27% 42.60% 0.061
Dindori 58.15% 47.17% 0.274 16.11% 55.19% 0.089
East Nimar 50.95% 47.94% 0.244 9.82% 40.43% 0.040
Guna 56.12% 47.56% 0.267 15.13% 44.60% 0.067
Gwalior 39.84% 45.25% 0.180 13.03% 42.64% 0.056
Harda 39.82% 47.12% 0.188 10.64% 39.71% 0.042
Hoshangabad 32.50% 44.41% 0.144 7.02% 40.30% 0.028
Indore 20.39% 47.69% 0.097 8.25% 43.53% 0.036
Jabalpur 33.72% 45.34% 0.153 8.65% 45.55% 0.039
Jhabua 72.38% 56.22% 0.407 34.57% 50.95% 0.176
Katni 45.70% 45.49% 0.208 10.69% 39.95% 0.043
Mandla 53.38% 47.41% 0.253 11.32% 40.48% 0.046
Mandsaur 38.65% 45.42% 0.176 16.14% 43.14% 0.070
Morena 38.97% 44.69% 0.174 16.98% 45.58% 0.077
Narsimhapur 36.35% 44.82% 0.163 9.76% 42.02% 0.041
Neemuch 37.30% 45.49% 0.170 18.49% 42.21% 0.078
Panna 47.17% 48.26% 0.228 18.49% 43.41% 0.080
Raisen 41.31% 45.10% 0.186 14.30% 41.87% 0.060
Rajgarh 48.71% 46.16% 0.225 14.07% 42.25% 0.059
Ratlam 51.83% 48.50% 0.251 20.12% 48.50% 0.098
Rewa 40.75% 45.10% 0.184 18.12% 40.41% 0.073
Sagar 44.82% 44.62% 0.200 29.25% 44.16% 0.129
Satna 40.66% 44.15% 0.180 9.64% 40.84% 0.039
Sehore 31.28% 46.57% 0.146 8.80% 45.02% 0.040
Seoni 46.39% 44.58% 0.207 14.99% 42.62% 0.064
Shahdol 48.90% 46.47% 0.227 17.43% 45.69% 0.080
Shajapur 39.29% 45.85% 0.180 11.36% 43.96% 0.050
Sheopur 53.22% 49.28% 0.262 30.07% 52.37% 0.157
Shivpuri 53.76% 46.78% 0.252 14.11% 40.84% 0.058
Sidhi 54.92% 48.40% 0.266 28.18% 43.50% 0.123
Singrauli 58.92% 50.83% 0.299 22.05% 49.96% 0.110
Tikamgarh 50.13% 45.27% 0.227 33.25% 51.04% 0.170
Ujjain 40.59% 45.57% 0.185 13.33% 46.33% 0.062
Umaria 50.70% 46.25% 0.234 22.44% 44.12% 0.099
Vidisha 51.78% 48.87% 0.253 34.46% 47.71% 0.164
West Nimar 39.87% 49.11% 0.196 14.61% 49.79% 0.073
INDIA MPI BASELINE REPORT 126 127
MaharashtraResults
Maharashtra: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
36.1%
1.4%
15.9%
6.5%
14.0%
4.2%
2.4%
39.5%
20.8%
48.0%
28.0%
13.9%
9.1%
6.6%
2.2%
27.9%
23.1%
10.3%
5.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
12.39%
0.83%
7.13%
4.27%
2.96%
12.46% 12.50%
5.27%
3.14%
10.12%
6.72%
3.80%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Maharashtra: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Maharashtra
Overview
Maharashtra Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
14.85%
Rural
Headcount Ratio Intensity MPI
22.83% 43.99% 0.1
Maharashtra: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.78%
MPI (HxA)
0.065
Maharashtra
Urban
Headcount Ratio Intensity MPI
5.55% 42.76% 0.024
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.7%
Child & Adolescent Mortality: 1.1%
Maternal Health: 9.1%
Years of Schooling: 10.9%
School Attendance: 7.6%
Cooking Fuel: 9.1%
Sanitation: 9.1%
Drinking Water: 3.9%
Electricity: 2.3%
Housing: 7.4%
Assets: 4.9%
Bank Account: 2.8%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Maharashtra State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 128 129
MaharashtraResults
0.014 to 0.0510.052 to 0.0890.090 to 0.127 0.128 to 0.165 0.166 to 0.203 0.204 to 0.241 0.242 to 0.280
3.59%
4.65%
5.29%
6.72%
8.19%
8.82%
10.17%
10.18%
10.19%
11.02%
12.24%
12.60%
13.38%
14.86%
15.24%
15.39%
15.45%
17.65%
17.84%
17.90%
18.22%
18.31%
18.47%
18.60%
18.75%
20.58%
22.53%
22.66%
23.39%
23.54%
27.48%
28.05%
29.41%
33.23%
52.12%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Mumbai
Mumbai Suburban
Pune
Nagpur
Bhandara
Wardha
Kolhapur
Sangli
Raigarh
Satara
Amravati
Solapur
Akola
Aurangabad
Thane
Sindhudurg
Ahmadnagar
Chandrapur
Osmanabad
Latur
Buldana
Nashik
Ratnagiri
Jalgaon
Gondiya
Garhchiroli
Washim
Bid
Parbhani
Yavatmal
Nanded
Hingoli
Jalna
Dhule
Nandurbar
Headcount Ratio (% of population who are multidimensionally poor)
Maharashtra: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Maharashtra.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Maharashtra
Multidimensional Poverty Index Score (District-wise)
Districts of Maharashtra are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.014 to 0.0510.052 to 0.0890.090 to 0.127 0.128 to 0.165 0.166 to 0.203 0.204 to 0.241 0.242 to 0.280
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 130 131
MaharashtraResults
Multidimensional Poverty in Maharashtra
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Maharashtra
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Maharashtra are as per the 2011 Census of India Districts of Maharashtra are as per the 2011 Census of India
Districts of Maharashtra Headcount Ratio Intensity MPI
Ahmadnagar 15.45% 43.67% 0.067
Akola 13.38% 40.05% 0.054
Amravati 12.24% 41.90% 0.051
Aurangabad 14.86% 42.85% 0.064
Bhandara 8.19% 38.79% 0.032
Bid 22.66% 43.15% 0.098
Buldana 18.22% 43.60% 0.079
Chandrapur 17.65% 43.00% 0.076
Dhule 33.23% 50.18% 0.167
Garhchiroli 20.58% 41.66% 0.086
Gondiya 18.75% 39.31% 0.074
Hingoli 28.05% 42.27% 0.119
Jalgaon 18.60% 45.58% 0.085
Jalna 29.41% 42.50% 0.125
Kolhapur 10.17% 40.38% 0.041
Latur 17.90% 41.30% 0.074
Mumbai 3.59% 39.73% 0.014
Mumbai Suburban 4.65% 42.97% 0.020
Nagpur 6.72% 38.02% 0.026
Nanded 27.48% 41.43% 0.114
Nandurbar 52.12% 53.78% 0.280
Nashik 18.31% 45.45% 0.083
Osmanabad 17.84% 41.75% 0.074
Parbhani 23.39% 40.59% 0.095
Pune 5.29% 39.45% 0.021
Raigarh 10.19% 45.55% 0.046
Ratnagiri 18.47% 40.77% 0.075
Sangli 10.18% 40.67% 0.041
Satara 11.02% 40.37% 0.045
Sindhudurg 15.39% 39.86% 0.061
Solapur 12.60% 42.62% 0.054
Thane 15.24% 46.94% 0.072
Wardha 8.82% 40.48% 0.036
Washim 22.53% 41.70% 0.094
Yavatmal 23.54% 43.56% 0.103
Rural Urban
Districts of Maharashtra
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ahmadnagar 18.57% 43.20% 0.080 3.38% 53.63% 0.018
Akola 17.03% 40.11% 0.068 6.83% 39.74% 0.027
Amravati 16.92% 42.30% 0.072 3.72% 38.59% 0.014
Aurangabad 21.41% 42.60% 0.091 6.67% 43.86% 0.029
Bhandara 11.22% 38.81% 0.044 0.96% 38.12% 0.004
Bid 23.97% 42.94% 0.103 18.03% 44.14% 0.080
Buldana 20.38% 43.93% 0.090 9.90% 40.97% 0.041
Chandrapur 25.06% 42.94% 0.108 2.57% 44.18% 0.011
Dhule 39.46% 50.53% 0.199 10.47% 45.42% 0.048
Garhchiroli 23.06% 41.70% 0.096 4.01% 40.04% 0.016
Gondiya 20.76% 39.33% 0.082 7.93% 39.02% 0.031
Hingoli 31.50% 42.37% 0.133 4.12% 36.84% 0.015
Jalgaon 21.73% 46.11% 0.100 9.72% 42.18% 0.041
Jalna 33.13% 42.13% 0.140 12.38% 47.01% 0.058
Kolhapur 13.97% 40.47% 0.057 1.69% 38.66% 0.007
Latur 20.17% 40.93% 0.083 10.03% 43.87% 0.044
Mumbai - - - 3.59% 39.73% 0.014
Mumbai Suburban - - - 4.65% 42.97% 0.020
Nagpur 11.26% 39.22% 0.044 4.83% 36.87% 0.018
Nanded 34.48% 41.53% 0.143 7.35% 40.07% 0.029
Nandurbar 55.92% 54.22% 0.303 20.68% 44.03% 0.091
Nashik 29.33% 44.39% 0.130 8.24% 48.87% 0.040
Osmanabad 18.99% 41.97% 0.080 10.16% 38.96% 0.040
Parbhani 27.84% 40.43% 0.113 12.84% 41.41% 0.053
Pune 8.03% 41.28% 0.033 3.62% 36.96% 0.013
Raigarh 18.39% 45.71% 0.084 2.53% 44.46% 0.011
Ratnagiri 21.38% 40.88% 0.087 2.32% 35.37% 0.008
Sangli 12.95% 40.51% 0.052 2.34% 43.20% 0.010
Satara 11.39% 40.49% 0.046 9.55% 39.81% 0.038
Sindhudurg 17.52% 39.86% 0.070 0.00% - 0.000
Solapur 15.92% 42.55% 0.068 5.80% 43.06% 0.025
Thane 50.10% 48.08% 0.241 5.52% 44.06% 0.024
Wardha 9.38% 40.64% 0.038 7.70% 40.08% 0.031
Washim 25.01% 41.97% 0.105 10.28% 38.34% 0.039
Yavatmal 27.32% 43.90% 0.120 7.09% 37.90% 0.027
INDIA MPI BASELINE REPORT 132 133
ManipurResults
Manipur: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
23.6%
1.8%
17.7%
5.4%
13.9%
2.4%2.5%
58.9%
28.7%
47.7%
35.1%
60.9%
28.5%
7.3%
1.9%
81.5%
76.8%
21.5%
4.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
13.41%
0.97%
10.14%
4.65%
1.77%
16.28%
11.68%
13.59%
3.49%
17.26%
6.91%
8.93%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Manipur: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Manipur
Overview
Manipur Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
17.89%
Rural
Headcount Ratio Intensity MPI
22.95% 45.07% 0.103
Manipur: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.44%
MPI (HxA)
0.08
Manipur
Urban
Headcount Ratio Intensity MPI
9.9% 42.17% 0.042
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.1%
Child & Adolescent Mortality: 1.0%
Maternal Health: 10.6%
Years of Schooling: 9.8%
School Attendance: 3.7%
Cooking Fuel: 9.8%
Sanitation: 7.0%
Drinking Water: 8.1%
Electricity: 2.1%
Housing: 10.3%
Assets: 4.1%
Bank Account: 5.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the
NFHS-5 Manipur State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 134 135
ManipurResults
0.034 to 0.054 0.055 to 0.075 0.076 to 0.096 0.097 to 0.116 0.117 to 0.137 0.138 to 0.158 0.159 to 0.180
8.47%
14.47%
14.75%
17.74%
21.2%
27.09%
28.52%
33.58%
37.66%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
Imphal West
Imphal East
Bishnupur
Thoubal
Churachandpur
Chandel
Ukhrul
Senapati
Tamenglong
Headcount Ratio (% of population who are multidimensionally poor)
Manipur: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Manipur.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Manipur
Multidimensional Poverty Index Score (District-wise)
Districts of Manipur are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.034 to 0.054 0.055 to 0.075 0.076 to 0.096 0.097 to 0.116 0.117 to 0.137 0.138 to 0.158 0.159 to 0.180
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 136 137
ManipurResults
Multidimensional Poverty in Manipur
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Manipur
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Manipur are as per the 2011 Census of India Districts of Manipur are as per the 2011 Census of India
Districts of Manipur Headcount Ratio Intensity MPI
Bishnupur 14.75% 40.98% 0.060
Chandel 27.09% 45.63% 0.124
Churachandpur 21.24% 47.32% 0.101
Imphal East 14.47% 44.21% 0.064
Imphal West 8.47% 40.05% 0.034
Senapati 33.58% 45.78% 0.154
Tamenglong 37.66% 47.79% 0.180
Thoubal 17.74% 42.66% 0.076
Ukhrul 28.52% 46.71% 0.133
Rural Urban
Districts of Manipur
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bishnupur 15.14% 39.06% 0.059 14.37% 42.93% 0.062
Chandel 28.98% 46.16% 0.134 17.43% 41.10% 0.072
Churachandpur 22.82% 47.44% 0.108 3.31% 38.10% 0.013
Imphal East 17.00% 44.32% 0.075 11.20% 44.01% 0.049
Imphal West 12.66% 40.49% 0.051 6.00% 39.49% 0.024
Senapati 34.76% 45.82% 0.159 3.83% 36.29% 0.014
Tamenglong 42.88% 48.32% 0.207 14.67% 41.06% 0.060
Thoubal 20.32% 42.57% 0.086 13.95% 42.86% 0.060
Ukhrul 33.28% 47.04% 0.157 7.39% 40.16% 0.030
INDIA MPI BASELINE REPORT 138 139
MeghalayaResults
Meghalaya: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
37.0%
3.1%
31.7%
19.7%
29.9%
6.2%
5.4%
77.1%
69.2%
38.6%
17.1%
33.5%
23.6%
8.2%8.1%
50.4%
55.9%
19.9%
9.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
23.84%
2.11%
22.48%
16.68%
5.34%
31.82%
18.58%
13.65%
6.43%
23.33%
19.41%
12.94%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Meghalaya: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Meghalaya
Overview
Meghalaya Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
32.67%
Rural
Headcount Ratio Intensity MPI
38.6% 48.37% 0.187
Meghalaya: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
48.06%
MPI (HxA)
0.157
Meghalaya
Urban
Headcount Ratio Intensity MPI
8.62% 42.47% 0.037
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 25.3%
Child & Adolescent Mortality: 1.1%
Maternal Health: 11.9%
Years of Schooling: 17.7%
School Attendance: 5.7%
Cooking Fuel: 9.7%
Sanitation: 5.6%
Drinking Water: 4.1%
Electricity: 2.0%
Housing: 7.1%
Assets: 5.9%
Bank Account: 3.9%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Meghalaya State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 140 141
MeghalayaResults
0.047 to 0.074 0.075 to 0.102 0.103 to 0.129 0.130 to 0.157 0.158 to 0.185 0.186 to 0.212 0.213 to 0.241
11.27%
23.68%
27.33%
39.78%
41.78%
46.16%
46.31%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
South Garo Hills
East Khasi Hills
West Garo Hills
West Khasi Hills
East Garo Hills
Jaintia Hills
Ri Bhoi
Headcount Ratio (% of population who are multidimensionally poor)
Meghalaya: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Meghalaya.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Meghalaya
Multidimensional Poverty Index Score (District-wise)
Districts of Meghalaya are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.047 to 0.074 0.075 to 0.102 0.103 to 0.129 0.130 to 0.157 0.158 to 0.185 0.186 to 0.212 0.213 to 0.241
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 142 143
MeghalayaResults
Multidimensional Poverty in Meghalaya
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Meghalaya
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Meghalaya are as per the 2011 Census of India Districts of Meghalaya are as per the 2011 Census of India
Districts of Meghalaya Headcount Ratio Intensity MPI
East Garo Hills 41.78% 47.85% 0.200
East Khasi Hills 23.68% 46.22% 0.109
Jaintia Hills 46.16% 52.24% 0.241
Ri Bhoi 46.31% 49.83% 0.231
South Garo Hills 11.27% 42.05% 0.047
West Garo Hills 27.33% 46.72% 0.128
West Khasi Hills 39.78% 46.61% 0.185
Rural Urban
Districts of Meghalaya
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
East Garo Hills 44.63% 48.42% 0.216 24.46% 41.61% 0.102
East Khasi Hills 39.27% 46.77% 0.184 4.52% 40.32% 0.018
Jaintia Hills 49.79% 52.42% 0.261 8.65% 42.02% 0.036
Ri Bhoi 47.85% 50.05% 0.240 27.96% 45.29% 0.127
South Garo Hills 12.12% 42.04% 0.051 1.32% 42.86% 0.006
West Garo Hills 29.44% 46.84% 0.138 3.52% 35.90% 0.013
West Khasi Hills 41.64% 46.63% 0.194 26.43% 46.34% 0.122
INDIA MPI BASELINE REPORT 144 145
MizoramResults
Mizoram: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
21.4%
2.3%
16.1%
7.9%
24.2%
13.9%
3.8%
2.5%
32.2%
16.8%
15.8%
4.7%
9.5%
4.7%
4.1%
1.8%
5.8%
3.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
6.20%
0.63%
5.98%
5.46%
2.32%
8.68%
5.70%
2.80% 2.98%
7.60%
6.65%
2.68%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Mizoram: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Mizoram
Overview
Mizoram Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
9.8%
Rural
Headcount Ratio Intensity MPI
20.48% 47.93% 0.098
Mizoram: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.4%
MPI (HxA)
0.046
Mizoram
Urban
Headcount Ratio Intensity MPI
1.42% 41.4% 0.006
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 22.2%
Child & Adolescent Mortality: 1.1%
Maternal Health: 10.7%
Years of Schooling: 19.6%
School Attendance: 8.3%
Cooking Fuel: 8.9%
Sanitation: 5.8%
Drinking Water: 2.9%
Electricity: 3.1%
Housing: 7.8%
Assets: 6.8%
Bank Account: 2.7%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Mizoram State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 146 147
MizoramResults
0.007 to 0.028 0.029 to 0.050 0.051 to 0.072 0.073 to 0.094 0.095 to 0.116 0.117 to 0.138 0.139 to 0.161
1.76%
3.45%
8.69%
10.12%
10.16%
12.74%
25.29%
30.50%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Aizawl
Serchhip
Kolasib
Champhai
Lunglei
Saiha
Mamit
Lawangtlai
Headcount Ratio (% of population who are multidimensionally poor)
Mizoram: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Mizoram.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Mizoram
Multidimensional Poverty Index Score (District-wise)
Districts of Mizoram are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.007 to 0.028 0.029 to 0.050 0.051 to 0.072 0.073 to 0.094 0.095 to 0.116 0.117 to 0.138 0.139 to 0.161
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 148 149
MizoramResults
Multidimensional Poverty in Mizoram
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Mizoram
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Mizoram are as per the 2011 Census of India Districts of Mizoram are as per the 2011 Census of India
Districts of Mizoram Headcount Ratio Intensity MPI
Aizawl 1.76% 39.01% 0.007
Champhai 10.12% 39.82% 0.040
Kolasib 8.69% 48.34% 0.042
Lawangtlai 30.50% 52.71% 0.161
Lunglei 10.16% 43.92% 0.045
Mamit 25.29% 50.58% 0.128
Saiha 12.74% 42.25% 0.054
Serchhip 3.45% 40.31% 0.014
Rural Urban
Districts of Mizoram
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Aizawl 6.70% 38.45% 0.026 0.52% 40.84% 0.002
Champhai 15.10% 39.88% 0.060 3.03% 39.36% 0.012
Kolasib 19.96% 50.67% 0.101 3.60% 42.52% 0.015
Lawangtlai 37.27% 53.06% 0.198 3.27% 36.57% 0.012
Lunglei 16.53% 44.02% 0.073 1.54% 42.46% 0.007
Mamit 29.91% 50.74% 0.152 4.97% 46.46% 0.023
Saiha 21.81% 42.00% 0.092 2.27% 44.98% 0.010
Serchhip 6.20% 40.63% 0.025 1.84% 39.69% 0.007
INDIA MPI BASELINE REPORT 150 151
NagalandResults
Nagaland: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
24.5%
2.1%
33.1%
13.6%
33.9%
4.8%
5.4%
69.3%
56.9%
23.3%
12.3%
21.2%
10.7%
3.3%
1.5%
71.0%
66.4%
28.7%
7.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
17.16%
1.38%
18.34%
11.28%
3.67%
23.97%
8.71%
6.90%
2.50%
23.98%
16.67%
15.82%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Nagaland: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Nagaland
Overview
Nagaland Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
25.23%
Rural
Headcount Ratio Intensity MPI
32.8% 46.67% 0.153
Nagaland: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
46.33%
MPI (HxA)
0.117
Nagaland
Urban
Headcount Ratio Intensity MPI
10.75% 44.37% 0.048
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 24.5%
Child & Adolescent Mortality: 1.0%
Maternal Health: 13.1%
Years of Schooling: 16.1%
School Attendance: 5.2%
Cooking Fuel: 9.8%
Sanitation: 3.6%
Drinking Water: 2.8%
Electricity: 1.0%
Housing: 9.8%
Assets: 6.8%
Bank Account: 6.4%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Nagaland State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 152 153
NagalandResults
0.033 to 0.059 0.060 to 0.086 0.087 to 0.114 0.115 to 0.141 0.142 to 0.169 0.170 to 0.196 0.197 to 0.224
8.14%
11.18%
15.35%
17.33%
23.71%
24.6%
27.2%
33.88%
37.33%
38.62%
45.56%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
Mokokchung
Kohima
Wokha
Dimapur
Zunheboto
Peren
Phek
Longleng
Kiphire
Tuensang
Mon
Headcount Ratio (% of population who are multidimensionally poor)
Nagaland: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Nagaland.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Nagaland
Multidimensional Poverty Index Score (District-wise)
Districts of Nagaland are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.033 to 0.059 0.060 to 0.086 0.087 to 0.114 0.115 to 0.141 0.142 to 0.169 0.170 to 0.196 0.197 to 0.224
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 154 155
NagalandResults
Multidimensional Poverty in Nagaland
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Nagaland
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Nagaland are as per the 2011 Census of India Districts of Nagaland are as per the 2011 Census of India
Districts of Nagaland Headcount Ratio Intensity MPI
Mon 45.56% 49.25% 0.224
Dimapur 17.33% 49.52% 0.086
Kiphire 37.33% 44.37% 0.166
Kohima 11.18% 41.48% 0.046
Longleng 33.88% 44.65% 0.151
Mokokchung 8.14% 39.96% 0.033
Peren 24.58% 46.76% 0.115
Phek 27.25% 42.59% 0.116
Tuensang 38.62% 46.59% 0.180
Wokha 15.35% 42.48% 0.065
Zunheboto 23.71% 42.88% 0.102
Rural Urban
Districts of Nagaland
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Mon 51.68% 49.95% 0.258 18.10% 40.30% 0.073
Dimapur 23.31% 50.14% 0.117 12.08% 48.45% 0.059
Kiphire 44.46% 44.51% 0.198 19.13% 43.54% 0.083
Kohima 18.86% 41.92% 0.079 5.60% 40.39% 0.023
Longleng 36.62% 44.89% 0.164 21.46% 42.85% 0.092
Mokokchung 11.19% 40.17% 0.045 2.49% 38.19% 0.009
Peren 28.26% 47.61% 0.135 14.23% 42.02% 0.060
Phek 29.18% 42.59% 0.124 14.37% 42.67% 0.061
Tuensang 46.06% 46.74% 0.215 13.59% 44.92% 0.061
Wokha 23.37% 42.76% 0.100 2.57% 38.36% 0.010
Zunheboto 26.84% 43.18% 0.116 13.80% 41.06% 0.057
INDIA MPI BASELINE REPORT 156 157
OdishaResults
Odisha: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
37.3%
2.2%
19.5%
16.7%
5.0%
21.0%
55.8%
19.2%
10.9%
80.9%
65.3%
70.4%
39.5%
13.4%
3.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
22.41%
1.51%
12.77%
13.77%
4.32%
28.77%
27.14%
9.89%
8.93%
24.87%
13.29%
6.49%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Odisha: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Odisha
Overview
Odisha Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
29.35%
Rural
Headcount Ratio Intensity MPI
32.66% 46.45% 0.152
Odisha: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
46.42%
MPI (HxA)
0.136
Odisha
Urban
Headcount Ratio Intensity MPI
12.33% 46.12% 0.057
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.4%
Child & Adolescent Mortality: 0.9%
Maternal Health: 7.8%
Years of Schooling: 16.8%
School Attendance: 5.3%
Cooking Fuel: 10.1%
Sanitation: 9.5%
Drinking Water: 3.5%
Electricity: 3.1%
Housing: 8.7%
Assets: 4.6%
Bank Account: 2.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Odisha State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 158 159
OdishaResults
0.046 to 0.083 0.084 to 0.120 0.121 to 0.158 0.159 to 0.196 0.197 to 0.233 0.234 to 0.271 0.272 to 0.310
11.64%
11.83%
14.97%
15.49%
18.62%
20.49%
20.75%
21.67%
21.88%
24.42%
24.53%
24.57%
24.75%
24.90%
28.05%
27.49%
28.43%
30.08%
33.03%
37.1%
38.0%
38.8%
41.78%
44.75%
44.90%
47.28%
48.14%
51.14%
58.71%
59.32%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Puri
Jagatsinghapur
Cuttack
Khordha
Jharsuguda
Nayagarh
Jajapur
Kendrapara
Ganjam
Baleshwar
Sambalpur
Anugul
Sundargarh
Bargarh
Balangir
Subarnapur
Bhadrak
Dhenkanal
Bauda
Debagarh
Nuapada
Gajapati
Kendujhar
Kandhamal
Mayurbhanj
Kalahandi
Rayagada
Koraput
Malkangiri
Nabarangapur
Headcount Ratio (% of population who are multidimensionally poor)
Odisha: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Odisha.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Odisha
Multidimensional Poverty Index Score (District-wise)
Districts of Odisha are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.046 to 0.083 0.084 to 0.120 0.121 to 0.158 0.159 to 0.196 0.197 to 0.233 0.234 to 0.271 0.272 to 0.310
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 160 161
OdishaResults
Multidimensional Poverty in Odisha
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Odisha
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Odisha are as per the 2011 Census of India Dis tricts of Odisha are as per the 2011 Census of India
Districts of Odisha Headcount Ratio Intensity MPI
Anugul 24.57% 43.44% 0.107
Balangir 27.49% 45.13% 0.124
Baleshwar 24.42% 44.85% 0.110
Bargarh 24.90% 42.95% 0.107
Bauda 33.03% 43.89% 0.145
Bhadrak 28.43% 43.37% 0.123
Cuttack 14.97% 43.20% 0.065
Debagarh 37.10% 47.61% 0.177
Dhenkanal 30.08% 44.55% 0.134
Gajapati 38.76% 47.24% 0.183
Ganjam 21.88% 44.93% 0.098
Jagatsinghapur 11.83% 41.40% 0.049
Jajapur 20.75% 44.12% 0.092
Jharsuguda 18.62% 42.70% 0.080
Kalahandi 47.28% 47.86% 0.226
Kandhamal 44.75% 46.99% 0.210
Kendrapara 21.67% 42.19% 0.091
Kendujhar 41.78% 50.24% 0.210
Khordha 15.49% 44.75% 0.069
Koraput 51.14% 51.77% 0.265
Malkangiri 58.71% 52.73% 0.310
Mayurbhanj 44.90% 46.89% 0.211
Nabarangapur 59.32% 50.87% 0.302
Nayagarh 20.49% 44.42% 0.091
Nuapada 37.98% 45.62% 0.173
Puri 11.64% 39.64% 0.046
Rayagada 48.14% 50.80% 0.245
Sambalpur 24.53% 43.08% 0.106
Subarnapur 28.05% 41.47% 0.116
Sundargarh 24.75% 45.29% 0.112
Rural Urban
Districts of Odisha
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anugul 26.11% 43.58% 0.114 17.42% 42.48% 0.074
Balangir 29.60% 45.29% 0.134 10.80% 41.71% 0.045
Baleshwar 25.84% 44.61% 0.115 11.65% 49.62% 0.058
Bargarh 25.50% 42.97% 0.110 18.72% 42.70% 0.080
Bauda 33.82% 43.87% 0.148 11.21% 44.97% 0.050
Bhadrak 29.76% 43.46% 0.129 19.77% 42.49% 0.084
Cuttack 17.50% 41.75% 0.073 7.58% 52.99% 0.040
Debagarh 38.79% 47.64% 0.185 10.89% 46.08% 0.050
Dhenkanal 33.40% 44.62% 0.149 2.30% 35.71% 0.008
Gajapati 42.90% 47.42% 0.203 8.84% 41.11% 0.036
Ganjam 26.18% 44.85% 0.117 5.89% 46.31% 0.027
Jagatsinghapur 11.70% 41.04% 0.048 13.13% 44.87% 0.059
Jajapur 20.39% 43.23% 0.088 24.68% 52.18% 0.129
Jharsuguda 24.25% 42.27% 0.103 9.97% 44.31% 0.044
Kalahandi 50.24% 47.79% 0.240 7.70% 53.93% 0.042
Kandhamal 46.88% 47.18% 0.221 20.72% 42.27% 0.088
Kendrapara 22.27% 42.31% 0.094 8.73% 35.73% 0.031
Kendujhar 46.87% 50.38% 0.236 15.97% 48.24% 0.077
Khordha 17.91% 43.01% 0.077 13.34% 46.82% 0.062
Koraput 56.92% 51.98% 0.296 15.33% 46.85% 0.072
Malkangiri 61.25% 53.02% 0.325 27.98% 44.97% 0.126
Mayurbhanj 47.91% 46.85% 0.224 8.10% 49.90% 0.040
Nabarangapur 62.43% 50.97% 0.318 21.51% 47.21% 0.102
Nayagarh 21.73% 44.40% 0.096 5.78% 45.35% 0.026
Nuapada 39.12% 45.73% 0.179 20.53% 42.51% 0.087
Puri 12.86% 39.73% 0.051 5.49% 38.64% 0.021
Rayagada 54.42% 51.01% 0.278 14.78% 46.81% 0.069
Sambalpur 28.79% 43.29% 0.125 14.24% 42.05% 0.060
Subarnapur 29.18% 41.30% 0.121 14.04% 45.75% 0.064
Sundargarh 29.86% 44.98% 0.134 14.66% 46.55% 0.068
INDIA MPI BASELINE REPORT 162 163
PunjabResults
Punjab: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
22.1%
1.4%
12.7%
7.3%
2.6%
1.9%
19.3%
1.7%
3.7%
36.4%
23.3%
17.3%
13.4%
0.4%0.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
4.41%
0.50%
3.09% 3.41%
1.41%
4.23%
3.02%
0.34% 0.22%
3.32%
0.60% 1.03%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Punjab: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Punjab
Overview
Punjab Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
5.59%
Rural
Headcount Ratio Intensity MPI
6.4% 43.2% 0.028
Punjab: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.75%
MPI (HxA)
0.024
Punjab
Urban
Headcount Ratio Intensity MPI
4.32% 45.02% 0.019
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.1%
Child & Adolescent Mortality: 1.7%
Maternal Health: 10.5%
Years of Schooling: 23.2%
School Attendance: 9.6%
Cooking Fuel: 8.2%
Sanitation: 5.9%
Drinking Water: 0.7%
Electricity: 0.4%
Housing: 6.5%
Assets: 1.2%
Bank Account: 2.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Punjab State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 164 165
PunjabResults
0.009 to 0.012 0.013 to 0.017 0.018 to 0.022 0.023 to 0.027 0.028 to 0.031 0.032 to 0.036 0.037 to 0.042
2.01%
2.96%
3.31%
3.49%
3.56%
3.75%
3.83%
4.49%
5.11%
5.1%
5.2%
5.6%
5.8%
7.24%
7.42%
7.71%
8.01%
8.31%
9.42%
9.99%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
Rupnagar
Faridkot
Jalandhar
Fatehgarh Sahib
Sangrur
Patiala
Ludhiana
Hoshiarpur
Sahibzada Ajit Singh Nagar
Gurdaspur
Kapurthala
Bathinda
Barnala
Shaheed Bhagat Singh Nagar
Amritsar
Muktsar
Moga
Tarn Taran
Firozpur
Mansa
Headcount Ratio (% of population who are multidimensionally poor)
Punjab: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Punjab.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Punjab
Multidimensional Poverty Index Score (District-wise)
Districts of Punjab are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.009 to 0.012 0.013 to 0.017 0.018 to 0.022 0.023 to 0.027 0.028 to 0.031 0.032 to 0.036 0.037 to 0.042
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 166 167
PunjabResults
Multidimensional Poverty in Punjab
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Punjab
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Punjab are as per the 2011 Census of India Districts of Punjab are as per the 2011 Census of India
Districts of Punjab Headcount Ratio Intensity MPI
Amritsar 7.42% 44.92% 0.033
Barnala 5.81% 43.39% 0.025
Bathinda 5.62% 44.01% 0.025
Faridkot 2.96% 42.69% 0.013
Fatehgarh Sahib 3.49% 43.61% 0.015
Firozpur 9.42% 43.30% 0.041
Gurdaspur 5.11% 43.67% 0.022
Hoshiarpur 4.49% 44.97% 0.020
Jalandhar 3.31% 39.09% 0.013
Kapurthala 5.19% 47.76% 0.025
Ludhiana 3.83% 45.35% 0.017
Mansa 9.99% 41.91% 0.042
Moga 8.01% 42.41% 0.034
Muktsar 7.71% 44.55% 0.034
Patiala 3.75% 41.82% 0.016
Rupnagar 2.01% 42.76% 0.009
Sahibzada Ajit Singh Nagar 5.05% 48.75% 0.025
Sangrur 3.56% 39.56% 0.014
Shaheed Bhagat Singh Nagar 7.24% 43.05% 0.031
Tarn Taran 8.31% 45.30% 0.038
Rural Urban
Districts of Punjab
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Amritsar 12.23% 46.71% 0.057 3.33% 39.32% 0.013
Barnala 6.29% 42.51% 0.027 4.80% 45.79% 0.022
Bathinda 4.61% 46.66% 0.022 6.87% 41.80% 0.029
Faridkot 2.87% 42.27% 0.012 3.20% 43.79% 0.014
Fatehgarh Sahib 3.22% 41.12% 0.013 4.08% 47.99% 0.020
Firozpur 10.31% 42.41% 0.044 7.11% 46.64% 0.033
Gurdaspur 5.96% 41.46% 0.025 3.20% 52.94% 0.017
Hoshiarpur 4.27% 46.46% 0.020 5.30% 40.47% 0.021
Jalandhar 3.03% 37.79% 0.011 3.54% 40.04% 0.014
Kapurthala 3.50% 40.51% 0.014 8.11% 53.17% 0.043
Ludhiana 2.85% 40.89% 0.012 4.44% 47.13% 0.021
Mansa 9.72% 41.81% 0.041 10.91% 42.18% 0.046
Moga 9.70% 42.26% 0.041 2.41% 44.37% 0.011
Muktsar 10.79% 44.86% 0.048 2.01% 41.50% 0.008
Patiala 5.51% 41.46% 0.023 1.72% 43.17% 0.007
Rupnagar 1.43% 42.72% 0.006 3.42% 42.80% 0.015
Sahibzada Ajit Singh Nagar 4.93% 42.70% 0.021 5.16% 53.68% 0.028
Sangrur 3.60% 39.13% 0.014 3.39% 41.32% 0.014
Shaheed Bhagat Singh Nagar 7.53% 41.55% 0.031 6.69% 46.35% 0.031
Tarn Taran 9.40% 45.82% 0.043 2.49% 34.82% 0.009
INDIA MPI BASELINE REPORT 168 169
RajasthanResults
Rajasthan: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
42.6%
3.0%
26.3%
17.1%
8.5%
26.8%
35.5%
20.5%
4.0%
69.9%
58.6%
53.9%
28.9%
8.7%
1.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
23.25%
2.09%
17.08%
13.37%
7.24%
27.69%
24.77%
13.11%
6.60%
18.71%
13.27%
2.22%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Rajasthan: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Rajasthan
Overview
Rajasthan Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
29.46%
Rural
Headcount Ratio Intensity MPI
35.22% 47.7% 0.168
Rajasthan: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.44%
MPI (HxA)
0.14
Rajasthan
Urban
Headcount Ratio Intensity MPI
11.52% 44.99% 0.052
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.7%
Child & Adolescent Mortality: 1.2%
Maternal Health: 10.2%
Years of Schooling: 15.9%
School Attendance: 8.6%
Cooking Fuel: 9.4%
Sanitation: 8.4%
Drinking Water: 4.5%
Electricity: 2.3%
Housing: 6.4%
Assets: 4.5%
Bank Account: 0.8%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Rajasthan State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 170 171
RajasthanResults
0.056 to 0.089 0.090 to 0.123 0.124 to 0.158 0.159 to 0.192 0.193 to 0.226 0.227 to 0.260 0.261 to 0.295
12.80%
13.30%
14.67%
15.48%
18.43%
18.51%
19.43%
22.31%
23.49%
24.02%
24.56%
25.23%
27.26%
27.69%
28.02%
28.32%
29.70%
29.96%
32.50%
32.74%
33.25%
33.43%
39.8%
39.9%
40.83%
41.19%
42.08%
44.69%
47.86%
50.97%
52.54%
53.06%
56.13%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Jhunjhunun
Kota
Sikar
Jaipur
Ganganagar
Ajmer
Hanumangarh
Nagaur
Churu
Bikaner
Pali
Tonk
Dausa
Bhilwara
Chittaurgarh
Jodhpur
Rajsamand
Alwar
Baran
Jhalawar
Sawai Madhopur
Bundi
Dhaulpur
Karauli
Bharatpur
Jalor
Sirohi
Dungarpur
Udaipur
Banswara
Pratapgarh
Jaisalmer
Barmer
Headcount Ratio (% of population who are multidimensionally poor)
Rajasthan: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Rajasthan.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Rajasthan
Multidimensional Poverty Index Score (District-wise)
Districts of Rajasthan are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.056 to 0.089 0.090 to 0.123 0.124 to 0.158 0.159 to 0.192 0.193 to 0.226 0.227 to 0.260 0.261 to 0.295
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 172 173
RajasthanResults
Multidimensional Poverty in Rajasthan
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Rajasthan
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Rajasthan are as per the 2011 Census of India Dis tricts of Rajasthan are as per the 2011 Census of India
Districts of Rajasthan Headcount Ratio Intensity MPI
Ajmer 18.51% 44.60% 0.083
Alwar 29.96% 45.16% 0.135
Banswara 50.97% 49.96% 0.255
Baran 32.50% 44.47% 0.145
Barmer 56.13% 52.63% 0.295
Bharatpur 40.83% 48.09% 0.196
Bhilwara 27.69% 46.49% 0.129
Bikaner 24.02% 46.97% 0.113
Bundi 33.43% 46.22% 0.155
Chittaurgarh 28.02% 47.30% 0.133
Churu 23.49% 44.22% 0.104
Dausa 27.26% 42.93% 0.117
Dhaulpur 39.82% 46.08% 0.183
Dungarpur 44.69% 49.32% 0.220
Ganganagar 18.43% 42.07% 0.078
Hanumangarh 19.43% 45.45% 0.088
Jaipur 15.48% 42.01% 0.065
Jaisalmer 53.06% 53.22% 0.282
Jalor 41.19% 49.97% 0.206
Jhalawar 32.74% 47.04% 0.154
Jhunjhunun 12.80% 43.62% 0.056
Jodhpur 28.32% 48.50% 0.137
Karauli 39.92% 45.92% 0.183
Kota 13.30% 45.23% 0.060
Nagaur 22.31% 46.53% 0.104
Pali 24.56% 46.16% 0.113
Pratapgarh 52.54% 50.28% 0.264
Rajsamand 29.70% 46.01% 0.137
Sawai Madhopur 33.25% 45.70% 0.152
Sikar 14.67% 43.29% 0.064
Sirohi 42.08% 50.51% 0.213
Tonk 25.23% 42.82% 0.108
Udaipur 47.86% 52.44% 0.251
Rural Urban
Districts of Rajasthan
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ajmer 25.93% 44.41% 0.115 6.07% 45.92% 0.028
Alwar 34.05% 45.26% 0.154 6.93% 42.38% 0.029
Banswara 53.51% 50.16% 0.268 16.55% 41.49% 0.069
Baran 35.91% 44.26% 0.159 19.32% 45.97% 0.089
Barmer 59.01% 52.84% 0.312 14.85% 40.50% 0.060
Bharatpur 44.26% 48.81% 0.216 27.53% 43.59% 0.120
Bhilwara 32.42% 46.05% 0.149 12.42% 50.16% 0.062
Bikaner 32.70% 47.38% 0.155 8.71% 44.21% 0.038
Bundi 37.84% 46.25% 0.175 16.89% 45.97% 0.078
Chittaurgarh 33.16% 47.52% 0.158 5.18% 41.00% 0.021
Churu 26.59% 44.22% 0.118 15.00% 44.23% 0.066
Dausa 29.81% 42.90% 0.128 10.20% 43.54% 0.044
Dhaulpur 44.12% 45.84% 0.202 23.36% 47.76% 0.112
Dungarpur 46.84% 49.40% 0.231 6.24% 38.76% 0.024
Ganganagar 21.33% 41.78% 0.089 10.68% 43.63% 0.047
Hanumangarh 19.66% 44.67% 0.088 18.57% 48.48% 0.090
Jaipur 21.20% 42.51% 0.090 9.49% 40.86% 0.039
Jaisalmer 57.23% 53.22% 0.305 17.17% 53.40% 0.092
Jalor 44.18% 49.99% 0.221 4.83% 47.43% 0.023
Jhalawar 37.13% 47.35% 0.176 9.02% 40.20% 0.036
Jhunjhunun 12.05% 41.48% 0.050 14.90% 48.50% 0.072
Jodhpur 37.72% 48.73% 0.184 10.14% 46.84% 0.047
Karauli 44.15% 45.76% 0.202 16.66% 48.19% 0.080
Kota 21.64% 45.46% 0.098 8.17% 44.85% 0.037
Nagaur 24.65% 46.97% 0.116 13.46% 43.51% 0.059
Pali 29.14% 46.59% 0.136 9.03% 41.40% 0.037
Pratapgarh 55.92% 50.33% 0.281 7.50% 45.36% 0.034
Rajsamand 33.99% 46.27% 0.157 4.60% 34.82% 0.016
Sawai Madhopur 35.11% 45.20% 0.159 25.66% 48.53% 0.125
Sikar 15.31% 42.07% 0.064 12.83% 47.47% 0.061
Sirohi 46.55% 50.33% 0.234 25.87% 51.70% 0.134
Tonk 29.75% 42.92% 0.128 12.04% 42.10% 0.051
Udaipur 57.23% 52.54% 0.301 2.12% 39.38% 0.008
INDIA MPI BASELINE REPORT 174 175
SikkimResults
Sikkim: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
13.3%
1.0%
5.4%
8.2%
9.5%
1.4%1.8%
42.2%
23.4%
10.4%
12.7%
2.3%
7.4%
0.6%0.7%
26.7%
26.2%
8.4%
6.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
2.87%
0.25%
1.75%
2.48%
0.36%
2.89%
1.13%
0.19% 0.07%
2.30%
1.84%
1.10%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Sikkim: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Sikkim
Overview
Sikkim Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
3.82%
Rural
Headcount Ratio Intensity MPI
4.25% 41.15% 0.018
Sikkim: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
41.2%
MPI (HxA)
0.016
Sikkim
Urban
Headcount Ratio Intensity MPI
2.8% 41.36% 0.012
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.4%
Child & Adolescent Mortality: 1.3%
Maternal Health: 9.3%
Years of Schooling: 26.3%
School Attendance: 3.8%
Cooking Fuel: 8.8%
Sanitation: 3.4%
Drinking Water: 0.6%
Electricity: 0.2%
Housing: 7.0%
Assets: 5.6%
Bank Account: 3.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Sikkim State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 176 177
SikkimResults
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
2.74%
3.90%
4.47%
4.66%
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%
South Sikkim
East Sikkim
North Sikkim
West Sikkim
Headcount Ratio (% of population who are multidimensionally poor)
Sikkim: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Sikkim.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.020
0.016
0.019
0.011
Sikkim
Multidimensional Poverty Index Score (District-wise)
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Sikkim
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
East Sikkim 4.88% 40.37% 0.020 2.92% 41.94% 0.012
North Sikkim 4.47% 41.66% 0.019 4.46% 41.15% 0.018
South Sikkim 2.89% 40.49% 0.012 1.98% 35.93% 0.007
West Sikkim 4.78% 42.38% 0.020 1.85% 42.86% 0.008
Multidimensional Poverty in Sikkim
Districts of Sikkim Headcount Ratio Intensity MPI
East Sikkim 3.90% 40.96% 0.016
North Sikkim 4.47% 41.57% 0.019
South Sikkim 2.74% 39.94% 0.011
West Sikkim 4.66% 42.39% 0.020
Districts of Sikkim are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.Districts of Sikkim are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 178 179
Tamil NaduResults
Tamil Nadu: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
24.8%
1.1%
6.7% 6.6%
1.0%
12.6%
20.2%
3.4%
6.4%
24.1%
17.1%
47.6%
27.4%
1.0%0.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
3.64%
0.30%
1.70%
2.27%
0.45%
3.63%
4.53%
1.21%
0.43%
2.64%
1.35% 1.48%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Tamil Nadu: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Tamil Nadu
Overview
Tamil Nadu Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
4.89%
Rural
Headcount Ratio Intensity MPI
7.32% 40.21% 0.029
Tamil Nadu: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
39.97%
MPI (HxA)
0.02
Tamil Nadu
Urban
Headcount Ratio Intensity MPI
2.49% 39.29% 0.01
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.1%
Child & Adolescent Mortality: 1.3%
Maternal Health: 7.2%
Years of Schooling: 19.3%
School Attendance: 3.9%
Cooking Fuel: 8.8%
Sanitation: 11.0%
Drinking Water: 3.0%
Electricity: 1.0%
Housing: 6.4%
Assets: 3.3%
Bank Account: 3.6%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Tamil Nadu State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 180 181
Tamil NaduResults
0.004 to 0.009 0.010 to 0.015 0.016 to 0.021 0.022 to 0.027 0.028 to 0.033 0.034 to 0.039 0.040 to 0.046
0.96%
1.52%
2.03%
2.29%
2.53%
2.73%
2.73%
3.02%
3.11%
3.73%
3.80%
4.16%
4.60%
4.76%
5.11%
5.26%
5.5%
5.9%
6.1%
6.3%
6.8%
6.56%
7.21%
7.23%
7.61%
8.18%
8.23%
8.64%
8.71%
9.18%
9.35%
11.71%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
Chennai
Kanniyakumari
The Nilgiris
Coimbatore
Thiruvallur
Namakkal
Erode
Kancheepuram
Tiruppur
Tiruchirappalli
Vellore
Dindigul
Karur
Theni
Krishnagiri
Dharmapuri
Tirunelveli
Tiruvannamalai
Madurai
Cuddalore
Salem
Thiruvarur
Ramanathapuram
Thanjavur
Perambalur
Nagappattinam
Thoothukkudi
Sivaganga
Ariyalur
Virudunagar
Viluppuram
Pudukkottai
Headcount Ratio (% of population who are multidimensionally poor)
Tamil Nadu: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Tamil Nadu.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Tamil Nadu
Multidimensional Poverty Index Score (District-wise)
Districts of Tamil Nadu are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.004 to 0.009 0.010 to 0.015 0.016 to 0.021 0.022 to 0.027 0.028 to 0.033 0.034 to 0.039 0.040 to 0.046
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 182 183
Tamil NaduResults
Multidimensional Poverty in Tamil Nadu
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Tamil Nadu
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Tamil Nadu are as per the 2011 Census of India Districts of Tamil Nadu are as per the 2011 Census of India
Districts of Tamil Nadu Headcount Ratio Intensity MPI
Ariyalur 8.71% 38.73% 0.034
Chennai 0.96% 41.79% 0.004
Coimbatore 2.29% 40.14% 0.009
Cuddalore 6.29% 39.61% 0.025
Dharmapuri 5.26% 39.68% 0.021
Dindigul 4.16% 38.87% 0.016
Erode 2.73% 42.54% 0.012
Kancheepuram 3.02% 39.03% 0.012
Kanniyakumari 1.52% 35.81% 0.005
Karur 4.60% 39.68% 0.018
Krishnagiri 5.11% 41.22% 0.021
Madurai 6.15% 39.28% 0.024
Nagappattinam 8.18% 40.35% 0.033
Namakkal 2.73% 41.11% 0.011
Perambalur 7.61% 39.81% 0.030
Pudukkottai 11.71% 39.18% 0.046
Ramanathapuram 7.21% 40.95% 0.030
Salem 6.56% 44.81% 0.029
Sivaganga 8.64% 38.50% 0.033
Thanjavur 7.23% 38.16% 0.028
The Nilgiris 2.03% 39.01% 0.008
Theni 4.76% 39.91% 0.019
Thiruvallur 2.53% 39.11% 0.010
Thiruvarur 6.79% 40.41% 0.027
Thoothukkudi 8.23% 40.42% 0.033
Tiruchirappalli 3.73% 38.20% 0.014
Tirunelveli 5.52% 40.35% 0.022
Tiruppur 3.11% 38.99% 0.012
Tiruvannamalai 5.92% 40.45% 0.024
Vellore 3.80% 37.30% 0.014
Viluppuram 9.35% 40.53% 0.038
Virudunagar 9.18% 39.45% 0.036
Rural Urban
Districts of Tamil Nadu
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ariyalur 8.69% 38.68% 0.034 8.86% 39.07% 0.035
Chennai - - - 0.96% 41.79% 0.004
Coimbatore 3.73% 38.92% 0.015 1.81% 40.99% 0.007
Cuddalore 7.07% 39.45% 0.028 4.73% 40.07% 0.019
Dharmapuri 5.63% 40.25% 0.023 3.98% 36.86% 0.015
Dindigul 5.57% 39.25% 0.022 1.88% 37.04% 0.007
Erode 4.62% 41.67% 0.019 1.07% 45.82% 0.005
Kancheepuram 3.76% 37.09% 0.014 2.63% 40.49% 0.011
Kanniyakumari 3.04% 35.85% 0.011 1.20% 35.79% 0.004
Karur 6.00% 40.18% 0.024 2.65% 38.10% 0.010
Krishnagiri 6.48% 41.35% 0.027 0.70% 37.37% 0.003
Madurai 10.09% 40.65% 0.041 4.08% 37.51% 0.015
Nagappattinam 9.36% 40.35% 0.038 3.98% 40.38% 0.016
Namakkal 2.70% 41.64% 0.011 2.77% 40.38% 0.011
Perambalur 8.89% 39.80% 0.035 1.14% 40.41% 0.005
Pudukkottai 14.52% 39.29% 0.057 1.62% 35.71% 0.006
Ramanathapuram 8.68% 40.58% 0.035 3.89% 42.86% 0.017
Salem 10.42% 46.02% 0.048 2.56% 39.73% 0.010
Sivaganga 11.24% 38.33% 0.043 2.08% 40.83% 0.008
Thanjavur 8.69% 38.17% 0.033 4.47% 38.11% 0.017
The Nilgiris 2.40% 41.81% 0.010 1.76% 36.36% 0.006
Theni 5.54% 40.03% 0.022 4.32% 39.82% 0.017
Thiruvallur 5.37% 40.18% 0.022 1.04% 36.19% 0.004
Thiruvarur 7.63% 39.99% 0.031 3.35% 44.33% 0.015
Thoothukkudi 12.75% 40.39% 0.051 3.93% 40.54% 0.016
Tiruchirappalli 5.15% 37.41% 0.019 2.24% 40.14% 0.009
Tirunelveli 6.92% 41.40% 0.029 4.01% 38.39% 0.015
Tiruppur 3.30% 38.31% 0.013 3.01% 39.40% 0.012
Tiruvannamalai 6.89% 40.52% 0.028 1.75% 39.22% 0.007
Vellore 4.70% 37.05% 0.017 2.88% 37.72% 0.011
Viluppuram 9.81% 40.81% 0.040 7.01% 38.49% 0.027
Virudunagar 13.20% 39.98% 0.053 5.02% 38.00% 0.019
INDIA MPI BASELINE REPORT 184 185
TelanganaResults
Telangana: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
31.1%
1.4%
10.9%
15.8%
12.8%
2.1%2.1%
31.7%
7.8%
49.3%
23.8%
27.8%
3.2%
1.2%
0.4%
25.5%
19.7%
7.5%
2.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
10.15%
0.78%
5.10%
8.50%
1.14%
10.49%
12.22%
4.45%
0.87%
8.32%
5.99%
2.73%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Telangana: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Telangana
Overview
Telangana Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
13.74%
Rural
Headcount Ratio Intensity MPI
20.35% 43.23% 0.088
Telangana: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.2%
MPI (HxA)
0.059
Telangana
Urban
Headcount Ratio Intensity MPI
5.1% 43.01% 0.022
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.5%
Child & Adolescent Mortality: 1.1%
Maternal Health: 7.2%
Years of Schooling: 23.9%
School Attendance: 3.2%
Cooking Fuel: 8.4%
Sanitation: 9.8%
Drinking Water: 3.6%
Electricity: 0.7%
Housing: 6.7%
Assets: 4.8%
Bank Account: 2.2%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Telangana State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 186 187
TelanganaResults
0.018 to 0.032 0.033 to 0.048 0.049 to 0.063 0.064 to 0.079 0.080 to 0.094 0.095 to 0.110 0.111 to 0.126
4.27%
5.83%
9.20%
12.45%
13.75%
15.3%
17.9%
21.44%
26.11%
27.43%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Hyderabad
Rangareddy
Karimnagar
Warangal
Khammam
Nalgonda
Medak
Nizamabad
Mahbubnagar
Adilabad
Headcount Ratio (% of population who are multidimensionally poor)
Telangana: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Telangana.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Telangana
Multidimensional Poverty Index Score (District-wise)
Districts of Telangana are as per the 2011 Census of India (erstwhile Andhra Pradesh). The colour represents the MPI score of a district. The co- lour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.018 to 0.032 0.033 to 0.048 0.049 to 0.063 0.064 to 0.079 0.080 to 0.094 0.095 to 0.110 0.111 to 0.126
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 188 189
TelanganaResults
Multidimensional Poverty in Telangana
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Telangana
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Telangana are as per the 2011 Census of India (erstwhile Andhra Pradesh) Dis tricts of Telangana are as per the 2011 Census of India (erstwhile Andhra Pradesh)
Districts of Telangana Headcount Ratio Intensity MPI
Adilabad 27.43% 46.03% 0.126
Hyderabad 4.27% 41.01% 0.018
Karimnagar 9.20% 41.10% 0.038
Khammam 13.75% 42.26% 0.058
Mahbubnagar 26.11% 43.54% 0.114
Medak 17.87% 42.68% 0.076
Nalgonda 15.34% 43.81% 0.067
Nizamabad 21.44% 44.51% 0.095
Rangareddy 5.83% 41.84% 0.024
Warangal 12.45% 40.50% 0.050
Rural Urban
Districts of Telangana
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Adilabad 32.68% 46.09% 0.151 12.58% 45.55% 0.057
Hyderabad - - - 4.27% 41.01% 0.018
Karimnagar 11.81% 40.19% 0.047 4.27% 45.84% 0.020
Khammam 17.47% 42.55% 0.074 2.15% 35.00% 0.008
Mahbubnagar 29.42% 43.55% 0.128 1.43% 42.86% 0.006
Medak 20.61% 42.73% 0.088 10.48% 42.39% 0.044
Nalgonda 16.01% 42.82% 0.069 12.71% 48.73% 0.062
Nizamabad 24.71% 44.61% 0.110 11.91% 43.85% 0.052
Rangareddy 12.05% 42.61% 0.051 3.29% 40.68% 0.013
Warangal 16.47% 40.41% 0.067 2.54% 41.92% 0.011
INDIA MPI BASELINE REPORT 190 191
TripuraResults
Tripura: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
28.0%
1.3%
13.5%
10.8%
18.8%
2.2%
2.7%
65.8%
56.2%
36.4%
26.4%
16.3%
13.9%
7.2%
1.8%
74.7%
67.0%
3.6%
3.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
12.01%
0.88%
7.79% 8.10%
1.67%
15.50%
11.08%
7.26%
4.30%
16.19%
9.42%
2.20%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Tripura: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Tripura
Overview
Tripura Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
16.65%
Rural
Headcount Ratio Intensity MPI
20.93% 45.34% 0.095
Tripura: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
45.02%
MPI (HxA)
0.075
Tripura
Urban
Headcount Ratio Intensity MPI
5.6% 41.96% 0.024
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 26.7%
Child & Adolescent Mortality: 1.0%
Maternal Health: 8.7%
Years of Schooling: 18.0%
School Attendance: 3.7%
Cooking Fuel: 9.8%
Sanitation: 7.0%
Drinking Water: 4.6%
Electricity: 2.7%
Housing: 10.3%
Assets: 6.0%
Bank Account: 1.4%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Tripura State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 192 193
TripuraResults
0.039 to 0.053 0.054 to 0.068 0.069 to 0.084 0.085 to 0.099 0.100 to 0.114 0.115 to 0.129 0.130 to 0.146
9.03%
17.03%
26.23%
30.65%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
West Tripura
South Tripura
Dhalai
North Tripura
Headcount Ratio (% of population who are multidimensionally poor)
Tripura: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Tripura.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Tripura
Multidimensional Poverty Index Score (District-wise)
Districts of Tripura are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.039 to 0.053 0.054 to 0.068 0.069 to 0.084 0.085 to 0.099 0.100 to 0.114 0.115 to 0.129 0.130 to 0.146
Multidimensional Poverty Index
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Tripura
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Dhalai 27.90% 47.33% 0.132 12.82% 46.55% 0.060
North Tripura 35.74% 47.77% 0.171 7.46% 41.35% 0.031
South Tripura 19.01% 41.84% 0.080 6.08% 45.12% 0.027
West Tripura 12.04% 43.91% 0.053 4.81% 40.73% 0.020
Multidimensional Poverty in Tripura
Districts of Tripura Headcount Ratio Intensity MPI
Dhalai 26.23% 47.29% 0.124
North Tripura 30.65% 47.49% 0.146
South Tripura 17.03% 42.02% 0.072
West Tripura 9.03% 43.20% 0.039
Districts of Tripura are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 194 195
Uttar PradeshResults
Uttar Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
44.5%
5.0%
35.4%
17.5%
11.9%
5.4%
67.5%
12.4%
4.9%
68.9%
50.5%
63.7%
31.2%
27.4%
9.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
30.48%
3.82%
25.29%
15.08%
9.97%
34.26%
31.80%
2.37%
18.35%
33.43%
8.87%
3.34%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Uttar Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Uttar Pradesh
Overview
Uttar Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
37.79%
Rural
Headcount Ratio Intensity MPI
44.32% 47.67% 0.211
Uttar Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.6%
MPI (HxA)
0.18
Uttar Pradesh
Urban
Headcount Ratio Intensity MPI
18.07% 47.06% 0.085
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.2%
Child & Adolescent Mortality: 1.8%
Maternal Health: 11.7%
Years of Schooling: 14.0%
School Attendance: 9.2%
Cooking Fuel: 9.1%
Sanitation: 8.4%
Drinking Water: 0.6%
Electricity: 4.9%
Housing: 8.9%
Assets: 2.3%
Bank Account: 0.9%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Uttar Pradesh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 196 197
Uttar PradeshResults
0.054 to 0.104 0.105 to 0.155 0.156 to 0.206 0.207 to 0.257 0.258 to 0.309 0.310 to 0.360 0.361 to 0.412
0.0%
Lucknow
Kanpur Nagar
Gautam Buddha Nagar
Ghaziabad
Jhansi
Baghpat
Meerut
Varanasi
Gorakhpur
Etawah
Jalaun
Saharanpur
Bijnor
Auraiya
Muzaffarnagar
Hamirpur
Mainpuri
Deoria
Firozabad
Mahamaya Nagar
Mau
Azamgarh
Allahabad
Bulandshahr
Agra
Rae Bareli
Ambedkar Nagar
Jyotiba Phule Nagar
Mahoba
Mathura
Lalitpur
Sultanpur
Moradabad
Pratapgarh
Ballia
Aligarh
Chandauli
Kanpur Dehat
Etah
Bareilly
Faizabad
Rampur
Farrukhabad
Banda
Jaunpur
Unnao
Ghazipur
Bh
adohi
Fatehpur
Mirzapur
Kushinagar
Basti
Pilibhit
Kannauj
Sant Kabir Nagar
Bara Banki
Kansiram Nagar
Sonbhadra
Maharajganj
Shahjahanpur
Hardoi
Chitrakoot
Kaushambi
Sitapur
Budaun
Siddharth Nagar
Gonda
Kheri
Balrampur
Bahraich
Shrawasti
12.16%
14.34%
17.08%
17.47%
20.27%
21.08%
21.11%
26.03%
26.26%
27.44%
27.67%
28.52%
29.78%
29.82%
29.85%
30.92%
31.32%
31.36%
32.01%
32.47%
32.70%
32.77%
32.77%
32.88%
34.13%
35.29%
33.59%
34.10%
34.84%
35.33%
35.98%
36.34%
36.86%
36.94%
37.11%
37.40%
37.91%
37.98%
38.47%
38.60%
38.73%
38.89%
39.18%
40.29%
40.78%
40.79%
41.04%
42.19%
42.66%
42.73%
42.94%
43.26%
43.50%
43.79%
43.3%
44.8%
47.8%
48.5%
49.1%
50.5%
51.2%
52.9%
56.06%
56.83%
57.10%
57.24%
59.26%
59.95%
69.45%
71.88%
74.3
8%
10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
Headcount Ratio (% of population who are multidimensionally poor)
Uttar Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Uttar Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Uttar Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Uttar Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.054 to 0.104 0.105 to 0.155 0.156 to 0.206 0.207 to 0.257 0.258 to 0.309 0.310 to 0.360 0.361 to 0.412
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 198 199
Uttar PradeshResults
Multidimensional Poverty in Uttar Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Uttar Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Uttar Pradesh are as per the 2011 Census of India Dis tricts of Uttar Pradesh are as per the 2011 Census of India
Districts of Uttar Pradesh Headcount Ratio Intensity MPI
Agra 33.59% 46.93% 0.158
Aligarh 37.40% 46.51% 0.174
Allahabad 32.77% 46.56% 0.153
Ambedkar Nagar 34.13% 43.52% 0.149
Auraiya 29.82% 44.92% 0.134
Azamgarh 32.77% 43.44% 0.142
Baghpat 21.08% 47.03% 0.099
Bahraich 71.88% 54.40% 0.391
Ballia 37.11% 45.12% 0.167
Balrampur 69.45% 53.77% 0.373
Banda 40.29% 46.12% 0.186
Bara Banki 44.77% 49.41% 0.221
Bareilly 38.60% 49.26% 0.190
Basti 43.26% 46.04% 0.199
Bhadohi 42.19% 45.53% 0.192
Bijnor 29.78% 45.10% 0.134
Budaun 57.10% 52.22% 0.298
Bulandshahr 32.88% 46.61% 0.153
Chandauli 37.91% 44.46% 0.169
Chitrakoot 52.86% 48.30% 0.255
Deoria 31.36% 45.43% 0.142
Etah 38.47% 45.61% 0.175
Etawah 27.44% 42.43% 0.116
Faizabad 38.73% 45.84% 0.178
Farrukhabad 39.18% 48.04% 0.188
Fatehpur 42.66% 47.32% 0.202
Firozabad 32.01% 46.68% 0.149
Gautam Buddha Nagar 17.08% 43.50% 0.074
Ghaziabad 17.47% 45.16% 0.079
Ghazipur 41.04% 44.95% 0.184
Gonda 59.26% 50.81% 0.301
Gorakhpur 26.26% 46.05% 0.121
Hamirpur 30.92% 44.12% 0.136
Hardoi 51.16% 48.14% 0.246
Jalaun 27.67% 43.80% 0.121
Jaunpur 40.78% 44.05% 0.180
Districts of Uttar Pradesh Headcount Ratio Intensity MPI
Jhansi 20.27% 44.28% 0.090
Jyotiba Phule Nagar 34.84% 48.24% 0.168
Kannauj 43.50% 47.26% 0.206
Kanpur Dehat 37.98% 43.87% 0.167
Kanpur Nagar 14.34% 44.09% 0.063
Kansiram Nagar 47.81% 49.57% 0.237
Kaushambi 56.06% 51.89% 0.291
Kheri 59.95% 51.32% 0.308
Kushinagar 42.94% 46.05% 0.198
Lalitpur 35.98% 44.31% 0.159
Lucknow 12.16% 44.57% 0.054
Mahamaya Nagar 32.47% 43.99% 0.143
Maharajganj 49.12% 45.88% 0.225
Mahoba 35.29% 43.76% 0.154
Mainpuri 31.32% 44.16% 0.138
Mathura 35.33% 44.40% 0.157
Mau 32.70% 44.82% 0.147
Meerut 21.11% 45.59% 0.096
Mirzapur 42.73% 47.11% 0.201
Moradabad 36.86% 48.05% 0.177
Muzaffarnagar 29.85% 47.79% 0.143
Pilibhit 43.26% 47.96% 0.207
Pratapgarh 36.94% 45.00% 0.166
Rae Bareli 34.10% 46.87% 0.160
Rampur 38.89% 49.66% 0.193
Saharanpur 28.52% 48.90% 0.139
Sant Kabir Nagar 43.79% 46.77% 0.205
Shahjahanpur 50.52% 48.90% 0.247
Shrawasti 74.38% 55.35% 0.412
Siddharth Nagar 57.24% 50.01% 0.286
Sitapur 56.83% 49.70% 0.282
Sonbhadra 48.46% 50.15% 0.243
Sultanpur 36.34% 47.34% 0.172
Unnao 40.79% 47.16% 0.192
Varanasi 26.03% 44.69% 0.116
INDIA MPI BASELINE REPORT 200 201
Uttar PradeshResults
Multidimensional Poverty in Uttar Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Uttar Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Rural Urban
Districts of Uttar Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Agra 43.50% 47.16% 0.205 22.93% 46.45% 0.107
Aligarh 44.82% 45.91% 0.206 22.72% 48.84% 0.111
Allahabad 39.89% 46.61% 0.186 13.07% 46.18% 0.060
Ambedkar Nagar 35.47% 43.31% 0.154 24.38% 45.79% 0.112
Auraiya 33.01% 44.30% 0.146 15.10% 51.12% 0.077
Azamgarh 36.21% 43.16% 0.156 5.94% 56.93% 0.034
Baghpat 22.51% 46.62% 0.105 16.63% 48.75% 0.081
Bahraich 74.52% 54.29% 0.405 43.15% 56.45% 0.244
Ballia 37.73% 45.41% 0.171 31.60% 42.03% 0.133
Balrampur 72.06% 54.00% 0.389 40.85% 49.37% 0.202
Banda 44.20% 45.40% 0.201 22.54% 52.53% 0.118
Bara Banki 48.96% 49.39% 0.242 15.66% 49.78% 0.078
Bareilly 46.42% 49.40% 0.229 26.21% 48.86% 0.128
Basti 46.28% 46.00% 0.213 4.97% 50.51% 0.025
Bhadohi 45.50% 45.32% 0.206 26.01% 47.29% 0.123
Bijnor 32.73% 44.55% 0.146 20.47% 47.91% 0.098
Budaun 66.39% 52.40% 0.348 15.46% 48.66% 0.075
Bulandshahr 34.98% 45.26% 0.158 26.97% 51.53% 0.139
Chandauli 42.03% 44.23% 0.186 16.60% 47.54% 0.079
Chitrakoot 55.31% 47.53% 0.263 38.39% 54.83% 0.210
Deoria 33.57% 45.53% 0.153 17.88% 44.26% 0.079
Etah 43.43% 45.61% 0.198 13.16% 45.63% 0.060
Etawah 30.73% 42.85% 0.132 17.81% 40.33% 0.072
Faizabad 41.91% 45.63% 0.191 18.18% 49.04% 0.089
Farrukhabad 45.59% 48.01% 0.219 16.04% 48.37% 0.078
Fatehpur 44.39% 47.09% 0.209 27.45% 50.63% 0.139
Firozabad 37.10% 45.27% 0.168 23.53% 50.38% 0.119
Gautam Buddha Nagar 24.26% 43.03% 0.104 13.37% 43.94% 0.059
Ghaziabad 29.40% 45.61% 0.134 12.81% 44.76% 0.057
Ghazipur 43.30% 45.02% 0.195 15.27% 42.77% 0.065
Gonda 61.95% 50.90% 0.315 22.08% 47.36% 0.105
Gorakhpur 31.48% 45.96% 0.145 4.18% 48.89% 0.020
Hamirpur 36.33% 44.11% 0.160 9.03% 44.22% 0.040
Hardoi 56.27% 48.52% 0.273 25.01% 43.85% 0.110
Jalaun 34.86% 43.14% 0.150 9.09% 50.39% 0.046
Jaunpur 41.81% 44.12% 0.184 28.12% 42.73% 0.120
Rural Urban
Districts of Uttar Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Jhansi 29.26% 44.51% 0.130 8.53% 43.25% 0.037
Jyotiba Phule Nagar 39.76% 48.12% 0.191 20.18% 48.98% 0.099
Kannauj 46.40% 47.27% 0.219 28.48% 47.21% 0.134
Kanpur Dehat 39.40% 43.82% 0.173 22.97% 44.88% 0.103
Kanpur Nagar 31.67% 44.06% 0.140 6.85% 44.16% 0.030
Kansiram Nagar 54.81% 49.60% 0.272 21.96% 49.26% 0.108
Kaushambi 56.16% 52.32% 0.294 55.20% 47.82% 0.264
Kheri 61.53% 51.65% 0.318 47.16% 47.74% 0.225
Kushinagar 44.94% 46.18% 0.208 18.85% 42.18% 0.079
Lalitpur 42.49% 44.53% 0.189 10.75% 40.84% 0.044
Lucknow 25.38% 45.36% 0.115 4.71% 42.16% 0.020
Mahamaya Nagar 36.70% 44.00% 0.161 17.88% 43.95% 0.079
Maharajganj 51.37% 45.83% 0.235 17.76% 47.96% 0.085
Mahoba 40.99% 44.21% 0.181 16.74% 40.17% 0.067
Mainpuri 34.15% 43.68% 0.149 18.11% 48.44% 0.088
Mathura 37.56% 43.74% 0.164 31.53% 45.73% 0.144
Mau 32.55% 43.89% 0.143 33.14% 47.55% 0.158
Meerut 23.27% 43.47% 0.101 20.10% 46.73% 0.094
Mirzapur 45.61% 47.60% 0.217 28.16% 43.05% 0.121
Moradabad 45.50% 48.61% 0.221 20.45% 45.67% 0.093
Muzaffarnagar 31.96% 47.16% 0.151 25.00% 49.65% 0.124
Pilibhit 48.88% 48.02% 0.235 15.96% 47.17% 0.075
Pratapgarh 39.26% 45.01% 0.177 0.83% 35.71% 0.003
Rae Bareli 36.42% 46.90% 0.171 6.53% 45.00% 0.029
Rampur 44.74% 49.85% 0.223 21.42% 48.52% 0.104
Saharanpur 32.38% 49.39% 0.160 19.62% 47.06% 0.092
Sant Kabir Nagar 45.25% 46.66% 0.211 29.97% 48.40% 0.145
Shahjahanpur 57.44% 49.02% 0.282 20.97% 47.47% 0.100
Shrawasti 75.65% 55.35% 0.419 38.63% 55.76% 0.215
Siddharth Nagar 59.37% 50.09% 0.297 21.42% 46.34% 0.099
Sitapur 60.84% 49.93% 0.304 28.75% 46.43% 0.133
Sonbhadra 55.25% 50.01% 0.276 9.68% 54.59% 0.053
Sultanpur 38.04% 47.39% 0.180 9.61% 44.47% 0.043
Unnao 45.29% 47.29% 0.214 14.81% 44.97% 0.067
Varanasi 34.78% 44.06% 0.153 15.85% 46.29% 0.073
Districts of Uttar
Pradesh are as per the 2011 Census of India Districts of Uttar Pradesh are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 202 203
UttarakhandResults
Uttarakhand: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
32.9%
2.6%
28.6%
9.8%
4.4%
9.0%
35.6%
13.8%
6.9%
52.1%
40.8%
34.1%
21.2%
2.2%
0.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
14.66%
1.63%
13.06%
6.74%
3.18%
15.80%
11.22%
3.21%
1.39%
12.35%
6.21%
3.23%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Uttarakhand: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Uttarakhand
Overview
Uttarakhand Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
17.72%
Rural
Headcount Ratio Intensity MPI
21.94% 43.78% 0.096
Uttarakhand: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.37%
MPI (HxA)
0.079
Uttarakhand
Urban
Headcount Ratio Intensity MPI
9.89% 46.82% 0.046
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.1%
Child & Adolescent Mortality: 1.7%
Maternal Health: 13.8%
Years of Schooling: 14.3%
School Attendance: 6.7%
Cooking Fuel: 9.6%
Sanitation: 6.8%
Drinking Water: 1.9%
Electricity: 0.8%
Housing: 7.5%
Assets: 3.8%
Bank Account: 2.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Uttarakhand State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 204 205
UttarakhandResults
0.031 to 0.043 0.044 to 0.055 0.056 to 0.067 0.068 to 0.079 0.080 to 0.092 0.093 to 0.104 0.105 to 0.117
6.88%
11.93%
13.41%
13.91%
13.96%
16.8%
19.5%
19.99%
22.41%
25.65%
23.20%
24.28%
24.76%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Dehradun
Garhwal
Nainital
Rudraprayag
Pithoragarh
Chamoli
Tehri Garhwal
Bageshwar
Champawat
Udham Singh Nagar
Uttarkashi
Haridwar
Almora
Headcount Ratio (% of population who are multidimensionally poor)
Uttarakhand: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Uttarakhand.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Uttarakhand
Multidimensional Poverty Index Score (District-wise)
Districts of Uttarakhand are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.031 to 0.043 0.044 to 0.055 0.056 to 0.067 0.068 to 0.079 0.080 to 0.092 0.093 to 0.104 0.105 to 0.117
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 206 207
UttarakhandResults
Multidimensional Poverty in Uttarakhand
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Uttarakhand
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Uttarakhand are as per the 2011 Census of India Districts of Uttarakhand are as per the 2011 Census of India
Districts of Uttarakhand Headcount Ratio Intensity MPI
Almora 25.65% 40.34% 0.103
Bageshwar 19.99% 41.08% 0.082
Chamoli 16.78% 41.32% 0.069
Champawat 22.41% 44.77% 0.100
Dehradun 6.88% 45.42% 0.031
Garhwal 11.93% 40.26% 0.048
Haridwar 24.76% 47.26% 0.117
Nainital 13.41% 43.74% 0.059
Pithoragarh 13.96% 41.06% 0.057
Rudraprayag 13.91% 40.28% 0.056
Tehri Garhwal 19.53% 40.64% 0.079
Udham Singh Nagar 23.20% 45.62% 0.106
Uttarkashi 24.28% 44.51% 0.108
Rural Urban
Districts of Uttarakhand
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Almora 27.71% 40.38% 0.112 2.98% 36.41% 0.011
Bageshwar 20.62% 41.06% 0.085 3.46% 44.05% 0.015
Chamoli 20.08% 41.32% 0.083 0.00% - 0.000
Champawat 22.68% 44.23% 0.100 20.90% 48.11% 0.101
Dehradun 12.29% 44.64% 0.055 3.64% 46.99% 0.017
Garhwal 13.85% 40.06% 0.055 1.86% 48.00% 0.009
Haridwar 29.55% 47.08% 0.139 17.98% 47.67% 0.086
Nainital 16.45% 43.15% 0.071 9.18% 45.20% 0.041
Pithoragarh 16.86% 41.06% 0.069 0.00% - 0.000
Rudraprayag 14.91% 40.28% 0.060 0.00% - 0.000
Tehri Garhwal 23.72% 40.64% 0.096 0.00% - 0.000
Udham Singh Nagar 26.68% 45.39% 0.121 17.62% 46.16% 0.081
Uttarkashi 26.13% 44.48% 0.116 1.34% 51.19% 0.007
INDIA MPI BASELINE REPORT 208 209
West BengalResults
West Bengal: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
33.6%
1.5%
14.4%
15.9%
14.1%
3.8%
2.7%
73.0%
61.6%
48.0%
32.0%
11.5%
5.1%
5.7%
2.5%
54.3%
47.2%
13.8%
4.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
16.22%
1.02%
9.44%
11.25%
2.82%
20.81%
16.93%
4.35%
3.74%
18.81%
8.66%
7.14%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
West Bengal: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in West Bengal
Overview
West Bengal Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
21.43%
Rural
Headcount Ratio Intensity MPI
25.8% 45.39% 0.117
West Bengal: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
45.49%
MPI (HxA)
0.097
West Bengal
Urban
Headcount Ratio Intensity MPI
11.67% 46% 0.054
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.7%
Child & Adolescent Mortality: 0.9%
Maternal Health: 8.1%
Years of Schooling: 19.2%
School Attendance: 4.8%
Cooking Fuel: 10.2%
Sanitation: 8.3%
Drinking Water: 2.1%
Electricity: 1.8%
Housing: 9.2%
Assets: 4.2%
Bank Account: 3.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 West Bengal State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 210 211
West BengalResults
0.013 to 0.044 0.045 to 0.075 0.076 to 0.107 0.108 to 0.139 0.140 to 0.171 0.172 to 0.203 0.204 to 0.236
2.80%
9.80%
11.34%
11.41%
12.84%
14.19%
14.93%
20.33%
22.02%
22.28%
22.42%
23.82%
27.2%
27.4%
27.6%
28.3%
35.70%
42.84%
49.69%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Kolkata
North 24 Parganas
Nadia
Darjeeling
Howrah
Purba Medinipur
Hugli
Barddhaman
Jalpaiguri
Koch Bihar
Dakshin Dinajpur
Pashchim Medinipur
Murshidabad
Bankura
Birbhum
South 24 Parganas
Maldah
Uttar Dinajpur
Puruliya
Headcount Ratio (% of population who are multidimensionally poor)
West Bengal: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of West Bengal.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
West Bengal
Multidimensional Poverty Index Score (District-wise)
Districts of West Bengal are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.013 to 0.044 0.045 to 0.075 0.076 to 0.107 0.108 to 0.139 0.140 to 0.171 0.172 to 0.203 0.204 to 0.236
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 212 213
West BengalResults
Multidimensional Poverty in West Bengal
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in West Bengal
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of West Bengal are as per the 2011 Census of India Districts of West Bengal are as per the 2011 Census of India
Districts of West Bengal Headcount Ratio Intensity MPI
Bankura 27.42% 44.58% 0.122
Barddhaman 20.33% 47.06% 0.096
Birbhum 27.61% 45.60% 0.126
Dakshin Dinajpur 22.42% 44.18% 0.099
Darjeeling 11.41% 44.97% 0.051
Howrah 12.84% 45.12% 0.058
Hugli 14.93% 44.23% 0.066
Jalpaiguri 22.02% 45.90% 0.101
Koch Bihar 22.28% 45.13% 0.101
Kolkata 2.80% 45.56% 0.013
Maldah 35.70% 45.66% 0.163
Murshidabad 27.23% 45.96% 0.125
Nadia 11.34% 42.60% 0.048
North 24 Parganas 9.80% 41.51% 0.041
Pashchim Medinipur 23.82% 43.50% 0.104
Purba Medinipur 14.19% 42.68% 0.061
Puruliya 49.69% 47.44% 0.236
South 24 Parganas 28.27% 45.67% 0.129
Uttar Dinajpur 42.84% 49.79% 0.213
Rural Urban
Districts of West Bengal
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bankura 29.38% 44.79% 0.132 7.34% 36.00% 0.026
Barddhaman 21.92% 48.56% 0.106 18.38% 44.86% 0.082
Birbhum 30.08% 45.76% 0.138 12.97% 43.41% 0.056
Dakshin Dinajpur 24.89% 44.45% 0.111 5.13% 35.03% 0.018
Darjeeling 15.24% 45.60% 0.069 4.93% 41.69% 0.021
Howrah 14.34% 45.55% 0.065 11.88% 44.79% 0.053
Hugli 17.23% 43.21% 0.074 10.92% 47.00% 0.051
Jalpaiguri 27.88% 46.12% 0.129 3.80% 40.68% 0.015
Koch Bihar 23.98% 45.36% 0.109 7.26% 38.51% 0.028
Kolkata - - - 2.80% 45.56% 0.013
Maldah 37.53% 45.79% 0.172 24.36% 44.42% 0.108
Murshidabad 27.50% 45.03% 0.124 26.33% 49.19% 0.130
Nadia 14.08% 42.39% 0.060 3.92% 44.73% 0.018
North 24 Parganas 13.86% 40.24% 0.056 6.52% 43.70% 0.028
Pashchim Medinipur 24.91% 43.54% 0.108 14.85% 42.91% 0.064
Purba Medinipur 14.74% 43.06% 0.063 9.95% 38.36% 0.038
Puruliya 49.76% 46.04% 0.229 49.30% 55.58% 0.274
South 24 Parganas 31.75% 45.76% 0.145 16.70% 45.12% 0.075
Uttar Dinajpur 46.23% 49.72% 0.230 19.24% 50.92% 0.098
INDIA MPI BASELINE REPORT 214 215
Andaman & Nicobar IslandsResults
Andaman & Nicobar Islands: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
22.0%
0.8%
5.1% 4.9%
0.9%
6.2%
33.6%
7.1%
1.6%
24.5%
20.2%
24.4%
12.0%
2.7%2.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
3.41%
0.31%
1.02%
1.95%
0.27%
3.15% 2.99%
1.18% 1.55%
3.51%
2.15%
0.11%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Andaman & Nicobar Islands: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Andaman & Nicobar Islands
Overview
Andaman & Nicobar Islands Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
4.3%
Rural
Headcount Ratio Intensity MPI
6.76% 40.86% 0.028
Andaman & Nicobar Islands: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
40.56%
MPI (HxA)
0.017
Andaman & Nicobar Islands
Urban
Headcount Ratio Intensity MPI
0.97% 37.76% 0.004
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 32.6%
Child & Adolescent Mortality: 1.5%
Maternal Health: 4.9%
Years of Schooling: 18.6%
School Attendance: 2.6%
Cooking Fuel: 8.6%
Sanitation: 8.2%
Drinking Water: 3.2%
Electricity: 4.2%
Housing: 9.6%
Assets: 5.9%
Bank Account: 0.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Andaman & Nicobar Islands UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 216 217
Andaman & Nicobar IslandsResults
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Andaman & Nicobar Islands: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.009
0.019
0.039
Andaman & Nicobar Islands
Multidimensional Poverty Index Score (District-wise)
Districts of Andaman & Nicobar Islands are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union
Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves
from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with
the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Andaman & Nicobar Islands.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
2.20%
5.28%
9.36%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%
South Andaman
Nicobar
North & Middle Andaman
Headcount Ratio (% of population who are multidimensionally poor)
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Andaman & Nicobar Islands
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Nicobar 5.28% 36.80% 0.019 - - -
North & Middle Andaman 9.47% 41.94% 0.040 6.33% 35.71% 0.023
South Andaman 4.44% 39.88% 0.018 0.85% 38.10% 0.003
Multidimensional Poverty in Andaman & Nicobar Islands
Districts of Andaman & Nicobar Islands Headcount Ratio Intensity MPI
Nicobar 5.28% 36.80% 0.019
North & Middle Andaman 9.36% 41.79% 0.039
South Andaman 2.20% 39.45% 0.009
Multidimensional Poverty Index
Districts of Andaman & Nicobar Islands are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 218 219
ChandigarhResults
4.90%
0.52%
3.20% 3.23%
1.46%
3.26%
4.84%
1.30%
0.48%
2.48%
1.20% 0.81%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Chandigarh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
Chandigarh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Chandigarh
Overview
Chandigarh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
5.97%
Rural
Headcount Ratio Intensity MPI
18.56% 47.88% 0.089
Chandigarh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.39%
MPI (HxA)
0.026
Chandigarh
Urban
Headcount Ratio Intensity MPI
5.45% 42.76% 0.023
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.5%
Child & Adolescent Mortality: 1.7%
Maternal Health: 10.3%
Years of Schooling: 20.7%
School Attendance: 9.4%
Cooking Fuel: 6.0%
Sanitation: 8.9%
Drinking Water: 2.4%
Electricity: 0.9%
Housing: 4.6%
Assets: 2.2%
Bank Account: 1.5%
23.1%
1.2%
11.0%
5.8%
1.8%
2.4%
6.4%
2.7%
4.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
4.9%
4.2%
19.0%
15.0%
0.5%0.1%
0.026
Chandigarh
Multidimensional Poverty Index Score (District-wise)
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Districts of Chandigarh are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Chandigarh UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 220 221
Dadra & Nagar HaveliResultsRESULT
A snapshot of multidimensional poverty in Dadra & Nagar Haveli
Overview
Dadra & Nagar Haveli Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
27.36%
Rural
Headcount Ratio Intensity MPI
44.67% 44.82% 0.2
Dadra & Nagar Haveli: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.57%
MPI (HxA)
0.122
Dadra & Nagar Haveli
Urban
Headcount Ratio Intensity MPI
4.89% 41.67% 0.02
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 33.1%
Child & Adolescent Mortality: 0.9%
Maternal Health: 5.8%
Years of Schooling: 9.5%
School Attendance: 9.0%
Cooking Fuel: 9.4%
Sanitation: 10.3%
Drinking Water: 4.0%
Electricity: 0.8%
Housing: 9.9%
Assets: 4.9%
Bank Account: 2.5%
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territory of Dadra & Nagar Haveli & Daman & Diu have been computed separately for their erstwhile regions.
Dadra & Nagar Haveli: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
Dadra & Nagar Haveli: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
45.0%
2.0%
13.4%
7.8% 7.7%
26.9%
57.1%
20.8%
10.9%
46.6%
25.7%
68.1%
36.9%
2.7%
0.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
24.22%
1.28%
8.46%
6.93% 6.56%
24.07%
26.29%
10.17%
2.12%
25.46%
12.60%
6.38%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the
NFHS-5 Dadra & Nagar Haveli District Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 222 223
Daman & DiuResultsINDIA MPI
A snapshot of multidimensional poverty in Daman & Diu
Overview
Daman & Diu Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
6.82%
Rural
Headcount Ratio Intensity MPI
5.19% 41.79% 0.022
Daman & Diu: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.18%
MPI (HxA)
0.03
Daman & Diu
Urban
Headcount Ratio Intensity MPI
7.41% 44.79% 0.033
x =
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 29.9%
Child & Adolescent Mortality: 0.9%
Maternal Health: 10.8%
Years of Schooling: 17.7%
School Attendance: 17.1%
Cooking Fuel: 2.3%
Sanitation: 7.2%
Drinking Water: 2.3%
Electricity: 0.0%
Housing: 1.7%
Assets: 5.0%
Bank Account: 5.1%
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territory of Dadra & Nagar
Haveli & Daman & Diu have been computed separately for their erstwhile regions.
Daman & Diu: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
20.9%
0.9%
14.7%
7.6%
4.7%
11.7%
8.1%
14.7%
12.5%
9.2%
7.9%
14.7%
34.8%
32.9%
10.3%
0.0%
0.5%
0.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
5.40%
0.33%
3.90%
3.20% 3.09%
1.44%
4.55%
1.42%
0.00%
1.08%
3.19% 3.25%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Daman & Diu: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
DOWNLOAD DATA
Note on comparison: The striped and dotted bars denote the provisional estimates of the uncensored headcount ratio based on the data avail- able in the NFHS-5 Daman District Factsheet and the NFHS-5 Diu District Factsheet (2019-20), respectively.
Dimension
Health Education Standard of Living (Daman & Diu)
NFHS-5: Standard of Living (Daman)
NFHS-5: Standard of Living (Diu)
INDIA MPI BASELINE REPORT 224 225
Daman & DiuResults
Daman & Diu: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
Multidimensional Poverty Index
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Daman & Diu.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
4.34%
7.46%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Diu
Daman
Headcount Ratio (% of population who are multidimensionally poor)
27.36%
Dadra & Nagar
Haveli
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Daman & Diu
Districts of Daman & Diu Headcount Ratio Intensity MPI
Daman 7.46% 45.16% 0.034
Diu 4.34% 37.64% 0.016
Rural Urban
Districts of Daman & Diu
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Daman 4.95% 44.77% 0.022 8.04% 45.21% 0.036
Diu 5.51% 38.37% 0.021 2.77% 35.72% 0.010
Districts of Daman & Diu are as per the 2011 Census of India
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.034
0.016
0.122
Dadra & Nagar Haveli, & Daman & Diu
Multidimensional Poverty Index Score (District-wise)
Districts of Dadra & Nagar Haveli, & Daman & Diu are as per the 2011 Census of India. Due to there being a relatively lower number of districts,
all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour
moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents
areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
INDIA MPI BASELINE REPORT 226 227
DelhiResultsINDIA MPI
A snapshot of multidimensional poverty in Delhi
Overview
Delhi Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
4.79%
Rural
Headcount Ratio Intensity MPI
3.41% 38.28% 0.013
Delhi: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.99%
MPI (HxA)
0.021
Delhi
Urban
Headcount Ratio Intensity MPI
4.8% 44.02% 0.021
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.0%
Child & Adolescent Mortality: 2.8%
Maternal Health: 13.2%
Years of Schooling: 20.1%
School Attendance: 9.1%
Cooking Fuel: 1.4%
Sanitation: 8.1%
Drinking Water: 3.8%
Electricity: 0.1%
Housing: 3.3%
Assets: 4.1%
Bank Account: 2.9%
Delhi: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
23.4%
1.9%
15.2%
5.9%
2.6%
23.7%
10.8%
5.5%
8.4%
2.2%
1.1%
26.7%
18.9%
0.3%0.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
3.92%
0.71%
3.34%
2.54%
1.15%
0.62%
3.58%
1.66%
0.06%
1.48% 1.80%
1.28%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Delhi: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Delhi UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 228 229
DelhiResults
2.29%
2.41%
2.86%
3.84%
4.26%
4.28%
6.06%
6.98%
7.35%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%
West Delhi
South West
North Delhi
Central Delhi
New Delhi
East Delhi
South Delhi
North West
North East
Headcount Ratio (% of population who are multidimensionally poor)
Delhi: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Delhi.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.010
0.018
0.018
0.025
0.011
0.031
0.012
0.033
0.017
Delhi
Multidimensional Poverty Index Score (District-wise)
Districts of Delhi are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
INDIA MPI BASELINE REPORT 230 231
DelhiResults
Multidimensional Poverty in Delhi
District-wise Headcount Ratio, Intensity and MPI Score
Districts of Delhi are as per the 2011 Census of India
Districts of Delhi Headcount Ratio Intensity MPI
Central Delhi 3.84% 43.78% 0.017
East Delhi 4.28% 41.77% 0.018
New Delhi 4.26% 43.27% 0.018
North Delhi 2.86% 41.15% 0.012
North East 7.35% 42.79% 0.031
North West 6.98% 47.21% 0.033
South Delhi 6.06% 41.76% 0.025
South West 2.41% 44.27% 0.011
West Delhi 2.29% 44.66% 0.010
Multidimensional Poverty in Delhi
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Delhi are as per the 2011 Census of India
Rural Urban
Districts of Delhi
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Central Delhi - - - 3.84% 43.78% 0.017
East Delhi - - - 4.28% 41.77% 0.018
New Delhi - - - 4.26% 43.27% 0.018
North Delhi 10.45% 34.86% 0.036 2.73% 41.55% 0.011
North East - - - 7.35% 42.79% 0.031
North West 0.62% 44.05% 0.003 7.08% 47.21% 0.033
South Delhi - - - 6.06% 41.76% 0.025
South West 4.03% 39.29% 0.016 2.38% 44.46% 0.011
West Delhi - - - 2.29% 44.66% 0.010
INDIA MPI BASELINE REPORT 232 233
Jammu & Kashmir, & LadakhResultsINDIA MPI
A snapshot of multidimensional poverty in Jammu & Kashmir, & Ladakh
Overview
Jammu & Kashmir, & Ladakh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
12.58%
Rural
Headcount Ratio Intensity MPI
16.39% 44.27% 0.073
Jammu & Kashmir, & Ladakh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.11%
MPI (HxA)
0.055
Jammu & Kashmir, & Ladakh
Urban
Headcount Ratio Intensity MPI
3.5% 42.32% 0.015
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 29.2%
Child & Adolescent Mortality: 1.3%
Maternal Health: 9.2%
Years of Schooling: 13.4%
School Attendance: 7.5%
Cooking Fuel: 9.7%
Sanitation: 9.1%
Drinking Water: 4.3%
Electricity: 1.4%
Housing: 8.1%
Assets: 5.7%
Bank Account: 1.2%
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territories of Jammu & Kash- mir, and Ladakh have been computed for their combined geographical region.
Jammu & Kashmir, & Ladakh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
Jammu & Kashmir, & Ladakh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
25.9%
1.8%
12.7%
6.8%
16.1%
3.7%
2.8%
14.2%
10.3%
29.6%
25.4%
3.9%
3.0%
45.2%
32.7%
23.7%
47.0%
24.3%
57.7%
2.8%
0.7%0.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
9.70%
0.85%
6.10%
4.47%
2.49%
11.25%
10.62%
4.97%
1.67%
9.44%
6.65%
1.43%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
DOWNLOAD DATA
Note on comparison: The striped and dotted bars denote the provisional estimates of the uncensored headcount ratio based on the data avail- able in the NFHS-5 Jammu & Kashmir UT Report and the Ladakh UT Factsheet (2019-20) respectively.
Dimension
Health Education Standard of Living (Jammu & Kashmir, & Ladakh)
NFHS-5: Education (Jammu & Kashmir) NFHS-5: Standard of Living (Jammu & Kashmir)
NFHS-5: Standard of Living (Ladakh)
INDIA MPI BASELINE REPORT 234 235
Jammu & Kashmir, & LadakhResults
1.51%
3.79%
6.51%
6.84%
6.97%
7.06%
7.43%
7.82%
8.36%
9.67%
11.07%
13.08%
16.08%
21.92%
24.27%
24.29%
26.83%
27.52%
28.92%
35.26%
Srinagar
Pulwama
Shupiyan
Badgam
Jammu
Baramula
Kulgam
Ganderbal
Anantnag
Samba
Bandipore
Kathua
Kupwara
Reasi
Punch
Kishtwar
Udhampur
Rajouri
Doda
Ramban
5.36%
19.4%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Leh (Ladakh)
Kargil
Headcount Ratio (% of population who are multidimensionally poor)
Jammu & Kashmir Ladakh
Jammu & Kashmir, & Ladakh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Jammu & Kashmir, & Ladakh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.006
0.050
0.030
0.030
0.041
0.034
0.016
0.106
0.039
0.028
0.079
0.067
0.097
0.033
0.021
0.057
0.136
0.163
0.111
0.123
0.117
Jammu & Kashmir, & Ladakh
Multidimensional Poverty Index Score (District-wise)
Districts of Jammu & Kashmir, & Ladakh are as per the Political Map of India 10
th
Edition (Survey of India). Due to there being a relatively lower
number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
INDIA MPI BASELINE REPORT 236 237
Jammu & Kashmir, & LadakhResults
Multidimensional Poverty in Jammu & Kashmir, & Ladakh
District-wise Headcount Ratio, Intensity and MPI Score
Districts of Jammu & Kashmir Headcount Ratio Intensity MPI
Anantnag 8.36% 46.54% 0.039
Badgam 6.84% 41.74% 0.029
Bandipore 11.07% 45.41% 0.050
Baramula 7.06% 43.09% 0.030
Doda 28.92% 47.10% 0.136
Ganderbal 7.82% 43.18% 0.034
Jammu 6.97% 43.33% 0.030
Kathua 13.08% 43.48% 0.057
Kishtwar 24.29% 45.52% 0.111
Kulgam 7.43% 43.79% 0.033
Kupwara 16.08% 41.57% 0.067
Pulwama 3.79% 42.07% 0.016
Punch 24.27% 43.48% 0.106
Rajouri 27.52% 44.68% 0.123
Ramban 35.26% 46.29% 0.163
Reasi 21.92% 44.45% 0.097
Samba 9.67% 42.77% 0.041
Shupiyan 6.51% 43.32% 0.028
Srinagar 1.51% 39.64% 0.006
Udhampur 26.83% 43.59% 0.117
Districts of Ladakh Headcount Ratio Intensity MPI
Kargil 19.43% 40.74% 0.079
Leh (Ladakh) 5.36% 38.83% 0.021
Multidimensional Poverty in Jammu & Kashmir, & Ladakh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Rural Urban
Districts of Ladakh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Kargil 23.42% 40.76% 0.095 3.46% 40.22% 0.014
Leh (Ladakh) 6.72% 37.93% 0.025 2.72% 43.14% 0.012
Rural Urban
Districts of Jammu & Kashmir
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anantnag 8.76% 44.65% 0.039 7.54% 50.93% 0.038
Badgam 7.99% 41.79% 0.033 1.63% 40.65% 0.007
Bandipore 12.58% 45.85% 0.058 6.27% 42.61% 0.027
Baramula 8.38% 42.81% 0.036 2.37% 46.71% 0.011
Doda 30.57% 47.14% 0.144 1.56% 35.71% 0.006
Ganderbal 8.43% 44.19% 0.037 5.99% 38.91% 0.023
Jammu 9.28% 45.51% 0.042 4.54% 38.64% 0.018
Kathua 14.75% 43.44% 0.064 0.87% 47.62% 0.004
Kishtwar 25.81% 45.55% 0.118 1.23% 35.71% 0.004
Kulgam 9.20% 43.75% 0.040 0.39% 47.62% 0.002
Kupwara 18.99% 41.59% 0.079 4.98% 41.24% 0.021
Pulwama 3.91% 42.25% 0.017 1.46% 33.33% 0.005
Punch 25.68% 43.55% 0.112 4.70% 37.86% 0.018
Rajouri 28.84% 44.78% 0.129 8.93% 40.11% 0.036
Ramban 36.49% 46.34% 0.169 5.23% 38.10% 0.020
Reasi 22.39% 44.45% 0.100 16.78% 44.50% 0.075
Samba 10.05% 42.70% 0.043 2.89% 47.39% 0.014
Shupiyan 6.81% 43.32% 0.030 1.91% 42.86% 0.008
Srinagar 7.92% 50.36% 0.040 1.42% 38.86% 0.006
Udhampur 29.73% 43.71% 0.130 10.05% 41.37% 0.042
Districts of
Jammu & Kashmir, & Ladakh are as per the Political Map of India 10th Edition (Survey of India) Districts of Jammu & Kashmir, & Ladakh are as per the Political Map of India 10th Edition (Survey of India)
INDIA MPI BASELINE REPORT 238 239
LakshadweepResultsINDIA MPI
A snapshot of multidimensional poverty in Lakshadweep
Overview
Lakshadweep Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
1.82%
Rural
Headcount Ratio Intensity MPI
1.16% 42.86% 0.005
Lakshadweep: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
36.15%
MPI (HxA)
0.007
Lakshadweep
Urban
Headcount Ratio Intensity MPI
2% 35.09% 0.007
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 46.1%
Child & Adolescent Mortality: 6.6%
Maternal Health: 10.9%
Years of Schooling: 0.0%
School Attendance: 16.2%
Cooking Fuel: 8.0%
Sanitation: 1.0%
Drinking Water: 4.1%
Electricity: 0.0%
Housing: 2.3%
Assets: 1.0%
Bank Account: 3.8%
Lakshadweep: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
1.82%
0.53% 0.86%
0.00%
0.64%
1.11%
0.14%
0.57%
0.00% 0.32% 0.14% 0.52%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Lakshadweep: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
31.5%
2.0%
6.5%
0.9%
1.4%
9.3%
1.5%
1.0%
5.6%
58.2%
40.6%
0.4%0.2% 0.0%0.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
0.007
Lakshadweep
Multidimensional Poverty Index Score (District-wise)
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Districts of Lakshadweep are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Lakshadweep UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 240 241
PuducherryResults
A snapshot of multidimensional poverty in Puducherry
Overview
Puducherry Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
1.72%
Rural
Headcount Ratio Intensity MPI
3.33% 36.74% 0.012
Puducherry: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
38.54%
MPI (HxA)
0.007
Puducherry
Urban
Headcount Ratio Intensity MPI
0.99% 41.28% 0.004
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 33.4%
Child & Adolescent Mortality: 3.6%
Maternal Health: 5.5%
Years of Schooling: 22.3%
School Attendance: 0.2%
Cooking Fuel: 9.7%
Sanitation: 10.2%
Drinking Water: 0.8%
Electricity: 0.8%
Housing: 7.9%
Assets: 3.0%
Bank Account: 2.6%
RESULT
Puducherry: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
21.9%
0.7%
4.1%
3.3%
1.2%
5.5%
17.6%
1.6%
5.3%
13.5%
7.7%
35.1%
15.1%
0.2%0.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
1.33%
0.28% 0.44%
0.89%
0.01%
1.35% 1.42%
0.11% 0.11%
1.10%
0.42% 0.37%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Puducherry: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Puducherry UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 242 243
PuducherryResults
Puducherry: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Puducherry.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.08%
1.30%
3.13%
5.18%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
Mahe
Puducherry
Karaikal
Yanam
Headcount Ratio (% of population who are multidimensionally poor)
Multidimensional Poverty Index
Districts of Puducherry Headcount Ratio Intensity MPI
Karaikal 3.13% 35.97% 0.011
Mahe 0.08% 35.71% 0.000
Puducherry 1.30% 39.28% 0.005
Yanam 5.18% 41.38% 0.021
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Puducherry
Rural Urban
Districts of Puducherry
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Karaikal 5.49% 36.17% 0.020 0.91% 34.86% 0.003
Mahe - - - 0.08% 35.71% 0.000
Puducherry 2.59% 37.15% 0.010 0.74% 42.52% 0.003
Yanam - - - 5.18% 41.38% 0.021
Districts of
Puducherry are as per the 2011 Census of India
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.000
0.005
0.011
0.021
Puducherry
Multidimensional Poverty Index Score (District-wise)
Districts of Puducherry are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the
States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through
yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI
scores. The legend provides the range of MPI scores represented by a colour.
INDIA MpI BASelINe reporT
ResultsSeCTIoN
244 245
SECTION
IV
TECHNICAL NOTES
&
DATA TABLES
INDIA MPI 246 247
Technical Notes Appendix
A.1 Policy for treatment of Missing Values
Any individual (and in extension household) for whom data for all indicators, and data for all constituents of an
indicator is not present, is not considered in the estimation sample of the national MPI and its disaggregation. It is
classified as a dropped observation.
For example, if an individual has data for eleven indicators of the national MPI but the information for one indicator is missing, that individual will not be considered in the estimation for the national MPI. Another example would be, supposing that in the indicator for drinking water, an individual has information for the type of drinking water source but information for round-trip time to the drinking water source is missing, then the individual is not considered in the estimation of the national MPI. Similarly, in the case of the indicator for sanitation, if the information for type of sanitation facility is available for an individual but the information for whether the facility is shared or exclusive is not available, then the individual is not considered for the estimation of the national MPI.
The exception to this policy is the maternal health indicator - the specific policy for which has been detailed sub-
sequently in this section.
A.2 Policy for the indicator on Bank Accounts
If an individual, when asked if they have a bank account, has replied that they “don’t know”, they are considered to
be deprived in the indicator for Bank Accounts.
In the case of the indicator for bank accounts, the NFHS-4 reports that 90.3% of individuals have stated that they
have a bank account, 9.5% stated that they do not have bank account and 0.1% of individuals responded “don’t
know” when asked if they have a bank account. For the national MPI, the 0.1% of individuals who responded with
“don’t know” (2,969 unweighted observations) have been treated as deprived in the indicator for bank accounts.
The rationale behind this is the assumption that if an individual is unaware of their ownership status for a bank ac-
count, then it may be considered analogous to them not having a bank account to begin with.
A
ANNEXURE
TECHNICAL NOTES
ESTIMATION DETAILS
A1
However, this assumption was not made discounting the possibility that there might be cases where the individual
has chosen to not disclose the information to the survey enumerator or the person responsible for the operation of
the bank account was not present in the household at the time of the survey. In such cases, the relatively low weight
assigned to the bank account indicator acts as a moderator, i.e., well-off individuals who have responded “don’t
know” to the bank account indicator will not be affected as they will need to be deprived in a substantial number
of other indicators to be considered as multidimensionally poor and on the other hand individuals who are already
multidimensionally poor by virtue of other indicators will be retained in the final estimation sample.
A.3 Policy for the indicator on Maternal Health
The indicator for maternal health is a composite of 2 discrete datapoints – the number of antenatal care visits a
woman received during her last pregnancy and the type of assistance (if any) that she received during the birth of
her last child. In order for her to be considered as deprived in the indicator for maternal health, she has to have a)
received less than 4 antenatal care visits (deprived in antenatal care) or b) not received assistance from a skilled
healthcare provider during childbirth (deprived in assisted delivery). In order to be deprived in the indicator for
maternal health, a woman must be deprived in either antenatal care or assisted delivery.
If the information for both antenatal care and assisted delivery are missing, then, adhering to the policy for treat-
ment of missing values, the woman for whom the information is missing, is not included in the estimation of the
national MPI.
The conundrum however arises, when the information for either antenatal care (or assisted delivery) is present, but
the information for assisted delivery (or antenatal care) is missing. Therefore, there are 9 possible scenarios which
may occur during the determination of the maternal health indicator.
The decision regarding the deprivation status of the maternal health indicator is fairly straightforward for outcomes
1 through 6 and outcome number 9. The problem lies with outcomes 7 and 8, where a woman is not deprived in
Antenatal Care but the information for Assisted Delivery is missing and vice versa. This is because the indicator for
which the information is missing may take a value of deprived or not deprived thereby determining the status of the
maternal health indicator as a whole. Thus, for observations falling in outcomes 7 and 8, it becomes impossible to
determine the actual deprivation status of the maternal health. A total number of 6,087 unweighted observations
fall in outcome 8 and there are no observations in outcome 7.
Outcome
Number
Deprived in Antenatal Care Deprived in Assisted Delivery Deprivation Status in Maternal Health
1 No No No
2 Ye s No Ye s
3 No Ye s Ye s
4 Ye s Ye s Ye s
5 Ye s Information Missing Ye s
6 Information Missing Ye s Ye s
7 No Information Missing ?
8 Information Missing No ?
9 Information Missing Information Missing
Obs. is dropped from estimation
sample
INDIA MPI BASELINE REPORT 248 249
Technical NotesAppendix
If the policy for treatment of missing values is to be applied, then these 6,087 indicators would be dropped from
the final estimation sample. This however would risk further reducing an already restricted sample (women who
have had at least one childbirth in the 5 years preceding the survey) of observations eligible for the maternal health
indicator.
Therefore, an exception to the policy for treatment of missing indicators has been made for the maternal health
indicator in order to retain the 6,087 observations in the final MPI estimation sample. In its place a different policy
has been utilized, the policy is as follows:
For individuals where the information for either antenatal care or assisted delivery is missing while the information
for the other is present and takes a value of “not deprived”, the individual is included in the estimation sample of
the national M
PI only if their deprivation score is higher than the second order cutoff and irrespective of the value
taken by the indicator on maternal health.
A.3.1 There are four steps involved to implement this policy, they have been outlined in the
following paragraphs.
4.4.2.i Step 1
Identify the number of observations for which either antenatal care or assisted delivery is not deprived while the
information for the other is missing. There are 6,087 observations which are not deprived in assisted delivery and
whose information for antenatal care is missing. There are no observations which are not deprived in antenatal care
and for whom the information on assisted delivery is missing.
4.4.2.ii Step 2
Within the 6,087 observations determine the ones where the deprivation score is above the second order cut-off
(i.e. c≥��) for 2 specific scenarios:
Scenario 1: Assume 6,087 observations are not deprived in maternal health and compute the deprivation scores for
them. Identify the observations for whom the deprivation score is above 33.33%. Scenario 1 yields the following
results,
4,908 observations are not multidimensionally poor, 1,071 are multidimensionally poor and 108 have missing values
in other indicators and have been dropped from the sample.
Scenario 2: Assume 6,087 observations are deprived in maternal health and compute the deprivation scores for
them. Identify the observations for whom the deprivation score is above 33.33%. Scenario 2 yields the following
results,
MPI Poor Freq. Percent
No 4,908 80.63
Ye s 1,071 17.59
Missing 108 1.77
Total 6,087 100.00
MPI Poor Freq. Percent
No 3,767 61.89
Ye s 2,212 36.34
Missing 108 1.77
Total 6,087 100.00
3,767 observations are not multidimensionally poor, 2,212 are multidimensionally poor and 108 have missing values in other indicators and have been dropped from the sample.
4.4.2.iii Step 3
Identify observations whose deprivation status remain unchanged across both scenario’s 1 and 2. That is, we identify the observations for whom the deprivation score remains above or below 33.33% irrespective of the value taken by the maternal health indicator.
4,838 (3,767 not deprived and 1,071 deprived) observations remain common across both the scenarios i.e. their
deprivation status (c
��
≥�� or c
��
<��) remains unchanged irrespective of the value taken by the maternal health indicator.
4.4.2.iv Step 4
Of the 6,087 identified ambiguous observations, it can be determined with absolute certainty that 4,838 obser-
vations will remain multidimensionally poor or not regardless of the value taken by the maternal health indicator.
Therefore, these 4,838 observations will be retained in the estimation sample of the national MPI.
A.4 Sample size
The national MPI utilizes 2,699,110 unweighted observations from the NFHS 4 as its estimation sample. This sample
size consists of de jure household members for whom the data for all twelve indicators of the national MPI is pres-
ent. Therefore, from the 2,869,043 unweighted observations present in the NFHS-4 microdata, 67,085 (2.34%) ob-
servations belonging to non-usual household members have been dropped. A further 102,848 observations (3.58%)
were dropped due to them missing the data for one or more component indicators of the national MPI.
The national MPI thus uses 94.08% of the total unweighted sample of the NFHS-4 for estimation. In comparison, the
global MPI uses 94.2% of the total unweighted sample of the NFHS-4 for estimation.
A.5 Micro-data Extraction, Treatment, and Visualization
The micro-data for the NFHS 4 was obtained from the official repository of the Demographic and Health Surveys
Program. The estimation of India's national MPI, its indicators, and related estimates was done utilizing the Birth
Recode (IABR74FL), Individual Recode (IAIR74FL), Men's Recode (IAMR74FL), and Person's Recode (IAPR74FL).
Extraction of data, adjustments for survey design and application of sample weights was completed adhering to
the procedures stated in the Standard Recode Manual for DHS-6. As the reporting of data was done up to the level
of urban and rural areas within a district, the occurrence of singleton sampling units within a strata was inevitable,
particularly in designated rural areas of primarily urban districts. For such occurrences, the standard errors have not
been reported and has been replaced with "*".
The processing of the data and computation of point estimates and estimate variance was carried out in STATA 16
(MP) and STATA 17 (MP). The final point estimates and standard errors were exported to Microsoft Excel for visuali-
sation. The choropleth maps were constructed in Tableau and QGIS using Shapefiles obtained from Survey of India.
INDIA MPI BASELINE REPORT 250 251
Index of tablesAppendix
• Alkir. Multidimensional Poverty Measures as Policy Tools. In V. Beck, H. Hahn, & R. Lepenies, Dimensions of
Poverty: Measurement, Epistemic Injustices, Activism (p. 200). Springer.
• Alkire, S., & Foster, J. (2011). Counting and Multidimensional Poverty Measurement. Journal of Public Economics, 95(7-
8), 476-487.
• Alkire, S., Foster, J. E., Seth, S., Maria Emma Santo, J. M., & Ballon, P. (2015). Multidimensional Poverty Measurement
and Analysis. Oxford: Oxford University Press.
• Alkire, S., Kanagaratnam, U., & Suppa, N. (2019). The Global Multidimensional Poverty Index (MPI) 2019. OPHI MPI
Methodological Note 47.
• Booth, C. (1903). Life and Labour of the People in London. London: Macmillan and Company.
• Bourguignon, F., Bénassy-Quéré, A., Dercon, S., Estache, A., Gunning, J. W., Kanbur, R., Spadaro, A. (2008). Millennium
Development Goals at Midpoint: Where do we stand and where do we need to go? European Report on Development.
• Brando, N., & Fragoso, K. P. (2020). Capability Deprivation and the Relational Dimension of Poverty: Testing Universal
Multidimensional Indexes. In V. Beck, H. Hahn, & R. Lepenies, Dimensions of Poverty: Measurement, Epistemic Injustices,
Activism (p. 308). Springer.
• Chakravarty, S. R. (2009). Inequality, Polarization, and Poverty. New York: Springer.
• Dotter, C., & Klasen, S. (2020). An Absolute Multidimensional Poverty Measure in the Functioning Space (and Relative
Measure in the Resource Space): An Illustration Using Indian Data. In V. Beck, H. Hahn, & R. Lepenies, Dimensions of Poverty: Measurement, Epistemic Injustices, Activism (p. 229). Springer.
•
Godinot, X., & Walker, R. (2020). Poverty in All Its Forms: Determining the Dimensions of Poverty Through Merg-
ing Knowledge. In H. H. Valentin Beck, Dimensions of Poverty: Measurement, Epistemic Injustices, Activism (p. 264). Springer.
•
Greve, B. (2020). Poverty: The Basics. New York: Routledge.
• Iqbal, K., Roy, P. K., & Alam, S. (2020). The impact of banking services on poverty: Evidence from sub-district level for
Bangladesh. Journal of Asian Economics.
• Koomson, I., Villano, R. A., & Hadley, D. (2020). Effect of Financial Inclusion on Poverty and Vulnerability to Poverty:
Evidence Using a Multi-Dimensional Measure of Financial Inclusion. Springer, 149(2), 613-639.
• Ministry of Health and Family Welfare. (2017). National Family Health Survey (NFHS-4). Government of India.
• OPHI, U. &. (2019). How to Build a National Multidimensional Poverty Index (MPI): Using the MPI to inform the SDGs.
New York: UNDP.
• Rowntree, B. S. (1901). Poverty: A study of town life. London: Macmillan and Company.
• Sen, A. (1979). Equality of What? The Tanner Lecture on Human Values.
• Sen, A. (1987). The Standard of Living. Cambridge : Cambridge University Press.
• Sen, A. (1999). Commodities and capabilities. Oxford: Oxford University Press.
• Townsend, P. (1979). Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living. Har-
mondsworth: Allen Lane.
• UNDP. (2010). Human Development Report 2010. New York: Palgrave Macmillan.
• UNICEF, WHO, World Bank, United Nations. (2019). Levels and Trends in Child Mortality Report 2018: Estimates devel-
oped by the United Nations Inter-agency Group for Child Mortality Estimation. New York City (NY): UNICEF.
• WHO. (2017). Global Accelerated Action for the Health of Adolescents: Guidance to Support Country Implementation.
WHO.
• WHO, UNICEF. (2014). Every Newborn Action Plan. Geneva: World Health Organization.
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Washington, DC: World Bank.
Page No.
State/UT
Table 1- State/UT-Wise: Headcount Ratio, Intensity, MPI 252
Table 2- State/UT-wise: Uncensored Headcount Ratio 253
Table 3- State/UT-wise: Uncensored Headcount Ratio (Rural) 254
T
able 4- State/UT-wise: Uncensored Headcount Ratio (Urban)
255
T
able 5- State/UT-wise: Censored Headcount Ratio
256
T
able 6- State/UT-wise: Censored Headcount Ratio (Rural)
257
Table 7- State/UT-wise: Censored Headcount Ratio (Urban) 258
T
able 8- State/UT-wise: Indicator Contribution to the MPI Score
259
Table 9- State/UT-wise: Indicator Contribution to the MPI Score (Rural) 260
T
able 10- State/UT-wise: Indicator Contribution to the MPI Score (Urban)
261
Districts
Table 11- District-wise: Uncensored Headcount Ratio
262
T
able 12- District-wise: Uncensored Headcount Ratio (Rural)
277
Table 13- District-wise: Uncensored Headcount Ratio (Urban) 292
Table 14- District-wise: Censored Headcount Ratio 307
Table 15- District-wise: Censored Headcount Ratio (Rural) 322
T
able 16- District-wise: Censored Headcount Ratio (Urban)
337
Table 17- District-wise: Indicator Contribution to the MPI Score 352
Table 18- District-wise: Indicator Contribution to the MPI Score (Rural) 367
Table 19- District-wise: Indicator Contribution to the MPI Score (Urban) 382
Standard Errors
Table 20- Standard Errors: State/UT-wise - Headcount Ratio, Intensity, MPI 397
Table 21- Standard Errors: State/UT-wise - Uncensored Headcount Ratio 398
Table 22- Standard Errors: State/UT-wise - Censored Headcount Ratio 399
Table 23- Standard Errors: District-wise - Headcount Ratio, Intensity, MPI 400
REFERENCES INDEX OF TABLES
INDIA MpI BASelINe reporT State/UTData Tables
252 253
Headcount Ratio, Intensity, MPIAllRuralUrban
State/UT
Headcount
Ratio
IntensityMPI
Headcount
Ratio
IntensityMPI
Headcount
Ratio
IntensityMPI
State
Andhra Pradesh12.31%43.23%0.05315.37%43.28%0.0674.91%42.83%0.021
Arunachal Pradesh24.27%47.26%0.11529.23%47.60%0.1398.15%43.18%0.035
Assam32.67%47.89%0.15636.16%48.07%0.1749.97%43.63%0.044
Bihar51.91%51.02%0.26556.01%51.14%0.28623.91%49.00%0.117
Chhattisgarh29.91%44.64%0.13435.73%44.83%0.16010.20%42.41%0.043
Goa3.76%40.16%0.0154.44%39.30%0.0173.34%40.84%0.014
Gujarat18.60%45.00%0.08427.40%45.12%0.1246.59%44.34%0.029
Haryana12.28%44.40%0.05514.86%44.38%0.0668.16%44.48%0.036
Himachal Pradesh7.62%39.43%0.0308.24%39.28%0.0321.46%48.24%0.007
Jharkhand42.16%47.91%0.20250.93%48.27%0.24615.26%44.24%0.067
Karnataka13.16%42.70%0.05619.01%42.79%0.0815.07%42.23%0.021
Kerala0.71%39.02%0.0030.95%39.81%0.0040.43%37.06%0.002
Madhya Pradesh36.65%47.25%0.17345.96%47.57%0.21913.82%44.62%0.062
Maharashtra14.85%43.78%0.06522.83%43.99%0.1005.55%42.76%0.024
Manipur17.89%44.44%0.08022.95%45.07%0.1039.90%42.17%0.042
Meghalaya32.67%48.06%0.15738.60%48.37%0.1878.62%42.47%0.037
Mizoram9.80%47.40%0.04620.48%47.93%0.0981.42%41.40%0.006
Nagaland25.23%46.33%0.11732.80%46.67%0.15310.75%44.37%0.048
Odisha29.35%46.42%0.13632.66%46.45%0.15212.33%46.12%0.057
Punjab5.59%43.75%0.0246.40%43.20%0.0284.32%45.02%0.019
Rajasthan29.46%47.44%0.14035.22%47.70%0.16811.52%44.99%0.052
Sikkim3.82%41.20%0.0164.25%41.15%0.0182.80%41.36%0.012
Tamil Nadu4.89%39.97%0.0207.32%40.21%0.0292.49%39.29%0.010
Telangana13.74%43.20%0.05920.35%43.23%0.0885.10%43.01%0.022
Tripura16.65%45.02%0.07520.93%45.34%0.0955.60%41.96%0.024
Uttar Pradesh37.79%47.60%0.18044.32%47.67%0.21118.07%47.06%0.085
Uttarakhand17.72%44.37%0.07921.94%43.78%0.0969.89%46.82%0.046
West Bengal21.43%45.49%0.09725.80%45.39%0.11711.67%46.00%0.054
Union Territory
Andaman & Nicobar Islands4.30%40.56%0.0176.76%40.86%0.0280.97%37.76%0.004
Chandigarh5.97%43.39%0.02618.56%47.88%0.0895.45%42.76%0.023
Dadra & Nagar Haveli27.36%44.57%0.12244.67%44.82%0.2004.89%41.67%0.020
Daman & Diu6.82%44.18%0.0305.19%41.79%0.0227.41%44.79%0.033
Delhi4.79%43.99%0.0213.41%38.28%0.0134.80%44.02%0.021
Jammu & Kashmir & Ladakh12.58%44.11%0.05516.39%44.27%0.0733.50%42.32%0.015
Lakshadweep1.82%36.15%0.0071.16%42.86%0.0052.00%35.09%0.007
Puducherry1.72%38.54%0.0073.33%36.74%0.0120.99%41.28%0.004
India25.01%47.13%0.11832.75%47.38%0.1558.81%45.25%0.040
T
able 1- STATE/UT-WISE: HEADCOUNT RATIO, INTENSITY, MPI
Uncensored Headcount RatioHealthEducationStandard of Living
State/UTNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh26.38%1.82%9.66%16.90%2.34%37.90%46.38%32.53%0.77%17.55%10.96%4.73%
Arunachal Pradesh21.04%1.97%28.34%17.77%8.15%57.79%38.94%14.95%11.83%76.15%23.36%15.40%
Assam39.67%2.90%25.44%16.19%6.55%77.12%51.25%17.71%21.78%75.90%19.95%15.40%
Bihar51.88%4.58%45.62%26.27%12.52%82.92%73.61%2.34%39.86%73.73%24.32%26.00%
Chhattisgarh43.02%3.32%24.70%13.47%5.38%78.04%65.39%18.30%3.64%63.31%14.92%5.74%
Goa24.65%0.57%7.14%4.70%0.96%14.91%21.43%4.11%0.18%16.16%2.97%4.02%
Gujarat41.37%2.21%14.77%9.83%6.68%48.79%37.16%12.51%3.75%24.25%13.60%9.41%
Haryana32.34%2.17%23.86%7.10%3.82%51.24%19.26%12.67%1.06%24.27%4.65%8.17%
Himachal Pradesh27.18%1.66%17.42%3.78%0.89%67.90%27.75%7.92%0.49%29.30%7.52%2.69%
Jharkhand47.99%3.32%33.07%18.32%8.19%82.14%75.38%31.06%18.79%61.78%21.37%8.98%
Karnataka33.56%1.34%12.36%8.70%3.54%45.54%43.11%14.51%1.71%37.30%10.05%8.83%
Kerala15.29%0.19%1.73%1.78%0.54%43.89%1.86%5.91%0.74%10.76%2.94%4.32%
Madhya Pradesh45.49%3.60%29.39%16.09%8.39%71.25%65.25%29.83%8.96%64.38%19.31%11.15%
Maharashtra36.09%1.42%15.95%6.54%4.20%39.49%47.97%13.89%6.59%27.90%13.97%10.35%
Manipur23.56%1.80%17.66%5.36%2.36%58.93%47.68%60.89%7.31%81.49%13.93%21.54%
Meghalaya37.05%3.10%31.70%19.71%6.15%77.08%38.61%33.52%8.18%50.40%29.88%19.91%
Mizoram21.37%2.30%16.11%7.93%3.75%32.18%15.82%9.48%4.08%24.17%13.95%5.81%
Nagaland24.50%2.07%33.06%13.63%4.81%69.28%23.28%21.23%3.25%70.98%33.91%28.66%
Odisha37.26%2.23%19.50%16.66%4.95%80.94%70.43%20.97%13.37%55.81%19.22%10.94%
Punjab22.11%1.39%12.70%7.29%2.59%36.40%17.30%1.93%0.39%19.30%1.72%3.71%
Rajasthan42.62%2.95%26.33%17.10%8.48%69.94%53.95%26.77%8.73%35.54%20.49%4.03%
Sikkim13.32%1.00%5.42%8.20%1.42%42.20%10.42%2.34%0.65%26.71%9.52%8.38%
Tamil Nadu24.76%1.15%6.70%6.61%1.03%24.07%47.57%12.61%0.97%20.18%3.38%6.35%
Telangana31.10%1.38%10.87%15.84%2.10%31.66%49.27%27.81%1.23%25.53%12.79%7.46%
Tripura28.02%1.28%13.49%10.79%2.19%65.84%36.39%16.31%7.18%74.66%18.76%3.63%
Uttar Pradesh44.47%4.97%35.45%17.52%11.91%68.85%63.69%5.38%27.43%67.52%12.44%4.88%
Uttarakhand32.85%2.58%28.56%9.79%4.37%52.07%34.11%9.03%2.17%35.59%13.84%6.90%
West Bengal33.62%1.50%14.38%15.86%3.83%73.02%47.99%11.53%5.75%54.26%14.11%13.81%
Union Territory
Andaman & Nicobar Islands22.05%0.83%5.11%4.87%0.92%24.53%24.38%6.20%2.72%33.61%7
.10%1.57%
Chandigarh23.11%1.16%11.05%5.83%1.76%4.85%19.04%2.35%0.48%6.40%2.71%3.97%
Dadra & Nagar Haveli45.00%2.01%13.44%7.76%7.73%46.62%68.11%26.95%2.74%57.06%20.81%10.92%
Daman & Diu20.92%0.90%14.69%7.56%4.72%9.24%34.80%11.67%0.03%8.08%14.74%12.52%
Delhi23.40%1.91%15.19%5.95%2.65%2.21%26.70%23.75%0.28%10.79%5.54%8.36%
Jammu & Kashmir & Ladakh25.89%1.85%12.72%6.84%3.72%45.21%47.04%14.16%2.78%29.62%16.12%3.95%
Lakshadweep31.47%1.96%6.50%0.95%1.43%58.15%0.44%9.31%0.05%1.54%1.02%5.62%
Puducherry21.87%0.66%4.13%3.29%1.21%13.51%35.06%5.49%0.24%17.59%1.65%5.35%
India37.60%2.69%22.59%13.88%6.40%58.48%51.97% 14.60%12.16%45.65%13.97%9.66%
T
able 2- STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT State/UTData Tables
254 255
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
State/UT (Rural)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh29.49%2.10%10.58%19.82%2.52%49.42%56.18%32.74%0.91%22.30%13.48%4.08%
Arunachal Pradesh22.53%2.20%30.44%21.05%8.66%71.47%42.86%17.73%15.24%85.41%28.33%18.11%
Assam41.67%3.13%27.39%17.75%7.23%85.39%53.55%18.64%24.48%81.85%21.71%16.60%
Bihar53.69%4.80%47.38%28.13%13.03%89.55%78.10%2.30%44.01%79.94%25.66%27.45%
Chhattisgarh46.32%3.66%27.30%15.48%5.93%92.60%74.80%21.33%4.56%75.53%17.85%6.20%
Goa31.95%0.39%9.31%3.47%1.18%28.46%17.74%6.54%0.11%26.81%4.04%4.96%
Gujarat48.81%2.76%18.04%12.73%8.23%74.69%53.90%15.79%5.55%37.96%19.21%10.42%
Haryana35.70%2.28%26.34%7.18%4.09%73.03%21.37%12.47%1.50%33.01%5.63%7.48%
Himachal Pradesh27.94%1.68%18.16%3.90%0.85%73.63%28.53%7.90%0.47%31.59%7.81%2.70%
Jharkhand52.11%3.83%37.29%21.80%9.55%93.57%86.78%34.66%23.97%75.78%25.43%10.49%
Karnataka37.53%1.66%13.49%11.90%4.18%67.69%57.50%16.90%2.34%50.51%14.37%10.27%
Kerala15.65%0.16%1.51%2.05%0.58%50.35%2.39%7.00%1.05%13.00%3.78%4.54%
Madhya Pradesh49.17%4.02%32.63%19.15%9.90%90.19%78.92%37.57%11.92%79.14%24.75%12.79%
Maharashtra42.64%1.49%17.94%8.58%5.11%66.77%54.94%22.82%8.51%45.12%20.01%10.68%
Manipur25.74%2.20%22.91%7.08%2.94%72.39%46.63%64.14%9.56%88.85%18.66%25.08%
Meghalaya39.24%3.62%36.88%23.37%7.21%89.69%40.87%37.73%9.90%56.27%35.19%22.19%
Mizoram25.99%3.26%26.23%15.31%5.99%65.59%25.96%13.47%8.96%38.36%26.58%9.68%
Nagaland26.64%2.60%38.08%18.14%5.49%86.52%20.45%21.39%4.73%82.23%45.95%38.42%
Odisha39.60%2.43%20.61%18.19%5.20%89.30%76.34%22.95%14.95%61.97%21.45%11.47%
Punjab23.73%1.53%13.19%8.21%2.48%53.46%19.81%2.52%0.37%27.16%1.70%3.50%
Rajasthan45.49%3.30%28.82%19.46%9.52%85.57%62.84%31.63%11.10%43.62%25.13%4.11%
Sikkim13.83%1.20%5.10%8.98%1.46%58.85%5.33%3.02%0.44%33.57%11.73%7.97%
Tamil Nadu29.28%1.45%7.43%8.95%1.26%38.63%65.28%10.02%1.31%26.21%4.94%6.78%
Telangana36.26%1.55%11.90%21.31%2.01%50.49%60.70%33.61%1.85%38.44%18.39%6.96%
Tripura29.86%1.55%15.70%13.01%2.80%79.54%38.49%21.30%9.63%86.18%23.28%4.29%
Uttar Pradesh47.98%5.52%38.85%18.86%12.34%83.99%74.83%4.48%34.84%80.28%13.90%4.89%
Uttarakhand34.98%2.67%31.05%10.13%4.32%71.59%38.95%13.03%2.99%48.75%18.55%6.95%
West Bengal37.23%1.78%16.13%18.34%4.06%89.07%52.03%11.37%7.18%67.31%16.79%14.76%
Union Territory
Andaman & Nicobar Islands24.49%1.04%6.49%6.86%1.12%41.05%33.19%10.77%4.45%50
.83%10.96%1.68%
Chandigarh51.55%0.00%4.12%18.56%7.22%22.68%69.07%0.00%0.00%15.46%1.03%1.03%
Dadra & Nagar Haveli58.04%3.18%16.18%11.62%10.98%77.12%88.34%29.71%4.48%84.02%29.54%14.76%
Daman & Diu25.52%0.59%16.20%8.73%4.36%25.66%28.59%13.39%0.12%7.88%9.41%10.63%
Delhi26.93%0.00%22.29%2.58%0.00%24.80%11.78%35.79%0.00%6.45%6.49%6.09%
Jammu & Kashmir & Ladakh29.19%2.11%15.81%7.47%4.43%60.43%53.04%18.81%3.80%37.65%20.79%4.35%
Lakshadweep34.33%1.93%6.95%2.31%1.55%73.64%0.77%7.68%0.00%2.60%0.90%10.72%
Puducherry23.28%0.82%5.79%3.51%1.43%28.29%53.67%2.00%0.60%31.86%2.91%3.36%
India42.36%3.20%26.49%16.98%7.52%77.41%62.88%16.44%16.80%59.52%17.82%10.74%
Uncensored Headcount Ratio: UrbanHealthEducationStandard of Living
State/UT (Urban)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh18.86%1.16%7.44%9.84%1.93%10.06%22.69%32.04%0.44%6.06%4.86%6.30%
Arunachal Pradesh16.19%1.19%21.50%7.08%6.47%13.27%26.15%5.91%0.74%45.99%7.16%6.58%
Assam26.67%1.41%12.74%6.03%2.15%23.32%36.34%11.65%4.21%37.17%8.46%7.59%
Bihar39.52%3.13%33.56%13.56%9.07%37.67%42.94%2.63%11.50%31.29%15.18%16.12%
Chhattisgarh31.84%2.19%15.91%6.65%3.52%28.71%33.50%8.06%0.52%21.96%5.02%4.19%
Goa20.26%0.68%5.84%5.43%0.83%6.77%23.64%2.65%0.23%9.76%2.32%3.46%
Gujarat31.21%1.46%10.32%5.86%4.57%13.45%14.31%8.03%1.29%5.53%5.94%8.04%
Haryana26.99%1.99%19.91%6.97%3.39%16.47%15.91%12.99%0.35%10.32%3.07%9.27%
Himachal Pradesh19.67%1.43%10.06%2.54%1.28%11.46%20.04%8.08%0.69%6.73%4.68%2.63%
Jharkhand35.34%1.75%20.15%7.67%3.99%47.11%40.41%20.01%2.92%18.86%8.94%4.35%
Karnataka28.07%0.91%10.80%4.27%2.65%14.85%23.18%11.21%0.83%19.00%4.06%6.82%
Kerala14.89%0.22%1.98%1.47%0.50%36.58%1.24%4.67%0.38%8.23%1.99%4.08%
Madhya Pradesh36.47%2.57%21.44%8.56%4.67%24.83%31.75%10.86%1.68%28.22%5.99%7.11%
Maharashtra28.44%1.34%13.62%4.17%3.15%7.66%39.83%3.46%4.35%7.81%6.92%9.96%
Manipur20.12%1.18%9.36%2.65%1.45%37.65%49.33%55.75%3.77%69.86%6.45%15.93%
Meghalaya28.18%0.96%10.68%4.87%1.84%25.95%29.44%16.48%1.17%26.62%8.37%10.68%
Mizoram17.74%1.55%8.17%2.14%2.00%5.95%7.87%6.35%0.25%13.04%4.03%2.76%
Nagaland20.42%1.07%23.47%5.00%3.49%36.31%28.71%20.93%0.42%49.46%10.87%10.02%
Odisha25.17%1.19%13.74%8.75%3.65%37.90%39.98%10.80%5.23%24.06%7.73%8.25%
Punjab19.62%1.18%11.95%5.86%2.75%10.01%13.42%1.02%0.40%7.16%1.75%4.04%
Rajasthan33.69%1.87%18.58%9.72%5.25%21.30%26.26%11.65%1.34%10.41%6.06%3.78%
Sikkim12.14%0.53%6.16%6.40%1.32%3.58%22.22%0.76%1.15%10.79%4.42%9.31%
Tamil Nadu20.30%0.85%5.97%4.29%0.79%9.68%30.05%15.18%0.63%14.22%1.84%5.93%
Telangana24.36%1.17%9.53%8.69%2.22%7.05%34.33%20.24%0.42%8.66%5.46%8.10%
Tripura23.28%0.57%7.79%5.08%0.61%30.58%30.98%3.47%0.87%45.00%7.13%1.93%
Uttar Pradesh33.87%3.30%25.18%13.44%10.61%23.11%30.06%8.13%5.03%28.99%8.04%4.83%
Uttarakhand28.91%2.40%23.95%9.15%4.48%15.84%25.13%1.61%0.64%11.18%5.08%6.80%
West Bengal25.58%0.86%10.48%10.33%3.32%37.18%38.95%11.88%2.56%25.14%8.13%11.70%
Union Territory
Andaman & Nicobar Islands18.74%0.55%3.23%2.19%0.64%2.13%12.44%0.00%0
.36%10.25%1.87%1.41%
Chandigarh21.94%1.21%11.33%5.30%1.53%4.12%16.97%2.45%0.50%6.02%2.78%4.09%
Dadra & Nagar Haveli28.08%0.48%9.88%2.75%3.51%7.04%41.85%23.37%0.47%22.08%9.49%5.93%
Daman & Diu19.24%1.01%14.13%7.13%4.85%3.24%37.07%11.04%0.00%8.15%16.68%13.21%
Delhi23.37%1.93%15.13%5.97%2.67%2.02%26.82%23.65%0.28%10.83%5.53%8.38%
Jammu & Kashmir & Ladakh18.06%1.24%5.37%5.33%2.04%9.00%32.79%3.10%0.34%10.52%5.04%2.99%
Lakshadweep30.69%1.97%6.38%0.58%1.39%53.95%0.35%9.75%0.06%1.25%1.05%4.23%
Puducherry21.24%0.59%3.39%3.20%1.12%6.86%26.69%7.05%0.08%11.18%1.08%6.24%
India27.63%1.62%14.41%7.38%4.04%18.84%29.12%10.74%2.44%16.59%5.91%7.41%
T
able 3- STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL) Table 4- STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (URBAN)
INDIA MpI BASelINe reporT State/UTData Tables
256 257
Censored Headcount RatioHealthEducationStandard of Living
State/UTNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh9.22%0.87%4.76%7.72%1.47%9.72%10.50%4.39%0.60%5.69%4.82%1.74%
Arunachal Pradesh13.80%1.19%14.86%13.48%5.90%21.29%16.65%6.19%7.15%23.29%12.85%9.23%
Assam25.46%2.18%17.78%14.26%5.64%31.64%24.43%8.25%14.71%31.39%13.90%10.51%
Bihar41.60%3.92%36.51%24.70%11.62%50.20%46.57%1.64%28.79%47.10%18.69%19.60%
Chhattisgarh24.04%2.25%16.96%10.91%4.31%29.14%26.64%10.19%2.78%26.78%10.43%3.40%
Goa2.96%0.20%1.39%2.24%0.59%2.06%2.81%0.30%0.00%1.83%0.86%0.79%
Gujarat15.42%1.11%8.74%6.68%4.79%17.26%15.51%4.74%2.90%11.41%8.20%4.36%
Haryana10.41%1.21%9.42%4.61%2.83%10.17%6.20%3.38%0.75%7.44%2.57%2.92%
Himachal Pradesh6.79%0.59%5.73%1.47%0.43%7.14%4.82%1.39%0.24%5.24%2.16%0.65%
Jharkhand34.42%2.74%26.51%16.46%7.17%41.25%39.40%17.47%13.58%35.93%15.54%6.63%
Karnataka10.08%0.71%5.35%5.45%2.36%11.58%11.04%3.46%0.97%9.43%4.99%3.39%
Kerala0.56%0.00%0.15%0.19%0.22%0.58%0.30%0.14%0.20%0.40%0.32%0.17%
Madhya Pradesh29.05%2.72%20.87%14.02%7.34%34.90%33.21%17.56%6.47%32.73%13.65%7.36%
Maharashtra12.39%0.83%7.13%4.27%2.96%12.46%12.50%5.27%3.14%10.12%6.72%3.80%
Manipur13.41%0.97%10.14%4.65%1.77%16.28%11.68%13.59%3.49%17.26%6.91%8.93%
Meghalaya23.84%2.11%22.48%16.68%5.34%31.82%18.58%13.65%6.43%23.33%19.41%12.94%
Mizoram6.20%0.63%5.98%5.46%2.32%8.68%5.70%2.80%2.98%7.60%6.65%2.68%
Nagaland17.16%1.38%18.34%11.28%3.67%23.97%8.71%6.90%2.50%23.98%16.67%15.82%
Odisha22.41%1.51%12.77%13.77%4.32%28.77%27.14%9.89%8.93%24.87%13.29%6.49%
Punjab4.41%0.50%3.09%3.41%1.41%4.23%3.02%0.34%0.22%3.32%0.60%1.03%
Rajasthan23.25%2.09%17.08%13.37%7.24%27.69%24.77%13.11%6.60%18.71%13.27%2.22%
Sikkim2.87%0.25%1.75%2.48%0.36%2.89%1.13%0.19%0.07%2.30%1.84%1.10%
Tamil Nadu3.64%0.30%1.70%2.27%0.45%3.63%4.53%1.21%0.43%2.64%1.35%1.48%
Telangana10.15%0.78%5.10%8.50%1.14%10.49%12.22%4.45%0.87%8.32%5.99%2.73%
Tripura12.01%0.88%7.79%8.10%1.67%15.50%11.08%7.26%4.30%16.19%9.42%2.20%
Uttar Pradesh30.48%3.82%25.29%15.08%9.97%34.26%31.80%2.37%18.35%33.43%8.87%3.34%
Uttarakhand14.66%1.63%13.06%6.74%3.18%15.80%11.22%3.21%1.39%12.35%6.21%3.23%
West Bengal16.22%1.02%9.44%11.25%2.82%20.81%16.93%4.35%3.74%18.81%8.66%7.14%
Union Territory
Andaman & Nicobar Islands3.41%0.31%1.02%1.95%0.27%3.15%2.99%1.18%1.55%3.51%2.15%0
.11%
Chandigarh4.90%0.52%3.20%3.23%1.46%3.26%4.84%1.30%0.48%2.48%1.20%0.81%
Dadra & Nagar Haveli24.22%1.28%8.46%6.93%6.56%24.07%26.29%10.17%2.12%25.46%12.60%6.38%
Daman & Diu5.40%0.33%3.90%3.20%3.09%1.44%4.55%1.42%0.00%1.08%3.19%3.25%
Delhi3.92%0.71%3.34%2.54%1.15%0.62%3.58%1.66%0.06%1.48%1.80%1.28%
Jammu & Kashmir & Ladakh9.70%0.85%6.10%4.47%2.49%11.25%10.62%4.97%1.67%9.44%6.65%1.43%
Lakshadweep1.82%0.53%0.86%0.00%0.64%1.11%0.14%0.57%0.00%0.32%0.14%0.52%
Puducherry1.33%0.28%0.44%0.89%0.01%1.35%1.42%0.11%0.11%1.10%0.42%0.37%
India19.90%1.88%14.71%10.71%5.23%23.13%21.32%5.53%8.29%20.56%8.87%5.37%
Censored Headcount Ratio: RuralHealthEducationStandard of Living
State/UT (Rural)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh11.29%1.09%6.03%9.56%1.68%12.80%13.57%5.71%0.74%7.51%6.28%1.83%
Arunachal Pradesh16.34%1.50%17.71%16.44%6.86%26.65%20.27%7.71%9.20%28.42%16.08%11.48%
Assam28.14%2.40%19.76%15.82%6.31%35.47%27.05%9.14%16.59%34.95%15.47%11.63%
Bihar44.94%4.21%39.32%26.69%12.29%54.70%50.70%1.75%31.89%51.58%19.99%21.01%
Chhattisgarh28.60%2.68%20.31%12.91%4.99%35.41%32.25%12.51%3.50%32.78%12.85%4.09%
Goa3.82%0.12%1.52%1.56%0.44%3.68%3.24%0.79%0.00%3.44%1.36%0.94%
Gujarat22.73%1.67%12.98%9.17%6.41%26.76%24.03%7.51%4.43%18.47%12.64%6.00%
Haryana12.64%1.42%11.61%4.90%3.29%13.93%7.26%4.28%1.06%9.97%3.18%3.09%
Himachal Pradesh7.34%0.62%6.19%1.56%0.43%7.83%5.18%1.46%0.25%5.73%2.33%0.68%
Jharkhand41.42%3.33%31.97%19.99%8.68%50.18%48.17%21.12%17.49%44.94%19.18%8.08%
Karnataka14.44%0.98%7.48%7.79%3.09%17.59%16.41%5.29%1.38%14.28%7.50%4.96%
Kerala0.72%0.01%0.17%0.26%0.25%0.84%0.47%0.22%0.35%0.62%0.54%0.25%
Madhya Pradesh36.18%3.22%26.07%17.31%9.02%45.21%42.65%23.08%8.65%42.47%17.95%9.10%
Maharashtra18.74%0.94%10.55%6.28%4.18%21.48%19.49%9.22%5.16%17.22%10.89%5.12%
Manipur16.88%1.21%14.09%6.25%2.30%21.33%14.24%17.50%4.78%22.28%9.82%11.43%
Meghalaya27.99%2.54%26.94%19.96%6.31%38.01%21.94%16.33%7.81%27.76%23.48%15.24%
Mizoram12.82%1.26%12.55%11.54%4.80%18.70%12.22%6.15%6.68%16.25%14.31%5.68%
Nagaland21.65%1.83%23.60%15.31%4.75%32.16%10.11%8.42%3.68%31.44%23.31%21.84%
Odisha25.04%1.68%14.21%15.23%4.62%32.21%30.35%11.11%9.99%27.95%14.93%7.05%
Punjab5.18%0.54%3.50%3.74%1.24%5.45%3.53%0.41%0.16%4.31%0.59%1.02%
Rajasthan27.62%2.48%20.33%15.75%8.33%34.28%30.28%16.29%8.46%23.50%16.59%2.50%
Sikkim3.04%0.35%1.88%2.65%0.33%3.99%0.85%0.27%0.05%2.91%2.39%1.32%
Tamil Nadu5.32%0.46%2.39%3.49%0.56%6.01%6.99%1.75%0.71%4.17%2.23%2.20%
Telangana14.87%1.00%7.41%12.50%1.36%16.63%18.34%6.91%1.25%13.28%9.38%3.70%
Tripura14.76%1.16%9.82%10.14%2.27%19.82%13.90%9.84%5.85%20.61%12.17%2.83%
Uttar Pradesh35.94%4.39%29.93%16.77%10.75%41.98%38.83%2.61%23.31%40.85%10.28%3.62%
Uttarakhand18.32%1.71%16.18%7.01%3.21%20.81%14.15%4.61%1.93%17.20%8.33%3.48%
West Bengal19.45%1.27%11.34%13.44%3.13%25.49%20.69%4.90%4.57%23.42%10.49%8.27%
Union Territory
Andaman & Nicobar Islands5.22%0.53%1.77%2.77%0.46%5.37%4.94%2.05%2.48%5.7
4%3.63%0.19%
Chandigarh17.53%0.00%4.12%14.43%7.22%16.49%18.56%0.00%0.00%5.15%1.03%1.03%
Dadra & Nagar Haveli39.75%2.08%13.36%11.09%10.11%41.77%42.99%16.31%3.76%43.32%21.36%10.61%
Daman & Diu3.43%0.00%2.47%3.31%1.61%2.58%3.44%0.39%0.00%0.91%2.29%2.44%
Delhi3.12%0.00%3.12%0.29%0.00%2.39%2.68%1.25%0.00%0.00%2.46%1.25%
Jammu & Kashmir & Ladakh12.63%1.04%8.23%5.40%3.17%15.21%14.07%6.90%2.24%12.83%8.90%1.84%
Lakshadweep1.16%0.00%0.00%0.00%1.16%1.16%0.00%1.16%0.00%0.00%0.00%0.00%
Puducherry2.60%0.36%1.12%0.94%0.00%3.20%3.17%0.12%0.19%2.36%0.75%0.96%
India26.01%2.40%19.33%13.75%6.49%31.47%28.60%7.40%11.64%28.09%11.86%6.84%
T
able 5- STATE/UT-WISE: CENSORED HEADCOUNT RATIO Table 6- STATE/UT-WISE: CENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT State/UTData Tables
258 259
Censored Headcount Ratio: UrbanHealthEducationStandard of Living
State/UT (Urban)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh4.21%0.36%1.71%3.29%0.94%2.25%3.09%1.19%0.27%1.30%1.30%1.52%
Arunachal Pradesh5.53%0.18%5.60%3.83%2.79%3.84%4.88%1.24%0.50%6.59%2.33%1.90%
Assam8.02%0.78%4.92%4.15%1.28%6.76%7.40%2.47%2.49%8.27%3.69%3.26%
Bihar18.83%1.91%17.32%11.15%7.09%19.45%18.36%0.90%7.59%16.51%9.88%9.97%
Chhattisgarh8.59%0.80%5.62%4.15%2.02%7.89%7.64%2.32%0.33%6.48%2.21%1.08%
Goa2.44%0.24%1.31%2.65%0.67%1.08%2.55%0.00%0.00%0.87%0.56%0.70%
Gujarat5.44%0.34%2.95%3.28%2.58%4.31%3.88%0.98%0.81%1.78%2.13%2.12%
Haryana6.86%0.87%5.92%4.14%2.10%4.17%4.52%1.94%0.24%3.41%1.59%2.64%
Himachal Pradesh1.37%0.27%1.16%0.65%0.42%0.26%1.24%0.72%0.15%0.43%0.55%0.41%
Jharkhand12.95%0.95%9.76%5.62%2.55%13.87%12.50%6.27%1.59%8.30%4.36%2.17%
Karnataka4.04%0.34%2.41%2.20%1.33%3.24%3.61%0.92%0.41%2.71%1.51%1.21%
Kerala0.38%0.00%0.12%0.10%0.20%0.28%0.10%0.05%0.03%0.14%0.08%0.08%
Madhya Pradesh11.57%1.51%8.12%5.95%3.24%9.63%10.09%4.04%1.15%8.86%3.10%3.10%
Maharashtra4.97%0.70%3.12%1.92%1.54%1.93%4.33%0.65%0.77%1.84%1.85%2.25%
Manipur7.91%0.59%3.90%2.14%0.93%8.30%7.64%7.41%1.44%9.34%2.32%4.97%
Meghalaya7.01%0.35%4.42%3.37%1.40%6.75%4.97%2.77%0.87%5.39%2.92%3.62%
Mizoram1.00%0.14%0.83%0.69%0.37%0.82%0.57%0.17%0.08%0.80%0.63%0.32%
Nagaland8.57%0.54%8.28%3.57%1.62%8.33%6.05%3.99%0.25%9.71%3.98%4.31%
Odisha8.89%0.64%5.39%6.27%2.77%11.07%10.60%3.58%3.44%8.98%4.85%3.62%
Punjab3.23%0.45%2.45%2.89%1.67%2.35%2.22%0.25%0.31%1.79%0.61%1.03%
Rajasthan9.65%0.90%6.98%5.96%3.86%7.17%7.64%3.23%0.84%3.80%2.93%1.32%
Sikkim2.48%0.00%1.46%2.09%0.43%0.33%1.78%0.00%0.13%0.90%0.56%0.57%
Tamil Nadu1.99%0.15%1.01%1.06%0.35%1.28%2.10%0.68%0.15%1.13%0.48%0.78%
Telangana3.99%0.50%2.08%3.27%0.85%2.48%4.21%1.23%0.36%1.84%1.55%1.47%
Tripura4.94%0.17%2.58%2.84%0.15%4.37%3.82%0.60%0.33%4.81%2.32%0.57%
Uttar Pradesh13.99%2.08%11.29%9.96%7.63%10.95%10.56%1.63%3.35%11.02%4.62%2.50%
Uttarakhand7.87%1.47%7.26%6.25%3.13%6.49%5.77%0.61%0.39%3.36%2.27%2.76%
West Bengal9.00%0.46%5.20%6.36%2.12%10.36%8.54%3.14%1.89%8.51%4.58%4.62%
Union Territory
Andaman & Nicobar Islands0.97%0.00%0.00%0.83%0.00%0.14%0.35%0.00%0
.28%0.48%0.14%0.00%
Chandigarh4.38%0.54%3.17%2.76%1.22%2.71%4.28%1.35%0.50%2.37%1.21%0.80%
Dadra & Nagar Haveli4.05%0.25%2.11%1.53%1.95%1.10%4.63%2.19%0.00%2.27%1.24%0.90%
Daman & Diu6.12%0.45%4.42%3.16%3.64%1.02%4.96%1.80%0.00%1.13%3.52%3.54%
Delhi3.93%0.72%3.35%2.56%1.16%0.61%3.59%1.66%0.06%1.49%1.80%1.28%
Jammu & Kashmir & Ladakh2.75%0.41%1.01%2.24%0.88%1.83%2.44%0.40%0.29%1.36%1.29%0.46%
Lakshadweep2.00%0.67%1.09%0.00%0.50%1.09%0.17%0.41%0.00%0.41%0.17%0.66%
Puducherry0.75%0.25%0.13%0.86%0.01%0.52%0.63%0.10%0.07%0.53%0.27%0.10%
India7.10%0.80%5.04%4.35%2.58%5.68%6.09%1.61%1.29%4.80%2.61%2.29%
Indicator ContributionHealthEducationStandard of Living
State/UTNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh28.88%1.37%7.46%24.19%4.59%8.69%9.40%3.92%0.54%5.09%4.31%1.56%
Arunachal Pradesh20.05%0.87%10.80%19.58%8.58%8.84%6.91%2.57%2.97%9.67%5.33%3.83%
Assam27.12%1.16%9.47%15.20%6.00%9.63%7.44%2.51%4.48%9.56%4.23%3.20%
Bihar26.19%1.23%11.49%15.55%7.32%9.03%8.38%0.29%5.18%8.47%3.36%3.52%
Chhattisgarh30.01%1.40%10.59%13.62%5.38%10.39%9.50%3.63%0.99%9.55%3.72%1.21%
Goa32.65%1.09%7.68%24.78%6.50%6.49%8.87%0.94%0.00%5.79%2.71%2.50%
Gujarat30.71%1.10%8.70%13.30%9.55%9.82%8.83%2.70%1.65%6.49%4.66%2.48%
Haryana31.83%1.85%14.39%14.08%8.65%8.88%5.42%2.95%0.65%6.50%2.24%2.55%
Himachal Pradesh37.65%1.63%15.88%8.16%2.38%11.31%7.64%2.20%0.38%8.30%3.43%1.04%
Jharkhand28.40%1.13%10.94%13.58%5.92%9.73%9.29%4.12%3.20%8.47%3.66%1.56%
Karnataka29.88%1.05%7.94%16.15%6.98%9.81%9.35%2.93%0.82%7.99%4.22%2.87%
Kerala33.98%0.14%4.48%11.28%13.61%10.01%5.15%2.42%3.49%6.87%5.60%2.97%
Madhya Pradesh27.96%1.31%10.04%13.49%7.07%9.60%9.13%4.83%1.78%9.00%3.75%2.02%
Maharashtra31.74%1.06%9.13%10.94%7.60%9.12%9.15%3.86%2.30%7.41%4.92%2.78%
Manipur28.10%1.01%10.63%9.75%3.71%9.75%6.99%8.14%2.09%10.34%4.14%5.35%
Meghalaya25.30%1.12%11.93%17.70%5.67%9.65%5.64%4.14%1.95%7.08%5.89%3.93%
Mizoram22.23%1.13%10.73%19.59%8.31%8.90%5.84%2.87%3.06%7.79%6.81%2.75%
Nagaland24.47%0.99%13.08%16.08%5.24%9.77%3.55%2.81%1.02%9.77%6.79%6.45%
Odisha27.41%0.92%7.81%16.85%5.29%10.06%9.48%3.46%3.12%8.69%4.64%2.27%
Punjab30.09%1.72%10.53%23.22%9.60%8.25%5.87%0.67%0.43%6.47%1.16%2.00%
Rajasthan27.73%1.25%10.19%15.95%8.64%9.44%8.44%4.47%2.25%6.38%4.52%0.76%
Sikkim30.44%1.31%9.28%26.33%3.79%8.76%3.42%0.57%0.22%6.98%5.58%3.33%
Tamil Nadu31.06%1.29%7.23%19.34%3.86%8.84%11.03%2.95%1.05%6.43%3.29%3.62%
Telangana28.51%1.10%7.16%23.86%3.19%8.42%9.80%3.57%0.69%6.68%4.80%2.19%
Tripura26.72%0.98%8.66%18.01%3.72%9.85%7.04%4.61%2.74%10.29%5.98%1.40%
Uttar Pradesh28.25%1.77%11.72%13.97%9.24%9.07%8.42%0.63%4.86%8.85%2.35%0.88%
Uttarakhand31.07%1.72%13.83%14.29%6.75%9.57%6.79%1.94%0.84%7.48%3.76%1.95%
West Bengal27.73%0.87%8.07%19.23%4.82%10.16%8.27%2.13%1.83%9.19%4.23%3.49%
Union Territory
Andaman & Nicobar Islands32.60%1.46%4.87%18.61%2.55%8.59%8.16%3.21%4.22%9
.57%5.86%0.30%
Chandigarh31.53%1.67%10.30%20.74%9.38%5.99%8.90%2.38%0.88%4.55%2.20%1.48%
Dadra & Nagar Haveli33.09%0.88%5.78%9.47%8.96%9.40%10.27%3.97%0.83%9.94%4.92%2.49%
Daman & Diu29.87%0.90%10.78%17.72%17.12%2.27%7.20%2.25%0.00%1.70%5.05%5.14%
Delhi31.03%2.82%13.22%20.12%9.11%1.40%8.09%3.75%0.14%3.34%4.08%2.89%
Jammu & Kashmir & Ladakh29.15%1.28%9.16%13.43%7.48%9.66%9.12%4.27%1.43%8.10%5.71%1.23%
Lakshadweep46.10%6.64%10.88%0.00%16.21%8.00%0.98%4.12%0.00%2.32%0.98%3.77%
Puducherry33.36%3.56%5.54%22.33%0.22%9.73%10.18%0.76%0.77%7.89%3.03%2.63%
India28.14%1.33% 10.40%15.14%7.39%9.34%8.61%2.23%3.35%8.31%3.58%2.17%
T
able 7- STATE/UT-WISE: CENSORED HEADCOUNT RATIO (URBAN) Table 8- STATE/UT-WISE: INDICATOR CONTRIBUTION TO THE MPI SCORE
INDIA MpI BASelINe reporT State/UTData Tables
260 261
Indicator Contribution: RuralHealthEducationStandard of Living
State/UT (Rural)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh28.28%1.36%7.55%23.94%4.21%9.16%9.71%4.08%0.53%5.37%4.49%1.31%
Arunachal Pradesh19.57%0.90%10.61%19.69%8.22%9.12%6.94%2.64%3.15%9.73%5.50%3.93%
Assam26.98%1.15%9.47%15.17%6.05%9.72%7.41%2.50%4.55%9.58%4.24%3.19%
Bihar26.15%1.23%11.44%15.53%7.15%9.09%8.43%0.29%5.30%8.58%3.32%3.49%
Chhattisgarh29.76%1.39%10.57%13.43%5.19%10.53%9.59%3.72%1.04%9.74%3.82%1.22%
Goa36.42%0.56%7.25%14.87%4.23%10.02%8.85%2.16%0.00%9.38%3.70%2.56%
Gujarat30.65%1.13%8.75%12.36%8.65%10.31%9.26%2.89%1.71%7.11%4.87%2.31%
Haryana31.95%1.80%14.68%12.39%8.31%10.06%5.24%3.09%0.77%7.20%2.30%2.23%
Himachal Pradesh37.77%1.59%15.93%8.00%2.22%11.52%7.62%2.14%0.37%8.42%3.42%1.00%
Jharkhand28.08%1.13%10.84%13.55%5.88%9.72%9.33%4.09%3.39%8.70%3.72%1.57%
Karnataka29.58%1.00%7.66%15.96%6.34%10.30%9.61%3.10%0.81%8.36%4.39%2.91%
Kerala31.98%0.19%3.73%11.49%11.04%10.56%5.94%2.79%4.42%7.84%6.84%3.18%
Madhya Pradesh27.58%1.23%9.94%13.20%6.87%9.85%9.29%5.03%1.88%9.25%3.91%1.98%
Maharashtra31.10%0.78%8.76%10.42%6.94%10.19%9.24%4.37%2.45%8.16%5.16%2.43%
Manipur27.21%0.97%11.35%10.07%3.71%9.82%6.56%8.06%2.20%10.26%4.52%5.26%
Meghalaya24.98%1.14%12.02%17.82%5.63%9.70%5.60%4.17%1.99%7.08%5.99%3.89%
Mizoram21.76%1.07%10.65%19.58%8.14%9.07%5.93%2.98%3.24%7.88%6.94%2.76%
Nagaland23.58%0.99%12.85%16.67%5.17%10.00%3.14%2.62%1.15%9.78%7.25%6.79%
Odisha27.51%0.92%7.80%16.74%5.08%10.11%9.53%3.49%3.14%8.78%4.69%2.21%
Punjab31.21%1.61%10.55%22.53%7.46%9.38%6.08%0.70%0.28%7.42%1.01%1.76%
Rajasthan27.41%1.23%10.08%15.63%8.27%9.72%8.58%4.62%2.40%6.66%4.70%0.71%
Sikkim28.94%1.68%8.93%25.26%3.12%10.87%2.31%0.73%0.13%7.92%6.52%3.60%
Tamil Nadu30.10%1.30%6.76%19.76%3.17%9.72%11.30%2.83%1.15%6.75%3.60%3.56%
Telangana28.16%0.95%7.02%23.68%2.57%9.00%9.93%3.74%0.68%7.19%5.08%2.00%
Tripura25.92%1.02%8.62%17.80%3.98%9.94%6.97%4.94%2.93%10.34%6.11%1.42%
Uttar Pradesh28.36%1.73%11.80%13.23%8.48%9.46%8.75%0.59%5.25%9.21%2.32%0.82%
Uttarakhand31.78%1.48%14.04%12.17%5.57%10.32%7.01%2.29%0.96%8.52%4.13%1.73%
West Bengal27.68%0.90%8.07%19.13%4.45%10.36%8.41%1.99%1.86%9.52%4.27%3.36%
Union Territory
Andaman & Nicobar Islands31.48%1.60%5.35%16.73%2.80%9.25%8.52%3.53%4.27%9
.89%6.25%0.33%
Chandigarh32.87%0.00%3.87%27.07%13.54%8.84%9.94%0.00%0.00%2.76%0.55%0.55%
Dadra & Nagar Haveli33.09%0.87%5.56%9.23%8.41%9.94%10.22%3.88%0.89%10.30%5.08%2.52%
Daman & Diu26.33%0.00%9.47%25.39%12.37%5.66%7.54%0.85%0.00%2.01%5.02%5.36%
Delhi39.81%0.00%19.91%3.73%0.00%8.71%9.78%4.56%0.00%0.00%8.95%4.56%
Jammu & Kashmir & Ladakh29.00%1.19%9.45%12.41%7.28%9.98%9.23%4.52%1.47%8.42%5.84%1.20%
Lakshadweep38.89%0.00%0.00%0.00%38.89%11.11%0.00%11.11%0.00%0.00%0.00%0.00%
Puducherry35.33%2.45%7.64%12.81%0.00%12.43%12.34%0.45%0.73%9.19%2.92%3.72%
India27.94%1.29%10.38%14.77%6.98%9.66%8.78%2.27%3.57%8.62%3.64%2.10%
Indicator Contribution: UrbanHealthEducationStandard of Living
State/UT (Urban)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh33.43%1.44%6.77%26.10%7.50%5.11%7.01%2.69%0.62%2.94%2.95%3.44%
Arunachal Pradesh26.20%0.42%13.27%18.13%13.19%5.20%6.60%1.68%0.67%8.92%3.15%2.57%
Assam30.71%1.49%9.42%15.89%4.89%7.40%8.10%2.71%2.73%9.05%4.04%3.57%
Bihar26.79%1.36%12.31%15.86%10.09%7.90%7.46%0.36%3.09%6.71%4.02%4.05%
Chhattisgarh33.09%1.53%10.82%16.00%7.79%8.68%8.41%2.56%0.36%7.14%2.43%1.19%
Goa29.76%1.49%8.01%32.40%8.23%3.78%8.88%0.00%0.00%3.03%1.95%2.45%
Gujarat31.05%0.97%8.42%18.71%14.74%7.02%6.33%1.59%1.32%2.91%3.48%3.46%
Haryana31.50%2.00%13.58%19.00%9.63%5.47%5.93%2.55%0.31%4.48%2.09%3.46%
Himachal Pradesh32.47%3.24%13.66%15.39%9.85%1.74%8.36%4.86%1.04%2.93%3.70%2.77%
Jharkhand31.98%1.18%12.05%13.88%6.30%9.79%8.82%4.43%1.12%5.85%3.08%1.53%
Karnataka31.46%1.34%9.39%17.15%10.38%7.22%8.02%2.05%0.92%6.03%3.35%2.70%
Kerala39.37%0.00%6.50%10.74%20.50%8.52%3.02%1.44%1.00%4.26%2.26%2.39%
Madhya Pradesh31.26%2.04%10.97%16.08%8.77%7.44%7.79%3.12%0.89%6.85%2.40%2.40%
Maharashtra34.91%2.45%10.98%13.49%10.84%3.87%8.69%1.31%1.55%3.70%3.71%4.51%
Manipur31.58%1.17%7.79%8.53%3.71%9.47%8.71%8.44%1.64%10.65%2.64%5.67%
Meghalaya31.91%0.79%10.06%15.34%6.38%8.78%6.47%3.61%1.13%7.02%3.80%4.71%
Mizoram28.42%2.05%11.78%19.73%10.47%6.65%4.64%1.36%0.65%6.53%5.12%2.59%
Nagaland29.93%0.95%14.46%12.47%5.66%8.31%6.04%3.98%0.25%9.68%3.97%4.30%
Odisha26.04%0.94%7.90%18.37%8.12%9.27%8.88%3.00%2.88%7.52%4.07%3.03%
Punjab27.62%1.94%10.47%24.73%14.30%5.75%5.43%0.60%0.77%4.37%1.49%2.53%
Rajasthan31.03%1.45%11.21%19.17%12.40%6.58%7.02%2.97%0.77%3.49%2.69%1.21%
Sikkim35.71%0.00%10.51%30.08%6.15%1.37%7.30%0.00%0.54%3.68%2.30%2.36%
Tamil Nadu33.91%1.28%8.64%18.10%5.94%6.21%10.23%3.32%0.75%5.48%2.34%3.79%
Telangana30.35%1.91%7.91%24.83%6.46%5.38%9.15%2.67%0.78%4.00%3.37%3.20%
Tripura35.02%0.59%9.14%20.15%1.07%8.85%7.73%1.21%0.67%9.73%4.70%1.15%
Uttar Pradesh27.42%2.04%11.06%19.53%14.96%6.13%5.91%0.91%1.88%6.17%2.58%1.40%
Uttarakhand28.31%2.64%13.05%22.48%11.26%6.68%5.93%0.62%0.40%3.46%2.34%2.83%
West Bengal27.93%0.71%8.08%19.75%6.59%9.19%7.57%2.78%1.68%7.55%4.06%4.10%
Union Territory
Andaman & Nicobar Islands44.14%0.00%0.00%37.90%0.00%1.78%4.50%0.00%3.66%6.23%1.78%0.0
0%
Chandigarh31.32%1.93%11.31%19.74%8.72%5.54%8.73%2.76%1.02%4.83%2.46%1.63%
Dadra & Nagar Haveli33.13%1.01%8.62%12.49%15.94%2.57%10.82%5.13%0.00%5.31%2.89%2.09%
Daman & Diu30.72%1.12%11.09%15.88%18.26%1.46%7.11%2.59%0.00%1.63%5.06%5.08%
Delhi30.98%2.84%13.19%20.20%9.16%1.36%8.09%3.75%0.14%3.35%4.05%2.88%
Jammu & Kashmir & Ladakh30.97%2.29%5.69%25.25%9.85%5.88%7.84%1.28%0.94%4.38%4.16%1.47%
Lakshadweep47.49%7.92%12.98%0.00%11.84%7.40%1.16%2.77%0.00%2.77%1.16%4.49%
Puducherry30.72%5.04%2.72%35.17%0.51%6.10%7.28%1.18%0.82%6.14%3.18%1.16%
India29.70%1.67%10.53%18.17% 10.80%6.79%7.27%1.92%1.54%5.74%3.12%2.74%
T
able 10- STATE/UT-WISE: INDICATOR CONTRIBUTION TO THE MPI SCORE (URBAN)Table 9- STATE/UT-WISE: INDICATOR CONTRIBUTION TO THE MPI SCORE (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
262 263
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Andhra Pradesh
Anantapur33.87%2.20%10.29%13.99%1.71%33.88%53.96%43.07%0.59%30.34%10.26%4.67%
Chittoor28.91%0.60%12.54%12.66%1.32%38.05%59.70%38.08%0.73%9.90%9.69%3.05%
East Godavari22.26%1.48%10.07%16.32%1.06%49.71%42.30%22.29%0.29%14.07%6.88%5.72%
Guntur16.25%1.50%8.20%19.56%1.43%18.26%34.80%32.38%0.50%17.46%11.95%6.01%
Krishna21.50%0.95%5.28%16.19%1.07%27.20%36.14%35.85%0.66%22.13%8.04%3.88%
Kurnool37.46%2.88%15.54%18.82%6.79%37.87%48.17%24.54%0.11%29.00%9.46%4.11%
Prakasam27.83%3.38%10.89%22.69%3.86%33.27%45.69%49.15%1.22%12.89%13.01%5.18%
SPSR Nellore23.19%1.29%6.00%15.98%2.71%42.83%46.78%36.17%1.79%11.73%10.17%6.97%
Srikakulam29.72%2.63%8.95%13.55%1.56%62.95%66.66%26.98%1.85%15.29%16.07%2.25%
Visakhapatnam25.93%1.99%10.33%15.22%2.96%46.97%47.00%21.42%1.00%14.41%15.97%4.69%
Vizianagaram35.38%1.67%9.98%19.64%1.07%52.52%69.73%20.70%1.13%17.16%17.57%5.01%
West Godavari19.07%1.72%7.05%19.27%2.02%37.46%38.91%30.44%0.85%18.37%10.47%5.57%
Y.S.R. Kadapa29.77%2.04%9.66%16.24%2.95%24.52%29.53%46.98%0.36%9.61%8.28%3.73%
Arunachal Pradesh
Anjaw11.78%1.51%28.62%19.76%4.87%79.70%56.77%14.31%10.25%85.95%32.81%7.42%
Changlang20.14%2.03%24.20%17.67%11.17%77.55%43.83%33.20%22.76%81.53%16.79%11.74%
Dibang Valley17.42%0.93%23.47%18.46%3.98%62.95%20.46%1.56%8.22%72.02%28.42%9.17%
East Kameng27.87%2.23%45.35%20.40%12.45%63.14%63.51%20.75%48.81%87.39%54.87%34.79%
East Siang13.48%0.28%20.02%9.82%4.61%59.68%26.63%2.00%4.07%73.30%8.44%3.40%
Kurung Kumey29.20%2.62%40.50%29.65%9.68%60.80%54.15%7.17%8.14%91.80%41.80%29.10%
Lohit28.43%4.26%28.82%22.47%10.31%78.52%37.60%9.78%20.75%81.90%21.35%16.63%
Lower Dibang Valley19.67%2.42%26.84%19.96%6.79%59.78%31.17%5.71%28.63%75.94%10.79%14.63%
Lower Subansiri16.54%0.82%19.91%17.08%6.11%35.09%27.37%2.45%1.19%74.01%14.69%21.31%
Papum Pare19.24%1.63%29.26%9.93%8.65%16.31%29.75%10.47%1.46%51.76%9.56%10.37%
Tawang12.10%1.94%32.17%40.75%6.95%55.32%43.37%6.25%3.43%65.79%33.90%26.74%
Tirap24.17%2.99%25.97%16.77%9.08%81.59%46.58%35.73%1.73%85.02%34.09%5.75%
Upper Siang15.09%0.34%18.41%15.12%4.47%79.97%44.02%10.87%6.84%83.39%26.13%10.54%
Upper Subansiri23.57%0.72%33.67%21.86%5.35%59.10%38.42%12.85%11.58%88.28%36.96%25.73%
West Kameng19.10%0.89%28.91%19.09%7.08%40.17%40.77%5.85%1.35%61.81%22.70%17.21%
West Siang14.33%0.97%22.58%10.81%2.97%53.30%25.23%12.65%4.87%80
.05%20.98%13.74%
Assam
Baksa32.81%2.43%23.76%10.26%2.41%85.37%42.42%18.23%16.86%81.64%9.76%10.99%
Barpeta44.84%2.78%29.85%17.29%6.03%79.56%63.95%2.13%27.56%82.97%18.85%21.59%
Bongaigaon37.60%1.83%36.12%16.85%6.06%75.01%53.21%25.22%10.87%74.03%13.58%15.27%
Cachar46.65%4.05%23.40%14.78%8.19%73.16%62.27%46.16%33.67%72.85%33.48%10.30%
Chirang37.14%2.24%30.64%15.75%3.09%83.13%67.04%29.83%27.87%82.65%13.24%17.67%
Darrang44.35%3.04%30.41%23.16%10.66%84.13%53.93%3.05%24.11%81.53%15.31%20.21%
Dhemaji33.59%3.02%23.92%8.21%5.08%90.32%56.42%8.47%36.34%87.48%18.70%20.13%
Dhubri47.54%2.36%37.89%27.31%13.27%85.40%66.68%10.64%29.16%85.37%32.27%35.79%
Dibrugarh40.83%1.44%17.38%16.84%4.84%72.23%40.12%1.31%23.62%70.20%19.00%14.97%
Dima Hasao30.40%3.82%30.09%14.32%5.63%76.28%38.85%54.47%20.68%73.29%45.91%13.92%
Goalpara43.57%2.74%33.13%22.59%10.45%81.86%53.19%13.38%30.37%75.14%24.27%18.53%
Golaghat36.63%2.43%15.18%10.69%3.17%85.55%36.54%6.10%11.48%80.98%12.77%12.14%
Hailakandi47.35%4.19%33.59%17.02%10.32%87.52%63.83%57.72%44.25%86.58%43.96%7.41%
Jorhat38.59%1.82%10.44%7.54%4.66%70.33%34.92%13.01%12.73%63.51%10.62%9.75%
Kamrup33.95%1.85%23.89%12.80%4.93%63.18%45.24%6.77%9.94%74.05%14.60%23.16%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Assam
Kamrup Metropolitan25.22%1.46%15.08%8.85%1.68%21.52%36.33%21.69%4.98%36.33%9.25%9.66%
Karbi Anglong31.96%4.27%32.54%19.99%7.82%87.98%56.52%48.73%17.85%88.19%19.82%12.95%
Karimganj49.58%5.55%38.08%13.53%8.22%78.46%60.01%44.55%27.26%71.39%39.83%8.44%
Kokrajhar31.81%2.76%27.44%18.49%4.62%83.21%59.44%23.26%25.09%77.86%17.58%16.68%
Lakhimpur32.70%2.31%19.36%9.08%4.46%83.82%51.62%26.22%24.20%78.82%18.33%11.43%
Marigaon43.42%5.39%28.87%18.53%7.03%83.67%58.03%2.69%21.52%85.22%19.70%16.45%
Nagaon41.11%3.61%28.17%15.42%4.51%81.96%52.83%8.72%16.19%79.07%17.38%8.76%
Nalbari31.85%2.19%20.88%9.08%3.00%66.17%46.45%1.87%16.59%75.25%10.13%10.46%
Sivasagar42.07%3.08%12.51%13.38%6.49%74.78%42.09%5.22%19.15%72.60%18.20%12.94%
Sonitpur32.98%2.29%21.34%16.73%5.18%77.55%37.50%29.52%20.75%69.82%10.67%8.69%
Tinsukia45.42%2.75%18.52%27.16%12.45%73.60%48.84%4.12%23.09%69.80%24.67%25.80%
Udalguri36.50%2.43%27.21%17.00%4.95%86.83%44.53%18.87%14.33%83.41%8.53%11.66%
Bihar
Araria58.06%7.48%46.80%42.27%19.07%94.86%86.28%0.53%50.16%89.42%26.80%37.60%
Arwal55.28%4.23%45.55%16.08%7.63%90.98%77.36%0.75%51.87%77.43%30.88%16.25%
Aurangabad47.96%3.56%38.83%13.61%8.54%84.52%72.48%1.70%39.97%68.67%21.63%18.10%
Banka55.04%4.35%42.92%28.26%9.85%91.43%84.25%8.95%52.35%77.93%26.04%28.22%
Begusarai51.07%5.53%45.22%28.57%11.05%84.32%64.26%2.09%35.20%73.71%27.33%18.59%
Bhagalpur48.28%3.46%43.48%27.09%11.21%79.08%66.14%3.37%30.28%67.36%27.77%22.01%
Bhojpur49.34%3.96%41.54%12.37%7.23%80.69%72.98%0.25%33.35%65.14%18.54%18.18%
Buxar48.45%5.96%41.62%13.24%3.84%77.55%70.63%0.25%33.22%70.28%19.41%13.62%
Darbhanga52.70%3.05%49.42%30.66%13.43%83.79%72.29%0.46%39.78%83.17%27.30%34.12%
Gaya62.84%7.67%50.55%20.88%9.81%84.32%71.46%4.99%26.58%67.98%28.37%22.39%
Gopalganj43.53%3.88%41.28%17.33%8.12%79.46%71.57%1.47%42.20%70.74%13.30%32.32%
Jamui61.05%5.43%55.52%29.53%12.13%90.67%84.90%20.89%36.86%77.68%29.89%21.76%
Jehanabad49.73%4.09%40.80%21.86%10.66%81.44%68.77%1.61%30.00%58.62%34.76%18.31%
Kaimur49.07%4.62%43.01%16.04%9.04%86.47%78.03%5.38%25.26%70.71%22.94%15.70%
Katihar50.30%4.41%45.59%38.10%20.02%90.12%77.25%1.19%66.98%82.13%21.75%34.31%
Khagaria53.82%5.78%50.06%35.28%15.95%87.78%69.17%1.29%45.48%78.51%37.11%29.61%
Kishanganj55.70%3.19%47.74%42.08%20.19%94.41%82.67%1.17%46.45%82.27%22.84%42.42%
Lakhisar
ai49.70%5.24%46.77%23.76%9.30%86.27%61.30%7.19%18.04%61.20%33.70%17.99%
Madhepura56.78%4.69%51.09%40.46%18.89%91.90%85.74%0.17%46.08%88.50%28.36%27.07%
Madhubani54.09%3.93%45.23%27.04%11.71%88.05%79.67%1.10%45.67%85.18%26.90%30.21%
Munger47.53%4.02%39.90%19.38%6.95%74.90%65.45%9.90%31.70%59.39%23.23%17.74%
Muzaffarpur49.74%3.07%40.85%24.80%11.42%80.13%71.13%0.58%26.70%79.62%18.94%24.95%
Nalanda52.55%4.22%42.48%23.15%14.69%78.13%69.00%3.50%30.17%52.62%28.18%20.13%
Nawada54.33%2.67%46.40%22.52%12.42%80.64%69.84%2.26%42.95%59.78%43.76%24.82%
Pashchim Champaran48.25%5.07%44.86%32.36%18.47%81.28%78.53%4.24%51.96%82.96%20.60%44.41%
Patna42.86%3.28%37.56%14.62%7.04%50.40%48.61%1.97%13.05%38.07%16.51%10.83%
Purba Champaran52.81%4.29%53.93%34.91%21.37%84.94%76.86%0.57%56.90%80.24%24.68%34.50%
Purnia60.35%7.22%49.70%38.21%16.05%92.82%85.08%0.17%49.52%86.05%26.46%30.07%
Rohtas50.66%5.30%45.15%10.10%4.28%83.74%72.80%1.34%18.55%61.49%21.94%11.82%
Saharsa57.27%5.97%51.54%35.09%19.58%89.19%83.38%0.80% 40.60%85.67%31.58%30.22%
Samastipur47.42%3.04%46.41%29.80%13.33%87.28%80.44%1.80%58.74%85.39%29.13%37.34%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIOTable 11- DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
264 265
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Bihar
Saran45.00%3.82%42.97%16.94%9.40%84.62%73.07%1.91%38.67%67.42%17.01%29.63%
Sheikhpura59.67%5.75%50.72%23.88%9.89%84.41%66.19%8.11%19.17%61.25%28.26%17.99%
Sheohar54.50%6.80%49.02%38.66%14.22%87.83%76.04%1.16%38.69%88.68%30.60%27.35%
Sitamarhi55.80%5.33%48.14%37.07%16.71%87.48%78.22%0.83%59.67%87.20%28.27%28.84%
Siwan45.20%4.85%42.40%12.89%8.54%78.05%74.69%1.74%45.07%65.02%13.72%17.38%
Supaul60.95%3.58%53.07%35.54%15.35%94.45%83.67%0.58%39.27%88.00%25.85%26.63%
Vaishali53.88%5.48%44.19%21.74%8.60%78.32%67.75%2.54%34.81%74.45%17.18%19.44%
Chhattisgarh
Bastar56.50%4.19%27.79%23.87%9.65%88.71%81.37%16.88%12.66%84.51%29.75%6.20%
Bijapur47.01%3.33%33.32%19.13%4.89%90.51%77.97%12.37%7.39%85.73%20.64%4.68%
Bilaspur36.30%3.40%28.35%10.19%5.28%74.56%60.73%17.46%0.46%61.59%8.19%4.29%
Dantewada57.96%6.23%26.31%35.33%22.36%85.34%77.12%17.34%12.56%84.26%42.43%8.92%
Dhamtari45.76%2.49%14.43%8.37%1.88%80.33%43.49%14.07%1.35%50.74%11.05%2.15%
Durg39.98%2.66%22.47%7.59%3.14%64.77%53.06%11.97%0.51%46.84%6.88%4.35%
Janjgir-Champa39.58%2.30%22.77%9.96%5.97%81.17%68.42%9.47%1.51%53.85%10.82%4.57%
Jashpur43.22%4.39%32.34%16.56%7.46%92.72%83.56%32.10%15.49%89.24%33.32%10.37%
Kabeerdham48.40%3.50%32.10%21.35%8.67%90.39%75.99%14.92%3.69%71.80%17.07%6.88%
Korba45.20%4.41%27.76%13.48%4.07%74.00%64.96%28.83%5.43%58.86%16.11%5.14%
Koriya40.70%3.24%30.12%17.72%4.26%80.70%74.71%41.01%9.04%75.78%23.38%7.00%
Mahasamund47.56%2.44%21.23%12.96%4.71%87.30%75.80%13.17%1.04%68.61%11.36%4.58%
Narayanpur53.56%3.53%35.01%27.72%12.07%90.31%85.37%17.15%21.65%88.62%28.74%7.39%
North Bastar Kanker47.41%3.64%18.69%10.77%3.83%86.65%61.10%9.08%3.61%76.56%18.42%4.91%
Raigarh38.56%2.78%21.97%13.37%3.92%81.02%71.53%14.45%3.90%71.91%16.44%9.51%
Raipur40.52%3.37%19.58%11.14%4.66%66.12%58.73%16.20%1.28%43.06%8.38%6.54%
Rajnandgaon43.40%3.41%26.38%6.25%1.57%83.33%53.06%15.53%1.04%66.96%4.87%1.42%
Surguja45.58%3.52%29.22%22.99%7.32%87.91%79.93%37.08%6.16%87.11%31.55%9.00%
Goa
North Goa26.37%0.53%6.88%3.83%0.41%15.84%18.78%3.77%0.31%18.95%3.51%4.04%
South Goa22.18%0.63%7.51%5.95%1.75%13.58%25.24%4.60%0.00%12.16%2.18%3.99%
Gujarat
Ahmadabad32.17%2.49%2.67%5.63%3.30%14.47%10.99%4.86%0.73%6.04%6.21%4.35%
Amreli28.89%1.24%15.67%10.22%4.35%49
.07%24.72%6.41%0.85%25.92%5.90%8.57%
Anand51.22%3.83%8.70%5.12%4.76%59.83%33.69%4.65%2.24%23.12%10.55%8.99%
Banas Kantha52.03%3.39%22.94%13.68%13.75%72.17%60.86%7.80%11.38%24.76%28.73%7.13%
Bharuch44.42%3.32%9.52%10.26%6.11%49.52%31.07%21.96%2.74%28.75%12.72%8.88%
Bhavnagar38.41%1.58%18.41%12.25%4.72%58.38%37.90%8.66%0.68%34.28%7.70%12.38%
Dang62.81%3.10%32.42%20.73%9.19%92.21%81.54%49.49%11.71%89.59%35.73%18.30%
Dohad59.23%5.41%27.11%17.43%12.73%86.60%80.12%36.21%20.51%71.46%36.11%14.92%
Gandhinagar44.37%2.30%6.81%11.12%6.65%48.77%39.45%4.81%4.94%17.04%11.62%7.52%
Jamnagar32.27%1.97%10.24%13.29%8.55%40.16%32.30%13.29%1.81%6.59%7.98%10.16%
Junagadh28.80%2.07%9.97%8.93%6.20%54.12%25.82%5.66%0.61%14.02%7.18%4.50%
Kachchh41.22%3.42%20.21%22.24%17.74%62.20%41.38%23.99%4.32%7.78%20.23%14.09%
Kheda52.28%3.26%21.72%6.18%6.63%72.30%48.00%8.02%3.94%39.75%19.19%18.53%
Mahesana41.27%1.63%12.44%4.49%3.87%43.55%34.23%8.81%2.71%13.73%8.00%3.61%
Narmada59.28%2.28%24.09%11.40%6.29%86.43%66.22%6.25%6.39%67.01%27.42%5.78%
Navsari42.13%1.27%2.87%6.40%2.27%47.13%30.42%34.64%1.22%40.11%7.02%5.02%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Gujarat
Panch Mahals59.69%1.79%25.56%7.23%7.48%78.76%64.61%24.17%13.55%62.47%29.80%18.24%
Patan48.09%3.00%16.84%13.29%6.97%68.12%44.34%6.64%5.89%23.43%20.27%8.01%
Porbandar29.12%0.73%15.97%9.56%2.47%49.92%25.59%18.69%0.52%9.20%7.00%7.93%
Rajkot31.46%1.32%11.33%8.04%5.72%34.64%28.10%11.40%1.59%7.55%4.57%7.09%
Sabar Kantha51.21%1.98%19.17%7.55%5.53%71.71%55.09%11.23%4.39%31.39%23.04%7.73%
Surat34.12%0.51%15.16%6.34%5.31%15.33%21.43%6.63%1.30%12.85%8.17%11.27%
Surendranagar43.26%2.22%18.93%17.65%10.01%64.55%53.69%23.12%4.02%17.95%14.13%9.25%
Tapi56.67%0.98%12.04%11.34%3.88%76.69%60.04%15.97%6.31%74.86%18.34%9.76%
Vadodara41.36%2.58%20.36%13.00%7.81%46.94%39.62%9.05%2.34%26.73%14.17%9.80%
Valsad38.42%1.87%16.08%10.81%5.17%43.45%39.31%37.31%1.22%35.77%15.32%13.69%
Haryana
Ambala26.49%1.15%12.58%2.67%1.07%33.60%9.05%1.05%0.15%12.71%0.85%2.44%
Bhiwani36.66%3.10%23.05%5.18%1.72%69.87%16.71%19.66%0.70%39.80%4.80%4.44%
Faridabad27.31%3.33%27.83%8.84%5.59%18.77%18.77%50.07%0.61%12.48%3.50%16.66%
Fatehabad37.03%1.66%23.30%9.49%2.73%63.88%11.61%2.23%0.25%29.96%2.29%6.62%
Gurgaon29.86%1.13%25.21%8.30%4.20%21.98%29.22%2.43%0.24%11.23%7.84%11.60%
Hisar29.62%2.37%23.80%5.95%2.24%63.70%13.77%16.56%0.84%24.20%2.60%5.36%
Jhajjar22.97%0.80%20.91%2.60%2.09%58.98%12.46%11.40%0.67%13.03%4.29%6.54%
Jind33.26%2.25%19.26%4.56%1.32%65.46%14.69%18.60%0.49%30.88%1.96%5.18%
Kaithal33.07%2.60%18.34%6.05%0.95% 60.40%20.83%7.53%0.13%26.81%2.38%2.45%
Karnal31.28%1.74%16.39%6.30%0.47%47.49%11.67%1.52%0.19%22.80%2.69%2.79%
Kurukshetra24.49%0.59%12.70%6.78%0.88%43.56%12.87%0.48%0.13%22.15%1.29%3.28%
Mahendragarh29.73%1.78%18.97%1.85%0.41%66.77%24.78%8.06%1.24%18.78%3.74%6.04%
Mewat61.16%9.04%64.83%26.74%31.65%84.17%49.24%33.34%9.74%69.42%24.48%22.01%
Palwal37.13%3.03%39.91%11.39%11.27%70.16%31.09%14.68%5.14%39.12%12.48%22.16%
Panchkula17.59%0.66%8.62%2.75%1.05%25.23%14.02%0.51%0.34%7.60%0.92%2.01%
Panipat27.66%1.12%20.73%8.00%3.05%38.42%8.20%1.20%0.00%15.63%1.78%1.82%
Rewari36.01%1.77%28.65%2.79%1.11%63.27%28.56%8.26%1.02%18.56%7.54%17.01%
Rohtak32.15%2.01%25.12%7.44%1.51%58.32%21.16%18.06%0.60%21.61%4.01%11.56%
Sirsa41.71%2.73%24.63%8.97%1.96%59.94%18.86%2.
90%0.70%37.13%3.24%4.35%
Sonipat29.97%0.51%23.56%3.60%1.69%54.79%17.45%6.91%0.43%15.58%2.47%5.09%
Yamunanagar30.29%0.35%11.84%4.81%2.09%38.71%18.54%0.46%0.13%23.68%1.82%5.57%
Himachal Pradesh
Bilaspur28.01%0.67%18.59%3.34%0.88%84.15%26.82%20.84%0.12%16.15%7.62%3.72%
Chamba30.03%3.11%23.59%6.83%1.35%78.48%22.29%12.33%1.63%38.56%12.69%1.94%
Hamirpur25.35%1.51%15.71%2.38%0.38%78.80%16.50%4.18%0.33%25.93%4.28%1.85%
Kangra28.19%1.67%11.52%2.93%0.15%70.94%32.04%4.65%0.00%28.85%3.70%4.10%
Kinnaur19.99%1.68%15.81%7.36%1.10%32.95%31.36%8.03%0.64%39.04%8.13%2.11%
Kullu22.71%1.03%17.67%3.97%2.02%64.61%31.73%7.43%0.48%49.99%9.36%1.55%
Lahul & Spiti15.04%1.43%13.66%11.23%0.75%43.46%44.89%4.36%1.95%69.68%8.56%1.79%
Mandi25.90%1.16%26.34%4.54%0.45%80.38%26.30%5.58%0.31%35.20%10.62%1.65%
Shimla26.22%1.58%13.53%2.84%1.32%38.84%26.44%6.06%0.53%33.81%9.08%3.35%
Sirmaur34.01%2.74%17.02%5.56%1.33%66.14%29.81%13.45%0.84%21.90%13.63%1.54%
Solan28.54%1.97%17.52%3.13%1.10%55.70%28.52%14.55%1.21%15.40%4.85%1.95%
Una23.68%1.32%17.87%3.05%1.61%71.52%29.12%1.95%0.22%21.44%4.19%3.44%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
266 267
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Jharkhand
Bokaro44.89%2.44%25.63%10.07%4.40%75.16%64.38%29.40%11.54%38.71%12.78%6.66%
Chatra59.19%4.03%48.03%23.26%9.74%90.95%84.89%38.75%56.40%77.77%25.26%10.76%
Deoghar53.27%4.84%39.27%17.01%6.89%81.68%79.11%24.83%18.83%63.66%17.60%5.90%
Dhanbad43.45%2.03%25.90%10.82%4.19%81.45%71.20%36.75%3.61%32.92%13.13%5.73%
Dumka52.18%3.54%32.57%27.70%7.32%91.13%87.89%24.03%29.17%85.15%26.85%12.58%
Garhwa50.51%5.13%43.59%16.04%10.66%94.88%88.90%19.27%48.29%85.08%29.41%19.27%
Giridih54.23%4.19%38.18%22.77%12.51%84.95%80.39%41.66%8.15%60.05%22.26%6.19%
Godda49.66%3.85%34.45%30.27%12.69%94.29%82.51%27.60%19.69%74.58%26.77%5.51%
Gumla45.52%1.79%33.43%14.45%8.09%90.12%83.29%58.09%21.52%85.33%20.17%7.75%
Hazaribagh48.98%3.68%30.73%12.10%5.98%82.96%71.75%38.12%3.08%48.53%16.72%2.80%
Jamtara55.65%3.25%39.34%25.03%9.13%96.65%85.23%16.27%15.90%73.06%20.98%7.83%
Khunti45.94%1.67%29.56%19.54%6.63%92.60%84.50%54.47%31.43%86.32%26.83%7.52%
Kodarma51.90%2.41%24.56%14.65%6.48%70.88%71.69%27.13%8.22%40.53%15.54%2.22%
Latehar44.81%2.67%37.32%25.69%12.20%95.18%87.45%31.67%43.84%87.63%29.97%20.76%
Lohardaga48.64%1.42%34.21%16.81%10.29%85.43%80.14%37.10%17.08%80.62%19.56%9.76%
Pakur56.04%2.95%38.90%38.26%19.28%94.08%86.53%20.18%22.57%81.97%30.98%6.65%
Palamu45.78%3.52%36.71%19.88%10.10%83.41%80.81%17.39%35.97%77.30%33.92%17.67%
Pashchimi Singhbhum53.01%6.88%41.39%28.59%16.37%89.82%83.93%39.50%30.98%83.00%38.52%20.06%
Purbi Singhbhum35.26%1.21%21.97%10.97%4.36%59.86%55.66%19.53%7.34%43.66%13.03%7.39%
Ramgarh44.44%2.41%26.58%10.75%3.95%82.63%59.88%40.13%3.83%35.86%14.13%6.79%
Ranchi41.83%2.39%24.29%9.93%2.88%61.14%61.22%30.39%6.20%45.46%12.41%5.77%
Sahibganj51.66%5.55%42.77%36.06%15.28%88.94%73.13%31.07%25.48%74.56%36.32%12.42%
Saraikela-Kharsawan50.49%3.81%34.89%15.17%4.45%78.57%74.59%28.34%13.94%60.43%18.05%7.80%
Simdega43.87%4.06%36.42%16.91%8.44%94.40%90.83%43.22%43.87%88.33%24.39%10.68%
Karnataka
Bagalkot45.63%2.32%12.83%10.67%4.45%66.52%77.09%10.81%2.27%53.58%13.68%12.14%
Bangalore23.87%0.47%14.95%2.44%2.30%3.96%13.49%8.02%0.42%5.42%2.15%6.39%
Bangalore Rural31.73%1.21%11.76%6.70%1.68%31.32%22.41%27.90%1.24%41.99%8.59%7.77%
Belgaum37.14%1.63%10.22%7.06%1.54%52.08%56.23%5.97%1.15%37.59%8.66%5.42%
Bellary45.00%3.45%15.89%16.46%7.93%52.73%61.26%9
.04%2.09%45.84%13.57%13.97%
Bidar40.33%1.94%11.60%8.66%2.76%67.68%72.09%13.99%1.97%47.04%15.66%9.30%
Bijapur34.48%1.28%12.84%12.28%6.33%71.40%77.57%25.44%2.98%49.99%17.51%13.04%
Chamrajnagar35.92%2.07%10.61%16.86%3.13%60.67%63.67%11.71%4.58%44.19%15.13%17.64%
Chikkaballapura38.03%1.25%7.55%10.12%2.96%49.33%50.40%25.01%2.14%41.69%15.39%9.01%
Chikmagalur27.24%0.68%14.64%8.77%2.42%50.91%36.58%21.94%2.78%49.69%9.58%16.28%
Chitradurga31.48%1.78%9.66%7.67%3.12%60.86%56.41%18.11%2.74%42.35%15.21%9.52%
Dakshina Kannada34.51%0.55%12.25%4.17%1.92%49.06%7.93%16.02%2.15%29.89%6.95%8.05%
Davanagere38.76%1.86%6.65%8.78%3.61%47.60%37.18%6.61%1.18%40.91%8.14%7.41%
Dharwad32.09%1.62%12.06%5.40%1.12%45.12%36.96%6.50%0.97%54.15%9.93%4.17%
Gadag30.49%1.49%10.38%10.57%7.30%76.02%69.54%25.69%2.09%54.77%18.88%9.44%
Gulbarga45.37%0.84%12.85%12.35%6.49%63.27%70.82%8.56%1.32%51.33%11.37%12.48%
Hassan25.49%1.43%6.80%9.65%1.73%43.20%33.15%12.62%2.45%44.95%8.58%7.19%
Haveri34.55%0.90%13.83%10.08%3.54%65.91%44.12%14.90%2.62%55.37%12.23%8.07%
Kodagu31.75%0.99%8.58%8.00%2.76%53.03%15.74%19.67%3.01%32.94%10.93%7.76%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Karnataka
Kolar35.29%1.54%10.59%6.84%1.65%39.13%39.56%35.83%0.86%29.75%8.15%5.84%
Koppal45.93%1.96%19.99%11.85%5.15%68.63%52.78%15.40%1.34%52.96%10.86%6.30%
Mandya25.53%0.77%8.65%7.38%3.08%42.35%41.99%7.33%1.15%36.38%7.61%9.10%
Mysore26.04%0.60%9.62%7.27%1.37%31.01%34.86%6.15%2.73%32.57%10.57%10.59%
Raichur39.52%1.91%18.19%21.17%8.16%72.56%72.38%21.15%2.04%50.23%19.78%14.45%
Ramanagara28.32%0.90%11.37%10.31%1.92%40.75%36.16%13.50%1.83%39.68%8.13%5.23%
Shimoga32.00%2.19%10.96%7.22%3.19%38.32%26.86%20.62%3.50%45.30%8.02%10.87%
Tumkur29.23%2.11%11.82%8.62%3.57%57.83%47.26%27.45%1.58%52.10%12.34%4.60%
Udupi33.98%0.84%8.80%10.48%2.08%57.57%9.87%36.57%1.43%28.83%5.93%7.13%
Uttara Kannada40.23%1.03%12.29%6.47%2.28%52.58%33.06%31.15%2.45%36.72%10.76%11.36%
Yadgir46.85%1.80%21.65%28.54%16.63%79.11%81.46%22.55%1.83%57.29%23.15%13.70%
Kerala
Alappuzha14.92%0.39%0.95%1.38%0.49%37.03%3.01%9.11%0.42%16.79%1.84%5.99%
Ernakulam8.96%0.24%1.76%1.00%0.10%20.34%0.73%2.97%0.02%9.63%1.47%4.32%
Idukki13.92%0.28%0.64%3.16%0.00%54.45%5.30%7.65%0.64%17.02%2.25%5.72%
Kannur17.19%0.23%3.63%2.18%0.52%44.46%1.05%7.92%1.33%7.72%3.33%3.65%
Kasaragod19.23%0.24%1.59%3.45%1.24%46.56%2.15%7.57%0.90%22.22%3.91%3.93%
Kollam11.83%0.11%1.50%1.01%0.75%39.91%1.91%4.92%1.86%8.45%3.55%7.61%
Kottayam10.58%0.13%0.98%0.98%0.00%38.65%0.89%5.93%0.35%7.69%1.66%2.10%
Kozhikode17.23%0.39%0.84%1.74%0.26%50.95%0.53%3.23%0.62%13.28%2.96%3.49%
Malappuram21.03%0.15%2.94%1.53%1.24%56.15%1.48%6.41%0.56%6.87%4.48%4.70%
Palakkad19.68%0.00%0.63%1.97%0.10%50.26%3.09%6.33%0.75%12.72%2.90%2.50%
Pathanamthitta11.54%0.00%0.53%1.96%1.22%42.86%2.98%7.18%0.77%9.44%1.77%7.04%
Thiruvananthapuram13.06%0.18%2.09%2.45%0.64%47.86%2.99%6.54%0.60%12.25%3.75%3.03%
Thrissur13.79%0.09%1.82%1.51%0.28%33.22%0.33%4.77%0.20%3.91%0.83%4.08%
Wayanad20.11%0.52%3.83%3.63%1.20%67.72%4.10%7.36%3.63%23.23%8.80%5.48%
Madhya Pradesh
Alirajpur63.37%6.72%54.00%46.85%42.03%90.13%84.68%22.84%7.08%84.80%27.93%13.88%
Anuppur43.15%3.36%28.20%12.68%4.76%83.88%79.90%48.72%20.16%76.11%24.40%6.07%
Ashoknagar49.57%2.38%30.57%18.07%10.04%85.20%78.73%31.58%11.39%84.47%18.49%8.35%
Balaghat54.68%3.19%26.31%10.81%3.46%84.37%71.
03%42.89%10.12%78.83%16.62%9.63%
Barwani65.20%4.84%44.78%36.47%31.58%80.52%80.73%25.27%10.18%75.41%33.78%14.16%
Betul42.69%3.38%27.71%14.47%4.73%74.59%69.03%32.00%6.38%70.43%23.47%8.90%
Bhind48.22%3.73%32.41%7.68%7.14%83.10%67.54%15.00%11.88%74.63%12.67%9.46%
Bhopal33.34%2.77%15.56%9.16%5.36%22.86%36.82%11.24%1.45%23.93%5.61%4.70%
Burhanpur47.49%3.53%32.56%24.74%13.82%54.60%59.27%16.74%9.22%53.66%21.81%12.69%
Chhatarpur46.94%5.96%38.12%19.17%12.04%86.70%85.47%47.75%20.18%74.72%21.43%16.56%
Chhindwara42.66%2.49%23.64%12.76%2.92%75.07%66.12%32.98%9.23%64.30%23.79%9.30%
Damoh42.11%4.82%32.12%15.52%7.93%86.27%76.86%53.31%12.38%82.33%29.30%21.61%
Datia44.96%5.25%28.39%10.06%4.20%80.16%64.10%24.74%9.47%88.10%15.53%6.73%
Dewas48.57%4.73%27.43%15.81%8.40%64.50%49.61%23.37%1.13%50.82%10.44%4.99%
Dhar51.21%4.76%33.95%23.56%10.88%68.28%64.56%23.77%4.08%55.10%18.07%10.07%
Dindori50.63%3.43%31.94%20.39%4.07%95.52%92.31%50.04%24.71%89.97%54.77%13.38%
East Nimar52.32%3.14%32.38%23.48%9.80%72.52%63.40%32.36%8.05%57.34%18.08%7.11%
Guna54.05%3.99%35.92%19.58%10.43%77.31%72.04%34.75%5.86%73.26%16.52%7.19%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
268 269
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Madhya Pradesh
Gwalior41.34%4.11%25.21%9.53%6.49%44.05%41.43%9.29%3.78%48.36%9.85%6.48%
Harda40.84%3.58%33.42%19.09%7.55%69.80%48.21%23.07%3.07%59.27%15.63%5.71%
Hoshangabad40.06%2.24%27.44%10.66%3.34%66.56%52.52%18.33%4.97%53.54%14.55%4.78%
Indore36.54%0.96%10.76%10.44%4.20%16.07%24.59%11.44%0.72%15.20%2.65%6.09%
Jabalpur37.70%2.44%19.91%8.80%6.89%50.17%49.02%16.51%4.20%40.33%15.13%5.24%
Jhabua57.92%6.17%42.63%41.44%34.48%92.16%87.28%36.19%10.88%82.42%42.25%29.50%
Katni42.17%4.84%27.62%11.79%4.42%82.63%77.93%31.59%15.74%76.16%25.45%12.51%
Mandla46.67%3.37%26.68%18.78%5.38%85.63%85.27%51.81%21.68%81.35%39.99%6.95%
Mandsaur48.57%2.95%31.00%15.13%4.93%71.19%68.27%37.08%1.83%60.11%10.77%17.93%
Morena44.30%3.93%30.33%9.93%8.63%77.49%61.96%16.81%11.04%72.41%13.63%5.59%
Narsimhapur39.68%2.30%25.25%13.11%1.97%79.10%56.27%16.08%6.40%70.68%26.97%17.74%
Neemuch45.47%2.68%26.48%17.20%7.14%64.54%66.39%30.77%1.90%55.72%10.57%15.67%
Panna40.40%4.00%33.65%18.17%9.23%88.20%79.05%38.56%18.15%81.99%23.47%15.96%
Raisen47.39%2.94%29.43%13.66%6.33%75.11%59.37%21.78%5.94%70.08%17.93%5.06%
Rajgarh48.98%3.29%28.71%18.44%6.68%81.45%79.66%56.69%4.11%65.59%18.92%7.22%
Ratlam49.40%2.53%28.18%21.86%10.09%66.39%64.65%22.08%5.33%57.78%25.05%29.85%
Rewa38.96%5.23%33.91%11.00%7.58%87.28%72.55%24.53%11.32%83.20%22.59%13.28%
Sagar41.29%3.32%37.07%12.46%6.06%83.50%72.11%38.90%14.23%79.43%22.57%12.44%
Satna36.93%4.20%29.12%12.34%5.28%78.62%67.55%21.17%9.30%78.74%21.45%7.94%
Sehore42.39%3.32%26.06%16.36%6.94%76.33%51.15%30.27%1.70%56.74%9.35%5.41%
Seoni46.92%1.99%23.51%13.48%4.01%83.19%76.24%44.63%16.11%77.75%27.10%12.85%
Shahdol41.47%3.87%28.86%15.31%6.79%85.90%83.10%50.96%21.40%80.55%26.76%12.87%
Shajapur47.40%2.55%19.27%18.80%10.39%75.32%62.69%39.70%2.78%54.89%12.05%9.42%
Sheopur54.35%4.22%32.38%24.35%11.64%85.23%83.40%28.56%16.47%80.33%28.86%10.03%
Shivpuri49.07%3.91%33.35%14.43%6.50%80.17%76.27%58.52%12.27%80.43%21.18%7.76%
Sidhi47.89%4.98%41.57%14.85%8.93%92.99%88.70%47.03%21.72%88.30%32.75%19.12%
Singrauli46.21%6.10%43.45%18.70%11.43%85.29%86.07%53.88%29.43%82.69%29.71%10.64%
Tikamgarh49.87%4.39%33.97%14.26%7.81%86.80%85.47%44.09%13.28%71.69%23.14%19.35%
Ujjain45.45%2.68%29.46%16.79%7.11%54.45%46.29%23.80%2.38%44.72%10
.29%16.68%
Umaria44.93%3.50%33.05%16.18%7.50%88.34%81.54%42.22%19.36%81.62%27.09%7.67%
Vidisha46.01%5.19%37.32%20.44%11.21%82.32%75.74%25.62%14.28%80.89%29.68%24.21%
West Nimar52.11%2.64%30.38%22.15%10.31%69.24%67.11%13.75%1.91%51.13%16.24%15.45%
Maharashtra
Ahmadnagar37.36%0.87%20.36%4.95%5.32%40.72%48.18%16.36%5.78%35.28%13.84%21.84%
Akola39.82%1.42%14.26%3.13%2.58%54.78%50.51%8.12%4.81%35.18%15.32%7.36%
Amravati36.10%0.97%11.47%5.17%0.81%49.18%33.31%9.14%6.24%37.94%15.01%8.71%
Aurangabad38.24%2.21%18.96%5.67%3.80%44.62%52.02%15.41%2.87%23.87%12.29%9.51%
Bhandara37.45%1.39%6.69%4.55%0.42%50.47%29.54%18.55%2.71%38.03%8.30%1.88%
Bid39.58%1.96%19.23%9.91%6.97%71.87%59.40%21.51%8.32%29.60%19.57%11.70%
Buldana40.99%0.88%14.69%7.65%3.46%67.20%53.58%18.58%3.29%39.84%15.29%5.47%
Chandrapur42.38%1.13%9.53%6.67%1.43%53.69%45.96%24.39%7.74%34.47%18.55%4.14%
Dhule45.55%1.48%25.98%15.86%13.49%62.97%71.00%9.22%13.22%50.46%23.43%18.04%
Garhchiroli38.17%0.76%13.76%10.68%2.59%77.23%67.65%18.68%5.87%52.56%17.65%7.27%
Gondiya46.04%1.20%12.46%5.25%0.44%75.81%41.80%27.60%3.04%72.53%12.23%4.59%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Maharashtra
Hingoli46.34%2.57%18.99%9.50%5.27%76.30%56.89%25.96%14.05%44.32%23.88%6.66%
Jalgaon31.67%2.08%20.63%10.99%5.70%46.58%55.54%6.62%7.68%30.72%16.01%12.39%
Jalna45.79%1.79%19.46%11.34%4.22%79.11%62.30%41.51%12.31%36.56%23.21%8.28%
Kolhapur34.63%1.31%17.01%4.22%3.00%35.80%32.53%6.18%2.45%38.64%9.50%12.99%
Latur40.78%1.01%16.00%5.32%2.64%65.44%56.30%33.02%5.22%32.44%20.35%9.31%
Mumbai22.53%0.96%7.86%4.18%2.51%1.57%60.33%0.83%0.52%2.40%11.72%11.58%
Mumbai Suburban24.89%1.81%13.34%5.14%3.46%2.44%75.10%0.87%7.43%2.93%9.04%14.70%
Nagpur34.60%0.97%9.21%4.30%1.23%23.00%27.72%6.88%1.65%24.02%6.72%6.41%
Nanded49.69%2.82%21.85%7.58%4.20%67.69%55.27%25.68%9.29%40.93%26.65%10.83%
Nandurbar56.51%1.66%25.46%28.36%22.39%77.96%75.03%25.74%26.89%76.33%45.43%17.99%
Nashik43.36%2.27%24.99%5.68%6.43%33.64%47.83%16.15%7.06%29.14%13.81%7.37%
Osmanabad37.16%1.47%14.44%7.71%1.41%63.08%71.55%32.55%8.89%35.74%15.91%8.62%
Parbhani49.26%1.01%18.64%6.85%4.54%74.63%64.55%16.19%7.28%32.45%16.96%8.12%
Pune27.24%1.12%12.28%4.69%1.89%15.51%36.87%6.65%3.97%10.07%7.86%8.29%
Raigarh35.61%1.01%13.45%6.64%2.33%23.66%27.07%8.94%5.60%16.46%8.96%13.56%
Ratnagiri35.42%0.63%12.97%6.59%4.03%66.21%33.47%17.50%3.33%53.29%22.11%19.83%
Sangli31.66%1.03%15.05%3.48%4.14%36.88%27.17%7.08%7.56%31.22%8.58%10.13%
Satara38.47%0.91%16.73%4.69%3.32%45.22%36.18%9.13%5.18%33.22%10.24%11.42%
Sindhudurg34.83%1.05%13.30%4.63%2.45%65.30%21.92%27.07%1.19%42.93%18.91%17.31%
Solapur35.04%1.41%13.40%7.03%3.16%48.06%47.03%21.15%8.45%24.73%10.40%5.71%
Thane30.76%1.16%15.00%6.37%6.08%20.12%37.22%14.14%8.77%17.17%12.14%9.58%
Wardha36.46%1.49%8.16%6.37%1.63%40.29%41.04%7.05%3.81%30.90%13.02%3.68%
Washim43.18%1.31%20.81%6.76%4.43%64.96%56.53%19.19%4.27%46.35%18.85%4.30%
Yavatmal44.97%1.14%17.06%7.21%4.92%64.61%56.32%24.02%9.07%48.48%22.22%9.67%
Manipur
Bishnupur25.12%1.49%8.15%3.03%1.99%62.00%59.79%61.56%3.64%89.89%6.11%19.26%
Chandel24.95%2.54%31.46%11.41%4.18%81.87%37.28%62.38%6.50%89.05%23.53%21.03%
Churachandpur27.71%3.03%22.14%10.32%2.88%58.16%32.47%49.82%8.89%80.89%24.00%20.19%
Imphal East23.02%1.33%10.46%4.42%2.50%49.62%53.20%71.21%2.95%77.16%8.62%17.99%
Imphal West17.23%1.09%11.18%1.68%0.60%38.59%50.31%48.31%2.22%70
.56%3.98%19.40%
Senapati30.50%3.11%38.46%10.57%4.89%82.13%39.57%65.89%7.36%89.11%23.53%28.70%
Tamenglong30.03%3.44%40.54%15.33%4.62%87.06%36.61%67.08%12.62%90.44%44.22%32.14%
Thoubal24.73%1.71%14.60%3.57%2.63%64.78%51.46%67.77%14.20%89.64%8.24%20.87%
Ukhrul25.70%1.99%33.73%6.79%2.10%89.63%37.51%59.34%22.31%85.55%44.48%34.01%
Meghalaya
East Garo Hills31.84%1.56%39.65%20.62%11.03%93.55%46.73%59.34%13.13%84.43%29.10%21.37%
East Khasi Hills38.68%2.65%21.72%14.25%2.91%52.71%32.61%17.91%3.96%32.21%25.58%22.36%
Jaintia Hills49.02%4.89%42.79%31.69%10.91%78.27%42.47%32.70%13.48%31.41%45.75%32.88%
Ri Bhoi44.83%5.20%43.97%32.36%8.89%89.75%41.60%32.43%13.48%53.23%46.12%27.60%
South Garo Hills22.00%3.45%15.63%5.96%2.62%90.16%13.25%27.03%0.92%54.16%7.51%2.07%
West Garo Hills27.64%2.39%26.78%14.96%5.07%82.84%55.20%45.87%6.60%68.08%20.07%6.22%
West Khasi Hills43.01%3.38%42.73%24.43%5.67%93.13%19.37%27.99%9.96%46.45%39.44%25.67%
Mizoram
Aizawl16.98%1.80%8.31%2.02%2.23%12.43%7.87%7.23%0.24%10.72%3.59%3.19%
Champhai24.48%2.37%21.97%8.26%2.86%45.79%12.38%4.00%0.10%28.41%12.01%4.07%
Kolasib22.05%2.47%15.13%8.82%3.68%29.05%12.44%3.75%0.94%27.28%13.26%4.98%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
270 271
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Mizoram
Lawangtlai29.52%2.99%28.53%23.30%10.68%53.95%33.42%25.68%24.92%47.94%40.11%12.56%
Lunglei20.27%2.38%17.89%9.20%2.80%45.06%20.88%6.20%0.77%29.80%15.38%7.54%
Mamit25.25%2.49%24.23%17.13%6.74%59.68%33.72%21.09%14.42%49.29%33.31%12.32%
Saiha29.80%3.91%25.89%6.70%3.84%37.87%19.54%8.09%0.57%23.01%18.68%6.31%
Serchhip21.01%2.48%13.98%4.27%1.27%35.44%7.94%5.01%0.26%15.37%8.35%1.51%
Nagaland
Mon35.64%1.75%49.71%23.34%9.34%94.61%17.64%27.87%13.44%91.32%62.98%51.16%
Dimapur22.18%1.60%27.10%13.02%5.15%34.36%31.72%22.31%1.58%54.24%10.28%21.31%
Kiphire32.95%3.70%40.55%15.08%5.47%89.66%18.63%15.84%1.74%79.09%53.41%42.36%
Kohima18.57%1.30%21.11%6.34%1.91%46.67%29.45%22.06%0.70% 44.00%16.15%10.57%
Longleng27.53%1.43%36.08%11.93%4.96%95.97%26.78%60.65%2.55%86.74%50.96%34.05%
Mokokchung15.88%1.09%15.23%7.38%1.60%59.97%9.23%17.50%0.51%67.55%13.85%18.41%
Peren23.21%3.25%39.13%14.39%3.09%84.19%24.89%40.95%2.25%79.08%31.96%22.13%
Phek20.13%3.68%34.95%12.60%3.98%91.59%15.34%5.33%1.45%91.23%53.04%28.79%
Tuensang33.51%4.01%48.04%18.24%4.96%91.79%26.21%7.56%2.67%84.13%49.07%37.42%
Wokha15.96%1.53%24.73%9.21%2.49%61.40%24.69%32.91%1.90%49.65%23.53%18.51%
Zunheboto21.13%0.66%28.22%11.12%5.26%86.73%18.02%16.28%0.37%83.54%40.52%33.61%
Odisha
Anugul33.80%3.95%15.50%11.47%2.73%78.62%64.15%30.10%11.63%41.02%17.20%7.43%
Balangir44.84%2.19%10.63%16.15%4.01%90.57%85.50%22.09%15.62%65.84%16.75%6.08%
Baleshwar37.66%1.09%23.43%11.12%3.82%86.32%61.76%10.07%9.55%69.26%12.37%13.07%
Bargarh40.46%2.45%13.01%11.26%2.86%86.41%72.08%19.01%15.13%62.18%14.23%7.29%
Bauda42.36%2.80%15.77%15.57%3.04%88.36%83.30%24.27%11.24%72.69%20.24%7.37%
Bhadrak39.11%1.57%24.06%9.97%3.36%89.25%75.92%9.78%11.46%75.15%14.10%23.25%
Cuttack25.74%1.28%18.90%7.36%2.18%66.97%60.43%11.61%7.21%33.80%10.00%13.17%
Debagarh42.75%2.56%23.77%19.80%5.01%93.67%67.72%22.01%17.52%67.30%31.08%16.51%
Dhenkanal35.61%1.66%15.82%14.00%3.12%79.58%66.62%48.45%10.94%51.33%19.34%13.87%
Gajapati36.64%3.48%25.61%26.87%9.30%82.77%62.21%38.62%11.40%45.93%39.70%7.90%
Ganjam28.15%2.14%16.94%20.09%3.80%65.10%58.50%17.83%8.66%28.10%15.95%5.79%
Jagatsinghapur25.18%1.77%15.11%6.46%0.81%86.79%67.52%8.99%6.56%39.99%12.13%7.45%
Jajapur37.40%2.06%23.27%9.10%2.68%83.78%68.41%16.62%6.
09%46.58%12.28%10.71%
Jharsuguda38.75%1.55%12.11%8.70%1.65%71.90%59.49%18.46%7.35%53.61%8.10%5.10%
Kalahandi41.41%1.83%21.33%28.84%8.47%92.52%85.40%27.27%31.17%75.93%27.94%16.98%
Kandhamal42.69%3.57%27.12%19.50%5.43%93.88%83.95%52.94%20.93%63.14%45.09%6.69%
Kendrapara33.19%1.69%20.51%8.69%1.70%87.72%73.45%11.93%6.62%58.51%16.17%6.76%
Kendujhar44.14%3.26%30.18%20.51%7.26%82.36%78.48%25.69%23.54%69.82%28.70%16.17%
Khordha22.51%1.02%16.72%11.36%3.80%55.25%52.48%22.63%3.32%28.12%9.53%8.64%
Koraput47.60%2.56%24.91%38.82%15.58%81.10%82.22%20.06%22.62%59.13%39.72%16.35%
Malkangiri58.90%6.94%28.08%43.56%17.12%94.63%83.26%19.56%11.53%76.07%35.56%9.26%
Mayurbhanj43.20%1.83%20.87%21.83%5.52%89.72%82.67%27.28%25.43%80.50%30.03%19.62%
Nabarangapur53.72%4.06%33.56%34.15%15.40%90.73%83.31%26.92%29.89%80.22%33.74%19.31%
Nayagarh25.31%1.77%19.48%16.21%2.74%78.38%67.10%28.52%5.93%39.59%14.29%10.04%
Nuapada49.05%2.52%16.59%21.37%7.34%90.56%80.02%18.59%16.83%71.33%17.54%5.85%
Puri22.94%1.61%15.43%5.88%1.23%79.80%59.61%15.30%4.04%34.29%7.57%6.11%
Rayagada46.62%5.50%25.96%36.80%12.98%83.60%76.90%16.26%19.18%59.51%36.58%15.80%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Odisha
Sambalpur40.90%1.56%11.42%14.73%2.51%74.24%65.98%16.98%10.33%62.14%13.77%4.01%
Subarnapur43.04%2.03%14.17%11.83%1.28%85.30%82.59%17.64%9.16%64.12%17.77%6.63%
Sundargarh40.96%2.09%13.07%11.81%3.85%75.77%63.11%21.48%13.75%60.64%13.46%5.47%
Punjab
Amritsar20.38%1.14%9.69%8.47%4.67%28.99%20.63%0.11%0.65%22.66%1.58%4.49%
Barnala21.79%2.16%13.08%10.73%1.72%49.50%13.50%1.00%0.05%21.38%1.32%3.79%
Bathinda19.29%1.21%12.20%9.52%3.36%39.57%14.05%6.08%0.45%19.50%2.24%4.38%
Faridkot22.24%0.60%6.98%6.64%4.00%36.67%13.96%6.96%0.66%25.51%0.80%1.55%
Fatehgarh Sahib22.08%1.98%12.45%5.80%1.08%32.85%14.96%0.24%0.06%9.71%1.14%1.26%
Firozpur23.81%1.61%11.44%12.54%4.53%51.17%21.92%8.08%0.39%41.52%1.97%3.30%
Gurdaspur20.02%1.63%15.66%4.56%1.03%31.90%24.58%0.70%0.31%18.21%1.90%3.11%
Hoshiarpur24.71%1.39%9.71%3.92%1.58%39.40%25.27%0.39%0.39%14.49%1.31%2.99%
Jalandhar19.74%1.43%11.52%3.57%0.94%23.34%10.96%0.15%0.25%11.43%0.38%3.83%
Kapurthala19.49%1.20%8.44%7.32%2.07%25.75%17.95%1.33%3.31%11.96%2.69%3.54%
Ludhiana21.11%0.89%10.18%6.05%3.24%22.86%15.37%0.20%0.05%9.82%2.41%4.66%
Mansa30.27%0.88%13.52%12.78%2.15%61.60%24.02%5.16%0.29%32.02%3.05%2.10%
Moga22.62%2.25%13.50%11.51%3.17%47.44%12.89%3.21%0.34%33.67%1.14%5.12%
Muktsar25.93%2.41%14.95%11.26%2.73%40.97%19.14%3.87%0.27%22.52%2.44%3.15%
Patiala19.09%1.55%20.03%4.73%1.26%35.16%11.15%0.98%0.08%14.40%1.78%3.42%
Rupnagar21.86%1.65%8.61%3.87%0.82%35.30%18.39%0.30%0.00%7.66%0.65%1.38%
Sahibzada Ajit Singh Nagar22.42%1.80%8.60%4.97%3.46%24.49%20.39%0.82%0.66%10.31%2.46%3.55%
Sangrur23.86%1.86%8.52%5.16%1.31%49.11%17.71%0.39%0.27%15.45%1.14%2.82%
Shaheed Bhagat Singh Nagar26.83%0.66%17.72%7.87%2.12%51.49%11.52%1.40%0.25%20.51%1.66%5.89%
Tarn Taran24.32%1.82%21.46%11.25%3.73%46.63%20.01%0.42%0.41%27.10%1.76%3.93%
Rajasthan
Ajmer41.69%2.07%20.42%13.27%5.21%52.66%30.22%30.52%1.21%11.71%8.53%1.16%
Alwar45.89%3.68%36.24%11.69%6.97%80.12%60.63%15.40%2.58%39.30%11.94%6.13%
Banswara50.98%2.16%24.66%23.63%15.91%87.98%79.37%28.59%31.05%76.14%49.79%2.29%
Baran45.22%2.03%21.55%15.33%8.22%75.47%66.41%26.39%6.62%57.92%21.47%2.25%
Barmer44.71%4.28%41.71%34.79%20.03%86.28%79.27%42.80%33.33%55.15%48.80%5.50%
Bharatpur45.29%4.00%40.70%14.27%12.73%85.37%65.59%47
.94%8.92%55.22%22.32%7.42%
Bhilwara36.37%2.27%21.94%20.73%8.35%73.94%65.65%35.85%2.96%23.93%15.07%2.48%
Bikaner40.17%2.57%26.57%18.03%9.53%63.22%34.17%15.75%8.88%23.56%15.61%3.31%
Bundi45.04%2.35%23.58%19.40%6.11%78.26%71.58%23.53%7.37%41.67%20.78%3.03%
Chittaurgarh36.60%2.05%24.10%21.81%7.27%75.25%68.18%24.82%3.89%40.03%12.56%3.68%
Churu40.32%1.88%33.51%16.46%5.56%70.79%23.77%21.38%4.47%21.61%22.10%7.48%
Dausa43.31%3.96%29.83%11.10%2.05%85.09%63.56%20.57%9.21%37.73%22.40%5.29%
Dhaulpur51.37%4.80%34.64%15.56%9.73%81.23%68.89%19.98%7.59%45.38%23.14%2.03%
Dungarpur54.65%2.31%24.20%22.01%8.99%86.08%68.18%32.89%21.75%70.78%43.55%2.15%
Ganganagar34.35%2.55%18.38%13.18%2.84%62.55%24.23%8.41%5.86%44.82%7.56%3.55%
Hanumangarh36.57%2.64%25.20%14.73%5.09%75.74%43.52%5.24%5.07%48.49%11.00%3.83%
Jaipur38.74%2.79%20.83%7.14%3.63%49.69%41.10%15.18%1.77%19.59%9.61%4.22%
Jaisalmer42.84%4.19%41.10%37.49%23.47%85.02%62.56%52.83%24.05%44.65%37.72%8.26%
Jalor51.69%3.36%32.48%24.73%14.06%74.91%60.58%30.70%16.86%38.23%36.93%3.91%
Jhalawar42.08%2.38%21.49%19.23%7.92%73.49%62.50%39.15%3.79%54.00%24.01%1.42%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
272 273
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Rajasthan
Jhunjhunun36.03%2.73%21.14%8.96%4.30%49.42%33.05%16.68%3.73%22.97%12.49%4.53%
Jodhpur39.28%2.88%25.29%19.51%12.09%57.83%47.51%30.85%8.76%21.49%16.78%3.47%
Karauli49.90%5.32%32.42%16.27%6.82%86.95%77.59%32.79%4.82%53.66%32.30%6.80%
Kota37.26%1.23%14.20%10.83%3.99%40.92%47.66%11.04%1.36%24.04%9.41%2.33%
Nagaur39.30%1.27%21.98%16.58%6.11%76.28%44.85%38.16%9.41%18.07%18.10%5.09%
Pali43.20%3.75%21.25%18.05%7.63%58.69%48.85%30.91%4.38%19.58%11.28%2.25%
Pratapgarh49.93%3.41%28.36%26.97%13.40%86.94%84.09%48.43%24.35%76.07%37.04%3.72%
Rajsamand41.26%3.32%25.31%19.14%6.28%77.12%67.98%32.89%3.82%22.30%22.51%2.42%
Sawai Madhopur45.13%4.71%30.54%13.76%8.33%83.46%63.14%31.10%13.65%41.41%24.89%5.73%
Sikar36.03%3.07%24.97%10.01%5.66%56.44%33.77%13.73%3.27%14.39%10.85%3.57%
Sirohi50.03%5.10%30.34%25.37%15.32%63.89%61.63%26.43%14.47%35.04%30.16%8.34%
Tonk43.00%1.79%20.23%14.48%5.64%79.07%64.52%32.24%2.01%31.53%14.97%1.15%
Udaipur53.49%3.74%25.75%28.43%15.14%77.51%71.18%39.74%17.49%54.06%35.41%4.15%
Sikkim
East Sikkim13.74%0.91%7.35%8.09%1.66%29.41%15.07%1.82%0.98%24.07%8.95%13.06%
North Sikkim10.86%0.45%5.11%11.78%1.56%52.23%9.63%2.20%0.27%33.57%12.77%6.41%
South Sikkim10.33%0.55%2.86%6.63%1.03%48.59%5.15%0.53%0.20%20.37%6.23%3.35%
West Sikkim16.26%1.82%3.99%9.34%1.27%61.32%6.04%5.55%0.49%37.98%13.68%3.94%
Tamil Nadu
Ariyalur28.02%1.03%5.60%10.86%0.55%50.41%71.61%7.50%1.33%32.58%4.01%8.98%
Chennai17.62%0.26%6.35%1.71%1.13%1.66%17.16%21.28%0.32%5.84%0.44%4.18%
Coimbatore23.24%0.42%3.58%4.71%1.04%11.84%41.86%6.31%1.13%14.96%2.60%6.16%
Cuddalore30.95%1.71%5.08%6.29%0.85%45.94%60.18%7.33%0.77%36.06%4.05%5.92%
Dharmapuri29.13%1.01%3.90%8.01%1.37%22.10%62.62%10.87%1.07%14.82%2.74%7.21%
Dindigul26.44%1.12%3.66%6.60%1.11%32.26%58.44%6.07%2.37%20.17%4.65%5.04%
Erode19.30%0.34%6.16%8.45%0.86%8.54%37.18%3.76%1.11%16.82%2.75%4.06%
Kancheepuram19.44%1.60%6.95%3.10%1.17%11.84%32.89%22.19%0.12%15.61%1.35%3.12%
Kanniyakumari18.00%1.22%7.97%2.26%0.46%39.08%14.37%8.34%0.57%20.20%2.15%5.68%
Karur29.00%0.87%7.99%8.29%1.55%19.48%52.26%7.95%1.04%13.39%3.15%3.80%
Krishnagiri32.39%2.54%6.22%6.28%0.88%35.35%56.02%12.83%0.84%17.28%2.00%3.68%
Madurai19.54%0.96%11.92%6.17%0.76%19.16%43.59%26.42%0
.67%16.57%3.18%9.27%
Nagappattinam30.29%1.56%9.13%6.31%1.75%38.83%58.14%12.59%0.90%39.12%4.50%6.67%
Namakkal19.23%0.17%7.04%7.67%0.89%10.46%48.62%8.71%0.41%17.88%3.05%10.19%
Perambalur27.08%1.77%10.06%8.37%1.65%35.12%62.18%18.12%0.90%23.11%4.09%6.13%
Pudukkottai33.00%0.78%14.04%7.06%0.92%59.23%64.81%24.20%2.30%30.60%4.07%8.48%
Ramanathapuram26.89%1.00%13.19%6.16%2.32%39.93%52.32%30.43%1.57%24.02%5.47%9.80%
Salem22.85%0.67%9.96%9.96%1.45%20.05%53.54%10.37%1.06%16.15%4.01%6.73%
Sivaganga30.26%1.12%5.56%6.50%1.40%43.19%51.83%18.26%1.41%21.83%5.15%5.94%
Thanjavur28.00%1.68%4.77%5.32%0.96%51.45%53.44%4.47%1.85%39.48%5.11%5.66%
The Nilgiris24.95%0.31%4.16%3.53%0.14%23.23%36.16%7.73%2.36%19.09%4.61%4.83%
Theni20.35%0.82%6.59%7.70%1.63%20.42%54.45%6.62%2.50%18.42%6.18%10.70%
Thiruvallur23.28%1.04%2.04%4.86%0.70%9.81%33.99%17.92%0.72%11.18%2.20%5.63%
Thiruvarur30.32%1.65%6.11%4.92%0.62%54.80%54.40%4.79%2.28%46.45%3.88%5.34%
Thoothukkudi24.51%1.17%12.94%10.28%1.06%27.90%48.15%9.74%1.38%24.39%6.40%12.65%
Tiruchirappalli26.55%1.78%6.79%6.29%0.77%25.26%53.24%5.14%0.78%17.24%3.56%5.88%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Tamil Nadu
Tirunelveli27.74%1.70%7.25%9.54%1.61%21.99%54.46%8.68%0.94%27.59%5.73%8.73%
Tiruppur21.40%1.31%3.33%9.71%0.46%6.12%48.60%5.89%0.39%17.19%2.59%5.82%
Tiruvannamalai29.14%0.79%5.97%8.56%0.62%32.40%68.78%5.81%1.09%18.30%5.11%6.41%
Vellore28.68%1.90%3.06%6.20%0.37%19.36%45.43%19.78%0.46%14.36%2.84%5.19%
Viluppuram30.32%1.49%9.44%10.32%1.08%40.45%68.07%7.78%0.70%29.44%2.97%4.54%
Virudunagar21.65%1.33%10.06%11.89%2.22%21.32%63.89%20.07%1.23%30.08%6.78%15.52%
Telangana
Adilabad40.26%2.25%15.24%23.65%3.80%61.11%69.58%25.82%2.32%38.99%21.47%9.66%
Hyderabad23.48%1.40%7.51%8.78%2.69%3.51%25.89%7.75%0.00%9.05%3.63%9.24%
Karimnagar29.06%0.97%10.37%17.42%0.93%23.63%41.85%35.83%0.48%24.58%9.97%7.54%
Khammam28.14%1.02%6.75%18.05%1.80%28.55%49.96%26.17%2.53%22.73%14.14%7.21%
Mahbubnagar39.19%2.34%18.29%22.40%3.55%55.13%69.41%35.21%1.66%30.70%20.87%4.66%
Medak36.40%2.72%13.29%16.75%1.47%53.00%45.82%17.71%1.56%47.47%15.26%11.26%
Nalgonda30.17%0.69%10.48%22.15%1.59%37.92%50.24%54.80%2.09%21.30%16.21%5.67%
Nizamabad35.65%2.81%15.13%19.36%1.96%42.95%55.91%21.32%2.02%42.15%14.73%8.63%
Rangareddy28.51%0.53%9.80%8.25%1.87%14.63%50.28%26.38%0.35%11.54%7.63%5.78%
Warangal30.01%0.45%6.18%12.93%1.27%36.52%53.12%33.89%1.43%36.91%15.59%6.47%
Tripura
Dhalai28.89%1.32%15.31%17.14%3.20%78.93%49.70%39.55%11.27%87.02%30.87%3.40%
North Tripura38.52%2.93%21.52%15.63%3.94%77.48%51.40%40.64%13.64%72.94%30.01%4.73%
South Tripura27.03%0.52%13.72%10.69%1.91%73.33%35.81%13.35%7.10%85.02%18.97%3.65%
West Tripura24.21%1.00%9.86%7.67%1.44%54.93%28.11%3.57%3.86%67.71%11.82%3.24%
Uttar Pradesh
Agra40.81%5.23%33.41%14.22%12.76%56.66%56.40%33.21%4.74%82.05%9.33%4.05%
Aligarh45.12%7.29%35.73%14.37%14.97%62.18%58.92%4.05%13.63%79.88%11.67%5.43%
Allahabad42.30%4.18%37.28%11.48%7.58%64.60%66.75%10.95%19.20%59.28%7.20%4.18%
Ambedkar Nagar49.96%5.02%33.52%9.57%4.77%86.13%78.55%0.25%25.47%82.14%6.26%3.19%
Auraiya38.01%2.96%34.36%7.93%6.76%72.91%64.77%3.51%29.71%75.47%11.65%2.69%
Azamgarh44.81%3.86%38.63%9.48%5.31%78.35%75.01%0.58%14.72%75.25%9.06%4.57%
Baghpat37.58%2.68%24.09%11.16%9.93%60.80%33.77%1.17%7.12%60.16%11.38%3.32%
Bahraich62.12%6.65%53.71%41.43%26.33%84.77%86.04%0.97%66.52%85.24%23.19%22.46%
Ballia41.52%6.02%41.81%11.63%8.59%77
.01%73.73%1.67%29.27%75.82%11.39%3.69%
Balrampur58.81%6.13%52.52%40.91%31.13%90.17%85.34%1.42%60.33%76.65%13.84%20.41%
Banda35.16%3.38%31.38%17.91%12.28%85.14%71.43%9.15%31.24%86.92%17.74%1.93%
Bara Banki44.29%3.36%33.32%25.42%14.20%71.43%78.79%1.89%48.12%76.27%17.19%7.15%
Bareilly43.49%5.04%27.52%27.70%19.87%58.58%43.05%1.17%29.16%51.39%15.39%4.35%
Basti48.42%4.55%43.66%18.12%8.79%80.19%81.54%0.12%29.06%75.72%8.70%3.75%
Bhadohi52.87%6.70% 40.60%13.75%9.45%74.68%77.13%15.76%17.75%82.87%10.61%2.84%
Bijnor45.26%3.36%32.15%16.33%9.93%66.48%36.28%0.99%19.20%50.70%14.07%4.48%
Budaun52.10%7.73%44.52%29.75%28.94%79.90%68.28%0.59%45.57%74.23%22.77%5.45%
Bulandshahr43.18%5.45%37.02%16.11%11.05%69.46%49.68%0.74%10.49%68.25%12.35%6.02%
Chandauli48.22%3.81%41.64%11.63%5.34%78.13%67.76%11.39%26.42%66.21%8.36%3.27%
Chitrakoot52.06%6.12%39.94%19.13%14.68%89.27%84.31%21.67%30.39%89.82%25.09%2.34%
Deoria42.74%3.85%35.19%13.26%5.28%68.11%75.19%0.57%28.21%67.25%9.90%3.43%
Etah43.38%6.37%38.31%11.78%9.26%75.81%74.52%3.07%31.98%70.69%16.59%4.06%
Etawah41.08%3.78%33.88%6.72%5.36%73.89%69.50%3.26%7.80%64.23%9.51%4.72%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
274 275
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Uttar Pradesh
Faizabad47.61%5.18%36.28%14.46%8.55%74.97%74.44%1.46%31.60%73.66%10.13%4.28%
Farrukhabad44.07%7.58%40.83%15.83%10.91%77.63%69.47%0.91%37.57%69.18%15.54%5.98%
Fatehpur43.09%2.38%32.41%17.67%13.37%82.11%72.63%6.48%53.52%83.63%15.94%3.05%
Firozabad42.55%5.74%36.00%14.72%11.54%63.07%63.24%7.77%12.57%57.24%14.39%6.41%
Gautam Buddha Nagar30.63%4.42%27.50%7.84%4.82%28.76%40.19%31.11%1.10%43.22%8.65%4.52%
Ghaziabad33.85%3.85%28.11%10.72%7.74%24.83%30.84%14.92%2.57%39.08%10.03%3.96%
Ghazipur45.45%5.27%36.83%12.59%7.42%85.54%80.41%2.34%29.46%82.35%7.85%3.59%
Gonda56.46%7.87%51.16%27.70%16.92%79.85%89.20%0.14%58.55%76.44%12.50%9.10%
Gorakhpur37.88%4.58%32.45%14.66%5.09%58.43%63.48%0.68%22.57%59.48%9.04%5.51%
Hamirpur40.86%3.11%32.44%12.23%7.11%80.74%51.50%7.38%23.38%68.64%17.24%2.77%
Hardoi48.21%5.58%41.01%21.28%16.25%77.59%70.86%2.23%56.08%81.49%14.49%6.21%
Jalaun34.07%3.24%27.37%10.11%7.52%68.88%50.93%8.60%10.10%64.68%12.92%2.24%
Jaunpur50.02%6.69%40.77%9.34%7.91%78.46%74.97%5.52%19.33%81.43%6.24%2.52%
Jhansi35.06%3.13%21.36%11.00%5.37%58.58%51.69%12.02%9.00%43.90%11.95%2.46%
Jyotiba Phule Nagar49.12%5.03%31.61%18.33%13.70%70.90%47.17%0.64%23.28%54.64%18.27%3.37%
Kannauj46.25%6.51%41.17%18.11%11.26%81.03%76.31%5.15%28.51%72.38%17.08%6.18%
Kanpur Dehat41.50%3.38%32.78%10.09%6.81%83.06%70.30%7.37%54.36%84.31%12.98%2.40%
Kanpur Nagar27.13%1.72%18.74%7.22%5.72%29.90%35.19%4.82%14.84%32.04%5.41%2.89%
Kansiram Nagar48.56%8.99%44.71%24.01%14.96%81.71%77.29%0.78%43.37%64.11%14.75%6.55%
Kaushambi50.64%5.75%40.03%32.16%19.96%82.71%80.19%10.69%49.63%83.38%16.71%5.39%
Kheri54.20%6.75%44.49%32.03%23.84%84.17%74.02%1.46%57.15%85.36%21.65%4.91%
Kushinagar46.71%5.56%46.18%16.96%6.34%76.17%75.94%2.43%49.41%70.04%10.98%3.85%
Lalitpur41.38%4.01%24.82%14.09%6.08%82.11%77.86%18.66%19.81%74.81%22.37%2.34%
Lucknow29.03%2.26%13.64%8.70%4.66%25.29%34.38%1.68%6.46%30.15%4.63%3.13%
Mahamaya Nagar39.45%3.93%33.08%11.82%8.23%71.38%67.56%5.95%10.92%94.30%10.47%3.24%
Maharajganj51.40%4.75%43.32%23.04%8.54%80.85%79.17%1.59%43.52%72.96%8.30%4.50%
Mahoba46.29%2.36%28.39%13.87%6.44%84.61%63.87%21.25%17.59%63.04%17.53%2.08%
Mainpuri39.83%7.23%38.41%8.79%5.74%76.13%71.11%2.25%16.22%71.05%12.55%8.49%
Mathura43.50%5.88%34.68%10.93%10.17%69.06%55.70%27.26%3.46%88.53%9
.74%4.18%
Mau48.11%4.99%37.62%11.62%9.25%78.86%71.84%0.46%14.96%73.45%9.15%2.81%
Meerut35.21%4.21%26.44%15.86%14.13%39.02%30.57%0.81%4.04%21.64%8.40%6.29%
Mirzapur47.97%5.92%43.84%14.10%9.04%82.91%76.23%18.01%27.87%88.10%8.89%3.36%
Moradabad48.39%6.04%29.54%24.07%16.63%57.49%45.01%0.83%20.65%47.55%18.01%3.64%
Muzaffarnagar42.70%3.61%31.56%18.20%15.29%64.10%42.97%1.18%5.78%46.90%14.77%3.49%
Pilibhit49.48%4.32%33.33%19.34%17.69%71.61%59.92%0.52%47.21%73.85%17.06%2.22%
Pratapgarh43.73%3.45%32.21%15.97%5.73%76.33%83.47%8.88%26.16%74.16%8.62%4.79%
Rae Bareli40.26%3.06%27.22%18.87%9.25%77.75%81.24%3.19%25.98%81.23%11.29%3.17%
Rampur44.55%3.43%25.37%32.04%22.76%69.77%43.32%1.48%17.95%60.51%14.50%3.03%
Saharanpur44.14%5.16%29.42%19.97%12.40%65.30%45.50%3.04%5.44%47.99%13.82%3.51%
Sant Kabir Nagar50.38%5.73%41.05%17.14%8.58%82.41%83.06%0.35%33.11%81.33%13.02%5.52%
Shahjahanpur50.24%7.45%38.78%20.86%20.87%76.42%65.72%0.58%45.43%80.35%18.52%3.50%
Shrawasti59.14%9.37%58.70%44.88%30.77%90.63%89.55%3.59%70.50%92.43%21.98%12.84%
Siddharth Nagar56.66%6.26%52.55%29.62%17.61%80.30%84.51%0.55%33.86%76.28%12.27%9.63%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Uttar Pradesh
Sitapur52.08%6.39%40.61%23.77%16.81%81.58%82.58%1.85%69.63%84.48%20.20%4.38%
Sonbhadra47.44%4.57%38.34%22.19%12.79%83.23%78.31%21.85%47.10%78.77%17.48%3.38%
Sultanpur44.66%6.65%34.71%16.01%8.35%83.02%81.07%4.97%22.56%83.62%6.38%4.29%
Unnao43.51%4.17%36.50%17.75%10.21%77.05%69.33%3.21%51.63%73.68%12.58%4.81%
Varanasi41.60%3.49%31.03%9.81%7.64%52.71%49.87%5.34%10.24%65.11%5.32%3.87%
Uttarakhand
Almora39.37%2.32%30.67%6.14%0.19%74.02%33.89%25.16%2.65%74.57%26.50%4.59%
Bageshwar36.18%1.64%30.70%6.44%0.51%80.84%31.44%21.81%2.38%52.37%29.73%2.41%
Chamoli28.36%2.53%29.02%5.35%0.14%67.63%34.99%12.31%4.22%55.68%26.52%3.27%
Champawat32.62%3.18%30.93%8.73%2.47%72.53%39.85%13.76%7.16%58.32%28.89%3.93%
Dehradun28.91%1.56%20.45%6.68%1.81%16.96%23.18%0.94%0.51%11.88%5.72%2.92%
Garhwal26.03%1.18%21.19%5.01%0.85%65.05%31.90%17.64%1.74%45.24%15.31%5.88%
Haridwar37.91%4.29%36.53%16.58%10.22%59.83%40.42%1.56%2.02%29.45%9.26%10.57%
Nainital30.93%2.32%26.09%5.79%3.88%40.91%27.20%4.90%1.22%25.17%12.45%9.10%
Pithoragarh23.30%2.33%24.25%6.79%0.41%65.45%37.64%19.19%2.54%51.22%23.97%3.32%
Rudraprayag23.90%0.77%23.52%6.52%0.38%71.63%31.31%22.72%0.92%45.34%20.95%3.78%
Tehri Garhwal35.12%2.61%29.36%8.07%0.00%68.22%33.80%28.13%1.83%39.70%21.15%1.64%
Udham Singh Nagar36.54%2.87%33.59%14.57%8.88%51.85%39.69%2.59%3.07%40.45%7.79%13.03%
Uttarkashi33.03%2.80%26.45%7.06%1.72%75.32%53.14%29.49%6.42%51.63%34.69%3.17%
West Bengal
Bankura43.81%2.07%8.52%16.66%3.31%84.95%68.59%17.07%9.80%57.49%15.34%8.19%
Barddhaman34.95%0.79%8.36%17.45%2.75%71.31%51.13%11.86%4.89%44.78%14.89%12.85%
Birbhum41.42%0.76%8.70%22.56%3.23%83.26%68.64%10.92%3.63%65.88%21.41%11.53%
Dakshin Dinajpur32.69%2.01%11.72%17.93%4.22%88.37%52.72%2.68%5.38%78.99%14.68%9.33%
Darjeeling25.38%0.68%15.61%8.52%2.26%43.32%35.52%32.81%3.61%32.83%11.53%13.79%
Howrah30.06%1.99%11.02%11.03%4.73%55.75%39.74%9.60%2.05%29.96%8.97%13.40%
Hugli28.40%1.21%10.15%13.61%1.88%69.37%42.61%6.15%4.73%34.72%10.75%14.83%
Jalpaiguri35.67%0.92%13.86%15.57%3.19%73.38%48.23%17.56%9.23%54.43%18.29%25.61%
Koch Bihar35.73%2.06%13.92%18.78%3.32%86.54%47.04%4.14%9.51%79.35%16.40%16.42%
Kolkata16.53%1.21%1.39%6.97%2.60%8.78%53.70%6.06%0.13%5.98%5.24%10.35%
Maldah40.61%2.97%27.89%24.87%5.48%83.93%53.47%18.
05%4.04%76.04%15.85%12.12%
Murshidabad38.65%1.31%21.12%20.62%4.65%82.40%46.16%7.16%6.93%56.76%16.82%12.09%
Nadia23.68%1.07%7.56%16.39%1.22%71.87%29.58%11.10%3.68%49.77%8.19%7.09%
North 24 Parganas20.28%0.65%11.18%9.39%2.93%51.72%31.50%9.79%2.49%41.61%8.06%12.27%
Pashchim Medinipur42.72%1.48%10.17%14.12%2.65%86.33%56.39%10.54%3.55%70.43%13.76%9.68%
Purba Medinipur31.93%1.43%13.56%9.90%2.88% 90.06%29.67%9.69%2.12%66.02%8.99%10.12%
Puruliya62.78%3.17%20.07%20.91%4.85%93.01%87.65%41.20%17.95%69.90%22.70%24.22%
South 24 Parganas32.36%2.10%25.02%16.27%6.12%82.94%47.38%10.71%11.95%69.01%21.93%20.73%
Uttar Dinajpur41.32%2.79%37.21%29.15%13.29%89.88%67.43%2.92%9.24%75.07%21.75%24.41%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
276 277
Uncensored Headcount RatioHealthEducationStandard of Living
UT DistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Andaman
& Nicobar
Nicobar14.48%0.31%12.16%3.76%4.09%47.10%13.98%0.01%0.27%75.54%10.41%1.61%
North & Middle Andaman25.22%0.88%5.74%7.50%0.44%55.71%49.77%10.84%7.30%68.89%15.65%2.61%
South Andaman21.60%0.87%4.11%3.96%0.77%9.78%15.44%5.02%1.16%15.18%3.37%1.15%
Chandigarh23.11%1.16%11.05%5.83%1.76%4.85%19.04%2.35%0.48%6.40%2.71%3.97%
Dadra & Nagar Haveli45.00%2.01%13.44%7.76%7.73%46.62%68.11%26.95%2.74%57.06%20.81%10.92%
Daman
& Diu
Daman18.01%0.85%15.42%7.61%5.57%4.03%36.94%14.19%0.00%7.38%17.32%14.94%
Diu32.21%1.09%11.84%7.38%1.43%29.49%26.49%1.88%0.16%10.80%4.69%3.10%
Delhi
Central Delhi16.88%1.35%6.56%5.62%3.97%0.76%24.32%7.81%0.55%9.73%5.38%7.21%
East Delhi18.70%2.30%13.05%6.08%2.70%0.59%20.07%7.20%0.05%18.34%5.14%14.66%
New Delhi18.72%1.58%10.30%3.09%1.63%5.25%35.22%16.13%0.22%13.07%4.49%8.04%
North Delhi25.88%2.48%11.69%4.28%3.28%1.57%22.33%12.10%1.06%14.70%6.56%11.33%
North East27.75%2.16%22.40%7.71%6.38%1.03%25.88%6.49%0.00%16.54%4.41%9.21%
North West23.01%2.39%16.48%8.47%3.56%3.79%34.51%26.01%0.87%7.82%7.81%11.38%
South Delhi22.86%2.22%11.36%7.16%1.54%3.59%28.36%27.06%0.20%4.80%8.83%7.30%
South West26.56%1.01%19.35%3.80%0.65%2.26%31.00%50.48%0.02%14.07%2.72%5.94%
West Delhi18.45%1.66%8.53%3.05%1.47%0.67%13.27%10.34%0.00%5.81%3.68%3.70%
Jammu & Kashmir
Anantnag18.29%1.84%11.54%7.95%4.54%32.14%43.44%6.42%3.49%19.09%16.94%4.77%
Badgam21.50%2.00%3.78%8.70%3.93%41.73%48.04%4.95%0.95%17.69%9.17%1.54%
Bandipore21.13%2.31%9.54%9.70%6.24%48.80%61.59%11.03%4.87%20.54%12.94%4.18%
Baramula26.63%2.08%10.09%5.48%3.55%29.79%36.83%9.84%0.10%20.98%11.23%3.79%
Doda28.95%2.63%31.68%10.44%9.51%86.66%54.65%15.69%6.07%69.62%47.18%5.40%
Ganderbal23.20%2.74%8.01%6.98%3.46%33.55%41.55%6.54%0.25%14.85%9.85%3.68%
Jammu25.68%0.77%9.35%5.72%1.43%28.64%36.88%4.53%0.12%17.62%4.33%4.11%
Kathua30.89%2.18%7.63%5.40%2.28%62.32%58.40%20.40%0.36%37.67%15.40%3.79%
Kishtwar28.85%3.58%22.78%12.60%7.10%75.30%58.80%14.50%12.91%56.89%34.63%2.75%
Kulgam19.88%2.67%8.40%8.34%4.00%53.67%45.54%3.37%2.85%21.09%14.39%2.75%
Kupwara34.54%2.78%16.62%4.80%4.18%53.29%59.10%13.13%6.18%28.39%23.62%3.20%
Pulwama18.41%1.94%5.20%4.64%1.53%24.56%30.24%5.67%1.63%9.50%9.93%2.35%
Punch34.37%2.35%25.34%5.18%3.12%79
.00%44.90%33.02%3.40%52.34%33.28%5.03%
Rajouri36.77%2.04%27.64%7.19%5.02%68.77%60.92%40.82%5.94%45.16%28.27%9.25%
Ramban31.71%2.65%28.32%17.94%12.19%81.93%58.07%23.47%11.30%67.51%44.54%3.66%
Reasi32.53%1.65%27.94%5.46%3.22%84.58%55.11%32.42%9.82%57.31%21.68%3.61%
Samba25.49%1.03%14.05%4.37%2.16%51.66%54.26%7.70%0.67%25.13%12.80%6.06%
Shupiyan20.96%1.74%8.12%6.37%3.41%60.96%39.52%7.20%3.11%17.03%12.14%2.35%
Srinagar15.20%0.89%3.00%4.93%2.08%2.61%31.90%2.97%0.06%8.24%1.63%2.10%
Udhampur38.85%1.77%10.95%8.65%4.67%75.45%65.85%45.06%2.55%57.92%27.04%4.89%
Ladakh
Kargil35.76%2.85%18.19%5.36%3.24%55.96%82.79%18.20%2.48%85.68%10.73%2.15%
Leh (Ladakh)16.66%1.28%4.30%9.00%1.12%11.27%82.32%33.91%0.16%91.01%7.27%1.50%
Lakshadweep31.47%1.96%6.50%0.95%1.43%58.15%0.44%9.31%0.05%1.54%1.02%5.62%
Puducherry
Karaikal27.37%0.58%5.38%4.02%0.49%23.80%30.66%3.56%0.36%28.10%2.69%4.07%
Mahe22.92%0.09%8.25%0.79%0.00%11.93%1.78%10.13%0.18%7.73%0.96%2.75%
Puducherry20.43%0.68%3.48%2.95%1.43%11.71%37.82%5.74%0.21%16.30%1.30%5.78%
Yanam25.96%1.12%8.12%8.81%0.97%6.92%26.09%4.90%0.40%7.01%4.54%4.36%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Andhra Pradesh
Anantapur36.78%1.86%12.06%16.52%2.10%43.92%65.52%35.02%0.62%38.30%11.61%4.55%
Chittoor32.42%0.43%12.15%13.97%1.41%49.27%67.14%28.63%0.87%12.16%11.18%2.98%
East Godavari24.04%1.78%11.14%18.67%1.40%60.91%49.01%22.21%0.38%16.87%8.38%5.11%
Guntur16.48%1.51%6.19%23.15%1.04%26.79%43.86%39.86%0.71%23.95%15.28%5.13%
Krishna25.10%0.83%5.08%20.55%1.18%39.10%44.26%44.85%0.54%30.79%10.37%2.85%
Kurnool42.97%3.97%18.25%22.37%7.37%48.55%55.55%26.76%0.16%38.11%11.10%3.14%
Prakasam28.69%3.51%11.44%24.97%4.05%36.36%48.73%47.48%1.05%14.64%14.34%4.22%
SPSR Nellore24.98%1.56%7.76%16.39%2.16%51.11%56.82%35.83%2.23%14.46%11.74%5.52%
Srikakulam31.98%3.10%9.60%15.45%1.54%69.40%73.72%29.40%2.22%16.87%18.72%2.71%
Visakhapatnam36.32%3.19%16.43%22.12%4.07%80.55%74.79%27.33%1.23%22.17%27.35%4.04%
Vizianagaram39.86%1.92%11.41%21.85%1.00%65.62%82.31%18.68%1.44%21.20%20.77%4.35%
West Godavari20.88%2.12%7.98%19.98%1.73%45.06%43.80%31.34%0.85%21.65%11.22%4.99%
Y.S.R. Kadapa32.01%2.02%7.82%22.61%4.25%35.22%42.46%38.03%0.56%13.39%11.27%2.83%
Arunachal Pradesh
Anjaw11.29%1.41%28.05%19.90%4.71%82.07%56.96%14.80%10.60%86.99%33.65%7.62%
Changlang19.87%2.26%26.43%18.90%11.59%82.19%45.86%35.39%25.13%85.23%17.87%12.88%
Dibang Valley19.65%0.63%26.03%20.81%3.54%72.11%20.93%1.84%10.22%79.22%33.63%11.09%
East Kameng27.86%2.89%43.18%26.70%16.03%85.71%75.81%25.52%70.61%95.48%73.26%43.10%
East Siang13.83%0.41%21.83%11.82%5.09%76.82%29.82%2.91%5.92%83.12%9.81%4.14%
Kurung Kumey29.20%2.68%40.50%29.88%9.51%61.70%54.18%7.30%8.34%91.72%42.78%29.61%
Lohit30.67%4.58%31.35%25.54%10.89%89.51%39.70%12.08%24.39%88.10%24.49%18.61%
Lower Dibang Valley20.31%2.60%28.64%22.37%6.70%70.75%31.54%7.13%36.27%84.99%11.13%16.46%
Lower Subansiri16.89%0.92%19.75%17.78%6.59%37.91%29.19%2.73%1.26%75.92%15.87%21.05%
Papum Pare24.85%1.16%33.85%14.57%8.93%32.99%31.48%15.15%3.25%73.89%14.67%15.26%
Tawang12.87%2.14%34.49%43.74%7.62%60.75%46.25%6.78%3.77%69.72%36.64%28.89%
Tirap25.97%3.50%29.87%19.44%10.04%93.52%51.22%38.53%2.11%93.37%38.63%6.92%
Upper Siang15.36%0.41%19.45%16.98%4.59%93.15%49.53%12.74%8.22%91.64%30.88%11.58%
Upper Subansiri23.93%0.91%35.58%26.29%5.09%73.52%42.22%16.25%14.65%94.90%45.42%30.32%
West Kameng20.98%1.10%32.53%21.09%7.59%47.96%42.52%7.08%1.58%67.46%26.06%20.45%
West Siang16.58%1.29%25.85%11.85%2.69%69.02%30.07%15.
93%6.37%90.24%26.38%16.17%
Assam
Baksa32.77%2.45%23.76%10.33%2.44%86.12%42.45%18.37%17.00%82.31%9.63%10.97%
Barpeta45.30%2.96%31.78%18.34%6.08%83.55%64.70%1.83%29.14%85.91%19.41%22.25%
Bongaigaon39.37%2.15%37.38%17.98%6.39%84.48%57.85%26.25%11.63%82.05%14.40%17.03%
Cachar51.27%4.71%25.59%16.94%9.74%83.84%68.24%53.62%38.89%82.65%38.63%11.43%
Chirang36.90%2.42%31.15%15.45%3.26%85.48%69.57%31.32%29.17%84.46%13.40%18.03%
Darrang44.95%3.18%31.85%24.30%11.33%88.36%55.01%3.27%25.73%84.41%16.02%21.00%
Dhemaji34.38%3.27%25.03%8.55%5.49%92.62%56.74%9.04%38.06% 90.40%18.95%21.27%
Dhubri49.23%2.25%40.18%29.52%14.23%92.48%69.65%10.75%31.67%90.93%34.97%37.89%
Dibrugarh44.86%1.59%19.04%19.08%5.78%86.62%39.79%1.50%28.30%77.89%21.73%16.81%
Dima Hasao34.09%4.38%36.64%19.66%7.74%96.01%44.33%61.10%29.03%89.33%60.56%18.61%
Goalpara45.99%2.73%36.12%25.37%11.51%90.23%55.97%14.33%34.93%82.13%26.79%20.58%
Golaghat36.84%2.71%16.02%11.02%3.35%91.42%38.01%6.78%11.71%84.61%13.47%13.23%
Hailakandi48.39%4.26%34.94%17.98%11.06%92.18%66.77%61.35%47.21%89.95%46.05%7.81%
Jorhat40.18%2.27%12.05%8.32%5.00%81.36%37.54%15.17%15.03%73.09%11.88%11.74%
Kamrup33.79%1.79%23.37%13.22%5.04%65.34%44.61%6.06%10.38%74.42%14.71%23.47%
T
able 12- DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
278 279
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Assam
Kamrup Metropolitan32.26%2.50%24.18%14.30%2.66%64.54%43.14%29.62%16.20%65.49%16.85%12.12%
Karbi Anglong33.41%4.25%33.95%21.12%8.33%91.53%57.66%49.59%19.42%90.38%20.88%13.79%
Karimganj52.11%5.80%39.34%14.63%8.99%84.26%63.30%48.22%29.20%75.70%41.65%8.91%
Kokrajhar33.08%2.93%28.44%18.93%4.90%86.69%60.85%24.19%26.36%81.75%18.31%17.35%
Lakhimpur34.44%2.57%20.26%9.33%4.31%88.77%52.00%28.07%26.79%81.68%19.01%12.05%
Marigaon44.36%5.71%30.03%19.65%7.16%87.32% 60.06%2.95%22.96%87.55%20.63%17.48%
Nagaon43.09%3.69%31.12%16.96%5.13%88.32%54.23%6.74%17.62%82.78%18.58%8.95%
Nalbari31.77%2.43%22.00%9.73%3.22%67.86%45.64%1.34%17.65%76.43%10.48%11.31%
Sivasagar43.59%3.38%13.09%14.12%7.05%80.70%42.21%5.73%20.88%75.46%19.02%13.25%
Sonitpur34.07%2.47%22.28%17.93%5.60%83.53%38.37%30.16%22.41%74.00%11.31%9.28%
Tinsukia50.96%2.84%20.41%32.71%14.14%88.17%53.23%4.84%28.52%79.74%28.96%30.33%
Udalguri36.19%2.57%27.88%17.94%5.06%90.37%44.78%18.96%15.12%85.79%8.97%11.76%
Bihar
Araria59.94%7.92%47.83%44.15%19.95%97.93%89.29%0.57%52.95%91.35%27.40%38.03%
Arwal56.30%4.57%46.50%16.37%7.79%93.01%77.72%0.81%55.03%79.05%32.35%16.49%
Aurangabad50.04%3.17%40.48%13.91%7.19%90.67%76.84%1.83%42.80%73.99%22.41%18.70%
Banka55.50%4.26%43.13%28.28%9.94%92.28%84.65%9.14%52.78%78.69%26.34%28.31%
Begusarai52.81%4.86%46.06%31.19%11.15%88.37%68.56%2.29%38.59%78.72%29.04%19.93%
Bhagalpur52.10%3.52%46.68%31.30%12.47%90.77%71.68%3.77%35.01%78.62%30.42%23.24%
Bhojpur51.20%4.79%43.63%12.27%7.52%87.28%77.42%0.30%37.84%72.20%20.11%18.69%
Buxar50.90%6.42%43.95%14.29%3.68%84.36%74.55%0.14%36.87%76.43%19.76%14.66%
Darbhanga54.37%3.09%50.81%32.69%13.82%88.32%74.83%0.51%42.21%87.69%28.24%34.75%
Gaya65.02%8.26%54.11%21.81%10.59%89.62%75.48%5.01%29.46%74.04%29.85%23.47%
Gopalganj43.14%3.76%41.10%16.62%7.24%80.52%71.59%1.58%42.29%70.29%12.49%31.39%
Jamui62.54%5.50%56.38%30.44%12.84%92.65%86.60%21.75%38.66%80.62%31.18%22.20%
Jehanabad50.97%4.47%43.59%23.91%11.38%90.86%73.55%1.86%34.05%66.09%38.24%20.07%
Kaimur50.89%4.48%44.20%16.19%9.09%91.10%80.24%5.73%26.78%74.68%23.34%16.36%
Katihar51.61%4.75%46.58%40.33%21.40%95.71%82.43%0.53%73.49%87.42%22.85%35.81%
Khagaria55.36%6.02%51.91%36.74%16.41%90.53%70.95%1.08%47.33%81.05%38.58%30.72%
Kishanganj56.40%3.16%48.29%43.22%20.86%97.19%83.88%1.1
4%47.82%84.23%23.85%42.40%
Lakhisarai50.64%6.00%47.04%24.60%9.40%89.90%62.65%7.78%20.00%65.20%35.99%19.69%
Madhepura58.29%4.92%52.01%41.98%19.38%95.30%87.68%0.18%48.09%91.10%28.64%28.24%
Madhubani54.78%3.57%45.10%27.58%11.98%89.41%80.61%1.11%46.80%86.24%26.92%30.59%
Munger48.80%4.94%42.56%22.04%7.46%85.68%72.21%12.14%39.02%71.36%26.64%17.57%
Muzaffarpur50.72%3.36%42.16%25.80%10.97%85.59%74.91%0.36%28.07%84.28%19.67%25.74%
Nalanda54.14%4.47%43.01%25.58%15.37%88.45%75.71%2.33%35.79%62.22%32.14%21.22%
Nawada53.58%2.90%46.67%24.57%13.18%86.86%75.27%2.56%48.41%65.58%47.80%27.01%
Pashchim Champaran50.05%5.41%47.36%36.89%18.11%92.48%87.40%3.26%60.02% 90.60%22.18%47.36%
Patna51.92%3.99%45.59%18.96%7.99%82.09%68.30%2.22%23.20%63.31%24.01%14.95%
Purba Champaran53.43%4.50%53.58%35.44%20.86%87.14%77.81%0.62%59.59%82.18%23.77%33.57%
Purnia62.74%6.90%53.22%41.89%17.60%97.65%88.00%0.00%54.08%90.55%28.43%32.65%
Rohtas51.98%5.51%46.52%10.33%4.06%90.04%76.82%1.54%19.67%67.92%22.05%11.90%
Saharsa59.23%6.41%53.12%36.27%20.32%92.89%85.71%0.86%42.64%88.58%32.97%31.55%
Samastipur47.87%3.17%47.37%29.99%13.79%89.31%82.07%1.80%60.53%87.19%29.14%38.03%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Bihar
Saran46.39%4.24%44.91%18.32%9.85%89.98%76.59%1.86%41.46%71.50%17.07%30.23%
Sheikhpura62.10%5.64%53.64%26.39%10.59%92.99%70.40%8.39%20.12%68.92%30.91%18.92%
Sheohar54.82%6.79%48.70%38.49%13.69%89.57%76.72%1.07%39.66%89.76%30.75%26.47%
Sitamarhi55.77%5.78%48.03%38.72%17.33%91.48%82.17%0.91%62.45%91.38%28.93%30.75%
Siwan46.05%4.94%42.73%12.80%8.28%80.92%76.07%1.50%46.67%66.75%13.85%16.78%
Supaul60.83%3.49%52.78%34.86%14.90%94.82%84.14%0.37%40.12%88.54%25.45%25.89%
Vaishali54.99%5.82%46.56%22.94%8.92%83.99%67.72%2.13%37.54%79.06%17.91%20.22%
Chhattisgarh
Bastar59.78%4.32%29.44%26.03%10.27%95.71%86.82%18.80%14.33%89.79%32.82%6.66%
Bijapur48.11%3.57%34.57%21.05%5.53%97.87%84.04%13.93%8.17%91.14%22.58%5.02%
Bilaspur38.87%4.26%32.37%11.39%5.65%89.97%72.58%22.76%0.59%75.19%9.53%4.21%
Dantewada59.81%6.93%29.91%39.82%25.49%95.33%84.87%18.15%14.55%91.44%48.47%10.01%
Dhamtari47.60%2.85%16.03%7.93%1.98%91.86%47.27%16.08%1.39%58.26%11.98%2.29%
Durg44.70%3.17%26.30%9.28%3.07%91.60%66.72%14.96%0.71%65.02%9.30%5.38%
Janjgir-Champa39.30%2.04%21.82%11.03%6.80%87.81%74.83%10.60%1.77%60.12%11.64%4.64%
Jashpur44.74%4.57%33.72%17.59%7.80%96.04%85.92%33.17%16.54%92.67%34.90%11.05%
Kabeerdham50.50%3.41%33.15%21.57%8.57%95.32%81.62%16.43%3.91%76.58%17.96%7.52%
Korba51.80%5.11%34.06%17.72%4.16%93.93%83.14%37.84%8.24%81.01%22.86%5.61%
Koriya46.25%3.69%33.09%21.21%5.18%95.16%86.80%49.11%12.63%92.29%28.84%7.27%
Mahasamund48.39%2.53%21.99%13.35%4.11%92.19%77.68%12.80%1.16%71.27%11.26%4.77%
Narayanpur55.80%3.67%37.66%30.92%13.71%97.17%92.40%19.68%25.01%94.02%32.12%8.13%
North Bastar Kanker50.19%3.60%19.08%11.84%4.28%94.38%64.56%9.72%4.02%84.01%20.11%5.30%
Raigarh41.21%3.23%23.67%14.88%4.43%90.03%77.85%16.32%4.54%78.98%18.36%10.12%
Raipur45.70%4.10%24.24%13.06%5.36%91.78%72.47%19.24%1.49%58.53%10.55%6.50%
Rajnandgaon45.29%3.07%26.75%6.09%1.35%93.99%56.56%17.18%1.26%76.22%4.99%1.38%
Surguja48.07%3.61%30.29%25.19%7.50%95.01%84.89%40.53%6.74%94.19%34.45%9.79%
Goa
North Goa34.84%0.65%8.26%3.02%0.00%28.54%18.83%6.29%0.18%29.13%4.63%6.15%
South Goa27.56%0.00%10.92%4.17%2.97%28.34%16.08%6.92%0.00%23.28%3.13%3.14%
Gujarat
Ahmadabad50.52%5.11%8.34%15.35%7.57%64.37%29.21%7.68%1.68%22.96%11.27%7.84%
Amreli28.75%1.00%13.77%10.92%4.93%58.50%30
.84%7.54%1.16%25.89%6.88%8.15%
Anand57.69%5.08%9.47%5.83%4.91%76.77%43.44%3.63%2.74%29.06%12.08%9.83%
Banas Kantha53.37%3.53%23.98%14.55%14.56%76.75%66.80%7.65%12.66%27.23%29.78%6.83%
Bharuch51.13%4.53%11.90%12.71%7.18%71.10%42.92%15.27%3.50%40.85%18.24%10.43%
Bhavnagar41.28%1.48%22.86%15.13%6.46%82.92%53.36%13.53%0.91%50.02%9.15%16.03%
Dang64.90%3.19%34.84%22.16%9.76%97.77%85.34%51.21%12.57%94.35%37.81%18.98%
Dohad61.70%6.01%28.63%18.79%13.29%93.78%85.84%40.15%22.79%77.33%39.61%13.92%
Gandhinagar49.05%3.21%6.90%8.85%3.65%63.43%53.17%4.29%2.73%18.71%13.09%9.29%
Jamnagar31.35%1.87%8.02%13.61%6.71%62.61%41.90%15.49%1.21%10.58%9.01%10.81%
Junagadh30.37%2.84%10.61%11.03%8.37%70.17%33.76%7.74%0.89%18.51%7.88%5.02%
Kachchh43.79%3.58%22.33%23.13%19.35%73.47%45.80%19.51%5.20%9.32%24.00%12.65%
Kheda53.42%3.01%20.13%4.86%5.58%77.90%55.42%8.41%3.08%43.60%22.02%16.91%
Mahesana46.79%1.91%14.21%5.76%5.28%55.86%39.39%9.17%3.36%17.37%10.49%3.92%
Narmada62.72%2.25%25.98%12.27%6.52%92.49%70.53%6.57%6.81%71.04%29.07%6.23%
Navsari48.92%1.73%1.61%7.18%1.94%64.70%36.46%38.00%1.77%55.55%8.36%4.76%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
280 281
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Gujarat
Panch Mahals65.50%2.09%28.83%8.05%8.59%91.72%75.72%28.33%15.88%71.50%34.27%19.36%
Patan50.38%2.78%15.82%15.32%7.47%79.38%50.66%6.42%6.53%27.30%23.37%7.57%
Porbandar34.02%0.78%15.81%12.65%3.97%76.00%35.05%27.28%0.99%14.01%11.55%8.56%
Rajkot32.37%0.87%8.44%9.59%4.50%63.02%41.43%13.24%1.30%14.61%6.80%6.94%
Sabar Kantha52.42%1.85%21.34%8.39%5.96%79.34%62.28%13.33%4.77%36.20%25.41%7.10%
Surat54.92%0.53%22.35%11.64%8.74%60.55%56.57%9.81%2.18%53.33%16.28%10.04%
Surendranagar49.32%3.09%24.32%22.82%13.59%81.81%68.35%27.45%5.59%23.43%18.67%11.53%
Tapi58.24%0.79%12.61%11.69%3.28%83.15%65.16%16.00%6.82%80.55%19.61%10.03%
Vadodara49.14%3.57%22.08%22.32%10.54%82.52%62.95%13.67%3.79%49.94%21.89%12.62%
Valsad43.41%1.75%19.99%14.34%5.66%61.17%51.32%42.42%1.79%50.42%20.59%14.92%
Haryana
Ambala28.13%0.75%13.94%3.45%0.68%52.94%13.23%1.68%0.27%18.21%1.29%0.84%
Bhiwani37.26%2.93%23.61%4.83%1.51%79.74%18.33%23.31%0.86%42.87%5.66%4.46%
Faridabad37.01%3.83%35.43%14.85%11.81%64.62%25.44%14.26%0.93%32.84%7.48%16.45%
Fatehabad39.18%2.08%25.49%9.53%3.05%76.62%13.24%2.68%0.32%37.43%2.50%6.74%
Gurgaon36.27%0.90%31.67%3.01%2.20%62.50%21.89%5.98%1.00%26.76%10.53%10.56%
Hisar33.49%2.19%29.64%6.72%2.26%82.08%15.09%21.66%0.95%31.86%3.06%5.66%
Jhajjar23.12%0.78%21.45%2.33%1.93%76.01%13.47%11.36%0.93%15.32%4.45%5.90%
Jind35.40%2.11%19.37%4.46%1.34%78.20%16.92%21.82%0.44%35.88%2.25%4.14%
Kaithal35.86%2.82%22.02%6.07%0.63%71.11%23.50%9.61%0.18%30.77%2.90%2.59%
Karnal36.55%1.89%17.66%6.38%0.38%62.07%13.78%2.19%0.24%29.55%2.81%2.66%
Kurukshetra27.18%0.55%15.01%5.86%0.71%53.03%14.87%0.67%0.00%27.67%1.18%3.68%
Mahendragarh31.42%1.88%18.92%2.09%0.49%74.27%26.66%8.61%1.47%20.97%4.37%6.19%
Mewat61.42%9.48%66.72%27.08%33.15%88.71%49.54%38.02%10.90%70.34%25.91%20.14%
Palwal37.58%3.00%40.92%12.52%13.00%84.12%36.92%18.79%6.79%44.92%14.61%25.56%
Panchkula23.08%1.35%14.03%4.25%1.60%58.36%30.29%1.21%0.41%14.97%2.28%2.67%
Panipat28.06%1.03%24.00%7.85%3.25%65.04%7.11%1.93%0.00%22.81%2.30%1.57%
Rewari35.67%1.96%28.60%2.46%0.50%78.70%28.90%10.38%1.15%21.85%8.33%19.04%
Rohtak35.52%1.88%28.94%5.31%1.24%79.27%22.59%30.12%0.76%29.52%5.01%11.42%
Sirsa43.84%2.91%29.78%10.24%2.28%78.96%21.44%2.
95%0.95%44.11%4.34%4.67%
Sonipat30.43%0.40%25.05%2.80%0.95%70.54%18.34%7.49%0.64%20.85%2.73%4.61%
Yamunanagar31.93%0.14%10.68%6.01%2.43%58.86%25.63%0.30%0.00%34.85%2.87%4.48%
Himachal Pradesh
Bilaspur28.84%0.71%18.95%3.05%0.93%88.86%27.44%19.61%0.13%16.69%7.81%3.61%
Chamba30.09%3.06%24.51%7.29%1.16%83.33%22.68%12.87%1.75%41.17%13.46%2.08%
Hamirpur25.81%1.62%15.95%2.46%0.13%81.45%16.40%4.49%0.36%26.46%4.50%1.99%
Kangra28.97%1.72%11.71%2.62%0.15%72.87%32.20%4.82%0.00%29.53%3.83%3.96%
Kinnaur19.99%1.68%15.81%7.36%1.10%32.95%31.36%8.03%0.64%39.04%8.13%2.11%
Kullu23.37%1.11%18.64%3.61%1.81%68.65%30.98%7.38%0.51%52.41%9.19%1.45%
Lahul & Spiti15.04%1.43%13.66%11.23%0.75%43.46%44.89%4.36%1.95%69.68%8.56%1.79%
Mandi25.84%0.91%26.10%4.83%0.18%85.04%26.19%4.43%0.33%37.26%11.16%1.52%
Shimla27.03%2.01%16.85%3.35%1.68%48.99%29.89%7.71%0.67%42.25%9.98%3.63%
Sirmaur36.69%2.37%17.74%6.24%1.52%73.38%31.99%15.37%0.96%24.86%14.93%1.48%
Solan30.63%2.19%18.59%3.52%1.12%67.78%30.26%13.46%0.65%16.88%4.43%1.86%
Una24.27%1.42%18.94%3.18%1.73%74.99%29.87%2.10%0.24%22.92%4.36%3.67%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Jharkhand
Bokaro53.48%2.93%32.89%12.02%4.22%96.02%87.43%39.83%19.05%56.97%17.22%7.69%
Chatra59.37%4.30%49.16%24.61%10.23%93.68%89.00%40.01% 60.09%81.78%26.86%11.35%
Deoghar58.22%6.01%43.58%19.30%7.64%92.06%90.13%26.08%22.62%73.96%20.04%6.40%
Dhanbad50.18%2.34%32.48%13.12%3.62%93.98%86.86%41.27%6.09%46.37%14.26%7.61%
Dumka53.47%3.66%34.13%29.47%7.51%95.74%92.04%24.67%31.12%89.57%28.16%13.22%
Garhwa51.36%5.10%44.32%16.24%11.18%96.21%90.39%19.64%49.33%87.12%30.01%19.68%
Giridih56.23%4.32%39.29%24.49%12.90%90.13%86.97%44.45%8.63%65.44%23.68%6.67%
Godda51.23%4.07%35.18%31.02%12.64%96.19%85.23%27.43%20.62%77.63%27.92%5.51%
Gumla46.08%1.92%34.15%15.23%8.30%94.37%86.65%59.32%22.78%90.53%20.90%8.09%
Hazaribagh50.30%3.52%33.50%13.37%6.33%90.31%81.45%41.04%3.62%54.39%19.05%3.04%
Jamtara57.44%3.39%40.91%27.17%9.83%98.60%89.63%16.41%17.43%78.91%22.57%8.28%
Khunti46.30%1.62%30.24%20.95%7.17%96.60%88.20%55.13%34.18%91.81%28.77%8.05%
Kodarma53.77%2.86%26.90%17.92%6.92%81.40%81.01%29.81%9.84%47.38%18.08%2.40%
Latehar45.35%2.78%38.24%26.86%12.49%96.82%89.93%33.02%46.50%91.02%30.87%21.43%
Lohardaga51.32%1.31%35.63%18.48%11.59%93.56%86.78%39.28%18.57%89.32%21.19%10.24%
Pakur56.18%3.07%39.35%39.21%19.09%95.87%88.73%20.26%24.16%84.28%32.28%6.94%
Palamu47.74%3.82%39.37%21.66%11.58%92.65%88.41%19.41%41.06%86.19%37.56%19.79%
Pashchimi Singhbhum55.38%7.77%44.70%31.96%17.84%97.39%89.60%43.89%34.72%92.57%42.27%22.43%
Purbi Singhbhum44.06%1.32%29.25%17.11%6.19%95.22%90.55%29.99%14.39%81.60%21.28%11.74%
Ramgarh49.14%1.98%29.25%12.74%3.66%93.60%69.05%46.52%5.14%50.79%16.81%6.64%
Ranchi49.94%3.17%29.51%13.38%3.50%86.02%80.94%42.65%9.36%69.45%16.95%7.81%
Sahibganj52.41%5.90%44.53%39.84%17.38%93.62%75.38%27.77%28.01%79.57%38.92%13.61%
Saraikela-Kharsawan56.45%4.70%37.91%16.17%4.69%96.08%89.07%38.54%17.90%79.84%22.27%7.58%
Simdega44.55%4.12%37.14%17.45%9.14%98.47%94.94%45.95%47.33%93.47%25.33%11.12%
Karnataka
Bagalkot43.78%2.69%13.53%12.86%4.63%76.38%86.12%8.08%2.71%62.86%17.02%13.93%
Bangalore38.71%1.55%42.83%1.16%1.58%12.04%11.26%6.29%0.00%7.70%8.03%7.99%
Bangalore Rural33.62%1.17%11.88%6.64%1.77%37.97%23.62%20.49%1.35%46.38%9.55%7.57%
Belgaum37.79%2.06%11.70%9.09%1.74%67.39%64.43%7.89%1.48%43.62%10.23%5.13%
Bellary47.14%3.14%17.38%20.87%8.45%62.56%74.61%12.29%2.
97%55.13%18.44%17.81%
Bidar44.58%2.07%11.74%10.98%3.50%84.78%85.88%16.42%2.71%52.60%19.67%10.11%
Bijapur34.94%1.71%14.81%15.11%7.28%82.78%86.10%28.00%3.57%59.43%19.80%14.13%
Chamrajnagar34.48%2.59%8.92%19.07%2.30%66.27%70.60%12.14%4.59%44.10%17.20%19.33%
Chikkaballapura38.75%0.66%7.71%11.79%3.80%62.85%58.71%16.15%2.22%49.19%18.32%9.30%
Chikmagalur30.13%0.83%15.20%9.51%2.53%59.42%37.15%21.92%2.14%53.07%10.59%15.64%
Chitradurga32.91%2.40%9.85%9.42%3.41%80.13%68.89%21.94%3.70%52.22%19.36%11.11%
Dakshina Kannada39.77%1.03%15.02%4.64%1.56%68.10%9.12%20.47%1.72%35.83%6.97%8.13%
Davanagere41.90%2.33%7.89%11.49%4.12%66.85%50.10%9.42%1.53%50.76%10.70%8.92%
Dharwad35.98%2.18%13.75%7.61%1.66%80.52%50.76%12.71%1.81%79.97%16.60%3.47%
Gadag30.45%1.03%10.44%12.44%5.80%91.80%78.82%24.28%2.39%68.27%23.56%10.64%
Gulbarga47.18%0.31%13.31%14.41%7.98%87.96%89.73%10.17%1.91%59.91%13.93%15.25%
Hassan26.62%1.31%7.19%12.04%1.91%56.12%40.67%12.84%3.04%49.05%10.02%7.74%
Haveri36.65%1.15%13.49%11.46%3.67%74.45%49.67%17.56%2.88%61.94%13.63%8.01%
Kodagu34.09%1.14%8.89%8.62%3.15%59.41%15.38%21.35%3.44%34.28%12.23%8.38%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
282 283
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Karnataka
Kolar39.04%1.27%11.67%7.64%1.43%54.09%48.78%20.19%1.15%37.84%8.76%5.64%
Koppal49.80%2.24%21.91%12.91%5.90%79.48%59.21%17.33%0.70%55.51%12.86%6.81%
Mandya25.03%0.97%8.18%8.56%2.83%52.45%47.73%8.36%1.44%42.72%8.94%8.89%
Mysore30.79%0.62%10.92%11.17%1.72%50.62%50.19%7.79%3.90%45.30%16.73%13.02%
Raichur42.18%1.66%20.81%25.07%9.89%88.88%86.66%25.55%2.33%61.18%25.09%18.74%
Ramanagara30.24%0.54%11.54%11.66%2.11%49.35%40.84%12.60%2.38%44.69%9.71%6.09%
Shimoga37.58%1.99%10.17%8.97%3.86%52.91%30.95%27.76%4.07%54.93%9.49%9.95%
Tumkur31.28%2.48%13.33%9.77%2.83%72.58%57.25%20.32%1.72%57.12%14.76%4.45%
Udupi36.57%1.15%10.03%11.09%2.70%66.14%10.96%42.03%1.63%34.02%6.93%6.36%
Uttara Kannada43.34%0.91%12.51%7.16%2.54%62.87%38.07%38.62%3.23%42.20%14.12%12.37%
Yadgir45.41%2.36%23.91%32.64%20.46%90.25%90.19%25.04%2.14%59.54%28.44%17.50%
Kerala
Alappuzha14.55%0.44%1.47%1.24%0.45%38.60%4.87%12.02%0.34%15.05%2.08%5.90%
Ernakulam10.51%0.00%1.78%1.68%0.32%34.61%0.99%5.00%0.06%14.06%1.66%5.92%
Idukki14.15%0.29%0.66%3.24%0.00%55.64%5.52%7.97%0.67%17.73%2.34%5.57%
Kannur12.68%0.32%0.00%2.86%0.56%42.20%1.03%9.28%1.51%8.50%4.21%3.45%
Kasaragod18.50%0.00%1.39%3.70%0.70%52.73%2.84%10.26%1.26%29.95%3.86%4.84%
Kollam14.31%0.21%1.30%1.09%1.20%45.81%2.41%6.16%3.02%11.10%5.17%7.78%
Kottayam10.04%0.17%1.32%1.06%0.00%42.68%0.73%5.68%0.47%6.53%1.85%1.98%
Kozhikode19.75%0.00%1.71%1.79%0.00%58.40%0.61%5.25%1.11%19.13%3.55%3.31%
Malappuram20.82%0.17%3.21%1.65%1.14%58.01%1.19%5.76%0.91%8.57%5.46%5.06%
Palakkad20.21%0.00%0.81%2.04%0.13%54.65%2.89%6.97%0.77%13.04%3.51%2.73%
Pathanamthitta11.13%0.00%0.58%2.11%1.35%43.31%3.29%6.86%0.81%9.55%1.88%7.31%
Thiruvananthapuram12.28%0.20%2.22%2.85%0.72%58.36%3.81%6.89%0.69%14.03%6.12%4.25%
Thrissur13.72%0.26%0.00%1.71%0.40%41.11%0.42%7.05%0.46%6.63%1.46%1.78%
Wayanad20.39%0.54%3.98%3.61%1.24%69.10%4.26%7.20%3.77%23.56%8.93%5.61%
Madhya Pradesh
Alirajpur66.19%7.26%55.87%49.89%44.51%95.92%90.08%23.46%7.66%89.71%29.76%14.76%
Anuppur47.37%3.94%30.41%14.42%5.48%95.70%93.29%56.68%25.28%89.55%29.34%7.08%
Ashoknagar50.39%2.17%29.10%19.28%11.04%94.03%85.52%36.33%13.52%89.08%20.76%9.38%
Balaghat57.87%3.04%27.97%11.43%3.32%92.37%7
6.64%47.79%10.66%86.00%17.87%9.70%
Barwani70.75%5.48%49.64%41.51%37.05%91.56%90.05%29.61%11.87%85.00%38.17%15.70%
Betul46.86%4.16%30.76%16.90%5.48%88.73%79.85%38.57%7.79%82.59%28.01%9.90%
Bhind49.21%3.65%31.55%7.75%7.51%93.09%77.63%20.03%16.01%83.19%15.58%10.96%
Bhopal35.06%2.88%17.86%13.23%8.42%74.23%73.03%23.65%4.57%69.98%16.40%9.30%
Burhanpur52.80%3.97%36.99%32.49%18.01%75.62%68.89%23.99%13.90%67.34%29.57%15.65%
Chhatarpur50.41%5.82%40.96%21.94%12.95%97.50%94.43%54.39%24.73%84.30%25.09%18.34%
Chhindwara45.79%2.62%25.14%14.93%3.36%89.75%75.61%41.28%12.00%75.14%29.50%10.57%
Damoh46.27%4.23%35.01%19.13%9.62%97.57%87.51%58.26%15.22%91.52%34.21%22.72%
Datia48.39%6.29%32.89%11.38%4.66%95.92%73.33%30.05%12.08%95.71%18.04%6.88%
Dewas49.81%4.47%29.88%18.22%10.23%82.58%56.92%27.75%1.39%61.81%12.94%5.10%
Dhar55.04%5.09%34.98%27.37%13.71%83.02%73.61%28.87%5.23%67.39%21.83%11.27%
Dindori51.65%3.60%32.22%20.84%4.16%98.04%94.35%52.02%25.41%92.50%56.08%13.59%
East Nimar57.24%3.51%34.18%27.47%11.20%87.82%73.25%38.10%9.91%69.31%21.06%8.24%
Guna56.61%3.97%38.21%22.80%12.22%93.78%84.65%45.17%7.77%85.53%21.41%8.73%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Madhya Pradesh
Gwalior45.33%5.45%32.52%15.37%9.07%87.46%68.18%20.88%9.98%73.70%19.60%7.45%
Harda43.18%4.11%35.08%23.06%9.59%83.63%53.69%28.66%3.77%68.76%18.57%5.97%
Hoshangabad43.73%2.50%30.34%13.15%3.86%85.11%64.39%25.38%6.72%66.42%18.75%5.13%
Indore43.68%1.46%13.84%13.78%6.75%55.76%40.07%16.82%0.23%38.17%6.32%10.87%
Jabalpur44.37%3.40%24.26%12.09%7.22%89.07%76.21%26.08%7.84%69.98%27.40%6.48%
Jhabua58.05%6.28%43.96%44.14%36.59%95.49%91.03%38.62%11.77%86.32%44.65%30.91%
Katni45.57%5.62%29.65%12.98%4.95%94.43%86.69%36.03%18.71%86.65%28.59%12.81%
Mandla49.19%3.72%28.79%20.50%6.05%94.25%92.16%56.95%24.70%88.86%44.18%7.34%
Mandsaur50.15%2.50%29.01%17.27%5.27%86.55%77.17%43.34%2.38%67.06%12.92%17.91%
Morena46.72%4.33%29.01%10.31%9.61%92.12%77.43%22.23%15.01%80.89%17.33%5.60%
Narsimhapur42.99%2.49%27.26%15.13%2.52%92.09%64.65%19.47%7.27%81.56%31.54%19.04%
Neemuch48.69%3.13%27.25%20.49%6.08%76.94%75.61%37.87%2.12%59.79%11.95%17.91%
Panna41.50%4.76%34.72%20.66%9.74%95.25%86.40%43.66%21.47%88.00%26.90%16.37%
Raisen50.67%3.40%30.87%15.69%7.85%89.80%68.90%26.93%7.87%82.91%19.78%6.12%
Rajgarh49.80%3.61%29.74%20.06%7.28%93.84%89.14%65.02%5.04%73.41%22.80%8.22%
Ratlam52.55%2.43%29.12%25.28%12.16%86.03%80.76%28.12%6.16%72.75%33.27%31.14%
Rewa40.35%5.50%33.31%12.00%8.33%96.07%79.00%27.69%13.45%92.61%24.55%14.29%
Sagar41.07%3.62%37.86%14.51%5.46%95.99%79.92%43.65%17.30%89.95%26.99%13.61%
Satna40.28%4.23%30.46%14.34%6.47%93.08%77.81%24.78%11.36%88.01%25.29%8.42%
Sehore43.58%3.49%28.09%18.35%7.78%88.32%60.05%33.07%1.95%66.09%11.19%5.75%
Seoni48.69%2.02%24.27%14.67%4.42%91.90%82.30%47.53%18.24%85.12%29.62%14.26%
Shahdol43.83%4.46%30.88%17.15%7.11%93.36%91.19%56.62%24.26%87.17%30.07%13.55%
Shajapur48.75%2.41%20.94%20.70%11.59%88.82%71.81%46.95%3.39%63.97%12.87%10.39%
Sheopur53.13%4.44%33.89%24.95%10.74%91.79%87.99%32.12%19.20%83.90%31.21%9.83%
Shivpuri51.48%4.29%35.55%16.60%7.25%95.54%87.36%65.16%14.88%88.61%25.06%9.16%
Sidhi48.41%5.16%42.67%15.16%9.53%95.53%90.94%49.19%22.58%91.90%34.40%20.15%
Singrauli49.83%6.97%47.06%20.37%12.26%95.73%95.88%62.87%34.66%94.16%34.51%11.12%
Tikamgarh50.14%3.67%33.76%14.94%7.09%92.05%88.65%47.62%14.73%76.57%25.55%20.70%
Ujjain50.08%2.54%32.96%20.58%8.61%80.30%61.62%32.81%3.63%61.58%1
4.58%19.52%
Umaria46.62%4.02%36.09%16.91%8.48%96.10%89.59%45.41%21.01%88.63%30.44%8.26%
Vidisha47.08%6.21%39.65%21.66%10.61%93.16%83.03%27.55%16.18%86.16%35.33%25.12%
West Nimar54.71%2.64%32.12%23.78%11.27%78.12%75.73%15.73%2.09%59.05%17.30%15.81%
Maharashtra
Ahmadnagar38.74%0.67%18.97%5.76%5.46%48.75%54.12%18.80%6.76%40.78%15.52%22.50%
Akola43.67%0.91%14.75%3.70%1.23%76.39%58.79%11.47%5.25%44.93%20.51%6.94%
Amravati39.12%0.65%12.95%6.87%0.64%66.04%41.96%12.37%9.36%50.93%20.82%8.37%
Aurangabad41.31%1.90%22.48%6.05%4.53%66.76%63.70%22.77%4.62%36.57%16.75%6.88%
Bhandara39.48%1.97%7.34%5.43%0.60%67.30%32.58%22.53%3.80%49.00%10.66%2.34%
Bid38.43%1.43%17.92%10.09%7.27%80.47%63.37%27.04%8.84%34.81%21.36%9.57%
Buldana43.29%0.82%15.09%8.76%4.14%78.56%58.20%21.43%3.91%46.34%17.46%5.03%
Chandrapur47.76%1.42%12.28%8.75%1.83%72.16%60.12%32.80%10.82%46.92%25.44%4.34%
Dhule47.92%1.10%26.93%19.23%15.39%77.47%80.97%11.11%16.00%62.76%28.77%21.44%
Garhchiroli38.65%0.73%13.08%11.69%2.85%85.55%73.00%21.49%6.71%59.24%20.30%8.04%
Gondiya48.52%1.14%12.29%5.71%0.32%82.44%45.03%29.78%3.60%78.16%13.51%4.66%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
284 285
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Maharashtra
Hingoli47.61%2.67%19.81%10.59%5.52%85.83%61.90%29.22%15.92%49.51%26.48%6.97%
Jalgaon33.35%2.27%22.41%12.81%6.27%58.14%60.29%7.35%9.05%35.47%19.15%14.13%
Jalna47.08%2.07%20.28%11.53%3.92%90.61%65.98%49.32%13.47%41.56%25.89%6.75%
Kolhapur39.49%1.82%18.45%5.74%3.94%49.50%38.09%7.83%1.48%48.61%11.22%13.82%
Latur40.29%0.59%16.85%5.95%1.81%77.60%61.66%38.81%5.62%36.37%22.90%8.56%
Mumbai------------
Mumbai Suburban------------
Nagpur41.23%1.72%5.59%5.78%0.59%51.74%41.60%10.37%5.24%42.98%13.23%6.19%
Nanded54.29%3.22%26.24%9.55%4.59%85.90%64.96%31.03%10.27%52.90%31.87%11.39%
Nandurbar59.49%1.41%26.03%31.07%23.83%85.21%80.18%25.98%28.99%82.82%49.76%18.43%
Nashik46.96%1.86%24.43%7.90%5.22%63.58%59.76%28.24%12.30%46.27%19.45%8.34%
Osmanabad37.59%1.48%13.72%8.62%1.56%69.33%73.53%35.85%9.60%37.78%17.81%8.76%
Parbhani53.72%0.41%18.66%8.10%4.29% 90.06%74.47%17.70%8.51%39.69%20.02%7.48%
Pune32.75%1.83%14.34%5.35%2.44%38.66%35.75%15.20%4.48%21.42%14.05%6.78%
Raigarh42.74%1.59%14.93%11.64%3.64%43.94%45.87%16.08%4.84%31.81%16.77%17.77%
Ratnagiri36.29%0.75%14.07%7.55%4.52%77.29%37.92%19.90%2.68%62.32%25.72%21.74%
Sangli34.60%1.40%16.64%3.46%4.53%47.76%29.37%9.40%9.34%38.14%11.12%8.30%
Satara41.90%0.86%17.18%4.89%2.03%52.97%34.05%8.90%4.21%37.69%10.88%12.72%
Sindhudurg37.12%1.20%13.39%4.75%2.78%73.90%22.69%28.11%1.12%47.95%21.21%19.24%
Solapur35.31%1.56%13.40%8.12%2.69%66.29%52.32%29.07%11.10%33.09%13.06%5.49%
Thane60.51%2.18%25.28%11.64%19.27%77.49%70.76%56.39%16.71%63.80%31.80%13.44%
Wardha35.30%0.95%6.14%7.40%0.76%54.57%46.04%8.39%4.41%42.63%15.10%3.89%
Washim44.20%1.15%20.44%7.86%3.92%75.62%63.95%23.08%4.04%53.15%21.87%4.50%
Yavatmal46.26%1.29%16.98%8.33%5.31%77.08%64.88%28.18%9.85%55.57%25.59%10.12%
Manipur
Bishnupur25.20%1.35%9.42%3.16%1.08%69.58%56.25%60.80%2.60%94.37%5.30%19.90%
Chandel24.84%2.95%31.44%12.66%4.20%84.45%35.94%64.92%7.76%92.96%25.34%21.72%
Churachandpur28.64%2.93%23.59%11.10%2.94%60.59%32.56%48.35%9.68%82.19%25.52%21.78%
Imphal East23.79%1.47%12.09%4.78%2.99%64.85%55.60%75.01%3.04%88.18%9.67%20.48%
Imphal West18.41%1.73%16.41%1.88%0.65%56.63%53.89%60
.15%3.58%83.14%5.56%23.71%
Senapati31.07%3.10%39.60%10.91%5.09%84.81%39.83%64.95%7.66%90.27%24.28%29.73%
Tamenglong31.73%3.71%44.86%18.03%5.37%92.48%37.73%67.03%15.21%94.62%51.69%36.10%
Thoubal26.33%1.77%17.73%3.92%3.11%74.94%53.36%66.74%16.49%94.30%8.17%24.05%
Ukhrul27.39%2.36%37.12%7.86%2.57%95.48%36.36%64.60%26.68%86.43%49.01%38.84%
Meghalaya
East Garo Hills31.91%1.60%41.51%22.19%12.43%97.45%49.55%61.21%15.08%87.32%32.27%23.41%
East Khasi Hills49.57%4.14%34.65%23.69%4.37%85.71%32.25%21.07%7.04%41.36%41.69%31.13%
Jaintia Hills51.38%5.25%46.02%34.18%11.63%84.94%43.69%34.68%14.78%34.40%49.29%35.14%
Ri Bhoi45.23%5.46%44.74%33.77%8.96%90.48%43.47%33.89%13.65%54.30%46.81%27.90%
South Garo Hills23.25%3.75%16.51%6.47%2.73%92.05%13.26%24.94%1.00%56.08%8.15%2.16%
West Garo Hills27.44%2.47%28.19%16.24%5.25%88.36%58.00%49.10%7.18%71.09%21.63%6.71%
West Khasi Hills42.03%3.75%45.14%25.29%6.14%94.70%20.16%30.83%10.26%47.87%41.42%27.19%
Mizoram
Aizawl20.65%4.04%18.06%5.09%2.16%58.84%7.78%11.53%0.48%15.03%8.22%2.64%
Champhai29.18%2.76%27.63%10.65%3.48%65.09%17.94%5.54%0.11%32.23%16.86%4.35%
Kolasib26.26%2.83%28.59%17.44%7.40%60.08%19.48%5.06%1.64%41.83%23.36%11.09%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Mizoram
Lawangtlai30.11%3.47%32.14%28.87%12.90%64.71%39.98%21.11%30.99%58.29%48.52%15.25%
Lunglei22.48%2.89%24.65%14.23%4.10%71.43%31.81%9.58%1.34%37.37%23.61%12.36%
Mamit25.29%2.67%27.11%20.28%7.68%68.54%38.61%24.57%17.28%57.10%38.30%14.59%
Saiha34.02%4.58%33.48%10.23%5.62%67.85%22.81%10.40%0.68%34.47%29.18%10.06%
Serchhip26.28%2.64%21.21%5.94%1.20%67.20%9.38%7.62%0.30%12.20%12.19%1.97%
Nagaland
Mon36.94%2.14%52.12%27.40%11.16%98.00%17.44%29.61%16.09%95.57%73.45%60.78%
Dimapur23.47%2.04%29.26%17.51%6.07%52.77%26.70%28.05%3.03%62.86%11.93%28.42%
Kiphire35.50%4.64%43.03%19.90%6.70%95.90%11.94%13.55%1.99%83.21%64.22%53.83%
Kohima19.50%2.45%26.76%11.01%1.05%86.77%26.83%26.40%1.20%65.56%28.22%15.89%
Longleng28.91%1.32%37.38%13.58%5.16%98.69%25.20%58.02%2.98%90.13%55.30%37.85%
Mokokchung15.23%1.54%18.17%10.53%2.46%78.72%6.75%10.57%0.79%78.52%20.26%26.18%
Peren24.00%3.90%40.83%18.68%2.65%89.77%27.10%37.84%2.61%85.78%38.34%26.38%
Phek20.95%3.61%36.74%13.87%3.96%96.10%15.14%5.28%1.67%90.77%56.63%31.82%
Tuensang35.83%4.30%53.64%22.87%3.93%97.62%27.55%7.56%3.30%89.17%59.12%46.31%
Wokha19.32%2.25%29.09%14.26%1.30%81.22%25.20%37.33%3.09%68.05%34.57%28.25%
Zunheboto21.69%0.67%32.18%14.34%6.19%92.90%15.44%11.07%0.49%87.60%49.58%41.81%
Odisha
Anugul35.79%4.05%16.00%12.23%2.47%84.71%68.98%33.87%12.61%44.14%18.93%6.81%
Balangir47.14%2.38%10.56%17.39%4.37%94.81%88.57%22.91%17.39%70.61%18.04%6.26%
Baleshwar38.90%1.22%24.16%11.70%3.83%92.42%64.72%10.47%10.24%74.00%13.32%13.64%
Bargarh41.65%2.69%13.29%11.14%2.83%90.67%74.85%20.07%15.90%65.51%14.66%7.15%
Bauda42.83%2.90%16.02%15.78%3.08%89.91%84.16%25.08%11.64%74.12%20.81%7.48%
Bhadrak40.57%1.59%24.30%10.24%2.79%91.61%80.29%11.28%11.84%78.32%15.10%23.30%
Cuttack27.03%1.72%20.55%7.12%1.78%80.49%70.44%14.31%7.95%39.41%11.18%13.50%
Debagarh43.46%2.73%24.46%20.67%5.25%96.27%69.46%22.95%18.09%69.78%32.34%16.70%
Dhenkanal37.50%1.60%17.29%15.66%3.39%87.94%73.70%52.49%12.25%56.48%21.64%15.53%
Gajapati38.80%3.96%26.77%28.73%10.11%89.94%65.57%43.57%12.56%49.48%44.69%8.56%
Ganjam30.44%2.52%19.21%23.10%4.44%78.09%66.58%20.73%9.63%33.38%18.40%5.21%
Jagatsinghapur25.65%1.68%14.84%6.42%0.89%90.25%67.01%8.90%5.22%38.60%12.28%7.33%
Jajapur36.97%1.85%22.51%8.91%1.83%86.36%69
.63%16.98%5.87%47.75%12.55%10.60%
Jharsuguda44.74%1.98%12.57%10.42%1.23%87.87%69.85%18.00%10.30%73.67%10.87%5.23%
Kalahandi42.77%1.56%22.32%30.35%9.10%96.33%89.67%28.15%33.17%79.64%29.52%17.73%
Kandhamal44.02%3.82%27.87%20.50%5.91%95.68%85.51%54.07%21.80%65.53%47.75%6.98%
Kendrapara33.81%1.61%21.25%8.98%1.78%88.19%73.84%12.46%6.76%59.55%16.62%6.86%
Kendujhar46.14%3.73%31.84%23.47%8.43%91.28%85.98%28.12%25.34%77.00%32.16%17.95%
Khordha26.58%1.41%17.68%10.37%1.50%79.87%74.58%33.51%3.69%39.16%9.43%7.15%
Koraput51.11%2.70%26.44%42.72%17.48% 90.00%86.37%20.20%24.88%65.72%44.87%17.86%
Malkangiri60.63%7.21%29.22%45.91%18.21%96.19%83.60%19.11%11.56%77.64%37.69%9.49%
Mayurbhanj45.45%1.98%21.89%23.19%5.68%94.52%86.44%28.98%26.54%84.04%31.63%20.93%
Nabarangapur55.31%4.34%34.52%36.34%16.10%94.61%85.15%26.84%31.44%83.45%35.60%20.31%
Nayagarh25.46%1.83%20.06%16.98%2.85%81.64%69.12%29.98%6.07%42.01%15.11%10.29%
Nuapada49.68%2.52%17.15%22.17%7.31%93.70%82.06%19.39%17.59%74.18%18.29%5.61%
Puri23.47%1.68%15.55%6.30%1.30%85.83%62.76%17.11%4.65%36.34%8.73%6.50%
Rayagada50.01%6.34%28.32%41.38%14.38%92.51%83.11%17.55%22.10%65.11%40.97%16.50%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
286 287
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Odisha
Sambalpur44.64%1.35%13.03%16.94%1.46% 90.00%76.18%22.88%13.81%76.76%16.54%4.29%
Subarnapur43.91%2.19%14.82%12.21%1.14%88.58%84.39%18.83%9.20%65.63%18.26%6.18%
Sundargarh45.52%2.32%13.71%13.38%3.85%93.29%74.44%24.51%18.96%79.89%16.20%4.89%
Punjab
Amritsar24.77%1.35%13.03%12.26%7.10%53.71%27.48%0.00%0.85%40.73%2.47%4.28%
Barnala22.82%2.35%13.04%11.50%1.11%60.59%13.48%1.41%0.00%25.67%0.90%2.75%
Bathinda19.57%1.00%11.57%9.43%4.12%61.01%18.91%6.59%0.63%29.09%1.95%4.92%
Faridkot22.23%0.68%5.35%6.88%4.91%45.36%14.40%7.52%0.89%29.41%0.79%1.15%
Fatehgarh Sahib23.64%2.04%12.98%5.18%0.63%44.27%14.40%0.05%0.00%12.87%1.11%0.85%
Firozpur23.18%2.03%10.43%14.31%4.49%63.23%25.24%9.63%0.54%52.59%2.17%3.16%
Gurdaspur23.18%1.55%15.35%4.84%0.61%42.07%28.44%0.62%0.00%23.84%2.29%2.96%
Hoshiarpur25.23%1.26%9.29%3.64%1.59%46.38%28.41%0.38%0.49%16.58%1.14%3.11%
Jalandhar19.21%1.11%11.06%3.16%0.38%40.64%14.52%0.34%0.54%14.79%0.44%2.12%
Kapurthala19.37%1.21%8.59%6.19%0.96%34.47%14.81%1.71%1.15%13.51%0.90%2.40%
Ludhiana21.81%1.16%8.50%4.89%1.34%51.97%8.49%0.53%0.13%18.78%1.21%4.69%
Mansa30.15%0.87%14.13%12.27%1.86%73.52%24.83%6.02%0.37%35.32%3.34%1.84%
Moga23.74%2.66%15.69%12.76%3.99%58.87%14.05%4.05%0.25%41.56%1.40%5.98%
Muktsar28.63%2.16%16.85%14.60%3.49%56.69%23.71%4.53%0.14%32.65%3.27%2.94%
Patiala20.75%2.77%22.37%7.26%1.50%60.87%15.14%0.82%0.15%23.16%2.01%4.63%
Rupnagar21.50%1.70%8.20%3.68%0.64%45.76%21.03%0.42%0.00%9.66%0.50%1.22%
Sahibzada Ajit Singh Nagar25.68%1.60%11.76%5.09%1.83%43.86%28.49%0.97%0.00%16.42%2.82%4.44%
Sangrur23.93%1.25%7.48%4.85%0.66%55.55%16.94%0.49%0.11%16.44%0.96%2.51%
Shaheed Bhagat Singh Nagar29.83%0.70%17.59%8.15%1.34%67.94%11.97%2.13%0.31%26.04%1.74%5.20%
Tarn Taran26.07%1.69%22.75%12.55%3.98%53.69%22.36%0.50%0.49%30.60%1.66%4.40%
Rajasthan
Ajmer46.73%2.81%24.88%16.85%6.02%78.76%39.86%44.20%1.65%15.58%11.84%1.00%
Alwar50.02%3.74%39.67%12.18%7.54%91.07%67.00%16.88%2.62%45.11%12.98%6.34%
Banswara52.86%2.15%25.50%24.98%16.48%92.68%83.68%29.41%32.83%80.58%52.48%2.41%
Baran47.10%1.96%20.92%16.88%8.68%85.75%71.96%29.36%7.70%64.99%23.81%1.26%
Barmer44.64%4.57%42.96%36.70%21.28%92.29%81.85%45.32%35.65%58.36%52.03%5.42%
Bharatpur46.56%4.59%43.57%15.74%14.15%94.20%70
.61%46.49%10.52%61.96%26.47%7.56%
Bhilwara38.96%2.71%22.69%23.22%9.22%88.33%76.46%43.17%2.96%29.79%16.67%2.19%
Bikaner42.18%2.89%32.00%22.30%11.57%90.13%43.87%23.26%13.57%35.01%22.68%3.18%
Bundi46.75%2.15%24.18%21.32%6.49%88.56%77.42%29.09%9.33%48.60%23.71%2.73%
Chittaurgarh38.45%1.96%24.72%25.29%8.91%88.19%75.34%29.35%4.71%47.05%14.50%4.00%
Churu42.53%1.88%33.82%16.74%4.99%88.25%24.93%21.21%5.48%25.32%27.05%7.70%
Dausa44.24%4.43%30.01%11.94%2.03%93.02%68.73%20.42%10.33%42.32%23.92%5.67%
Dhaulpur55.17%5.42%36.54%15.89%9.64%92.64%75.71%21.61%9.13%51.78%25.44%2.41%
Dungarpur55.79%2.44%25.36%22.87%9.38%89.60%70.68%33.93%22.97%74.25%45.83%2.28%
Ganganagar36.64%2.14%20.37%13.91%3.17%76.30%26.51%9.68%6.88%54.81%9.18%3.20%
Hanumangarh35.95%2.47%23.22%14.52%3.63%86.88%47.94%6.21%5.99%54.97%12.27%3.89%
Jaipur43.68%3.04%25.34%8.78%4.08%82.25%57.05%21.25%3.00%29.68%14.00%2.84%
Jaisalmer44.79%4.22%42.80%39.74%25.40%91.63%66.63%55.67%26.79%46.29%41.55%8.55%
Jalor53.21%3.46%33.99%26.31%14.94%80.33%63.25%31.36%18.06%40.91%39.46%4.15%
Jhalawar44.14%2.68%23.17%22.03%8.68%84.70%69.03%44.40%4.21%61.22%27.36%1.24%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Rajasthan
Jhunjhunun36.61%2.33%20.64%6.44%1.68%60.19%37.53%16.38%4.13%25.32%14.70%4.22%
Jodhpur42.14%3.91%29.31%23.06%14.76%83.31%65.37%45.28%13.01%29.44%23.41%3.71%
Karauli52.02%6.26%34.28%16.27%6.34%94.46%83.41%36.61%5.68%59.10%35.76%7.16%
Kota44.28%1.60%16.78%14.82%4.31%77.93%69.29%22.68%2.67%47.58%16.18%2.21%
Nagaur41.96%1.60%21.97%18.11%7.07%85.51%48.55%36.98%11.27%20.79%20.95%5.83%
Pali46.45%3.78%24.06%20.27%8.64%70.05%51.55%37.27%5.14%24.28%13.50%2.49%
Pratapgarh50.96%3.67%29.19%28.74%13.97%91.33%88.01%50.42%26.18%80.62%39.62%4.00%
Rajsamand43.99%3.31%26.39%21.96%7.35%86.94%75.81%37.69%4.47%24.18%25.98%2.66%
Sawai Madhopur45.33%4.86%31.80%13.24%6.90%92.45%69.81%30.50%15.85%47.08%26.96%5.82%
Sikar36.88%3.77%24.50%8.28%4.22%71.14%39.70%14.91%4.27%17.19%12.00%3.45%
Sirohi51.51%5.33%32.65%26.39%16.07%72.35%66.95%30.38%16.04%41.14%34.05%8.51%
Tonk41.64%2.31%20.63%17.01%5.45%93.05%73.34%41.64%2.70%40.81%18.76%1.15%
Udaipur58.50%4.37%29.98%33.91%18.24%92.22%82.64%47.46%21.08%64.18%41.87%4.71%
Sikkim
East Sikkim14.71%1.29%8.14%9.35%1.91%55.12%7.84%2.69%0.64%36.98%13.23%15.06%
North Sikkim10.52%0.54%5.48%11.84%1.45%60.89%7.19%2.64%0.32%38.60%14.35%7.41%
South Sikkim10.53%0.51%2.29%7.37%1.23%57.98%2.22%0.63%0.13%22.77%7.15%3.60%
West Sikkim16.66%1.90%4.17%9.51%1.16%63.64%5.00%5.79%0.51%38.97%13.83%4.03%
Tamil Nadu
Ariyalur27.33%1.04%5.57%11.03%0.00%50.42%73.84%7.55%1.20%31.17%4.15%8.84%
Chennai------------
Coimbatore29.65%0.86%1.21%5.83%1.51%25.31%60.94%6.42%1.28%20.09%3.72%7.27%
Cuddalore33.88%1.82%6.27%6.81%0.74%55.89%73.47%5.39%0.57%40.33%4.72%5.71%
Dharmapuri29.64%1.30%3.89%8.99%1.59%26.57%70.36%11.22%1.38%17.19%2.88%7.81%
Dindigul30.29%1.20%4.16%7.23%1.15%42.87%69.30%3.83%2.84%21.66%6.04%3.53%
Erode21.22%0.00%3.48%10.73%1.36%14.99%49.56%2.34%1.83%19.80%4.28%4.23%
Kancheepuram26.01%1.45%8.54%4.83%0.99%27.64%52.96%13.53%0.34%21.92%2.65%3.35%
Kanniyakumari21.20%0.00%8.85%2.73%1.37%46.70%16.93%2.92%0.47%23.69%1.07%1.76%
Karur34.80%0.71%9.50%9.51%2.12%28.81%66.16%9.90%1.46%15.48%3.55%2.91%
Krishnagiri35.20%2.72%6.42%7.21%1.15%46.09%68.47%10.06%0.99%21.12%2.62%4.34%
Madurai24.27%1.46%10.23%12.84%1.84%37.77%65.
93%27.19%0.48%22.41%6.83%10.72%
Nagappattinam34.83%1.79%9.10%7.31%1.58%46.74%67.30%9.83%1.06%44.03%5.29%6.82%
Namakkal17.26%0.26%6.28%8.13%0.97%12.69%52.14%3.00%0.43%20.01%3.55%11.52%
Perambalur29.11%2.01%10.82%9.54%1.08%40.83%69.03%16.68%1.04%25.71%4.83%6.27%
Pudukkottai34.97%1.
A
INDIA
National
Multidimensional
Poverty Index
Baseline Report
Based on NFHS-4 (2015-16)
NITI Aayog, 2021
Based on the National Family Health Survey-4 (2015-16)
INDIA
BASELINE REPORT
NATIONAL
MULTIDIMENSIONAL
POVERTY INDEX
Copyright@ NITI Aayog, 2021
NITI Aayog,
Government of India,
Sansad Marg, New Delhi - 110001, India
Visit the dashboard:
sdgindiaindex.niti.gov.in/MPI
Contact us at:
e-mail: sanyukta.samaddar@nic.in
Source of Maps: Census of India 2011 &
Political Map of India 10
th
Edition (Survey of India)
Cover &
Report Design by Sourav Das,
Data Analytics Officer (MPI-SDG), NITI Aayog sourav.das.3195@gmail.com
INDIA MPI BASELINE REPORT II IIIFOREWORD
Vice Chairperson
National Institution for Transforming India
Government of India
DR. RAJIV KUMAR
MESSAGE
The Sustainable Development Goals framework, adopted by 193 countries in 2015, has redefined de-
velopment policies, government priorities, and metrics for measuring development progress across
the world. The SDG framework, with seventeen Global Goals and 169 targets, is significantly wider
in scope and scale relative to the Millennium Development Goals, its predecessor. The expansion
of scope includes the significant development of recognising the need to address poverty in all its
forms and dimensions. This has been articulated in the SDG framework through target 1.2 –which
is aimed at reducing “at least by half the proportion of men, women and children of all ages living
in poverty in all its dimensions according to national definitions”. The development of the National
Multidimensional Poverty Index of India is an important contribution towards instituting a public
policy tool which monitors multidimensional poverty, informs evidence-based and focused inter-
ventions, thereby ensuring that no one is left behind.
India’s national MPI measure uses the globally accepted and robust methodology developed by the
Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development
Programme (UNDP), who have been key partners in ensuring the public policy utility and technical
rigour of the Index. Importantly, as a measure of multidimensional poverty, it captures multiple and
simultaneous deprivation faced by households. This report presents an in-depth analysis of the
headcount ratio and intensity of multidimensional poverty at the national, State/UT, and district
levels. I am certain that the results and findings of this baseline edition of the index will be of great
relevance and interest to policy makers and administrators in States and districts, researchers and
scholars, and the wider public.
This baseline report of India’s first ever national MPI measure is based on the reference period of
2015-16 of the National Family Health Survey (NFHS). The national MPI measure has been construct-
ed by utilising twelve key components which cover areas such as health and nutrition, education
and standard of living.
It has been envisaged as a comprehensive tool to expedite goal-oriented action to measure multi-
dimensional poverty and steer its systematic eradication. Since the data reference period of NFHS
2015-16, India has made remarkable strides through flagship programmatic interventions in housing,
electricity, sanitation, drinking water, and cooking fuel, among others. Apart from building infra-
structure and ensuring basic services, focused initiatives to improve health and nutritional outcomes
have been undertaken expeditiously during this period. The baseline report will be updated upon
the release of the NFHS-5 (2019-20) dataset. I am confident that India’s multisectoral approach to
address poverty and the impact of the flagship programmes and schemes will be reflected in that
edition, thereby improving the national MPI score further.
I congratulate Shri Amitabh Kant, CEO, NITI Aayog, for encouraging the SDG team at NITI Aayog,
led by Ms. Sanyukta Samaddar, Adviser, to develop India’s inaugural Multidimensional Poverty Index.
My compliments and sincere thanks to the officials of State Governments, UTs, Union ministries,
OPHI and UNDP, who have contributed towards this report.
DR. RAJIV KUMAR
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT IV VFOREWORD
The year 2021 marks the sixth anniversary of the adoption of the Sustainable Development Goals
which envisages to end poverty in all its forms everywhere. Steered by the Government of India’s
Global Indices for Reforms and Growth (GIRG) initiative, the National Multidimensional Poverty
Index (MPI) for India is aimed at leveraging the monitoring mechanism and methodology of the
globally recognised MPI to rigorously benchmark national and subnational performance and drive
programmatic actions and reforms.
India’s national MPI captures multiple and simultaneous deprivations faced by households across
the three macro dimensions of health, education and living standards. It highlights the need for a
whole-of-government approach towards addressing poverty and its multidimensionality. This mul-
tisectoral approach must be horizontally and vertically integrated across all levels of governance.
Estimates of national MPI headcount ratio and intensity have been furnished not only for the coun-
try and States but also for all the districts, which is a unique feature of this report. This will not only
enable the analysis of comparative and relative performance among States and UTs but also among
the districts of a State. This assumes salience given the federal structure of the country and the im-
portance of the involvement of district administrations for effective implementation of interventions
and schemes.
As a multidimensional poverty measure and a policy tool, this inaugural baseline Report provides us
with important insights into the degree of success of multi-sectoral interventions to address various
aspects of poverty. With the release of the National Family Health Survey 2019-20, the subsequent
update of the Report will capture the remarkable strides the country has taken to improve the lives
of households across these multifaceted parameters.
Simultaneously, under the GIRG initiative, reform areas and actions are being formulated to utilise
the insights made available through the Index to ramp up our efforts further to aggressively and
systematically eradicate poverty. My appreciation goes to the Union Ministries and State/UT Gov-
ernments, and all officials who have been engaging with us in this endeavour. I also compliment
our technical partners, Oxford Poverty and Human Development Initiative (OPHI) and the United
Nations Development Programme (UNDP), for their support.
I congratulate the SDG team at NITI Aayog led by Ms. Sanyukta Samaddar, Adviser, who has been
steering our overall national MPI efforts in bringing out our first national Multidimensional Poverty
Index & Dashboard and our continuous engagement with key stakeholders, the Governments of
States and UTs and Union Ministries through her commendable leadership.
Chief Executive Officer
National Institution for Transforming India
Government of India
AMITABH KANT
MESSAGE
AMITABH KANT
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT VI VIIFOREWORD
Director
Human Development Report Office
United Nations Development Programme
PEDRO CONCEIÇÃO
MESSAGE
I congratulate the Government of India, NITI Aayog, and my UNDP colleagues on the launch of In-
dia's first National Multidimensional Poverty Index: Baseline Report & Dashboard.
Since its development in 2010, the Multidimensional Poverty Index has served as a valuable analyti-
cal tool to identify the most vulnerable people – the poorest among the poor and revealing poverty
patterns within countries and over time, thereby enabling policy makers to target resources and
design policies more effectively.
This new Indian national version of the index complements the 10 indicators of the global MPI with
metrics on maternal health and bank account under the dimensions of health and standard of liv-
ing, respectively. The resulting work offers powerful insights that can enhance India’s capability to
reduce poverty as the country builds back stronger from the effects of the COVID-19 pandemic.
National and state averages can mask important distinctions. The Indian national MPI’s granular data
– which cover not only all the States and Union Territories, but also all the districts – can empower
policymakers and local officials to manage complexity and scale, inform evidence-led policy, design
programs, and track progress. It can also help fine-tune the policies and programmes of State gov-
ernments who are at the forefront of public service delivery, social protection, and multidimensional
poverty reduction in India’s federal structure. It will also be a tool to inform public dialogue across a
range of stakeholders, including civil society, the research community, and businesses.
We at UNDP reaffirm our commitment to our partners Government of India and NITI Aayog in the
collective journey to eradicate poverty in all its forms and accelerate the achievement of the Sus-
tainable Development Goals.
PEDRO CONCEIÇÃO
20 September 2021 New York United States of America
INDIA MPI BASELINE REPORT VIII IXFOREWORD
Resident Representative
United Nations Development Programme
India
SHOKO NODA
MESSAGE
To end poverty in all its forms everywhere is at the core of the comprehensive, expansive, and in-
terdependent framework of the 2030 Agenda for Sustainable Development adopted by the world
in 2015. The Multidimensional Poverty Index (MPI) is a systematic, robust, and nuanced measure to
estimate our progress towards achieving this goal. MPI as a measure was first developed by Oxford
Poverty and Human Development Initiative (OPHI) and United Nations Development Programme
(UNDP) for inclusion in UNDP’s flagship Human Development Report in 2010, where it has been
since published.
India’s National MPI: Baseline Report & Dashboard is a milestone in its journey towards realising the
SDGs, in particular –target 1.2 of the 2030 Agenda which specifically focuses on addressing poverty
in all its dimensions. The national MPI is being launched at an important juncture where I am sure
it will provide key insights into the development scenario at the national, State and district levels,
especially for India’s sustainable recovery from the COVID-19 pandemic. On this important national
endeavour, it has been a genuine pleasure to collaborate with NITI Aayog –Government of India’s
premier thinktank, and with our partner OPHI.
India’s national MPI will provide crucial insights into multiple cross-sectoral indicators for decision
makers at the national, State and district levels. From the perspective of planning and policy design,
it will complement existing metrices in tracking progress, informing high-impact interventions and
simultaneously engendering evidence-based policy making.
I would like to express my heartfelt gratitude to Dr Rajiv Kumar, Vice Chairperson, NITI Aayog, for
his visionary leadership and unwavering commitment in steering the process of instituting India’s
national MPI. I also extend my sincere appreciation to Shri Amitabh Kant, CEO, NITI Aayog, for his
continuous engagement and encouragement in this endeavour. UNDP is committed to continue and
strengthen this important partnership. Lastly, I commend Dr Sabina Alkire, Director, OPHI, and her
team for the technical support in this exercise.
SHOKO NODA
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT X XIFOREWORD
It has been an honour to collaborate on India’s National MPI: Baseline Report & Dashboard under
the leadership of NITI Aayog, Government of India. This baseline report is a contribution towards
measuring and monitoring progress on achieving target 1.2 of the Sustainable Development Goals
on multidimensional poverty. It not only presents the MPI results – plus headcount ratio and inten-
sity – at the national level, but also at the levels of States and all the districts of the country. The
high-resolution disaggregation by indicators makes it a powerful policy tool to benchmark progress
and inform policy making.
Similar to the interlinked nature of the goals and targets of the 2030 Agenda, as a measure, India’s
national MPI reflects the interlinkages across the indicators at the level of households. This is salient
to designing and implementing coordinated multisectoral policies and focused interventions. As
a policy tool, it can be utilised by decision makers to focus on reducing overall multidimensional
poverty by targeting to reduce deprivations in each of its dimensions and indicators across the
country at the sub-national and district levels. This disaggregation is crucial, especially in a country
as diverse as India, for not only reflecting information on inter-State variations in outcomes, but also
intra-State variations. The National MPI: Baseline Report & Dashboard is based on the rich dataset
of the National Family Health Survey (NFHS) 2015-16. The follow-up to this baseline report, with the
release of the NFHS 2019-20, will make it possible to estimate multidimensional poverty reduction
trends as envisaged under SDG target 1.2.
I would like to express my sincere gratitude to Dr Rajiv Kumar, Vice Chairperson, NITI Aayog for his
leadership, guidance and constant encouragement extended to this important project. Our techni-
cal assistance in this regard is a product of our strengthened partnership with UNDP India.
I would also like to congratulate Shri Amitabh Kant, CEO, NITI Aayog and his energetic, dedicated,
and professional SDG team led by Ms Sanyukta Samaddar, Adviser, NITI Aayog, for producing this
technically rigorous measure.
Director
Oxford Poverty and Human Development Initiative
Department of International Development,
University of Oxford
DR. SABINA ALKIRE
MESSAGE
DR. SABINA ALKIRE
20 September 2021 Oxford United Kingdom
INDIA MPI BASELINE REPORT XII XIIIFOREWORD
Our relentless efforts at NITI Aayog in adopting, implementing, and monitoring the Sustainable
Development Goals at the national and subnational levels have been anchored on the core principle
of “Leaving No One Behind”. Instituting a robust SDG monitoring framework, promoting healthy
competition among the States, strengthening the statistical systems, and building partnerships with
a range of stakeholders have primarily aimed at reaching the farthest behind first. Given India’s de-
velopment context, the most effective way to ensure development of the most disadvantaged sec-
tions is through faster poverty reduction. To implement this strategy, the first step is to estimate and
monitor poverty levels, using a framework which defines poverty in all its forms, which is relevant to
the present and aligns with the SDGs and our national context. This is precisely what our efforts at
NITI Aayog regarding Multidimensional Poverty Index (MPI) aim to achieve.
The national MPI, an aggregate measure which defines poverty, in simple terms, as the deprivation
in crucial and basic parameters of health, education, and living standards, is a significant departure
from the way poverty has been understood and conceptualised historically. This shift in focus from
income or consumption expenditure as the basis of poverty estimation is founded on the policy
narrative at the national level that human and capability development along with access to basic
infrastructure is at the centre of India’s development policy. The national MPI is an outcome of NITI
Aayog’s focussed strategy to execute this policy directive and the mandate given by the Cabinet
Secretariat to improve India’s position in global rankings of important and globally accepted indices.
This baseline report on the national MPI estimates at the national, State/ UT, and district levels
based on NFHS-4 (2015-16) is a pivotal first step in bringing multidimensional poverty as a tool to
the policy table. The national MPI will complement monetary poverty statistics, throw light on “how
many are poor” and “how poor are the poor”, track poverty over time, offer disaggregated esti-
mates by States, districts, and population groups, and support priority-based resource allocation
and targeted reforms, all at a granular level. We expect that the Report will play an instrumental role
in sensitizing governments, researchers, civil society, the public, and other stakeholders on the need
for and importance of MPI as a powerful policy instrument.
Adviser (SDG)
National Institution for Transforming India
Government of India
SANYUKTA SAMADDAR
FOREWORD
In this nationally important endeavour, we owe our deepest gratitude to the Central Ministries and the State Governments, which have strongly supported the national MPI initiative. The insightful discussions we have had during the MPI Coordination Committee meetings, and the detailed delib- erations held with the States as part of our SDG-MPI workshops in more than 20 States in the past four months, have contributed immensely to improving the framework. We hope that the State- specific reform action plan will aid faster reduction of multidimensional poverty and will result in better outcomes as measured by subsequent NFH Surveys.
To fully realise the potential of the MPI as a policy tool for focused interventions towards addressing
multidimensional poverty, utilising it at the level of States and districts is of paramount importance.
In this regard we thank all the State Governments and especially the ones where we organised over
20 workshops in the State capitals with Chief Secretaries and senior Government officials from the
departments and districts. These workshops facilitated building interest and engendering under-
standing and acceptability of this important tool for governance. The positive, enthusiastic and
constructive feedback from our State governments has played a very important role in the process
of developing the national MPI.
We are unequivocally thankful to Dr Sabina Alkire, Director, Oxford Poverty and Human Develop-
ment Initiative, and her team for offering technical advice from time to time. As the designer of the
global MPI, Sabina’s deep knowledge on the subject based on her experience of working with coun-
tries across the globe, has contributed significantly to the robustness of the national MPI.
We would like to place on record our gratitude to Ms Shoko Noda, Resident Representative, UNDP
India, for her support to the project, as a key partner. We are certain that the MPI initiative will offer
crucial inputs to UNDP’s SDG efforts across the country. We are also grateful to the United Nations
Resident Coordinator and his office for their support to the project.
Our deepest thanks goes to Prof KS James, Director and Senior Professor, International Institute for
Population Sciences (IIPS), and his team, for their unswerving support to the project. IIPS has been
kind enough to share its expertise in Demographic and Health Surveys which has supported the
project technically to a great extent.
Finally, sincere appreciation to my team in the SDG Vertical in NITI Aayog: Alen John, Farha Anis,
Soumya Guha, Sourav Das, Sundar Mishra and Vidya Warrier. Special acknowledgments are due
to the core team, comprising Soumya Guha, SDG Officer, and Sourav Das, Data Analytics Officer,
who have remarkably shouldered the responsibility of this inordinately vast range of computations,
estimations and visualisations at all levels, and the development of the reform action plan and the
MPI Dashboard.
We are grateful to Shri Amitabh Kant, CEO, NITI Aayog, for his consistent support to the idea of mul-
tidimensional poverty, and the project and its initiatives. The project would not have been a success
without the relentless support of our Hon’ble Vice Chairman, Dr Rajiv Kumar who has thrown his
weight behind all efforts, with passion, to pursue the execution of the national MPI project.
We sincerely hope that the national MPI project and its outcomes will act as a strong force which
will further accelerate SDG achievements for our country.
SANYUKTA SAMADDAR (IAS)
20 September 2021 New Delhi India
INDIA MPI BASELINE REPORT XIV XV
CONTENTS
Message from the Vice Chairperson, NITI Aayog II
Message from the CEO, NITI Aayog IV
Message from the Director, Human Development
Report Office, United Nations Development Programme
VI
Message from the Resident Representative,
United Nations Development Programme, India
VIII
Message from the Director, Oxford Poverty and Human Development Initiative
X
Foreword by Adviser, NITI Aayog XII
contents
1. Introduction 2
2.Developing India’s MPI 6
PAGE 1
Context & Introduction
3. Methodology 14
4.Way forward 28
5. India 32
States
Andhra Pradesh 50
Arunachal Pradesh 56
Assam 62
Bihar 68
Chhattisgarh 74
Goa 80
Gujarat 84
Haryana 90
Himachal Pradesh 96
Jharkhand 102
Karnataka 108
Kerala 114
Madhya Pradesh 120
Maharashtra 126
Manipur 132
Meghalaya 138
Mizoram 144
Nagaland 150
Odisha 156
Punjab 162
Rajasthan 168
Sikkim 174
Tamil Nadu 178
Telangana 184
Tripura 190
Uttar Pradesh 194
Uttarakhand 202
West Bengal 208
PAGE 31
III
National & State/UT Results
6. Technical Notes 246
7.References 250
8. Index of tables 251
9.Data Tables 250-414
PAGE 245
IV
Technical Notes & Data Tables
Union Territories
Andaman & Nicobar Islands 214
Chandigarh 218
Dadra & Nagar Haveli 220
Daman & Diu 222
Delhi 226
Jammu & Kashmir, & Ladakh 232
Lakshadweep 238
Puducherry 240
National & State/UT
Results
PAGE 31
I
PAGE 13
Methodology & Way Forward
II
Chapter 1SeCTIoN
SECTION
I
CONTEXT
&
INTRODUCTION
INDIA MPI 2 3
IntroductionCHAPTER Chapter 1
INDIA’S NATIONAL MPI
The Resolution of the United Nations General Assem-
bly on 25 September 2015 established the 17 Sustain-
able Development Goals (SDG). SDG 1 in its entirety
(“End poverty in all its forms everywhere”) is multi-
dimensional in nature and definition. While target 1.1
seeks to eradicate extreme poverty –measured as
people living on less than $1.25 a day (subsequently
increased to $1.90/day), target 1.2 aims at reducing
multidimensional poverty, as defined by national defi-
nitions, by half.
The Multidimensional Poverty Index (MPI) has been
used by the United Nations Development Programme
in its flagship Human Development Report since 2010
and is the most widely employed non-monetary pov -
erty index in the world (Godinot & Walker, 2020). It
captures overlapping deprivations in health, education
and living standards (UNDP, 2010). It complements
income poverty measurements because it measures
and compares deprivations directly. In this context, a
national Multidimensional Poverty Index for India will
enable estimation of poverty not only at the level of
the States but also for all the 700 plus districts (600
plus in 2015-16, 700 plus in 2019-20) across twelve
indicators, capture simultaneous deprivations and in-
dicator-wise contribution to poverty, and most impor-
tantly, will facilitate formulation of sectoral policies
and targeted interventions which contribute towards
ensuring that “no one is left behind”. The district-wise
estimation of the national MPI will also ensure reach-
ing out to the furthest behind first through focused
efforts on specific indicators and dimensions.
This chapter provides a brief overview of the national and international context of measuring poverty across multiple dimensions, the conceptual framework be- hind multidimensional measures and how they con- tribute towards ending poverty in all its forms. The latter half of this chapter discusses the salience and features of the national MPI and the purpose of such a measure
1.1 India - Multidimensional Poverty Index:
National context and Global Goals
The history of poverty estimation in India dates back
to as early as 1901 when Dadabhai Naoroji estimated
poverty in the country based on the cost of a subsis-
tence diet. In 1938, the National Planning Committee
suggested a poverty line estimation based on living
standards followed by the authors of the Bombay Plan
in 1944. Addressing and ending poverty has been part
of the national agenda since independence. Various
committees, working groups and scholars including
the working group of 1962, Dandekar and Rath in 1971
and the Y.K. Alagh taskforce in 1979 were engaged
in the estimating the headline statistic of poverty to
inform public policy. Similarly, the Expert Groups un-
der Lakdawala (1993) and Tendulkar (2009) and the
Rangarajan Committee (2014) undertook the exercise
of estimating monetary poverty. Globally, the focus
on reducing monetary poverty was mirrored in the
Millennium Development Goals target of halving the
proportion of people living on less than $ 1.25 a day
between 1990 and 2015.
The adoption of Transforming our world: the 2030 Agenda for Sustainable Development by 193 countries of the United National General Assembly, including In- dia, brought institutionalised focus in measuring, and addressing poverty in “all its forms” enshrined in its preamble and explicated under Goal 1. The impact of this was also reflected in the work of the World Bank, the custodian of the International Poverty Line (IPL) statistic, which convened a high-level Commission led by Sir Anthony Atkinson and supported by an advi- sory board of global poverty measurement experts.
One of the specific tasks of this commission was to
go “beyond Goal 1.1” and engage with “complemen-
tary indicators and multidimensionality” (World Bank,
2017). The Commission recommended the inclusion
of a multidimensional poverty measure based on the
counting approach, seen in the Alkire-Foster method
of the MPI. The Global Coalition of Partners to End
Child Poverty in its submission to this Commission
emphasised on the importance of an aggregate mul-
tidimensional poverty measure and its potential to
powerfully summarize and communicate global and
national figures to a wider audience, therefore mobil-
ising public support to end poverty in all its dimen-
sions.
In India, the Cabinet Secretariat’s mandate on the MPI
is aimed at utilising the monitoring mechanism of the
Index to gauge and benchmark poverty reduction to
improve the country’s performance and therefore,
reach out to the furthest behind by identifying reform
areas and formulating reform actions on each of its
components. The development of the India MPI is in-
tended to drive competition among the States and
Union Territories and provide insights on multidimen-
sional poverty at the subnational and district levels.
1.2 Conceptual framework of poverty and
its multidimensionality
Early 20th century examples of attempts at capturing
poverty and its multidimensionality include studies
by Booth (1903) and Rowntree (1901) in the United
Kingdom. For a measure of wellbeing, application
of multidimensional approaches dates back to the
Level of Living Survey conducted in Sweden in 1968
(World Bank, 2017). Townsend’s landmark study in
1979 serves as an early example of a multidimension-
al approach towards relative deprivation across 60
indicators which covered dimensions like diet, cloth-
ing, fuel and light, housing, conditions and security
of work, recreation, health, and education. Similarly,
the European Union’s portfolio of social indicators to
monitor social protection and inclusion is inherently
multidimensional in nature. The need for complemen-
tary non-monetary statistics also stems from the rec-
ognition that income is one of the many dimensions and not the only attribute of wellbeing (Chakravarty, 2009).
The theoretical underpinnings of a non-monetary
approach towards poverty and instead, as a multidi-
mensional phenomenon is drawn from the capability
approach (Sen 1979, 1987, 1999). The capability ap-
proach suggests that functionings and capabilities are
two integral parts of a person’s quality of life and well
being where functionings are the “beings and doings”
that they value and have reason to value –such as be-
ing healthy and nourished; and capabilities reflect the
freedom that they have in achieving valuable func-
tionings. Therefore, to arrive at the conclusion that a
household or individual is deprived in basic capabili-
ties, it is pertinent to examine and consider multiple
dimensions of well being (Dotter & Klasen, 2020).
Multidimensional measures complement monetary
measures by capturing information they lack –such as
broader qualitative aspects of life such as child mor-
tality, housing conditions, and other basic services
such as water and sanitation (Greve, 2020). This is of
significance to policy formulation and targeted inter-
ventions in the context of intra-country or intra-re-
gion heterogeniety in development.
Simple headcount related measures do not provide
information on the depth of poverty, as the Atkin-
son Commission observes, and therefore, potentially,
while the extent of poverty captured by the head-
count ratio can reduce, the poorest may fall even
further behind. To address this, the Multidimensional
Poverty Index, based on the Alkire-Foster method,
employs an adjusted headcount ratio (MPI score)
which is arrived at by multiplying the headcount ra-
tio with the average deprivation among the MPI poor
(Alkire & Foster, Counting and Multidimensional Pov-
erty Measurement, 2011). In 2010, this measure of mul-
tidimensional poverty replaced the Human Poverty
Index (HPI) in UNDP’s Human Development Report. It
draws from the capability approach by including mul-
tiple dimensions of poverty across the dimensions of
health, education and living standards, and examines
the “fundamental objective features” which affect the
poor (Brando & Fragoso, 2020).
1.3 Ending poverty in all its forms: Multidi-
mensional Poverty Index
The multidimensionality of poverty is an integral part
of the Sustainable Development Goals. Target 1.2. re-
fers to reducing “at least by half the proportion of
men, women and children of all ages living in poverty
in all its dimensions according to national definitions”.
The interlinked nature of the SDGs is reflected in mul-
1
INTRODUCTION
12
INDIA MPI BASELINE REPORT 4 5
IntroductionChapter 1
tidimensional poverty measures as well, since they
examine deprivations in areas such as nutrition (Goal
2), health (SDG 3), education (SDG 4) and living stan-
dards related indicators such as water and sanitation
(SDG 6), and electricity and clean cooking fuel (SDG
7), among others.
Development of multidimensional poverty measures
was motivated to not only capture multiple but also
overlapping deprivations faced by the poor, which
was not sufficiently reflected in monetary poverty
measures (Alkire, 2020). Measures such as the MPI,
based on the Alkire-Foster methodology, consider si-
multaneous deprivations and disadvantages articulat-
ed through indicators in the broad dimensions of ade-
quate health and nutrition, education, and standard of
living. MPI can be utilised as a complementary policy
tool to monetary poverty, to build and illuminate a
more comprehensive depiction of poverty. The infor-
mation and insights from this tool can drive cross-sec-
toral policies which address the interconnected and
simultaneous disadvantages and deprivations faced
by the poor. Two distinctive normative conditions are
fulfilled by the MPI –the recognition that non-mone-
tary deprivations are integral to what can be termed
as poverty, and that deprivations often simultane-
ously overlap. As discussed above, MPI not only con-
cerns itself with the headcount ratio of proportion
of people who are multidimensionally poor but also
includes important information on average depriva-
tions or “depth of poverty”. The Index also provides
information of policy relevance on each of the indica-
tors by way of deconstructing each of the indicators
for sectoral focus and elucidating cross-sectoral over-
laps exhibited through multiple deprivations. There-
fore, the MPI as a measure of multiple dimensions of
poverty complements monetary poverty statistics,
enables close monitoring of individual indicators and
dimensions which overlap with several SDGs, allows
for disaggregation at the levels of States and districts,
and urban and rural to better inform policy focus, and
engenders integrated cross-sectoral policy actions by
capturing multiple deprivations in its methodology.
Apart from its theoretical and statistical merits, the
MPI produces clear and easy insights to communicate
policy relevant information for a wider audience.
In 2010, the Multidimensional Poverty Index (MPI)
developed by Sabina Alkire and James Foster was
adopted by the United Nations Development Pro-
gramme (UNDP) in their Human Development Re-
port as a replacement for its Human Poverty Index.
The MPI serves as a complementary measure to the
more traditional measures that are based on income
or consumption. The rationale for multidimension-
al measurement of poverty is that wellbeing can be
adversely impacted in many forms that may only be
indirectly related to an individual’s income or level of consumption. Deprivations can include an over- lap related to poor health, undernourishment, and inadequate access to clean cooking fuel, electricity, water, sanitation, and housing. Importantly, the MPI also reflects some of our national priorities articulat- ed through focused interventions on housing, clean water, sanitation, cooking fuel, electricity along with our universal education goals, improved nutrition and good health for all.
1.4 National MPI: Salience and features
A national MPI is a headline statistic of multidimen- sional poverty that is used (OPHI, 2019):
• To compare poverty across subnational regions,
• To track poverty over time,
• To highlight “how” poor are the people in poverty,
using direct information from the set of MPI indi-
cators.
• National MPIs are always reported along with sev-
eral intuitive statistics that show the level and com- position of poverty by indicator. These are:
•
Incidence, ‘H’ which shows the percentage of peo- ple who are multidimensionally poor.
• Intensity, ‘A’ which shows the percentage of weighted deprivations the average multidimen- sionally poor person suffers from.
• The national MPI is constructed directly from each person’s profile of deprivations across each indica- tor, built from a single household survey that cap- tures the data on all the indicators. So, the national MPI is always reported together with its composi- tion by indicator. This is done in one of two ways: (1) analysing the percentage of people who are multidimensionally poor and deprived in each in- dicator one by one, and (2) analysing the weighted contributions of each indicator to the national MPI.
1.5 Purpose of National MPI as a measure
A national MPI statistic for a country is tailored to the
national priorities and therefore, countries choose
their own set of dimensions, indicators, weights, and
cut-offs, according to their plans and contexts (OPHI,
2019). National MPIs are disaggregated by subnation-
al regions, urban or rural areas, age, and other factors.
They are also always reported with the indicator-wise
deconstruction and breakdown. These details can
guide and monitor national policies such as budget
allocation, targeting specific interventions, and poli-
cy coordination across sectors. The purpose of con-
structing such a measure is discussed below:
•
Enhanced high-level view of poverty at the na-
tional le
vel: The international community, includ-
ing government officials, international agencies,
academia, and society, understand poverty today as a complex, multidimensional phenomenon. The national MPI provides a high-level view of the level of multidimensional poverty and its change with time. This presents an overall picture of poverty in the country, while also enabling closer and more in-depth analyses of areas of interest such as re- gions –State or districts, and specific sectors –un- der the dimensions of health and nutrition, educa- tion and living standards.
• Complements monetary poverty measures: The national MPI complements existing monetary pov-
erty statistics. The dimensions of the Index have proven to help identify and achieve targeted policy interventions. The ability to provide a better de- piction of poverty and inform more precise policy actions has been an incentive in every country that has developed a national MPI so far. Evidence has shown that people who are experiencing multiple deprivations in crucial areas of their lives, such as education, health, safety, or employment, may not be income poor (Bourguignon, et al., 2008), and policies to reduce income poverty may not affect other deprivations. Furthermore, public action in areas like education, infrastructure, and hous- ing, which might only impact income in the next generation, are not well captured by traditional monetary metrics. In contrast, a national MPI that includes such indicators can show rapid improve- ments in these areas, making visible the impact of social policies and interventions more directly.
•
Information to shape policy: A national MPI can guide coordinated actions by several ministries and departments, provide clear goals and targets for each indicator, and act as a monitoring and ac- countability tool within the government. One rea- son for this is that it allows robust disaggregation by groups (such as between urban and rural areas, subnational regions, gender, age groups,). One can also unpack the numbers to analyse the compo- sition of poverty by dimensions and indicators – nationally, and at the level of States and districts, which allows for more efficient policy design, pol- icy coordination and focus, and assignment of re- sources.
• The MPI is based on each person’s or household’s
pr
ofile of the overlapping or “joint” deprivations
they experience: This provides new information that is not available in many other measures of poverty estimation. For example, 20 percent of the population may not have access to adequate san- itation and 20 percent may have insufficient edu- cation, but these two indicators separately do not provide information on the degree of intersection
of the population without adequate sanitation and those without an adequate education. Elucidating such overlaps is a specific feature of the MPI. This additional information is extremely relevant for identifying the poorest of the poor, who experi- ence serious and multiple deprivations at the same time. It is also useful for guiding multisectoral and integrated policies, because it highlights the com- plexity of simultaneous deprivations.
• Provides incentives for leaving no one behind
and
reaching the furthest behind first: By reflect-
ing the intensity of poverty (detailing the multiple deprivations that a family has at the same time), the national MPI has an advantage over headcount poverty measures since efforts to reduce the pro- portion of simultaneous hardships faced by the poor will reduce the MPI even if they have not yet moved out of poverty. For example, if a poor per- son goes from being deprived in 90 percent of the indicators to being deprived in 50 percent of them, then the MPI goes down, even if they are still iden- tified as multidimensionally poor. This further bol- sters the incentive for focussing on the poorest of the poor, because if any deprivation of any multidi- mensionally poor person is removed, the MPI falls.
•
Adaptable to national context and transparent: The design of the national MPI is flexible, as the di- mensions, indicators and weights can be adapted to the national context. These are attributes that can be defined by policymakers to accurately char- acterize poverty in diverse contexts. The national MPI is also transparent and easy to implement. This provides legitimacy for official estimates. In addi- tion, it is intuitive and easy to communicate to the media, private sector and civil society as seen in countries which have implemented such a mea- sure.
• Robus It is crucial that an official poverty measure be robust. It means that the pol- icy conclusions are not overly sensitive to small changes in its own components, like indicators, cut-offs or weights. This is because in a pluralist societies people often agree on a broad range of priorities but disagree on details. A measure that is robust to a number of specifications has more legitimacy among a wider group of citizens and stakeholders. Furthermore, the national MPI can be rigorously applied, using standard errors and tests of statistical inference. This means that pol- icymakers can ensure that their statements –such as “poverty has reduced” refer to statistically sig- nificant changes.
INDIA MPI 6 7
Developing India’s MPICHAPTER Chapter 2
THE PROCESS
DEVELOPING INDIA’S MPI
2.1 The Global Indices for Reforms &
Growth (GIRG) mandate
In early 2020, the Cabinet Secretariat, Government
of India, identified 29 global indices to monitor, anal-
yse and evaluate with the aim of improving India’s
position in global rankings. Under this mandate, also
known as the Global Indices for Reforms and Growth
(GIRG) mandate, NITI Aayog was identified as the
nodal agency for the Multidimensional Poverty Index
(MPI).
The GIRG exercise is aimed at leveraging the moni-
toring mechanism of important social, economic and
other internationally recognised indices to drive re-
forms and growth. The primary goal of this exercise is
to gauge India’s performance - not only on the overall
country's result in the indices but on the parameters
and sub-parameters as well; and subsequently draft
an action plan to improve in those areas and sub-areas.
It was widely recognised that this exercise would re-
quire a whole-of-government approach in letter and
spirit. As one of the first steps in this direction, NITI
Aayog, as the nodal Ministry for the MPI, identified
all relevant Union Ministries and departments which
mapped not only to the broad dimensions of the in-
dex but also to the individual components, parame-
ters and sub-parameters. For example -improvement
in dimensions such as living standards required co-
ordination among Ministries concerned with cooking
gas, electricity, rural and urban development, minis-
tries which work on housing, sanitation and drinking
water, and so on. This exercise also enabled the iden- tification of existing government actions articulated through schemes and policies which were definition- ally impacting the parameters and sub-parameters of the index: for example, various nutrition, and child and maternal health related schemes which impact the MPI dimension of health. It underscored the im- portance of inter-ministerial coordination and part- nership for effective and focused action.
The emphasis of the GIRG initiative is not only to im-
prove country’s performance and ranking but to lever-
age them as tools for systemic reforms in the policies
and processes aimed at improving and enhancing the
ease of living. In this context, NITI Aayog has been co-
ordinating with the concerned Ministries and depart-
ments mapped to the indicators and sub-indicators
of the index to develop a roadmap and action plan.
Parameter and sub-parameter linked reform areas
and actions are being developed by the inter-ministe-
rial committee on the index. The exercise includes the
identification of reform actions, duration, sub-national
applicability, priority and target setting for implemen-
tation by Ministries and departments.
As the nodal agency, NITI Aayog is also responsible
for constructing an indigenised index for monitoring
the performance of States and Union Territories and
ranking them. The national MPI for India is a milestone
contribution towards this effort. It aims to become the
primary tool for monitoring progress on SDG 1 ‘end-
ing poverty in all its forms everywhere’ in the coun-
try and simultaneously foster competition among
the States and Union Territories to expeditiously take action towards this goal. In this endeavour, engage- ment with publishing agencies and relevant technical partners was impressed upon from the outset. Build- ing partnerships and engaging with- i) the publishing agencies— United Nations Development Programme (UNDP) and Oxford Poverty and Human Develop- ment Initiative (OPHI) and ii) other technical partners such as the survey implementors of the National Fam- ily Health Survey— International Institute for Popula- tion Sciences (IIPS) of Ministry of Health and Family Welfare, has been critical in developing the national MPI for States and districts and ensuring its technical rigour and robustness.
To institutionalise this inter-ministerial effort for de-
veloping the national MPI and formulating reform ar-
eas and actions, the Multidimensional Poverty Index
Coordination Committee (MPICC) was constituted by
NITI Aayog, which included twelve Ministries and de-
partments.
2.2 MPI Coordination Committee (MPICC)
The inter-ministerial coordination committee con-
stituted under NITI Aayog included Ministries and
departments pertaining to areas such as health, ed-
ucation, nutrition, rural development, drinking water,
sanitation, electricity, and urban development, among
others. It also included experts from Ministry of Statis-
tics and Programme Implementation and the publish-
ing agencies – OPHI and UNDP.
The composition of the MPICC drew from the multidi-
mensional nature of the indicators and sub-indicators
within the index. This brought forth a cross-sectoral
collection of perspectives on policies and interven- tions to improve achievements at the level of house- holds. It also highlighted various approaches required for improving outcomes of intrinsic significance such as nutrition and education; and material infrastructure and household services such as electricity, drinking water, cooking fuel and others.
As critical stakeholders in the process, the MPICC en-
gaged in extensive discussions on the index, its the-
oretical underpinnings, technical computations and
indicators. The inaugural MPICC round tables on dif-
ferent facets of the index enabled a technical exposi-
tion of the computation of the index, the Alkire-Foster
method, dimensions, indicators and cut-offs. Conse-
quently, deliberations on two facets of the GIRG ex-
ercise -i) developing an India index or national MPI
and ii) identification of reform areas and actions, took
place simultaneously.
NITI Aayog in collaboration with the publishing agen-
cies (OPHI and UNDP) led the discussion on develop-
ing the national iteration of the index, with the MPICC.
The guiding principle of this exercise was the objec-
tive of developing a national measure which can con-
tribute towards measuring the progress under SDG 1
-of halving poverty in its multidimensional form, and
subsequently supporting evidence-based policymak -
ing to formulate government action to address it.
Members from each Ministry of the MPICC explicat-
ed their experience in their domain in relation with
public service delivery and the macro challenges in
each sector in a demographically and geographically
diverse country such as India. Their rich experience in
identifying past, present and future challenges in their
respective sectors informed the discussion on indica-
2
22
MPI Coordination Committee
Inter-Ministerial Coodrination Committee on the MPI
MEMBER MINISTRIES
NITI Aayog
Ministry of Statistics and Programme Implementation
1
2
Ministry of Women and Child Development 3
Ministry of Petroleum and Natural Gas 4
Ministry of Power 5
Ministry of Housing and Urban Affairs 6
Department of Health and Family Welfare
Department of Rural Development
7
8
Department of Food and Public Distribution 9
Department of School Education and Literacy 10
Department of Drinking Water and Sanitation 11
Department of Financial Services 12
SUBJECT MATTER EXPERTS
United Nations Development Programme
Oxford Poverty and Human Development Initiative
1
2
INDIA MPI BASELINE REPORT 8 9
Developing India’s MPIChapter 2
tor selection and formulating the reform areas. This
was followed by technical feasibility assessment of
the indicators in the NFHS and selection of respective
weights. Cognizance was taken to enable the national
MPI to simultaneously track the performance of the
country, and all States and districts on the global indi-
cators of the Index. Drawing from the GIRG mandate,
the consensus was twofold: i) to adapt the MPI to the
national and local contexts while tracking the global
MPI indicators and ii) to estimate it not only at the
level of the States, but districts as well.
2.2.1
Engagement with States: Building
c
onsensus on M
PI at the subnational level
State and Union Governments are pivotal stakehold- ers which make up the institutional bulwark of the country. With 36 States and Union Territories and over 700 districts –subnational entities represent the myriad socio-political, geographical and econom- ic diversity in the country. For a public policy tool such as the national MPI to fully realise its potential, utilisation of its results and findings by State and UT governments is crucial. Simultaneously, the success of identification and implementation of reform areas and actions to improve the lives of households and indi- viduals, would significantly be influenced by the level of adoption at the level of States. Therefore, build- ing consensus on the need to create a national MPI and the model thereof, developing capacities, under- standing and appetite for this novel policy tool, with our primary stakeholders, ie., the State governments and policy makers and implementers at the sub-na- tional level, was felt to be imperative at the stage of MPI project design. Close collaboration and extensive engagement with subnational governments –through State MPI workshops with top policy makers and all line departments in the States, was recognised as a pivotal driver of outreach on the Index.
NITI Aayog, at the time of writing, had organised MPI
workshops in the capitals of over 20 States. These
workshops were chaired by the Chief Secretaries
and other senior officials of the State Governments,
district level officials, and in some cases by the Chief
Ministers. The meetings covered the basics of MPI,
mandate from Cabinet Secretariat, India’s position in
global rankings, index estimations, status of the State
on MPI and district-wise performance, MPI parameter
dashboard, national MPI, and reform action plan. The
technical sessions on computation were conducted in
the presence of the State Statistical Officers and dis-
trict officials to generate feedback and insights from
their experience on the ground. The feature of the in-
dex –to estimate multidimensional poverty for the dis-
tricts of a State, generated significant interest among
the stakeholders for the measure itself. Insights on
district performance including that of headcount ra- tio, intensity of poverty, factors causing poverty, cen- sored and uncensored deprivations and contribution to MPI, were of keen interest to the departments and district administrations.
Deliberations in these 20 State-level MPI workshops
largely focused on the State-specific developmental
specificities, experience in domains of public service
delivery, challenges in the related sectors contextu-
al to the developmental reality of the State, and in-
tra-State diversities. Issues related to convergence
of inter-departmental action, synergies across line
departments to achieve the broad outcomes aligned
with the parameters of the Index, challenges to the
efficacy of implementation of the aligned Centrally
Sponsored Schemes and State schemes, analysis of
sub-optimal effiencies in public service delivery, as-
sessment of the adequacy of financial, technical, and
human resources, level of capacities of the State sa-
tistical systems to generate high frequency data for
periodic monitoring, inter alia, were some of the most
recurring discussion points that emerged during the
MPI workshops in the States. The common outcome of
these numerous State meetings was consensus that it
is important to monitor and reduce multidimensional
poverty, MPI is a technically robust and contextually
relevant instrument, and progress monitoring at dis-
trict level is crucial.
The draft State Reform Action Action Plan with an
illustrative set of reform areas and actions for each
indicator to achieve progress as measured by the In-
dex was also presented to the Chief Secretaries and
Heads of Departments in these workshops. The re-
form action plan has to be further customised by the
State Governments to align it with its own context and
reality. Therefore, the workshops paved the way for i)
the introduction and deliberation on this important
measure of multidimensional poverty which goes as
granular as the district, ii) presentation on the causal
factors to the MPI estimates and the areas of improve-
ments and iii) discussions on formulation of reform ar-
eas and actions for improving the lives of households
in the sectors under the purview of the MPI.
The process of developing the national MPI, since its
beginning, has been a collaborative one. As the tool
has to have both policy utility and technical robust-
ness, no stone has been left unturned in ensuring
that expert opinions, ground realities, and practical
approaches have been accommodated. Both central
ministries and State governments contributed im-
mensely to this process. The road ahead too will be
collaborative, with active involvement of all relevant
stakeholders.
Assam
Andhra Pradesh
Arunachal Pradesh
Goa
Bihar
Chhattisgarh
Jammu & Kashmir
Gujarat
Himachal Pradesh
Kerala
Jharkhand
Karnataka
Uttar Pradesh
Tamil Nadu
Uttarakhand
Meghalaya
Madhya Pradesh
Maharashtra
Sikkim
Nagaland
Odisha
WORKSHOPS AND CONSULTATIONS HELD
WITH 21 STATES AND UNION TERRITORIES
States/UTs remaining States/UTs where workshops and consultations were held
INDIA MPI BASELINE REPORT 10 11
Developing India’s MPIChapter 2
ENGAGEMENT WITH STATES & UNION TERRITORIES ENGAGEMENT WITH STATES & UNION TERRITORIES
Developing India’s MPIChapter 2
SECTION
II
METHODOLOGY
&
WAY FORWARD
INDIA MPI 14 15
MethodologyCHAPTER Chapter 3
COMPUTING INDIA’S MPI
METHODOLOGY
3.1 The Alkire-Foster Methodology
At the core of the MPI is the Alkire-Foster (AF) meth-
odology. The AF methodology is a general framework
for measuring multidimensional poverty that identi-
fies people as poor or not poor based on a dual-cutoff
counting method. The first order cut-off within each
component indicator is applied to determine which
person is “deprived” in that indicator. The information
across all indicators is then aggregated to arrive at
a deprivation score for each individual. The second
order cut-off is then applied to identify the individ-
uals who are multidimensionally poor. The AF meth-
odology is an extension of the widely accepted Fos-
ter-Greer-Thorbecke (FGT) class of poverty measures
and has a range of technical and practical advantages
that make it favorable for use in non-monetary pover-
ty estimation.
Poised within a family of axiomatic measures, the AF
methodology achieves multiple technical milestones
associated with poverty measures including dimen-
sional monotonicity, subgroup decomposability, scale
and replication invariance, poverty and deprivation
focus and symmetry. This ability of the AF methodol-
ogy to provide an idea of not only the degree of pov-
erty but also its composition and distribution is what
makes it a powerful tool for decision-making.
The AF methodology’s intuitive counting approach
for poverty identification, explicit consideration of
joint distributions, consistent partial indices and most
importantly, its ability to utilize ordinal or binary data, make it adaptable to existing data systems without the need to introduce any specialized modules within surveys that relate only to the estimation of multidi- mensional poverty.
The dual-cutoff approach of the AF methodology also
mitigates a number of issues that arise from the union
and intersection approaches in the measurement of
multidimensional poverty with the former tending to-
wards overestimation and the latter tending towards
underestimation. The flexibility it provides (within
bounds of logic and reason) in terms of selection of
indicators, determination of first and second order
cutoffs and indicator weights adds a layer of custom-
ization essential for the construction of a multidimen-
sional poverty measure suited to the national context.
3.2 Steps in computing the MPI
The process of computing the MPI can be divided into
2 broad categories: i) Identification, and ii) Aggre-
gation.
3.2.1 Identification
i.
Detmine the set of indicators to be used in the
MPI and group thematically similar indicators into dimensions. For example, years of schooling and school attendance are indicators under the di- mension of education.
3
31
ii. Set the deprivation cut-offs for each indicator, i.e.,
the level of achievement considered normatively sufficient in order for an individual to be consid- ered not deprived in an indicator. E.g., the individ- ual has completed at least six years of schooling.
iii.
Apply the cutoff and determine whether the indi-
vidual is deprived in each indicator.
iv. Select weights to be applied to each indicator
such that the sum of the weights for all indicators adds up to 1. Optionally, the weights of the indica- tors could be such that the weight attributable to each dimension (i.e. the sum of the weights of the indicators in that dimension) is the same.
v.
Calculate the weighted sum of deprivations for
each individual. This is known as their deprivation score.
vi.
Apply the second order cutoff, i.e., the proportion
of weighted deprivations that an individual needs to experience to be identified as multidimension- ally poor. India’s national MPI follows the second order cutoff of 33.33 percent used in the global MPI measure.
3.2.2 Aggregation
i. Det
as multidimensionally poor in the population. This is known as the headcount ratio (H) of the MPI or the incidence of poverty. The headcount ratio broadly explains ‘how many are poor’.
ii.
Determine the average share of weighted indica-
tors in which multidimensionally poor individuals are deprived i.e., add the deprivation scores of the poor and divide it by the total number of poor in- dividuals. This is known as the intensity of poverty (A) in the MPI or the breadth of poverty, which broadly explains ‘how poor are the poor’.
iii.
Compute the MPI score (M
0
) as the product of the
partial indices of Headcount Ratio and Intensity.
3.3 Indicators in India’s National MPI
The national MPI model retains the ten indicators of the global MPI model, essentially to be closely aligned to the global methodology and rankings. India’s MPI has three equally weighted dimensions – health, edu- cation, and standard of living - which are represented by twelve indicators (Figure 1).
Figure 1: INDICATORS IN INDIA'S NATIONAL MPI
Education
Standard
of Living
1/3
1/3
1/3
1/6
1/6
1/6
1/12
1/12
1/21
1/21
1/21
1/21
1/21
1/21
1/21
A household is considered deprived if any child between the ages of 0 to 59 months, or woman
between the ages of 15 to 49 years, or man between the ages of 15 to 54 years -for whom nutri-
tional information is available - is found to be undernourished.
A child/adolescent under 18 years of age has died in the family in the five-year period preceding
the survey.
A household is deprived if any woman in the household who has given birth in the 5 years pre-
ceding the survey, has not received at least 4 antenatal care visits for the most recent birth, or has
not received assistance from trained skilled medical personnel during the most recent childbirth.
Not even one member of the household aged 10 years or older has completed six years of
schooling.
Any school-aged child is not attending school up to the age at which he/she would complete
class 8.
A household cooks with dung, agricultural crops, shrubs, wood, charcoal or coal.
The household has unimproved or no sanitation facility or it is improved but shared with other
households.
The household does not have access to improved drinking water or safe drinking water is at least
a 30-minute walk from home (as a round trip).
The household has no electricity.
The household has inadequate housing: the floor is made of natural materials, or the roof or wall
are made of rudimentary materials.
The household does not own more than one of these assets: radio, TV, telephone, computer,
animal cart, bicycle, motorbike, or refrigerator; and does not own a car or truck.
No household member has a bank account or a post office account.
Nutrition
Child & Adolescent
Mortality
Antenatal Care
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
Dimension Weights Indicator Deprived if
Health
INDIA MPI BASELINE REPORT 16 17
MethodologyChapter 3
3.3.1 Dimension: Health
The ‘health’ dimension comprises parameters repre-
senting nutrition, child mortality and maternal health.
The indicators for Nutrition and Child Mortality echo
the definitions and cutoffs followed by their coun-
terparts in the Global MPI. The indicator for Maternal
Health is unique to India’s national MPI. A point of note
is that in the national MPI, the Child Mortality indica-
tor has been renamed as Child-Adolescent Mortality.
According to the parlance of the Indian statistical sys-
tem, the use of the term “Child Mortality” is usually
associated with mortality under 5 years of age. Given
that the indicator in the MPI refers to deaths below 18
years of age, the indicator has been renamed so as
to mitigate confusion arising from the nomenclature.
Digressing from the precedent set by the Global MPI
measure, the indicators in the dimension for Health
are not equally weighted. Nutrition – with a weight of
1/6, carries half the dimension weight of 1/3. The re-
maining dimension weight is split between Child-Ad-
olescent Mortality and Maternal Health, with each in-
dicator having a weight of 1/12. The sharing of weights
between the Child-Mortality and Maternal Health Mor-
tality prevents the overall MPI measure from favoring
households with no children or households with no
births in the last 5 years, while allowing for the mon-
itoring of deprivations in the domains of childbirth,
and access to antenatal and maternal care. The shared
weights also allow for the indicator on Nutrition to re-
tain its weight in the Global MPI, thus enabling unifor-
mity in reporting across both.
3.3.1.i Nutrition
A household is considered deprived if any child be-
tween the ages of 0 to 59 months, or woman be -
tween the ages of 15 to 49 years, or man between the
ages of 15 t
o 54 years -for whom nutritional informa-
tion is available- is found to be undernourished.
A woman (15 to 49 years) or a man (15 to 54 years) is
considered undernourished if their Body Mass Index
(BMI) is below 18.5 kg/m
2
. Children under 5 years of
age are considered malnourished if their z-score of
height-for-age (stunting) or weight-for-age (under-
weight) is below minus two standard deviations from
the median of the reference population.
It is to be noted that even if a single member of the
household is identified as undernourished, the entire
household is treated as deprived in nutrition. This is
owing to two primary reasons: i) the unit of analysis is
the household, and ii) the indicator for nutrition oper- ates within the implicit principle of shared positive or negative externality, wherein the debilitating effects of undernourishment on one household member will have a direct or indirect effect on other members of the same household.
Contributing to nearly one-third of the multidimen-
sional poverty in India, nutrition is arguably one of the
most important indicators in India’s national MPI. Mal-
nutrition has significant consequences to early child-
hood development as well as to the health and overall
wellbeing of adults. The indicator for nutrition carries
a weight of 1/6 and its definition is aligned with the
Global MPI.
3.3.1.ii Child-Adolescent Mortality
A household is deprived if any child or adolescent
under 18 years of age has died in the household in
the five-year period preceding the survey.
The child-adolescent mortality indicator is based on
birth history data provided by mothers aged 15-49
years. However, if the data from the mother is missing,
and if the male in the household reported no child-ad-
olescent mortality, then the household is reported to
be not deprived. A household with no children would
also be treated as not deprived.
For the five-year period preceding NFHS 2015-16, the
neonatal mortality rate (i.e., the probability of death
within the first month of life) was 30 deaths per 1000
live births. This means that one newborn in 33 live
births died during the neonatal period. The under-five
mortality rate during this time period stood at 50
deaths per 1000 live births, indicating that one in 20
children in India, died before their fifth birthday. A sig-
nificant proportion of neonatal deaths are a result of
preventable diseases and, lack of access to pediatric
healthcare. Certain demographic risk factors can also
be identified with under five deaths being significant-
ly higher among vulnerable population groups (for in-
stance, Scheduled Tribes, and Scheduled Castes) and
among the lowest wealth quintiles (Ministry of Health
and Family Welfare, 2017).
The rationale behind the indicator on Child-Adoles-
cent Mortality and the age group it considers is that it
is indicative of the set of deprivations experienced by
the household which may have contributed to the un-
fortunate demise of a child in the household and the
effect of that incident on the set of deprivations that
the household may experience over time.
The death of a child or adolescent in a household is emblematic of a larger set of deprivations already experienced by the household. Factors such as, lack of access to healthcare, infectious diseases, malnutri- tion, iron-deficiency anemia, an unsafe environment are all contributors to child and adolescent mortal- ity (WHO, 2017). The death of a child or adolescent therefore indicates the deprivations experienced by a household in one or more of these factors. Further- more, it highlights the risks that other living children or adolescents in the household are being exposed to.
Child-Adolescent mortality also possesses multiple
negative externalities which directly affect all indi-
viduals and in extension the deprivation status of the
individuals in that household. These externalities can
manifest in a number of different ways over time.
For example, the death of a school-going child aged
10 years or older may deprive the household of their
only member who had attained more than six years
of schooling, thereby depriving them of the positive
externalities that are associated with having a house-
hold member with formal education. Another exam-
ple, where a more direct effect can be observed is
where a 17-year-old adolescent who was an earning
member of a household in the lowest wealth quintile
passes away resulting in their sibling having to drop
out of school.
It is therefore that the death of a child or adolescent
below the age of 18 years in a household is normative-
ly considered a tragedy for the household and is in-
cluded as a determinant for multidimensional poverty.
The indicator for Child-Adolescent Mortality carries a
weight of 1/12 and its definition remains aligned with
the Global MPI.
3.3.1.iii Maternal
Health
A household is deprived if any woman in the house-
hold who has given birth in the 5 years preceding the
survey has not received at least 4 antenatal care
visits for the most recent birth or has not received assistance from trained skilled medical personnel during the most recent childbirth.
Introduced as a new indicator to India’s national MPI, the indicator for Maternal Health is a union of two dis- tinct components – antenatal care and assisted deliv-
ery. The indicator captures if a woman in the house- hold who has given birth in the 5 years preceding the survey has received at least 4 antenatal care visits and has received assistance from skilled medical person-
nel during the most recent childbirth. Not fulfilling any one of the two criteria would cause the household to be considered deprived. If the household has not had any births in the 5 years preceding the survey, it would be considered to be not deprived in this indicator. The indicator carries a weight of 1/12.
Antenatal care (ANC) and assisted delivery, even
when taken in isolation, form a critical prerequisite to
positive healthcare outcomes for mothers and new-
borns alike. With a significant percentage of maternal
deaths occurring during the period of pregnancy, the
four-visit antenatal care model outlined in the WHO
clinical guidelines has been instrumental in the early
identification of complications in pregnancy, monitor-
ing of foetal growth and the management of compli-
cations through the referral of mothers to the appro-
priate facility for further treatment.
In India, as per NFHS-4, only 51 percent of women had
received at least 4 ANC visits during their most recent
pregnancy with the highest proportion of women be-
ing in Kerala (90 percent) and the lowest in Bihar (14
percent). There also exists a significant disparity of
ANC among income groups with women in the high-
est wealth quintile being almost twice as likely to have
received ANC from a skilled provider than women in
the lowest wealth quintile.
Of all globally reported child deaths, 2.5 million oc-
curred within the first month of life and 2 million were
stillbirths (UNICEF, WHO, World Bank, United Nations,
2019). According to NFHS-4, in India, approximately
60 percent of deaths below 18 years are neo-natal and
infant deaths occurring before the completion of the
first month since birth and before the completion of
one year since birth, respectively.
The causes of nearly 80 percent of new-born deaths
can be identified and there are solutions to address
them, preventing death or life-long disability (WHO,
UNICEF, 2014). These causes are - complications due
to prematurity, intrapartum deaths, and neonatal in-
fections. Thus, ANC cannot be looked at in isolation
as prevention of intrapartum deaths requires quality
care provided during childbirth. In India, 81 percent of
live births were assisted by a skilled provider. 93 per-
cent of women who had received four or more ANC
visits also received skilled assistance during delivery
as compared to only 60 percent of women who had
no ANC visits.
It is based on this premise that India’s national MPI
digresses from the precedence of Afghanistan, Gua-
INDIA MPI BASELINE REPORT 18 19
MethodologyChapter 3
temala, Panama, and Pakistan (which have indicators
for either one of the two, ANC or assisted delivery in
their respective national MPIs). India’s MPI seeks to
adopt a stricter union measure when determining the
deprivation status of an individual in Maternal Health -
ensuring that an expectant mother must receive both
4 or more antenatal care visits and be assisted by
skilled personnel during childbirth.
India being a signatory to the 2030 Agenda, the ma-
ternal health indicator in the national MPI aims to en-
force strict compliance to the SDG targets of reduc-
ing maternal mortality and end preventable deaths of
new-borns in the country.
3.3.2 Dimension:
Education
The ‘Education’ dimension is represented by param- eters pertaining to school attendance and years of schooling, with each indicator – weighted at 1/6 - car- rying half of the dimension weight (1/3) for Education. The definitions and cut-offs for the indicators remain unchanged and aligned with the Global MPI.
3.3.2.i Years of Schooling
A household is deprived if not even one member of the household aged 10 years or older has completed six years of schooling.
Years of schooling has a shared positive effect on the household, wherein even if one member has more than six years of schooling, the positive effect of that education, be in terms of increase in economic oppor- tunities such as the ability to enter high paying em- ployment or in terms of improvement in social stand- ing, is shared among all members of the household.
A point to be noted is that because of the nature of
the indicator, an individual living in a household where
there is at least one member with six years of school-
ing is considered to be non-deprived, even though
they themselves may not have attended school. The
indicator carries a weight of 1/6.
3.3.2.ii School Attendance
A household is deprived if any school-aged child is
not attending school up to the age at which he/she
would complete class 8.
The indicator for school attendance is the logical pre-
cursor to the indicator for years of schooling. A child
not attending school is indicative of both the present
set of deprivations experienced by the household as
well as the possible future deprivations that may un-
fold as a result of the child not attending school. A child not attending school is emblematic of a greater set of deprivations being experienced by the house- hold that acts as an impediment to the education of the child. Similarly, because the child is not attending school, the household members will be deprived of the positive externalities that arise from having a for- mally educated member in the household.
An individual living in a household where there is at
least one child not attending school, is treated as de-
prived in this indicator, even though they themselves
may have completed schooling. The indicator has a
weight of 1/6.
3.3.3 Dimension: Standard of Living
Lastly, the dimension for ‘Standard of Living’ compris- es parameters representing access of the household to basic services such as electricity, clean cooking fuel, improved and safe drinking water, improved sanitation, pucca housing (proper flooring, roof and walls), bank account, and household assets. All indi- cators with the exception of the indicator for bank ac- counts – which is unique to India’s national MPI – align with the global definitions and cutoffs. The dimension weight of 1/3 is split evenly across all the seven indica- tors therefore giving each a weight of 1/21.
3.3.3.i Cooking Fuel
A household is deprived if the primary source of cooking fuel is dung, agricultural crops, shrubs, wood, charcoal or coal.
Improved or safe sources of cooking fuel include elec- tricity, LPG/natural gas, biogas. A point of importance here is that simply the presence of an improved/safe source of cooking fuel in the household is not enough to warrant a “not deprived” status. The household must also be utilizing the improved/safe source of cooking fuel as their primary source of cooking fuel - i.e. a household may have a LPG connection and stove, but if wood/coal is the primary (most used) fuel for cooking, then the household will be considered to be deprived in the indicator.
3.3.3.ii Sanitation
The household has unimproved or no sanitation fa- cility or it is improved but shared with other house-
holds.
Improved sanitation includes any non-shared toilet of the following types: flush/pour flush toilets to piped sewer systems, septic tanks, and pit latrines; venti-
lated improved pit (VIP)/biogas latrines; pit latrines with slabs; and twin pit/composting toilets. It must be noted that exclusive access to an improved sanitation facility, which is not shared with members of another household, is required for a household to be consid- ered not deprived in this indicator.
3.3.3.iii Drinking Water
A household is deprived if it does not have access to improved drinking water or safe drinking water is more than a 30-minute walk from home (as a round trip).
Safe or improved sources of drinking water include piped water supply, public taps, standpipes, tube wells, boreholes, protected dug wells and springs, rainwater, and community reverse osmosis (RO) plants. Even if a household has access to an improved water source, it will be considered deprived in this in- dicator if the source is more than 30-minute roundtrip walk from home.
3.3.3.iv Electricity
A household is deprived if it has no electricity.
Access to household electricity has a multiplier effect on any household and deprivation in this basic and essential service is ground for treating any household as deprived.
3.3.3.v Housing
A household is deprived if it has inadequate hous-
ing: the floor is made of natural materials, or the roof or w
alls are made of rudimentary materials.
Mud/clay/earth, sand and dung are considered rudi- mentary/natural materials.
3.3.3.vi
Ownership of Assets
The household is deprived if it does not own more than one of these assets: radio, T
V, telephone, com-
puter, animal cart, bicycle, motorbike, or refrigera-
tor; and does not own a car or truck.
In the case of the indicator for assets, the criteria for the car or truck ownership acts as an exclusion cri- teria. Therefore, even if a household does not have a radio, television, telephone, computer, animal cart, bicycle, motorbike, or refrigerator, but has either a car or a truck, then the household will be treated as non-deprived.
3.3.3.vii Bank Account
No household member has a bank account or a post office account.
The indicator for bank accounts is an additional indi- cator in India’s national MPI. The ownership of a bank account or post office account is the key to financial inclusion of the hitherto unbanked households. The access of a household to a bank account is critical for availing the benefits of several flagship government programs aimed at reduction of poverty, increas- ing access to education, and creation of livelihoods – which often utilize direct benefit transfers. Bank accounts also play an important role in the delivery of public services, access to institutionalized credit and also act as long-term savings instruments - ei- ther through self-deposits or through institutionalized savings schemes.
Extensive evidence suggests that there exists a strong
and positive correlation between access to financial
services and improved capabilities and functionings.
Empirical studies that have analyzed spatial data have
cited significant correlation between areas with lower
banking access and higher or relatively severe inci-
dences of poverty (Iqbal, Roy, & Alam, 2020). Oth-
er studies which have probed demographic datasets
have concluded that financial inclusion plays an im-
portant role in preventing a household’s exposure to
future poverty while also aiding in sustained escapes
from poverty, especially female-headed households
(Koomson, Villano, & Hadley, 2020).
These factors necessitate the inclusion of an indica-
tor pertaining to financial inclusion in India’s national
MPI not only to identify the geographical regions and
population sub-groups where immediate intervention
is required but also to ensure that the efforts to fast-
track financial inclusion in India are sustained.
The SDG target 8.1 focuses on strengthening the ca-
pacity of domestic financial institutions to encourage
and expand access to banking and financial services
for all. The inclusion of the indicator for bank accounts
thus allows for the national MPI to have a larger cov-
erage of and increased cross-linkages with the SDGs.
At the global front, the national MPIs of Rwanda and
Sierra Leone also include indicator for bank accounts
with the former having included it in a new dimen-
sion titled as “Social Services and Economic Inequal-
ity” and the latter having kept in in the dimension for
“Standard of Living”.
INDIA MPI BASELINE REPORT 20 21
MethodologyChapter 3
3.4 Computing the MPI
As stated previously, the process of computing the
MPI is divided into two distinct stages – identification
and aggregation. Identification involves obtaining the
deprivation score for every individual followed by
censoring of deprivation scores to identify the multi-
dimensionally poor for a given cutoff. Aggregation in-
volves the estimation of two partial indices, headcount
ratio and intensity, the product of which provides us
with the MPI. Each of the aforementioned concepts
has been detailed in the following paragraphs.
3.4.1 Identifying the poor
Based on the AF methodology, identification of the
poor is dependent on a set of within-indicator depri-
vation cutoffs as well as an across-indicators depri-
vation cutoff (hence the term dual-cutoff approach).
The cutoffs within indicators (also known as the
first-order cutoff) are used to determine the depri-
vation score while the across-indicator cutoff (also
known as the second-order cutoff) is used to finally
determine who is multidimensionally poor. Both con-
cepts have been detailed in the following sections.
3.4.1.i Deprivation Score
Each individual (and in extension everyone in the
same household), is first marked as deprived (denot-
ed by 1) or not deprived (denoted by 0) in each of
the indicators based on their achievement (or lack
thereof) in the respective first order cutoffs for each
indicator.
For example, if an 18-year-old individual (referred to
as A for the sake of simplicity) has 3 years of school-
ing, she does not meet the first order cutoff for the
indicator on years of schooling (any individual aged
10 years or older must have at least 6 years of school-
ing). Therefore, A is considered deprived in the indica-
tor for years of schooling and assigned a score of 1 for
that indicator. Conversely, individual B has 7 years of
schooling and is 12 years old, therefore B is assigned a
score of 0 for the indicator on years of schooling. This
process is repeated for each indicator until individuals
A and B have been assigned a score for all indicators.
The next step is to determine the counting vector also
known as the deprivation score for the individual. The
deprivation score is the sum of the weighted status of
all the indicators for an individual.
Extending the previous example, individual A is de-
prived in the indicator for years of schooling. The
weighted status of the indicator for A would then be
1 (the number assigned to her denoting that she is
Deprivation Status
If the achievement of an individual �� in indicator �� is denoted by ��
����
,
the first order cutoff for indicator �� is denoted by ��
��
, and the status
of the individual is denoted as g
0
����
, then
g
0
����
=1 if ��
����
<��
��
& g
0
����
=0 otherwise for all ��=1,2⋯n & ��=1,2⋯d
Note to the Reader
In order to facilitate ease of reading, the section on the computa-
tion of the MPI has been divided into two columns, the left column
provides the descriptive overview of the process of arriving at In-
dia’s national MPI and its various partial indices while the right
column provides the associated mathematical notations and il-
lustrations for the concepts provided in the left column.
Example: Finding g
0
����
for Individual A
Steps in Computing the MPI
Identification 1
Build a deprivation profile by applying cutoffs within an indicator
Identify who is multidimensionally poor by applying a cutoff across
all indicators
Calculate the Intensity of Poverty
(A):
On average, how poor are the poor?
Calculate the Headcount Ratio
(H):
How many are poor?
Compute the MPI by taking the product of H and A (MPI=HxA)
Aggregation 2
Has 6 years of schooling
Does not have 6 years of schooling
Indicator Deprived? Status (g
0
����
)
Individual
A
1 Ye s
0 No
deprived) multiplied by 1/6 (which is the weight as-
signed to the indicator for years of schooling. Thus,
A’s weighted status for indicator on years of schooling
would be 1/6 or 0.166. Following this, the weighted
status for individual B would be 0. This is repeated for
all the indicators following which the weighted scores
are added, giving us the deprivation scores for A and
B. Because the weight structure follows the AF meth-
odology, the sum of the relative weights of all the in-
dicators equals to 1.
3.4.1.ii
Po
The second-order cutoff (��), defined in the AF meth- odology as the poverty cutoff is the deprivation score which is the identifier for multidimensional poverty. Individuals with a deprivation score greater than or equal to the second-order cutoff are identified as multidimensionally poor.
For example, if the second-order cutoff is 0.33 and
individual A has a deprivation score of 0.54, then she
is considered multidimensionally poor. Likewise, if in-
dividual B has a deprivation score of 0.28, she will not
be considered multidimensionally poor even though
she has a non-zero deprivation score.
India for its national MPI has adopted the second-or-
der cutoff of 0.33 which is also the standard cutoff
used globally. Thus, for an individual to be considered
as multidimensionally poor, she should be deprived in
at least 1/3rd of the weighted indicators.
It is at this juncture that the potential of the AF meth-
odology is realized. The union method of multidimen-
sional poverty identification considers an individual to
be poor if she is deprived in even one indicator – lead-
ing to overestimation, while the intersection method
only considers an individual as poor if she is deprived
in all indicators, leading to underestimation. Neither
of these provides any useful insights to a policy mak-
er. The AF methodology, with its dual cutoff approach
thus provides a realistic middle ground for poverty
estimation.
3.4.1.iii Censoring
Following the computation of the deprivation scores
for all individuals, those individuals for whom the
score is less than the second order cutoff, is replaced
with 0. This step in known as censoring in multidimen-
sional poverty estimations.
Following our example, the deprivation score of indi-
vidual A (0.54) will remain unaltered while the score
of individual B (0.28) will be replaced with 0.
Counting Vector and Deprivation Score
The counting vector for individual �� up to the ��
th
indicator (denoted
by c
��
), also known as deprivation score, is their status in each indi-
cator (g
0
����
) multiplied by the weight (w
��
) assigned to that indicator.
The deprivation score (or weighted deprivation) of individual ��
can thus be denoted as:
c
��
= w
1
g
0
��1
+
w
2
g
0
��2
+
...+ w
��
g
0
����
w
��
g
0
����
or
d
��=1
⅀c
��
= w
��
=1where
d
��=1
⅀
Example: Calculating the Deprivation Score for Individual A
Indicator WeightsDeprived?Status (g
0
����
) Score (w
��
g
0
����
)
1/6 Ye s 1 0.16X = Nutrition
Child & Adolescent Mortality 1/12 No 0 0X =
Maternal Health 1/12 Ye s 1 0.08X =
Years of Schooling 1/6 Ye s 1 0.16X =
School Attendance 1/6 No 0 0X =
Cooking Fuel 1/21 Ye s 1 0.04X =
Sanitation 1/21 No 0 0X =
Electricity 1/21 No 0 0X =
Drinking Water 1/21 No 0 0X =
Housing 1/21 Ye s 1 0.04X =
X = Assets 1/21 No 0 0
X = 1/21 No 0 0 Bank Account
Deprivation Score (c
��
) 0.48=
Applying the Poverty Cut-off
The identification function for multidimensional poverty is denot- ed by ρ. The function ρ is dependent on the deprivation status of
an individual (��
��
) given the cutoffs within an indicator (��) as well as
on the cutoffs across indicators (��) and is therefore represented by:
ρ
��
(��
��
;��)=
1 if c
��
≥�� and ρ
��
(��
��
;��)=0 otherwise
Therefore, the function ρ considers an individual �� as multidimen-
sionally poor when her deprivation score (c
��
) is greater than of
equal to the second-order cutoff (��).
Example: Applying the Poverty-Cutoff
Censored Deprivation Scores
Censored scores are denoted as c
��
(��) to differentiate them from
deprivation scores c��. Thus, if c
��
<��, then c
��
(��)=0 and if c
��
≥�� then
c
��
(��)=c
��
. Thus, c
��
(��) is the deprivation score of the multidimension-
ally poor.
Example: Censoring in MPI
Deprivation
Score (c
��
)
Higher than
0.33? (c
��
≥��)
Is MPI Poor?
Score
ρ
��
(��
��
;��)
Individual A
No No 0 Individual B 0.20
0.48 Ye s Ye s 1
Deprivation
Score (c
��
)
Higher than
0.33? (c
��
≥��)
Is MPI Poor?
Censored
Deprivation
Score (c
��
(��))
Individual A
No No 0 Individual B 0.20
0.48 Ye s Ye s 0.48
INDIA MPI BASELINE REPORT 22 23
MethodologyChapter 3
3.4.2 Headcount Ratio
Following the identification of multidimensionally
poor individuals, the next step is to determine the
proportion of multidimensionally poor individuals in
the total population. This is known as the headcount
ratio of multidimensional poverty or the incidence of
poverty and is the first of two partial indices used to
determine the MPI. The headcount ratio (denoted by
H) answers the question ‘how many are poor?’ India’s
national MPI identifies 25.01 percent of the population
as multidimensionally poor.
3.4.2.i Uncensored (
Raw) Headcount Ratio
While the headcount ratio (H) provides the proportion of multidimensionally poor individuals in the popula- tion, the uncensored headcount ratio (denoted by h
��
)
provides the proportion of individuals who are de- prived in an indicator �� irrespective of whether they are multidimensionally poor or not.
The uncensored headcount ratios of the indicators in
India’s MPI have been provided in Figure 2. Each bar
represents the percentage of India’s population who
are deprived in that indicator.
3.4.2.ii Censored
Headcount Ratio
Akin to its uncensored counterpart, the censored headcount ratio (denoted by h
��
(��)) provides the pro-
portion of individuals who are multidimensionally poor and deprived in an indicator ��.
The censored headcount ratios of the indicators in In-
dia’s MPI have been provided in
Figure 3. Each bar rep-
resents the percentage of individuals who are multi-
dimensionally poor and are deprived in that indicator.
3.4.3 Intensity of
Poverty
The intensity of poverty (denoted by A) is the average proportion of deprivations which is experienced by multidimensionally poor individuals. It is the average deprivation score of all multidimensionally poor indi- viduals. A is the second partial index used in the con-
struction of the MPI and answers the question how poor are the poor?
3.4.4
TPI
The Multidimensional Poverty Index reflects both the incidence and the intensity of multidimensional pov-
erty. The index (denoted by M
0
) is the product of the
two partial indices, the headcount ratio (H ) and inten-
sity (A) of multidimensional poverty. This can also be defined as the share of population that is multidimen- sionally poor adjusted by the intensity of deprivation.
Headcount Ratio
where q is the total number of multidimensionally poor individ-
uals identified (i.e., the total number of individuals for whom
ρ
��
(����;��)=1) and �� is the total population. In this report, the head-
count ratio has been reported as a percentage (H×100 ).
H=
q
��
Uncensored Headcount Ratio
where denotes the sum of the deprivation status up to the ��
th
individual for the indicator �� and �� is the total population. In this
report, the uncensored headcount ratios have been reported as
percentages (h
��
×100).
g
0
����
��
��=1
⅀h
��
=
1
��
g
0
����
��
��=1
⅀
Censored Headcount Ratio
where �� is the total population, and g
0
����
(��) is the censored depriva-
tion score of individual �� in indicator �� using a second-order cutoff (��) of 33.33 percent. In this report, the censored headcount ratios have been reported as percentages (h
��
(��)×100).
g
0
����
(��)
��
��=1
⅀h
��
(��)=
1
��
Intensity
Where c
��
(��) is the censored deprivation score (i.e. deprivation
score of multidimensionally poor individuals) up to the ��
th
individu-
al and q is the number of multidimensionally poor individuals.
c
��
(��)
q
��=1
⅀A=
1
q
Multidimensional Poverty Index
M
0
=H��A
q
��
c
��
(��)
q
��=1
⅀
1
q
H��A=�� = c
��
(��)
��
��=1
⅀
1
��
= w
��
g
0
����
(��)
��
��=1
⅀
1
��
d
��=1
⅀or
The MPI, therefore, is the share of weighted deprivations faced by
multidimensionally poor individuals divided by the total popula-
tion. The MPI is therefore known as the adjusted headcount ratio.
Percentage of the total population of India who are deprived in each indicator
37.6%
2.7%
22.6%
13.9%
6.4%
14.6%
45.6%
14.0%
9.7%
58.5%
41.4%
52.0%
29.8%
12.2%
3.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population deprived
19.90%
1.88%
14.71%
10.71%
5.23%
23.13%
21.32%
5.53%
8.29%
20.56%
8.87%
5.37%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population MPI poor & deprived
Figure 2: Uncensored Headcount Ratio
Percentage of total population of India who are multidimensionally poor and deprived in each indicator
Figure 3: Censored Headcount Ratio
Dimension Health Education Standard of Living NFHS-5: Standard of Living
Note on comparison: The striped bars denote the provisional estimates for the uncensored headcount ratio based on the data available
in the NFHS-5 India Factsheet (2019-20).
INDIA MPI BASELINE REPORT 24 25
MethodologyChapter 3
3.4.4.i Why is the adjustment important?
An understandable question at this point would be as
to why the adjustment (using the intensity of poverty)
is required when the headcount ratio already identi-
fies who is multidimensionally poor.
Traditionally, poverty measures (such as poverty lines)
would utilize a single threshold to determine if an in-
dividual was poor or not. However, this would only
convey the information regarding number of people
in poverty but not the extent of their poverty.
Therefore, any change in the level of deprivations (for
better or for worse) faced by an individual in pover-
ty would not affect the poverty measure unless the
change was substantial enough to make the individual
cross the determined poverty threshold.
To put it in simpler terms, traditional poverty mea-
sures would remain unaltered if an individual who is
already poor became poorer, or an individual who is
poor became less poor but not enough to cross the
poverty line. This means that these measures do not adhere to the axiom of dimensional monotonicity in poverty measurement i.e., if the number of depriva- tions faced by poor individuals decreases, then the overall poverty measure should also decrease and vice versa.
M
0
(or the MPI) estimated by the AF methodology is
dependent both on the headcount ratio as well as the
intensity of poverty, and therefore may change if the
headcount ratio decreases/ increases (i.e. the abso-
lute number of people in poverty decreases/ increas-
es) or if the deprivations faced by multidimensionally
poor individuals decrease/ increase (which may hap-
pen without changing the headcount ratio). There-
fore, the MPI adheres to the axiom of dimensional
monotonicity.
Thus, for policy makers the MPI presents a responsive
measure that improves not only when the absolute
number of individuals in poverty decreases, but also
when the severity of poverty experienced by a multi-
dimensionally poor individual decreases.
Headcount Ratio
The Headcount Ratio is computed by di-
viding the total number of multidimen-
sional poor (q) by the total population (��)
q=7+5 & ��=7+5+4
H=
q
��
7+5
7+5+4
12
16
0.75= = =
In this illustration, 75% of individuals are
multidimensionally poor
Intensity of Poverty
The Intensity of multidimensional poverty is computed by summing the weighted deprivation scores of all the MPI poor di- vided by the total number of MPI poor
On an average, an MPI poor individual is deprived in 59% of weighted indicators
0.68��7+0.48��5
7+5
= 0.596=c
��
(��)
q
��=1
⅀A=
1
q
Multidimensional Poverty Index
The MPI score is the product of the head- count ratio and intensity. It is known as the adjusted headcount ratio
MPI=H��A=0.75��0.596=0.447
Example: Calculating the Headcount Ratio, Intensity and MPI for 3 Households
Indicator
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Electricity
Drinking Water
Housing
Assets
Bank Account
Deprivation Score (c
��
) =
Censored Deprivation Score (c
��
(��))=
Members of HH1 and HH2 are
multidimensionally poor
HH2
5 members
Status (g
0
����
)
1
0
1
1
0
1
0
0
0
1
0
0
HH1
7 members
Status (g
0
����
)
1
0
1
1
1
1
1
0
0
1
0
0
=
=
=
=
=
=
=
=
=
=
=
=
1/6X
1/12X
1/12X
1/6X
1/6X
1/21X
1/21X
1/21X
1/21X
1/21X X
1/21
X 1/21
Weights
HH2
5 members
0.16
0
0.08
0.16
0
0.04
0
0
0
0.04
0
0
Score (w
��
g
0
����
)
HH3
4 members
0
0.08
0.08
0
0
0.04
0
0
0
0
0
0
Score (w
��
g
0
����
)
HH3
4 members
Status (g
0
����
)
0
1
1
0
0
1
0
0
0
0
0
0
0.68
0.68
0.48
0.48
0.20
0
HH1
7 members
0.16
0
0.08
0.16
0.16
0.04
0.04
0
0
0.04
0
0
Score (w
��
g
0
����
)
X =
3.5 Deconstruction of Estimates & Indicators
One of the defining characteristics of the AF meth-
odology is sub-group decomposability, i.e. breaking
down estimates by sub-groups such as geographical
region and population groups. The AF methodology
also allows for deconstruction by indicators that can
allow the determination of the contribution of each
indicator to the MPI. This contribution can be deter-
mined for the total population as well as for each
sub-group. This ability to “drill-down” through the
estimates lends importance to the MPI at every ad-
ministrative level in India, from the Union Government,
the State Government and even the district adminis-
tration.
3.5.1
Estimates by geographical level and
popula
tion sub-groups
In order to arrive at the estimates for the headcount ratio, intensity and the adjusted headcount ratio (and the sub-components under the same), each sub- group is treated as the total population over which the estimates are computed.
For example, when computing the estimates for Dis-
trict ��, we will take all households in District �� and com-
pute the MPI like we would do for the total population,
i.e., we will carry out the end to end process of as-
signing deprivation scores, applying the second-order
cutoff, determining who is multidimensionally poor
and compute the aggregate estimates for only the
population in District ��.
Similarly, if we would like to look even further and de-
termine the estimates for the rural areas within Dis-
trict ��, then we would carry out the identification and
aggregation process for only the population living in
the rural area within District ��.
It would be prudent to note that a simple average of
sub-group estimates will not provide the estimate for
the parent group. Thus, taking the average of district
MPIs for a state will not provide the state MPI, nor will
taking the average of state MPIs provide the national
MPI. Only the population weighted sum of the sub-
group MPIs will provide the MPI for the larger group
it is a part of.
3.5.2 Contribution of Indicators
The MPI can be deconstructed into its component
censored indicators. Therefore, we can not only look
at the MPI for a certain sub-group, but we can also
look at the factors (i.e., indicators) which are contrib-
uting to multidimensional poverty for that sub-group.
Estimates for a Region: Example, Headcount Ratio
where q
��
is the total number of multidimensionally poor individ-
uals in region ��, where �� can be any region of interest such as a
State, a District, and even urban or rural areas within a selected
State or District. Intensity of poverty will then include the average
deprivations experienced by the multidimensionally poor individ-
uals in region ��.
H
��
=
q
��
��
��
Dissaggregation by Urban and Rural Areas
Let us assume that the MPI for District �� is MPI
��
and the MPI for the
urban and rural areas within District �� is MPI
u��
and MPI
r��
, therefore,
Where ��
��
denotes the total population in District ��, ��
u��
is the pop-
ulation living in the urban areas of District ��, and ��
r��
is the popula-
tion living in the rural areas of District �� assuming that ��
��
= ��
u��
+ ��
r��
.
MPI
��
=
��
u��
��
��MPI
u��
+MPI
r��
��
r��
��
��
Dissaggregation by Districts
Taking the above example forward, if we wish to arrive at the MPI for India from the MPI of the 640 districts in India’s national MPI, then:
Where, MPIc and �� are India’s MPI and population respectively,
MPI
��
and ��
��
are the MPI and population for the ��
th
district with ��
taking a value up to 640, equivalent to the number of districts in
the country as of 2011.
MPI
c
=
��
1
��
��
�� ��
MPI
1
+ MPI
��
MPI
2
+...+
��
2
��
2
��
��
MPI
��
640
��=1
⅀MPI
c
=
1
��
or
Disaggregations in this Report
The Baseline Report on the MPI Provides:
National, State and District level estimates of Headcount Ratio, Intensity
and the MPI based on the NFHS-4.
Uncensored and Censored Headcounts for 12 indicators of the national MPI up to the level of the Districts
Contribution of each of the 12 indicators to India's MPI Score up to the level of the Districts
Headcount Ratio, Intensity and the MPI for Urban and Rural areas upto the level of the Districts
INDIA MPI BASELINE REPORT 26 27
MethodologyChapter 3
The contribution of indicators is determined by divid-
ing the weighted censored headcount ratio for each
indicator by the MPI. This is multiplied by 100 to arrive
at the percentage contribution.
Analogous to the process of disaggregation by geo-
graphical and population sub-groups, the contribu-
tion of each region (e.g. how much does a district
contribute to the national figure) or of each popula-
tion group (e.g. how much does female/male poverty
contribute to the national figure) can be computed
through the method illustrated, where the weighted
censored headcounts is replaced by the population
weighted MPI for the sub-group.
3.5.2.i Why is looking at contributions import-
ant?
The contribution of an indicator provides an insight
into the relative deprivation in a particular indicator
based on the weight attached to that indicator. When
looking at the censored or uncensored headcount
ratios, we can gauge, in absolute terms, how many
individuals in the total population are deprived in an
indicator (for uncensored) and how many multidi-
mensionally poor individuals are deprived in an indi-
cator (for censored).
However, a high percentage of absolute deprivation
in an indicator may not result in a high MPI. While the
number of individuals experiencing joint deprivations
Determining the Contribution of an Indicator
The process for determining the contribution of an indicator is a
derivative of the fact that the sum of weighted censored head-
count ratios for all indicators provides us with the MPI.
As shown earlier, the censored headcount ratio is represented as
h
��
(��) where �� is the indicator and �� is the second-order cutoff at
which the censoring was done. Therefore,
Where, MPI
c
is India’s MPI, w
��
is the weight of the ��
th
indicator with
�� taking a value up to 12 - equivalent to the number of indicators
in India’s national MPI. Thus, the contribution of each indicator �� is,
Contribution
��
=
w
��
h
��
(��)
MPI
c
��100
MPI
c
= w
1
h
1
(��)+w
2
h
2
(��)+...+w
��
h
��
(��)
w
��
h
��
(��)
12
��=1
⅀MPI
c
=or
Dimension Indicator
Uncensored
Headcount
Censored
Headcount (CH)
Weight (W) Contribution
Health
Nutrition 37.60% 19.90% 1/6 28.14%
Child-Adolescent Mortality2.69% 1.88% 1/12 1.33%
Maternal Health 22.59% 14.71% 1/12 10.40%
Education
Years of Schooling 13.88% 10.71% 1/6 15.14%
School Attendance 6.40% 5.23% 1/6 7.39%
Standard of
Living
Cooking Fuel 58.48% 23.13% 1/21 9.34%
Sanitation 51.97% 21.32% 1/21 8.61%
Drinking Water 14.60% 5.53% 1/21 2.23%
Electricity 12.16% 8.29% 1/21 3.35%
Housing 45.65% 20.56% 1/21 8.31%
Assets 13.97% 8.87% 1/21 3.58%
Bank Account 9.66% 5.37% 1/21 2.17%
MPI (M
0
) = Sum of (CH × W) = 0.118
Figure 4: CONTRIBUTION OF INDICATORS TO INDIA'S MPI SCORE
across multiple indicators is one of the determinant
factors of the MPI, the weights assigned to those in-
dicators also play an important role. In order to un-
derstand this with more clarity, we can look at Figure
4 that portrays the uncensored headcount, censored
headcount, and contribution for each indicator in In-
dia’s national MPI.
Taking the case of the indicator for maternal health,
one can see that the uncensored headcount (i.e.,
percentage of total population) deprived is 22.59
percent. Similarly, 14.71 percent of multidimensional-
ly poor individuals are deprived in maternal health.
However, the contribution of the indicator to the MPI
is 10.40 percent. Similarly, for the indicator for years
of schooling, the converse can be observed with both
censored and uncensored headcounts being lower
than the contribution to the MPI score. Therefore, in
order to arrive at an objective assessment of poverty
it is important to consider all three factors:
I.
The uncensored headcount gives us the absolute
number of individuals who are deprived in an indi-
cator; it gives us the status of deprivations among
the entire population.
II.
The censored headcount gives us the proportion
of individuals who are multidimensionally poor and deprived in an indicator; it gives us the composi- tion of deprivations among the multidimensionally poor.
III.
The contribution of an indicator gives us the per-
centage contribution of an indicator to the overall MPI considering the weights attached to each in- dicator.
From the point of view of a policy maker, the un- censored headcount outlines the broader priorities for intervention required for the benefit of the entire population, the censored headcount outlines the im- mediate priorities required for the benefit of the mul- tidimensionally poor population, and the contribution outlines where interventions would lead to the reduc- tion of the overall MPI of the population.
3.6 The Data Source & Unit of Analysis
The MPI captures the multiple deprivations faced by an individual and by extension, a household. These deprivations lie across a broad spectrum of domains such as health, education, access to basic infrastruc- ture, and ownership of assets, to name a few. The aim of the MPI is therefore to identify the various set of in- dicators in which an individual is deprived at the same time. Thus, the prerequisite for the construction of the MPI is that all the data required for it, must come
from the same single survey, otherwise the creation of household deprivation profiles will not be possible. Therefore, it is neither possible nor feasible to collate data on a single household from several different sur- veys i.e., health indicators from the different rounds of National Sample Surveys, education indicators from the National Achievement Surveys etc.
3.6.1 The National Family Health Survey
The globally established practice is to use the Demo- graphic and Health Surveys (DHS) in countries where it is available, for the computation of the MPI. This has several benefits, as the DHS allows for cross-country comparisons, can be disaggregated at multiple levels by geography or by population sub-groups, and most importantly, collects data across all the dimensions critical to the computation of the MPI. The DHS for India is the National Family Health Survey (NFHS), which is conducted by the International Institute for Population Sciences (IIPS) under the aegis of the Min- istry of Health and Family Welfare (MoHFW), Govern- ment of India. This is the baseline report for India’s national MPI, and has been computed using the data from the 4
th
round of the NFHS conducted in 2015-16.
The NFHS-4 captures the data for 28,69,043 individ- uals across 6,28,892 households. The data is repre- sentative at the national, state and district levels, and can be further disaggregated into urban and rural ar- eas to provide granular estimates. The NFHS covers all States and Union Territories and provides data for 640 administrative Districts defined in the 2011 cen- sus.
The national MPI will be updated upon the release of
the data for the 5
th
round of the NFHS conducted in
2019-20.
3.6.2 The Unit of Identification & Analysis
The unit of identification, i.e., the entity that is iden-
tified as poor or non-poor for India’s national MPI
is the household. The information for all members
in a household is considered altogether. Therefore,
all members in a household are assigned the same
deprivation scores. This also acknowledges the in-
tra-household positive or negative externalities in fac-
tors such as nutrition, maternal health and education.
The unit of analysis i.e., the unit for analyzing and
reporting of the results is the individual. Therefore,
the headcount ratio provides the percentage of indi-
viduals who are poor rather than the percentage of
households who are poor. This approach treats every
individual as equal in terms of reporting and differen-
tial treatment of the deprivations faced by individuals
within the same household.
INDIA MPI 28 29
Way forwardCHAPTER Chapter 4
REFORMS & PROGRESS
WAY FORWARD
This baseline National MPI Report and Dashboard is
a landmark first step in bringing multidimensional
poverty as a tool to the policy table at the nation-
al and subnational levels in India. It is expected that
the report will play an instrumental role in sensitizing
government, researchers, civil society, citizens, and
other stakeholders on the need for and importance
of MPI as a powerful policy instrument. At the higher
levels, MPI could be used as an input to the design
of development policies schemes, budget allocations,
and target setting. At the lower levels, for instance,
of that of district, MPI could decide priority of execu-
tion and delivery. With every revision of MPI based on
new survey data, actions could be redesigned to shift
focus to those who need it the most. NITI Aayog will
play a key role in charting this path and supporting
the stakeholders in their actions, through the follow-
ing approaches.
4.1 Trend analysis based on estimates from
NFHS-5
While this report is an indispensable first step in
mainstreaming MPI, it is based on a dataset which
is five years old. The success of numerous develop-
ment interventions in the recent past have resulted in
progress in key parameters on health, education, and
standard of living. For instance, saturation of village
electrification and toilets was achieved in 2018 and
2019, respectively. The NFHS-5, conducted during
2019-20, is expected to capture the progress achieved in these areas. The unit-level data of the survey re- quired for MPI estimations is likely to be published before the end of 2021, based on which the nation- al MPI figures will be revised, at the national, State/ UT, and district levels. A trend analysis, using the two datasets, will also be carried out. This will clearly point out areas of focus for the near future. The decision to conduct subsequent National Family Health Surveys once in every three years will increase the frequency of MPI revisions and reduce the lag in the reflection of development outcomes in poverty estimates. A high- er frequency of NFHS will also address the issue of stagnation of India’s global rankings in MPI and reflect the improvements adequately. Corresponding to the revision of MPI estimates, the MPI dashboard, which will present national, State, and district-level MPI and related figures, will be updated.
4.2 Reform action plan for the States/UTs
Between two consecutive NFH Surveys, focused gov- ernment action to fill the gaps and reduce depriva- tions will result in improved outcomes. The reform action plan is a tool designed to support the States in this crucial endeavor. The plan maps the government schemes and policies which have a direct bearing on the health, education, and living standards outcomes which MPI captures, to each national MPI indicator. Further, the plan also identifies indicators under these
4
44
schemes and programmes whose achievements are directly and positively correlated with MPI outcomes. This implies that progress, as measured by these indicators, will result in improvement of the corre- sponding national MPI indicators. To demonstrate, one of the indicators in the reform action plan, under the “nutrition” indicator of national MPI, is “number of Anganwadi Centres having weighing scales as a proportion of total number of Anganwadi Centres”, which is mapped to the ICDS scheme. Improvement as measured under this indicator will result in bet- ter monitoring of nutritional outcomes by the AWCs which will in turn trigger action to improve nutrition, whose success will consequently bring about reduc- tion in levels of malnutrition. This change will result in reduction of deprivation under the “nutrition” indica- tor of the national MPI, leading to a better MPI score, ceteris paribus.
While NITI Aayog has prepared a template for the re-
form action plan through consultations with central
ministries, it is important to note that the States are
being encouraged to suitably modify it, taking into
account their realities, development challenges, and
priorities. Though national MPI measures outcomes,
the reform action plan will invariably consist of admin-
istrative and input indicators. The idea is that these
input indicators will act as high- frequency proxies for
the outcome indicators. In the coming months, NITI
Aayog aims to support the States in developing effec-
tive reform action plans which will periodically cap-
ture progress under development programmes and
schemes, and contribute to reduction in deprivations
and multidimensional poverty.
4.3 Progress dashboard
While the periodic NFH Surveys will measure out-
comes and will be used for revising MPI estimates,
there is a need to strengthen implementation which
will eventually result in improved outcomes. To moni-
tor the progress of the implementation, the Develop-
ment Monitoring and Evaluation Office (DMEO), an
attached office under NITI Aayog, is in the process of
developing a progress dashboard.
This dashboard will track the progress of the reforms
implemented by the States to improve outcomes
which will eventually reflect in reduced multidimen-
sional poverty. Though this dashboard will not mon-
itor outcomes directly, nevertheless it plays a crucial
role, as the implementation of reform actions by the
States is the only way the country can achieve faster
poverty reduction and correspondingly a better posi-
tion in global MPI rankings.
4.4 Technical support to States
While NITI Aayog will continue to estimate and pub- lish MPI figures based on NFHS data periodically, the States are encouraged to pursue analysis at multiple levels. Household surveys could be designed and carried out to estimate MPI at block or district lev-
els, with higher frequency. This will offer insights into block-level estimates, which are not possible from NFHS owing to its sample design and size and deliver more frequent estimations at the district levels. The experience of the Government of Andhra Pradesh, which carried out a household survey in 2016 exclu- sively to estimate MPI at the State and district lev-
els is an example in this regard. NITI Aayog has in- house capabilities and will be willing to offer technical support to the States, should they be interested in surveys for this purpose. This support could include design of indicators, sampling design, questionnaire development, training of enumerators, data cleaning, processing, and analysis, report structuring, and ac- tion plan for improvement. On one hand, this support will result in disaggregated and more frequent MPI estimates and corresponding action plans for poverty reduction, while on the other hand, it will contribute significantly to improving state capacity in poverty estimation, monitoring, and reduction.
While the aforementioned are the clear actions
planned for the near future to further mainstream MPI
as a powerful policy tool at the national and subna-
tional levels, the long-term actions will depend on
how the project and its associated initiatives will un-
fold. Depending on the acceptance at various levels,
NITI Aayog will accordingly design and implement or
support further initiatives to fast track the adoption
of MPI. Some of this could include State-specific MPI
reports, focus on disaggregated MPI etc. The util-
ity, relevance and acceptability of a national MPI as
a powerful policy tool for fast tracking development
and leaving no one behind at the national and local
levels, will eventually shape the discourse on devel-
opmental policy in the coming days in the country as
much as in the global arena.
INDIA MpI BASelINe reporT
ResultsSeCTIoN
30 31
SECTION
III
NATIONAL
&
STATE/UT RESULTS
INDIA MPI BASELINE REPORT 32 33
IndiaResults
India: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
37.6%
2.7%
22.6%
13.9%
6.4%
14.6%
45.6%
14.0%
9.7%
58.5%
41.4%
52.0%
29.8%
12.2%
3.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population deprived
19.90%
1.88%
14.71%
10.71%
5.23%
23.13%
21.32%
5.53%
8.29%
20.56%
8.87%
5.37%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population MPI poor & deprivedIndia: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI DOWNLOAD DATA
A snapshot of multidimensional poverty in India
Overview
India Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
25.01%
Rural
Headcount Ratio Intensity MPI
32.75% 47.38% 0.155
India: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.13%
MPI (HxA)
0.118
India
Urban
Headcount Ratio Intensity MPI
8.81% 45.25% 0.04
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.1%
Child & Adolescent Mortality: 1.3%
Maternal Health: 10.4%
Years of Schooling: 15.1%
School Attendance: 7.4%
Cooking Fuel: 9.3%
Sanitation: 8.6%
Drinking Water: 2.2%
Electricity: 3.3%
Housing: 8.3%
Assets: 3.6%
Bank Account: 2.2%
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 India Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 34 35
IndiaResults
Percentage of population who are multidimensionally poor in each State/UT
India: Headcount Ratio
The size of the bar represents the percentage of population who are multidimensionally poor in each State/UT of India.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.052 0.053 to 0.105 0.106 to 0.158 0.159 to 0.211 0.212 to 0.265
States Union Territories
1.72%
1.82%
4.30%
4.79%
5.97%
6.82%
0.71%
3.76%
3.82%
4.89%
5.59%
7.62%
9.80%
12.58%
12.28%
12.31%
13.16%
13.74%
14.85%
16.65%
17.72%
17.89%
18.60%
21.43%
27.36%
24.27%
25.23%
29.35%
29.46%
29.91%
32.67%
32.67%
36.65%
37.79%
42.16%
51.91%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Puducherry
Lakshadweep
Andaman & Nicobar Islands
Delhi
Chandigarh
Daman & Diu
Jammu & Kashmir, & Ladakh
Dadra & Nagar Haveli
Kerala
Goa
Sikkim
Tamil Nadu
Punjab
Himachal Pradesh
Mizoram
Haryana
Andhra Pradesh
Karnataka
Telangana
Maharashtra
Tripura
Uttarakhand
Manipur
Gujarat
West Bengal
Arunachal Pradesh
N
agaland
Odisha
Rajasthan
Chhattisgarh
Assam
Meghalaya
Madhya Pradesh
Uttar Pradesh
Jharkhand
Bihar
Headcount Ratio (% of population who are multidimensionally poor)
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territories of Jammu & Kash-
mir, and Ladakh have been computed for their combined geographical region. Similarly, the estimates for the present Union Territory of Dadra
& Nagar Haveli & Daman & Diu have been computed separately for their erstwhile regions.
Multidimensional Poverty Index Score (State/UT-wise)
India: States & Union Territories
The colour represents the MPI score of a State/UT. The colour moves from green, through yellow, to red as the MPI score increases. Green rep- resents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.052 0.053 to 0.105 0.106 to 0.158 0.159 to 0.211 0.212 to 0.265
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 36 37
IndiaResults
0.000 to 0.0490.050 to 0.0990.100 to 0.149 0.150 to 0.199 0.200 to 0.249 0.250 to 0.299 0.300 to 0.349 0.350 to 0.399 0.400 to 0.450
Multidimensional Poverty Index Score (District-wise)
India: Districts
Districts of Jammu and Kashmir, and Ladakh are as per the Political Map of India 10
th
Edition (Survey of India). Other districts are as per the
Census of India, 2011. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score
increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the
range of MPI scores represented by a colour. Regions with no data are shown in grey.
Multidimensional Poverty Index Score (Rural and Urban)
India: States & Union Territories
The colour represents the MPI score of a State/UT. The colour moves from green, through yellow, to red as the MPI score increases. Green rep- resents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.056 0.057 to 0.114 0.115 to 0.171 0.172 to 0.228 0.229 to 0.286
MPI: Rural
MPI: Urban
INDIA MPI BASELINE REPORT 38 39
IndiaResults
State/UT-wise percentage of population deprived
Uncensored Headcount: Nutrition
Union TerritoriesStates
20.92%
13.32%
15.29%
21.04%
21.87%
22.05%
21.37%
22.11%
23.11%
23.40%
23.56%
25.9%
24.5%
24.6%
24.8%
26.38%
27.18%
28.02%
31.47%
31.10%
32.34%
32.85%
33.56%
33.62%
36.09%
37.05%
37.26%
39.67%
41.37%
42.62%
43.02%
44.47%
45.00%
45.49%
47.99%
51.88%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Daman & Diu
Puducherry
Andaman & Nicobar Islands
Chandigarh
Delhi
Jammu & Kashmir & Ladakh
Lakshadweep
Dadra & Nagar Haveli
Sikkim
Kerala
Arunachal Pradesh
Mizoram
Punjab
Manipur
N
agaland
Goa
Tamil Nadu
Andhra Pradesh
Himachal Pradesh
Tripura
Telangana
Haryana
Uttarakhand
Karnataka
West Bengal
Maharashtra
Meghalaya
Odisha
Assam
Gujarat
Rajasthan
Chhattisgarh
Uttar Pradesh
Madhya Pradesh
Jharkhand
Bihar
% of population deprived in nutrition
State/UT-wise percentage of population deprived
Uncensored Headcount: Child & Adolescent Mortality
Union Territories
0.66%
0.83%
0.19%
0.57%
0.90%
1.16%
1.00%
1.15%
1.28%
1.34%
1.38%
1.4%
1.4%
1.5%
1.7%
1.85%
1.80%
1.82%
1.91%
1.96%
2.01%
1.97%
2.07%
2.17%
2.21%
2.23%
2.30%
2.58%
2.90%
2.95%
3.10%
3.32%
3.32%
3.60%
4.58%
4.97%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
Puducherry
Andaman & Nicobar Islands
Daman & Diu
Chandigarh
Jammu & Kashmir & Ladakh
Delhi
Lakshadweep
Dadra & Nagar Haveli
Kerala
Goa
Sikkim
Tamil Nadu
Tripura
Karnataka
Telangana
Punjab
Maharashtra
West Bengal
Himachal Pradesh
Manipur
Andhra Pradesh
Arunachal Pradesh
N
agaland
Haryana
Gujarat
Odisha
Mizoram
Uttarakhand
Assam
Rajasthan
Meghalaya
Jharkhand
Chhattisgarh
Madhya Pradesh
Bihar
Uttar Pradesh
% of population deprived in child & adolescent mortality
States
Definition: A household is considered deprived if any child between the ages of 0 to 59 months, or woman between the ages of 15 to 49 years,
or man between the ages of 15 to 54 years -for whom nutritional information is available - is found to be undernourished.
Definition: A household is deprived if any child or adolescent under 18 years of age has died in the household in the five-year period preceding
the survey.
INDIA MPI BASELINE REPORT 40 41
IndiaResults
State/UT-wise percentage of population deprived
Uncensored Headcount: Maternal Health
Union TerritoriesStates
4.13%
5.11%
1.73%
5.42%
6.50%
6.70%
7.14%
9.66%
11.05%
10.87%
12.36%
12.7%
13.4%
12.7%
13.5%
14.69%
14.38%
14.77%
15.19%
15.95%
16.11%
17.42%
17.66%
19.50%
23.86%
24.70%
25.44%
26.33%
28.34%
28.56%
29.39%
31.70%
33.06%
33.07%
35.45%
45.62%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
Puducherry
Andaman & Nicobar Islands
Lakshadweep
Chandigarh
Jammu & Kashmir & Ladakh
Dadra & Nagar Haveli
Daman & Diu
Delhi
Kerala
Sikkim
Tamil Nadu
Goa
Andhra Pradesh
Telangana
Karnataka
Punjab
Tripura
West Bengal
Gujarat
Maharashtra
Mizoram
Himachal Pradesh
Manipur
O
disha
Haryana
Chhattisgarh
Assam
Rajasthan
Arunachal Pradesh
Uttarakhand
Madhya Pradesh
Meghalaya
Nagaland
Jharkhand
Uttar Pradesh
Bihar
% of population deprived in maternal health
State/UT-wise percentage of population deprived
Uncensored Headcount: Years of Schooling
Union TerritoriesStates
0.95%
3.29%
1.78%
3.78%
4.87%
5.83%
4.70%
5.36%
5.95%
6.54%
6.61%
6.8%
7.6%
7.1%
7.3%
7.76%
7.93%
8.20%
8.70%
9.79%
9.83%
10.79%
13.47%
13.63%
15.84%
15.86%
16.09%
16.19%
16.66%
16.90%
17.10%
17.52%
17.77%
18.32%
19.71%
26.27%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Lakshadweep
Puducherry
Andaman & Nicobar Islands
Chandigarh
Delhi
Jammu & Kashmir & Ladakh
Daman & Diu
Dadra & Nagar Haveli
Kerala
Himachal Pradesh
Goa
Manipur
Maharashtra
Tamil Nadu
Haryana
Punjab
Mizoram
Sikkim
Karnataka
Uttarakhand
Gujarat
Tripura
Chhattisgarh
N
agaland
Telangana
West Bengal
Madhya Pradesh
Assam
Odisha
Andhra Pradesh
Rajasthan
Uttar Pradesh
Arunachal Pradesh
Jharkhand
Meghalaya
Bihar
% of population deprived in years of schooling
Definition: A household is deprived if not even one member of the household aged 10 years or older has completed six years of schooling. Definition: A household is deprived if any woman in the household who has given birth in the 5 years preceding the survey has not received
at least 4 antenatal care visits for the most recent birth or has not received assistance from trained skilled medical personnel during the most
recent childbirth.
INDIA MPI BASELINE REPORT 42 43
IndiaResults
Definition: A household is deprived if the primary source of cooking fuel is dung, agricultural crops, shrubs, wood, charcoal or coal.
State/UT-wise percentage of population deprived
Uncensored Headcount: School Attendance
Union TerritoriesStates
2.80%
0.30%
1.10%
0.90%
1.80%
2.10%
2.70%
1.70%
2.50%
2.20%
2.50%
2.70%
2.40%
5.40%
5.40%
4.40%
5.20%
9.80%
0.92%
1.21%
1.43%
1.76%
2.65%
3.72%
4.72%
7.73%
0.54%
0.89%
0.96%
1.03%
1.42%
2.10%
2.19%
2.34%
2.36%
2.59%
3.54%
3.75%
3.82%
3.83%
4.20%
4.37%
4.81%
4.95%
5.38%
6.15%
6.55%
6.68%
8.15%
8.19%
8.39%
8.48%
11.91%
12.52%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
Jammu & Kashmir
Andaman & Nicobar Islands
Puducherry
Lakshadweep
Chandigarh
Delhi
Jammu & Kashmir & La
Daman & Diu
Dadara & Nagar Havelli
Kerala
Himachal Pradesh
Goa
Tamil Nadu
Sikkim
Telangana
Tripura
Andhra Pradesh
Manipur
Punjab
Karnataka
Mizoram
Haryana
West Bengal
Maharashtra
Uttarakhand
Nagaland
Odisha
Chhattisgarh
Meghalaya
Assam
Gujarat
Arunachal Pradesh
Jharkhand
Madhya Pradesh
Rajasthan
Uttar Pradesh
Bihar
% of population deprived in school attendance
State/UT-wise percentage of population deprived
Uncensored Headcount: Cooking Fuel
Union TerritoriesStates
7.90%
14.70%
23.70%
32.70%
1.10%
4.20%
7.70%
20.20%
25.70%
40.60%
3.10%
17.10%
7.80%
16.80%
23.30%
16.40%
20.80%
23.40%
28.20%
21.90%
35.10%
40.50%
40.80%
46.80%
28.70%
56.20%
53.10%
50.50%
56.90%
58.60%
59.90%
61.60%
69.20%
60.50%
67.00%
65.30%
68.10%
63.20%
2.21%
4.85%
9.24%
13.51%
24.53%
45.21%
46.62%
58.15%
14.91%
24.07%
31.66%
32.18%
36.40%
37.90%
39.49%
42.20%
43.89%
45.54%
48.79%
51.24%
52.07%
57.79%
58.93%
65.84%
67.90%
68.85%
69.28%
69.94%
71.25%
73.02%
77.08%
77.12%
78.04%
80.94%
82.14%
82.92%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
Daman
Diu
Ladakh
Jammu & Kashmir
Delhi
Chandigarh
Daman & Diu
Puducherry
Andaman & Nicobar Islands
Jammu & Kashmir & Ladakh
Dadara & Nagar Havelli
Lakshadweep
Goa
Tamil Nadu
Telangana
Mizoram
Punjab
Andhra Pradesh
Maharashtra
Sikkim
Kerala
Karnataka
Gujarat
Haryana
Uttarakhand
Arunachal Pradesh
Manipur
Tripura
Himachal Pradesh
Uttar Pradesh
Nagala
Rajasthan
Madhya Pradesh
West Bengal
Meghalaya
Assam
Chhattisgarh
Odisha
Jharkhand
Bihar
% of population deprived in cooking fuel
Definition: A household is deprived if any school-aged child is not attending school up to the age at which he/she would complete class 8.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of school attendance is based on the NFHS-5 State/
UT Reports. Final estimates based on the microdata may vary.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of cooking fuel is based on the NFHS-5 State/UT Reports and Factsheets. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 44 45
IndiaResults
Definition: A household is deprived if it does not have access to improved drinking water or safe drinking water is more than a 30-minute walk
from home (as a round trip).
Definition: The household has unimproved or no sanitation facility or it is improved but shared with other households.
State/UT-wise percentage of population deprived
Uncensored Headcount: Sanitation
Union TerritoriesStates
0.20%
15.00%
12.00%
18.90%
15.10%
36.90%
1.30%
12.70%
4.70%
13.40%
15.00%
12.10%
12.30%
18.30%
21.20%
26.40%
26.00%
17.10%
17.10%
25.20%
22.70%
27.40%
35.10%
28.00%
32.00%
23.80%
31.50%
28.90%
31.20%
34.90%
23.20%
39.50%
50.60%
43.30%
0.44%
19.04%
24.38%
26.70%
34.80%
35.06%
47.04%
68.11%
1.86%
10.42%
15.82%
17.30%
19.26%
21.43%
23.28%
27.75%
34.11%
36.39%
37.16%
38.61%
38.94%
43.11%
46.38%
47.57%
47.68%
47.97%
47.99%
49.27%
51.25%
53.95%
63.69%
65.25%
65.39%
70.43%
73.61%
75
.38%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
32.90%Daman
57.70%Ladakh
24.30%Jammu & Kashmir
10.30%Diu
Lakshadweep
Chandigarh
Andaman & Nicobar Islands
Delhi
Daman & Diu
Puducherry
Jammu & Kashmir & Ladakh
Dadara & Nagar Havelli
Kerala
Sikkim
Mizoram
Punjab
Haryana
Goa
Nagaland
Himachal Pradesh
Uttarakhand
Tripura
Gujarat
Meghalaya
Arunachal Pradesh
Karnataka
Andhra Pradesh
Tamil Nadu
Manipur
Maharashtra
West Bengal
Telangana
Assam
Rajasthan
Uttar Pradesh
Madhya Pradesh
Chhattisgarh
Odisha
Bihar
Jharkhand
% of population deprived in sanitation
State/UT-wise percentage of population deprived
Uncensored Headcount: Drinking Water
Union TerritoriesStates
10.30%
1.70%
7.40%
1.60%
5.40%
5.00%
4.70%
5.10%
5.40%
9.10%
6.90%
13.90%
14.90%
10.70%
3.20%
9.30%
23.60%
28.50%
2.35%
5.49%
6.20%
9.31%
11.67%
14.16%
23.75%
26.95%
1.93%
2.34%
2.34%
4.11%
5.38%
5.91%
7.92%
9.03%
9.48%
11.53%
12.51%
12.61%
12.67%
13.89%
14.51%
14.95%
16.31%
17.71%
18.30%
20.97%
21.23%
26.77%
27.81%
29.83%
31.06%
32.53%
33.52%
60.89%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Jammu & Kashmir
Chandigarh
Puducherry
Andaman & Nicobar Islands
Laksha
Daman & Diu
Jammu & Kashmir & Ladakh
Delhi
Dadara & Nagar Havelli
Punjab
Bihar
Sikkim
Goa
Uttar Pradesh
Kerala
Himachal Pradesh
Uttarakhand
Mizoram
West Bengal
Gujarat
Tamil Nadu
Haryana
Maharashtra
Karnataka
Arunachal Pradesh
Tripura
Assam
Chhattisgarh
Odisha
Nagaland
Rajasthan
Telangana
Madhya Pradesh
Jharkhand
Andhra Pradesh
Meghalaya
Manipur
% of population deprived in drinking water
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of drinking water is based on the NFHS-5 State/UT
Reports. Final estimates based on the microdata may vary.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of sanitation is based on the NFHS-5 State/UT Re- ports and Factsheets. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 46 47
IndiaResults
Definition: A household is deprived if it has inadequate housing: the floor is made of natural materials, or the roof or walls are made of rudi-
mentary materials.
Definition: A household is deprived if it has no electricity.
State/UT-wise percentage of population deprived
Uncensored Headcount: Electricity
Union TerritoriesStates
0.50%
0.70%
0.50%
0.20%
0.10%
0.10%
0.10%
2.40%
0.30%
0.30%
0.50%
0.70%
0.40%
0.50%
0.70%
0.40%
0.40%
0.90%
0.40%
1.50%
1.20%
2.40%
1.80%
2.50%
2.20%
1.80%
1.90%
8.10%
1.90%
1.60%
5.20%
3.00%
5.70%
7.40%
9.00%
3.70%
0.03%
0.05%
0.24%
0.28%
0.48%
2.72%
2.74%
2.78%
0.18%
0.39%
0.49%
0.65%
0.74%
0.77%
0.97%
1.06%
1.23%
1.71%
2.17%
3.25%
3.64%
3.75%
4.08%
5.75%
6.59%
7.18%
7.31%
8.18%
8.73%
8.96%
11.83%
13.37%
18.79%
21.78%
27.43%
39.86%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0
Diu
Daman
Jammu & Kashmir
Ladakh
Daman & Diu
Lakshadweep
Puducherry
Delhi
Chandigarh
Andaman & Nicobar Islands
Dadara & Nagar Havelli
Jammu & Kashmir & Ladakh
Goa
Punjab
Himachal Pradesh
Sikkim
Kerala
Andhra Pradesh
Tamil Nadu
Haryana
Telangana
Karnataka
Uttarakhand
Nagaland
Chhattisgarh
Gujarat
Mizoram
West Bengal
Maharashtra
Tripura
Manipur
Meghalaya
Rajasthan
Madhya Pradesh
Arunachal Pradesh
Odisha
Jharkhand
Assam
Uttar Pr
Bihar
% of population deprived in electricity
0.00%
0.00%
State/UT-wise percentage of population deprived
Uncensored Headcount: Housing
Union TerritoriesStates
25.40%
16.70%
9.50%
14.70%
23.30%
19.70%
26.20%
23.10%
24.00%
36.40%
55.90%
47.20%
66.40%
65.30%
67.00%
69.30%
76.80%
1.54%
6.40%
8.08%
10.79%
17.59%
29.62%
33.61%
57.06%
10.76%
16.16%
17.55%
19.30%
20.18%
24.17%
24.25%
24.27%
25.53%
26.71%
27.90%
29.30%
35.54%
35.59%
37.30%
50.40%
54.26%
55.81%
61.78%
63.31%
64.38%
67.52%
70.98%
73.73%
74.66%
75.90%
76.15%
81.49%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
Jammu & Kashmir
Lakshadweep
Chandigarh
Dama
Delhi
Puducherry
Jammu & Kashmir & Ladakh
Andaman & Nicobar Islands
Dadara & Nagar Havelli
Kerala
Goa
Andhra Pradesh
Punjab
Tamil Nadu
Mizoram
Gujarat
Haryana
Telangana
Sikkim
Maharashtra
Himachal Pradesh
Rajasthan
Uttarakhand
Karnataka
Meghalaya
West Bengal
Odisha
Jharkhand
Chhattisgarh
Madhya Pradesh
Uttar Pradesh
Nagaland
Bihar
Tripura
Assam
Arunachal Pradesh
Manipur
% of population deprived in housing
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of electricity is based on the NFHS-5 State/UT Re-
ports and Factsheets. Final estimates based on the microdata may vary.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of housing is based on the NFHS-5 State/UT Re- ports. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 48 49
IndiaResults
Definition: No household member has a bank account or a post office account.
State/UT-wise percentage of population deprived
Uncensored Headcount: Assets
Union TerritoriesStates
1.02%
1.65%
2.71%
1.72%
2.94%
2.97%
3.38%
4.65%
5.54%
7.10%
7.52%
9.5%
10.0%
11.0%
12.4%
12.79%
13.60%
13.84%
13.93%
13.95%
13.97%
14.11%
14.74%
16.12%
14.92%
18.76%
19.22%
19.31%
19.95%
20.81%
20.49%
21.37%
23.36%
24.32%
29.88%
33.91%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
Lakshadweep
Puducherry
Chandigarh
Delhi
Andaman & Nicobar Islands
Daman & Diu
Jammu & Kashmir & Ladakh
Dadra & Nagar Haveli
Punjab
Kerala
Goa
Tamil Nadu
Haryana
Himachal Pradesh
Sikkim
Karnataka
Andhra Pradesh
Uttar Pradesh
Telangana
Gujarat
Uttarakhand
Manipur
Mizoram
Maharashtra
West Bengal
Chhattisgarh
Tripura
O
disha
Madhya Pradesh
Assam
Rajasthan
Jharkhand
Arunachal Pradesh
Bihar
Meghalaya
Nagaland
% of population deprived in assets
State/UT-wise percentage of population deprived
Uncensored Headcount: Bank Accounts
Union TerritoriesStates
3.00%
2.10%
3.00%
2.60%
3.30%
3.60%
3.40%
2.90%
6.90%
5.10%
4.40%
5.00%
4.50%
3.70%
9.10%
4.20%
4.00%
7.10%
1.57%
3.95%
3.97%
5.35%
5.62%
8.36%
10.92%
12.52%
2.69%
3.63%
3.71%
4.02%
4.03%
4.32%
4.73%
4.88%
5.74%
5.81%
6.35%
6.90%
7.46%
8.17%
8.38%
8.83%
8.98%
9.41%
10.35%
10.94%
11.15%
13.81%
15.40%
15.40%
19.91%
21.54%
26.00%
28.66%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Jammu & Kashmir
Andaman & Nicobar Islands
Jammu & Kashmir & Ladakh
Chandigarh
Puducher
Lakshadweep
Delhi
Dadara & Nagar Havelli
Daman & Diu
Himachal Pradesh
Tripura
Punjab
Goa
Rajasthan
Kerala
Andhra Pradesh
Uttar Pradesh
Chhattisgarh
Mizoram
Tamil Nadu
Uttarakhand
Telangana
Haryana
Sikkim
Karnataka
Jharkhand
Gujarat
Maharashtra
Odisha
Madhya Pradesh
West Bengal
Arunachal Pradesh
Assam
Meghalaya
Manipur
Bihar
Nagaland
% of population deprived in bank accounts
Definition: The household is deprived if it does not own more than one of these assets: radio, TV, telephone, computer, animal cart, bicycle,
motorbike, or refrigerator; and does not own a car or truck.
Legend NFHS-4 Estimates (2015-16) NFHS-5 Provisional Estimates (2019-20)
Note on comparison: The NFHS-5 provisional estimates of the uncensored headcount ratio of bank accounts is based on the NFHS-5 State/UT Reports and Factsheets. Final estimates based on the microdata may vary.
INDIA MPI BASELINE REPORT 50 51
Andhra PradeshResults
Andhra Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
26.4%
1.8%
9.7%
16.9%
2.3%
11.0%
1.7%
37.9%
16.4%
46.4%
22.7%
32.5%
9.3%
0.8%0.5%
17.5%
14.7%
4.7%
3.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
9.22%
0.87%
4.76%
7.72%
1.47%
9.72%
10.50%
4.39%
0.60%
5.69%
4.82%
1.74%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Andhra Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Andhra Pradesh
Overview
Andhra Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
12.31%
Rural
Headcount Ratio Intensity MPI
15.37% 43.28% 0.067
Andhra Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.23%
MPI (HxA)
0.053
Andhra Pradesh
Urban
Headcount Ratio Intensity MPI
4.91% 42.83% 0.021
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Nutrition: 28.9%
Child & Adolescent Mortality: 1.4%
Maternal Health: 7.5%
Years of Schooling: 24.2%
School Attendance: 4.6%
Cooking Fuel: 8.7%
Sanitation: 9.4%
Drinking Water: 3.9%
Electricity: 0.5%
Housing: 5.1%
Assets: 4.3%
Bank Account: 1.6%
Health Education Standard of Living
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Andhra Pradesh State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 52 53
Andhra PradeshResults
0.034 to 0.042 0.043 to 0.050 0.051 to 0.059 0.060 to 0.068 0.069 to 0.076 0.077 to 0.085 0.086 to 0.094
8.31%
8.55%
8.98%
9.11%
9.96%
10.34%
11.67%
13.24%
14.01%
15.10%
15.63%
19.00%
20.69%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0%
Guntur
East Godavari
Krishna
West Godavari
Y.S.R. Kadapa
Chittoor
SPSR Nellore
Anantapur
Srikakulam
Visakhapatnam
Prakasam
Vizianagaram
Kurnool
Headcount Ratio (% of population who are multidimensionally poor)
Andhra Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Andhra Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Andhra Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Andhra Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.034 to 0.042 0.043 to 0.050 0.051 to 0.059 0.060 to 0.068 0.069 to 0.076 0.077 to 0.085 0.086 to 0.094
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 54 55
Andhra PradeshResults
Multidimensional Poverty in Andhra Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Andhra Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Andhra Pradesh are as per the 2011 Census of India Dis tricts of Andhra Pradesh are as per the 2011 Census of India
Districts of Andhra Pradesh Headcount Ratio Intensity MPI
Anantapur 13.24% 42.08% 0.056
Chittoor 10.34% 42.35% 0.044
East Godavari 8.55% 41.46% 0.035
Guntur 8.31% 41.21% 0.034
Krishna 8.98% 41.74% 0.037
Kurnool 20.69% 45.56% 0.094
Prakasam 15.63% 44.52% 0.070
SPSR Nellore 11.67% 44.41% 0.052
Srikakulam 14.01% 41.57% 0.058
Visakhapatnam 15.10% 47.03% 0.071
Vizianagaram 19.00% 42.51% 0.081
West Godavari 9.11% 39.90% 0.036
Y.S.R. Kadapa 9.96% 41.91% 0.042
Rural Urban
Districts of Andhra Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anantapur 17.02% 42.02% 0.072 5.05% 42.52% 0.021
Chittoor 12.24% 42.13% 0.052 4.90% 43.94% 0.022
East Godavari 10.34% 41.36% 0.043 3.05% 42.51% 0.013
Guntur 10.77% 41.64% 0.045 4.06% 39.23% 0.016
Krishna 12.08% 41.88% 0.051 3.09% 40.66% 0.013
Kurnool 25.67% 45.66% 0.117 9.58% 44.95% 0.043
Prakasam 17.04% 44.74% 0.076 9.68% 42.91% 0.042
SPSR Nellore 12.93% 43.97% 0.057 8.15% 46.34% 0.038
Srikakulam 16.89% 41.57% 0.070 0.00% - 0.000
Visakhapatnam 25.50% 47.21% 0.120 3.08% 45.29% 0.014
Vizianagaram 23.09% 42.49% 0.098 4.42% 42.85% 0.019
West Godavari 9.39% 39.93% 0.037 7.94% 39.76% 0.032
Y.S.R. Kadapa 13.68% 42.36% 0.058 3.55% 38.90% 0.014
INDIA MPI BASELINE REPORT 56 57
Arunachal PradeshResults
Arunachal Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
21.0%
2.0%
28.3%
17.8%
8.1%
15.0%
76.1%
23.4%
15.4%
57.8%
46.8%
38.9%
17.1%
11.8%
5.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
13.80%
1.19%
14.86%
13.48%
5.90%
21.29%
16.65%
6.19%
7.15%
23.29%
12.85%
9.23%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Arunachal Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Arunachal Pradesh
Overview
Arunachal Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
24.27%
Rural
Headcount Ratio Intensity MPI
29.23% 47.6% 0.139
Arunachal Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.26%
MPI (HxA)
0.115
Arunachal Pradesh
Urban
Headcount Ratio Intensity MPI
8.15% 43.18% 0.035
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 20.1%
Child & Adolescent Mortality: 0.9%
Maternal Health: 10.8%
Years of Schooling: 19.6%
School Attendance: 8.6%
Cooking Fuel: 8.8%
Sanitation: 6.9%
Drinking Water: 2.6%
Electricity: 3.0%
Housing: 9.7%
Assets: 5.3%
Bank Account: 3.8%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Arunachal Pradesh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 58 59
Arunachal PradeshResults
0.037 to 0.061 0.062 to 0.087 0.088 to 0.112 0.113 to 0.138 0.139 to 0.163 0.164 to 0.189 0.190 to 0.215
8.84%
12.85%
14.55%
15.95%
15.97%
17.13%
22.56%
22.86%
23.56%
26.5%
28.3%
29.8%
31.30%
31.97%
39.69%
44.08%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
East Siang
Papum Pare
West Siang
Upper Siang
Lower Subansiri
Dibang Valley
West Kameng
Anjaw
Lower Dibang Valley
Changlang
Tirap
Upper Subansiri
Tawang
Lohit
Kurung Kumey
East Kameng
Headcount Ratio (% of population who are multidimensionally poor)
Arunachal Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Arunachal Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Arunachal Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Arunachal Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.037 to 0.061 0.062 to 0.087 0.088 to 0.112 0.113 to 0.138 0.139 to 0.163 0.164 to 0.189 0.190 to 0.215
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 60 61
Arunachal PradeshResults
Multidimensional Poverty in Arunachal Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Arunachal Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Arunachal Pradesh are as per the 2011 Census of India Dis tricts of Arunachal Pradesh are as per the 2011 Census of India
Districts of Arunachal Pradesh Headcount Ratio Intensity MPI
Anjaw 22.86% 42.92% 0.098
Changlang 26.53% 48.04% 0.127
Dibang Valley 17.13% 41.40% 0.071
East Kameng 44.08% 48.84% 0.215
East Siang 8.84% 41.61% 0.037
Kurung Kumey 39.69% 47.46% 0.188
Lohit 31.97% 51.28% 0.164
Lower Dibang Valley 23.56% 45.96% 0.108
Lower Subansiri 15.97% 43.84% 0.070
Papum Pare 12.85% 45.95% 0.059
Tawang 31.30% 45.62% 0.143
Tirap 28.30% 48.12% 0.136
Upper Siang 15.95% 41.41% 0.066
Upper Subansiri 29.78% 45.02% 0.134
West Kameng 22.56% 43.86% 0.099
West Siang 14.55% 45.19% 0.066
Rural Urban
Districts of Arunachal Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anjaw 22.79% 43.04% 0.098 24.96% 39.73% 0.099
Changlang 28.65% 48.31% 0.138 7.65% 39.07% 0.030
Dibang Valley 20.54% 41.38% 0.085 3.18% 41.81% 0.013
East Kameng 56.81% 49.70% 0.282 15.64% 41.83% 0.065
East Siang 10.67% 41.94% 0.045 4.84% 40.02% 0.019
Kurung Kumey 39.82% 47.65% 0.190 34.25% 38.43% 0.132
Lohit 36.66% 51.15% 0.188 12.82% 52.84% 0.068
Lower Dibang Valley 26.75% 46.16% 0.123 11.89% 44.31% 0.053
Lower Subansiri 16.65% 44.36% 0.074 10.07% 36.31% 0.037
Papum Pare 20.25% 46.96% 0.095 6.85% 43.55% 0.030
Tawang 34.26% 45.61% 0.156 1.73% 45.87% 0.008
Tirap 33.16% 48.40% 0.160 6.13% 41.11% 0.025
Upper Siang 18.42% 41.38% 0.076 3.62% 42.31% 0.015
Upper Subansiri 35.30% 45.54% 0.161 8.98% 37.22% 0.033
West Kameng 26.47% 43.97% 0.116 5.43% 41.49% 0.023
West Siang 17.82% 45.94% 0.082 4.72% 36.65% 0.017
INDIA MPI BASELINE REPORT 62 63
AssamResults
Assam: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
39.7%
2.9%
25.4%
16.2%
19.9%
6.6%
4.4%
77.1%
60.5%
51.3%
31.5%
17.7%
14.9%
21.8%
7.4%
75.9%
69.3%
15.4%
3.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
25.46%
2.18%
17.78%
14.26%
5.64%
31.64%
24.43%
8.25%
14.71%
31.39%
13.90%
10.51%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Assam: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Assam
Overview
Assam Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
32.67%
Rural
Headcount Ratio Intensity MPI
36.16% 48.07% 0.174
Assam: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.89%
MPI (HxA)
0.156
Assam
Urban
Headcount Ratio Intensity MPI
9.97% 43.63% 0.044
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.1%
Child & Adolescent Mortality: 1.2%
Maternal Health: 9.5%
Years of Schooling: 15.2%
School Attendance: 6.0%
Cooking Fuel: 9.6%
Sanitation: 7.4%
Drinking Water: 2.5%
Electricity: 4.5%
Housing: 9.6%
Assets: 4.2%
Bank Account: 3.2%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Assam State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 64 65
AssamResults
0.052 to 0.081 0.082 to 0.110 0.111 to 0.140 0.141 to 0.170 0.171 to 0.199 0.200 to 0.229 0.230 to 0.260
11.04%
16.94%
20.24%
20.60%
23.59%
24.23%
25.32%
25.69%
26.22%
27.71%
28.97%
29.46%
30.5%
31.2%
32.1%
33.8%
36.2%
36.70%
36.75%
37.73%
38.22%
39.41%
40.15%
42.37%
46.02%
51.06%
51.07%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Kamrup Metropolitan
Nalbari
Jorhat
Golaghat
Baksa
Lakhimpur
Sonitpur
Sivasagar
Kamrup
Dhemaji
Dibrugarh
Udalguri
Nagaon
Dima Hasao
Kokrajhar
Bongaigaon
Chirang
Tinsukia
Marigaon
Karbi Anglong
Darrang
Barpeta
Goalpara
Cachar
Karimganj
Dhubri
Hailakandi
Headcount Ratio (% of population who are multidimensionally poor)
Assam: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Assam.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Assam
Multidimensional Poverty Index Score (District-wise)
Districts of Assam are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.052 to 0.081 0.082 to 0.110 0.111 to 0.140 0.141 to 0.170 0.171 to 0.199 0.200 to 0.229 0.230 to 0.260
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 66 67
AssamResults
Multidimensional Poverty in Assam
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Assam
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Assam are as per the 2011 Census of India Districts of Assam are as per the 2011 Census of India
Districts of Assam Headcount Ratio Intensity MPI
Baksa 23.59% 43.45% 0.102
Barpeta 39.41% 46.52% 0.183
Bongaigaon 33.77% 45.80% 0.155
Cachar 42.37% 49.54% 0.210
Chirang 36.20% 45.71% 0.165
Darrang 38.22% 49.49% 0.189
Dhemaji 27.71% 45.03% 0.125
Dhubri 51.06% 50.85% 0.260
Dibrugarh 28.97% 47.05% 0.136
Dima Hasao 31.24% 49.90% 0.156
Goalpara 40.15% 50.65% 0.203
Golaghat 20.60% 45.64% 0.094
Hailakandi 51.07% 49.21% 0.251
Jorhat 20.24% 43.62% 0.088
Kamrup 26.22% 45.03% 0.118
Kamrup Metropolitan 11.04% 47.03% 0.052
Karbi Anglong 37.73% 48.05% 0.181
Karimganj 46.02% 48.45% 0.223
Kokrajhar 32.14% 46.11% 0.148
Lakhimpur 24.23% 46.80% 0.113
Marigaon 36.75% 47.71% 0.175
Nagaon 30.51% 47.10% 0.144
Nalbari 16.94% 44.69% 0.076
Sivasagar 25.69% 49.16% 0.126
Sonitpur 25.32% 46.55% 0.118
Tinsukia 36.70% 52.07% 0.191
Udalguri 29.46% 44.74% 0.132
Rural Urban
Districts of Assam
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Baksa 23.76% 43.46% 0.103 8.57% 40.48% 0.035
Barpeta 41.34% 46.61% 0.193 18.78% 44.34% 0.083
Bongaigaon 36.86% 45.89% 0.169 16.12% 44.75% 0.072
Cachar 49.01% 49.84% 0.244 7.28% 38.55% 0.028
Chirang 37.00% 45.62% 0.169 25.80% 47.34% 0.122
Darrang 40.28% 49.60% 0.200 9.78% 43.64% 0.043
Dhemaji 29.23% 45.09% 0.132 9.02% 42.62% 0.038
Dhubri 54.88% 51.04% 0.280 12.52% 42.15% 0.053
Dibrugarh 33.94% 47.20% 0.160 9.30% 44.85% 0.042
Dima Hasao 41.55% 50.78% 0.211 8.08% 39.71% 0.032
Goalpara 45.38% 50.80% 0.231 6.65% 44.34% 0.029
Golaghat 21.81% 45.67% 0.100 9.78% 45.19% 0.044
Hailakandi 54.12% 49.28% 0.267 8.39% 42.47% 0.036
Jorhat 23.01% 43.82% 0.101 9.00% 41.49% 0.037
Kamrup 26.27% 45.07% 0.118 25.72% 44.64% 0.115
Kamrup Metropolitan 20.96% 48.15% 0.101 8.46% 46.31% 0.039
Karbi Anglong 39.63% 48.46% 0.192 20.01% 40.63% 0.081
Karimganj 49.72% 48.56% 0.241 6.53% 39.86% 0.026
Kokrajhar 33.60% 46.24% 0.155 8.53% 37.81% 0.032
Lakhimpur 26.06% 46.82% 0.122 8.14% 46.22% 0.038
Marigaon 39.00% 47.61% 0.186 13.43% 50.76% 0.068
Nagaon 33.16% 47.39% 0.157 11.37% 40.83% 0.046
Nalbari 18.32% 44.83% 0.082 4.03% 38.77% 0.016
Sivasagar 28.02% 49.22% 0.138 1.95% 40.75% 0.008
Sonitpur 27.27% 46.55% 0.127 1.37% 46.43% 0.006
Tinsukia 44.13% 52.47% 0.232 8.50% 44.16% 0.038
Udalguri 30.62% 44.88% 0.137 8.45% 35.37% 0.030
INDIA MPI BASELINE REPORT 68 69
BiharResults
Bihar: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
51.9%
4.6%
45.6%
26.3%
24.3%
12.5%
9.8%
82.9%
63.2%
73.6%
50.6%
2.3%
1.7%
39.9%
3.7%
73.7%
65.3%
26.0%
4.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
41.60%
3.92%
36.51%
24.70%
11.62%
50.20%
46.57%
1.64%
28.79%
47.10%
18.69%
19.60%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Bihar: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Bihar
Overview
Bihar Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
51.91%
Rural
Headcount Ratio Intensity MPI
56.01% 51.14% 0.286
Bihar: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
51.02%
MPI (HxA)
0.265
Bihar
Urban
Headcount Ratio Intensity MPI
23.91% 49% 0.117
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 26.2%
Child & Adolescent Mortality: 1.2%
Maternal Health: 11.5%
Years of Schooling: 15.5%
School Attendance: 7.3%
Cooking Fuel: 9.0%
Sanitation: 8.4%
Drinking Water: 0.3%
Electricity: 5.2%
Housing: 8.5%
Assets: 3.4%
Bank Account: 3.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Bihar State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 70 71
BiharResults
0.138 to 0.168 0.169 to 0.199 0.200 to 0.231 0.232 to 0.262 0.263 to 0.293 0.294 to 0.324 0.325 to 0.356
29.20%
40.50%
40.55%
40.74%
41.84%
40.99%
42.75%
42.80%
43.90%
43.94%
44.48%
45.41%
45.6%
46.6%
47.6%
48.0%
50.7%
51.7%
52.2%
52.7%
54.67%
55.47%
56.45%
57.83%
55.87%
57.50%
58.23%
60.03%
61.48%
62.38%
63.29%
63.46%
64.01%
64.10%
64.13%
64.43%
64.65%
64.75%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Patna
Bhojpur
Siwan
Rohtas
Munger
Buxar
Gopalganj
Saran
Lakhisarai
Aurangabad
Kaimur
Jehanabad
Bhagalpur
Nalanda
Vaishali
Muzaffarpur
Begusarai
Nawada
Arwal
Sheikhpura
Gaya
Madhubani
Samastipur
Darbhanga
Pashchim Champaran
Banka
Khagaria
Sheohar
Saharsa
Katihar
Purnia
Sitamarhi
Jamui
Supaul
Purba Champaran
Madhepura
A
raria
Kishanganj
Headcount Ratio (% of population who are multidimensionally poor)
Bihar: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Bihar.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Bihar
Multidimensional Poverty Index Score (District-wise)
Districts of Bihar are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.138 to 0.168 0.169 to 0.199 0.200 to 0.231 0.232 to 0.262 0.263 to 0.293 0.294 to 0.324 0.325 to 0.356
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 72 73
BiharResults
Multidimensional Poverty in Bihar
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Bihar
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Bihar are as per the 2011 Census of India Districts of Bihar are as per the 2011 Census of India
Districts of Bihar Headcount Ratio Intensity MPI
Araria 64.65% 55.12% 0.356
Arwal 52.18% 47.83% 0.250
Aurangabad 43.94% 46.91% 0.206
Banka 57.83% 50.39% 0.291
Begusarai 50.68% 51.17% 0.259
Bhagalpur 45.60% 51.97% 0.237
Bhojpur 40.50% 46.54% 0.188
Buxar 41.84% 45.48% 0.190
Darbhanga 56.45% 51.77% 0.292
Gaya 54.67% 49.67% 0.272
Gopalganj 42.75% 47.21% 0.202
Jamui 64.01% 50.71% 0.325
Jehanabad 45.41% 50.42% 0.229
Kaimur 44.48% 47.79% 0.213
Katihar 62.38% 53.82% 0.336
Khagaria 58.23% 54.38% 0.317
Kishanganj 64.75% 53.88% 0.349
Lakhisarai 43.90% 50.62% 0.222
Madhepura 64.43% 54.42% 0.351
Madhubani 55.47% 51.16% 0.284
Munger 40.99% 48.98% 0.201
Muzaffarpur 48.00% 49.82% 0.239
Nalanda 46.61% 50.65% 0.236
Nawada 51.72% 50.53% 0.261
Pashchim Champaran 57.50% 52.83% 0.304
Patna 29.20% 47.26% 0.138
Purba Champaran 64.13% 52.78% 0.339
Purnia 63.29% 54.52% 0.345
Rohtas 40.74% 44.38% 0.181
Saharsa 61.48% 54.84% 0.337
Samastipur 55.87% 52.58% 0.294
Saran 42.80% 48.35% 0.207
Sheikhpura 52.70% 49.41% 0.260
Sheohar 60.03% 51.84% 0.311
Sitamarhi 63.46% 52.67% 0.334
Siwan 40.55% 46.23% 0.187
Supaul 64.10% 51.78% 0.332
Vaishali 47.64% 48.72% 0.232
Rural Urban
Districts of Bihar
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Araria 67.70% 55.26% 0.374 25.32% 50.26% 0.127
Arwal 54.20% 47.91% 0.260 26.94% 45.76% 0.123
Aurangabad 46.46% 46.72% 0.217 20.42% 50.83% 0.104
Banka 58.29% 50.38% 0.294 45.09% 50.65% 0.228
Begusarai 54.00% 51.56% 0.278 36.84% 48.76% 0.180
Bhagalpur 52.69% 52.36% 0.276 21.64% 48.81% 0.106
Bhojpur 44.10% 46.48% 0.205 23.35% 47.06% 0.110
Buxar 46.15% 45.37% 0.209 9.91% 49.31% 0.049
Darbhanga 59.35% 51.91% 0.308 24.26% 48.01% 0.116
Gaya 59.48% 49.92% 0.297 23.84% 45.75% 0.109
Gopalganj 41.80% 46.56% 0.195 55.16% 53.64% 0.296
Jamui 66.42% 50.99% 0.339 32.44% 43.16% 0.140
Jehanabad 50.51% 50.47% 0.255 12.01% 48.83% 0.059
Kaimur 46.87% 47.54% 0.223 8.22% 69.51% 0.057
Katihar 66.36% 53.95% 0.358 22.90% 50.15% 0.115
Khagaria 60.58% 54.51% 0.330 11.05% 40.01% 0.044
Kishanganj 66.46% 53.82% 0.358 47.85% 54.58% 0.261
Lakhisarai 45.68% 50.96% 0.233 35.30% 48.49% 0.171
Madhepura 66.52% 54.54% 0.363 20.93% 46.73% 0.098
Madhubani 56.38% 51.27% 0.289 32.89% 46.67% 0.153
Munger 47.23% 49.56% 0.234 23.71% 45.74% 0.108
Muzaffarpur 50.42% 49.62% 0.250 22.40% 54.63% 0.122
Nalanda 51.95% 50.71% 0.263 24.32% 50.12% 0.122
Nawada 56.17% 50.80% 0.285 18.39% 44.41% 0.082
Pashchim Champaran 64.37% 52.92% 0.341 18.95% 51.08% 0.097
Patna 45.46% 47.66% 0.217 13.80% 46.02% 0.064
Purba Champaran 65.68% 52.54% 0.345 47.76% 56.29% 0.269
Purnia 68.08% 54.91% 0.374 29.73% 48.26% 0.143
Rohtas 43.97% 44.22% 0.194 19.13% 46.92% 0.090
Saharsa 64.08% 54.99% 0.352 25.54% 49.56% 0.127
Samastipur 56.98% 52.63% 0.300 29.72% 50.40% 0.150
Saran 45.34% 48.39% 0.219 19.50% 47.45% 0.093
Sheikhpura 58.17% 49.52% 0.288 28.23% 48.38% 0.137
Sheohar 60.65% 51.74% 0.314 46.82% 54.65% 0.256
Sitamarhi 65.50% 53.05% 0.347 42.30% 46.54% 0.197
Siwan 40.97% 46.32% 0.190 32.72% 44.14% 0.144
Supaul 63.54% 51.62% 0.328 76.33% 54.78% 0.418
Vaishali 50.89% 48.78% 0.248 20.39% 47.50% 0.097
INDIA MPI BASELINE REPORT 74 75
ChhattisgarhResults
Chhattisgarh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
43.0%
3.3%
24.7%
13.5%
5.4%
18.3%
63.3%
14.9%
5.7%
78.0%
67.0%
65.4%
23.2%
3.6%
1.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
24.04%
2.25%
16.96%
10.91%
4.31%
29.14%
26.64%
10.19%
2.78%
26.78%
10.43%
3.40%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Chhattisgarh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Chhattisgarh
Overview
Chhattisgarh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
29.91%
Rural
Headcount Ratio Intensity MPI
35.73% 44.83% 0.16
Chhattisgarh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.64%
MPI (HxA)
0.134
Chhattisgarh
Urban
Headcount Ratio Intensity MPI
10.2% 42.41% 0.043
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.0%
Child & Adolescent Mortality: 1.4%
Maternal Health: 10.6%
Years of Schooling: 13.6%
School Attendance: 5.4%
Cooking Fuel: 10.4%
Sanitation: 9.5%
Drinking Water: 3.6%
Electricity: 1.0%
Housing: 9.6%
Assets: 3.7%
Bank Account: 1.2%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Chhattisgarh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 76 77
ChhattisgarhResults
0.076 to 0.105 0.106 to 0.136 0.137 to 0.166 0.167 to 0.197 0.198 to 0.227 0.228 to 0.257 0.258 to 0.289
18.59%
20.00%
21.82%
23.14%
23.16%
25.66%
27.03%
29.85%
30.82%
31.86%
38.2%
39.6%
41.2%
45.85%
46.95%
47.37%
51.52%
54.59%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Dhamtari
Durg
Raipur
Rajnandgaon
Janjgir-Champa
Bilaspur
North Bastar Kanker
Mahasamund
Raigarh
Korba
Koriya
Kabeerdham
Bijapur
Jashpur
Bastar
Surguja
Narayanpur
Dantewada
Headcount Ratio (% of population who are multidimensionally poor)
Chhattisgarh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Chhattisgarh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Chhattisgarh
Multidimensional Poverty Index Score (District-wise)
Districts of Chhattisgarh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.076 to 0.105 0.106 to 0.136 0.137 to 0.166 0.167 to 0.197 0.198 to 0.227 0.228 to 0.257 0.258 to 0.289
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 78 79
ChhattisgarhResults
Multidimensional Poverty in Chhattisgarh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Chhattisgarh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Chhattisgarh are as per the 2011 Census of India Districts of Chhattisgarh are as per the 2011 Census of India
Districts of Chhattisgarh Headcount Ratio Intensity MPI
Bastar 46.95% 48.22% 0.226
Bijapur 41.20% 44.52% 0.183
Bilaspur 25.66% 43.25% 0.111
Dantewada 54.59% 52.92% 0.289
Dhamtari 18.59% 40.74% 0.076
Durg 20.00% 41.97% 0.084
Janjgir-Champa 23.16% 41.55% 0.096
Jashpur 45.85% 46.09% 0.211
Kabeerdham 39.56% 46.50% 0.184
Korba 31.86% 45.87% 0.146
Koriya 38.24% 44.89% 0.172
Mahasamund 29.85% 42.03% 0.125
Narayanpur 51.52% 49.42% 0.255
North Bastar Kanker 27.03% 43.47% 0.117
Raigarh 30.82% 43.48% 0.134
Raipur 21.82% 43.50% 0.095
Rajnandgaon 23.14% 40.37% 0.093
Surguja 47.37% 46.60% 0.221
Rural Urban
Districts of Chhattisgarh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bastar 51.19% 48.30% 0.247 16.97% 46.57% 0.079
Bijapur 44.90% 44.75% 0.201 12.81% 38.16% 0.049
Bilaspur 31.38% 43.44% 0.136 8.45% 41.18% 0.035
Dantewada 61.59% 53.31% 0.328 15.26% 44.11% 0.067
Dhamtari 20.83% 40.08% 0.083 10.22% 45.81% 0.047
Durg 27.45% 42.46% 0.117 8.11% 39.34% 0.032
Janjgir-Champa 24.89% 41.84% 0.104 12.81% 38.17% 0.049
Jashpur 48.56% 46.17% 0.224 9.92% 41.10% 0.041
Kabeerdham 42.28% 46.36% 0.196 15.58% 49.78% 0.078
Korba 44.08% 46.15% 0.203 9.75% 43.52% 0.042
Koriya 48.48% 45.13% 0.219 14.25% 42.97% 0.061
Mahasamund 30.90% 41.87% 0.129 20.07% 44.30% 0.089
Narayanpur 57.36% 49.95% 0.287 19.37% 40.77% 0.079
North Bastar Kanker 29.65% 43.62% 0.129 4.34% 34.64% 0.015
Raigarh 35.16% 43.47% 0.153 4.07% 44.10% 0.018
Raipur 28.74% 43.61% 0.125 9.94% 42.96% 0.043
Rajnandgaon 24.96% 40.25% 0.100 14.70% 41.31% 0.061
Surguja 51.63% 46.59% 0.241 10.23% 47.12% 0.048
INDIA MPI BASELINE REPORT 80 81
GoaResults
Goa: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
24.6%
0.6%
7.1%
4.7%
3.0%
1.0%0.9%
14.9%
3.1%
21.4%
12.1%
4.1%
1.6%
0.2%0.0%
16.2%
9.5%
4.0%
2.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
2.96%
0.20%
1.39%
2.24%
0.59%
2.06%
2.81%
0.30% 0.00%
1.83%
0.86% 0.79%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Goa: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Goa
Overview
Goa Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
3.76%
Rural
Headcount Ratio Intensity MPI
4.44% 39.3% 0.017
Goa: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
40.16%
MPI (HxA)
0.015
Goa
Urban
Headcount Ratio Intensity MPI
3.34% 40.84% 0.014
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 32.7%
Child & Adolescent Mortality: 1.1%
Maternal Health: 7.7%
Years of Schooling: 24.8%
School Attendance: 6.5%
Cooking Fuel: 6.5%
Sanitation: 8.9%
Drinking Water: 0.9%
Electricity: 0.0%
Housing: 5.8%
Assets: 2.7%
Bank Account: 2.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Goa State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 82 83
GoaResults
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
3.33%
4.37%
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%
North Goa
South Goa
Headcount Ratio (% of population who are multidimensionally poor)
Goa: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.014
0.017
Goa
Multidimensional Poverty Index Score (District-wise)
Districts of Goa are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States
of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow,
to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The
legend provides the range of MPI scores represented by a colour.
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Goa. The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Goa
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
North Goa 4.74% 39.27% 0.019 2.45% 42.90% 0.011
South Goa 3.99% 39.34% 0.016 4.58% 39.32% 0.018
Multidimensional Poverty in Goa
Districts of Goa Headcount Ratio Intensity MPI
North Goa 3.33% 40.92% 0.014
South Goa 4.37% 39.32% 0.017
Districts of Goa are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 84 85
GujaratResults
Gujarat: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
41.4%
2.2%
14.8%
9.8%
13.6%
6.7%
5.2%
48.8%
35.1%
37.2%
26.0%
12.5%
5.4%
3.7%
2.4%
24.2%
23.3%
9.4%
4.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
15.42%
1.11%
8.74%
6.68%
4.79%
17.26%
15.51%
4.74%
2.90%
11.41%
8.20%
4.36%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Gujarat: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Gujarat
Overview
Gujarat Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
18.6%
Rural
Headcount Ratio Intensity MPI
27.4% 45.12% 0.124
Gujarat: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
45%
MPI (HxA)
0.084
Gujarat
Urban
Headcount Ratio Intensity MPI
6.59% 44.34% 0.029
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.7%
Child & Adolescent Mortality: 1.1%
Maternal Health: 8.7%
Years of Schooling: 13.3%
School Attendance: 9.5%
Cooking Fuel: 9.8%
Sanitation: 8.8%
Drinking Water: 2.7%
Electricity: 1.6%
Housing: 6.5%
Assets: 4.7%
Bank Account: 2.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Gujarat State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 86 87
GujaratResults
0.024 to 0.059 0.060 to 0.095 0.096 to 0.132 0.133 to 0.168 0.169 to 0.205 0.206 to 0.241 0.242 to 0.278
5.87%
8.74%
8.94%
9.22%
10.28%
10.51%
11.68%
11.94%
13.18%
14.81%
16.57%
17.85%
17.90%
21.19%
20.16%
21.24%
24.85%
25.16%
25.50%
27.76%
28.6%
31.2%
37.1%
41.62%
55.05%
57.33%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Ahmadabad
Rajkot
Porbandar
Surat
Junagadh
Mahesana
Navsari
Amreli
Jamnagar
Anand
Gandhinagar
Bharuch
Bhavnagar
Valsad
Patan
Vadodara
Sabar Kantha
Surendranagar
Kheda
Tapi
Kachchh
Banas Kantha
Narmada
Panch Mahals
Dohad
Dang
Headcount Ratio (% of population who are multidimensionally poor)
Gujarat: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Gujarat.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Gujarat
Multidimensional Poverty Index Score (District-wise)
Districts of Gujarat are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.024 to 0.059 0.060 to 0.095 0.096 to 0.132 0.133 to 0.168 0.169 to 0.205 0.206 to 0.241 0.242 to 0.278
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 88 89
GujaratResults
Multidimensional Poverty in Gujarat
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Gujarat
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Gujarat are as per the 2011 Census of India Districts of Gujarat are as per the 2011 Census of India
Districts of Gujarat Headcount Ratio Intensity MPI
Ahmadabad 5.87% 40.50% 0.024
Amreli 11.94% 42.81% 0.051
Anand 14.81% 41.91% 0.062
Banas Kantha 31.24% 46.14% 0.144
Bharuch 17.85% 43.87% 0.078
Bhavnagar 17.90% 41.88% 0.075
Dang 57.33% 48.54% 0.278
Dohad 55.05% 46.89% 0.258
Gandhinagar 16.57% 47.19% 0.078
Jamnagar 13.18% 46.25% 0.061
Junagadh 10.28% 43.48% 0.045
Kachchh 28.60% 49.80% 0.142
Kheda 25.50% 42.50% 0.108
Mahesana 10.51% 43.68% 0.046
Narmada 37.11% 43.32% 0.161
Navsari 11.68% 41.43% 0.048
Panch Mahals 41.62% 45.56% 0.190
Patan 21.19% 43.95% 0.093
Porbandar 8.94% 42.31% 0.038
Rajkot 8.74% 43.61% 0.038
Sabar Kantha 24.85% 43.46% 0.108
Surat 9.22% 44.64% 0.041
Surendranagar 25.16% 48.23% 0.121
Tapi 27.76% 41.38% 0.115
Vadodara 21.24% 46.11% 0.098
Valsad 20.16% 48.08% 0.097
Rural Urban
Districts of Gujarat
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ahmadabad 22.84% 41.29% 0.094 2.10% 38.60% 0.008
Amreli 13.12% 42.87% 0.056 8.62% 42.53% 0.037
Anand 18.77% 41.95% 0.079 5.24% 41.63% 0.022
Banas Kantha 34.41% 46.08% 0.159 9.33% 47.60% 0.044
Bharuch 23.03% 44.71% 0.103 8.67% 39.90% 0.035
Bhavnagar 26.12% 41.57% 0.109 5.51% 44.15% 0.024
Dang 60.97% 48.69% 0.297 19.54% 43.62% 0.085
Dohad 59.50% 47.11% 0.280 14.92% 39.17% 0.058
Gandhinagar 15.85% 41.45% 0.066 17.35% 52.83% 0.092
Jamnagar 13.97% 43.61% 0.061 12.34% 49.45% 0.061
Junagadh 13.34% 44.36% 0.059 4.23% 37.98% 0.016
Kachchh 31.75% 49.78% 0.158 21.32% 49.85% 0.106
Kheda 26.02% 41.61% 0.108 23.88% 45.50% 0.109
Mahesana 13.64% 43.86% 0.060 1.95% 40.18% 0.008
Narmada 40.14% 43.37% 0.174 8.15% 40.95% 0.033
Navsari 15.07% 40.93% 0.062 4.16% 45.43% 0.019
Panch Mahals 48.39% 45.55% 0.220 2.27% 46.77% 0.011
Patan 23.22% 44.44% 0.103 13.77% 40.94% 0.056
Porbandar 12.59% 42.13% 0.053 4.86% 42.81% 0.021
Rajkot 10.69% 42.68% 0.046 7.45% 44.49% 0.033
Sabar Kantha 28.36% 43.55% 0.124 6.06% 41.44% 0.025
Surat 26.60% 46.65% 0.124 4.62% 41.57% 0.019
Surendranagar 34.64% 48.30% 0.167 0.75% 39.61% 0.003
Tapi 29.61% 41.52% 0.123 9.33% 36.87% 0.034
Vadodara 34.44% 47.05% 0.162 8.51% 42.41% 0.036
Valsad 27.42% 48.73% 0.134 7.82% 44.21% 0.035
INDIA MPI BASELINE REPORT 90 91
HaryanaResults
Haryana: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
32.3%
2.2%
23.9%
7.1%
3.8%
12.7%
24.3%
4.6%
8.2%
51.2%
40.5%
19.3%
15.0%
1.1%
0.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
10.41%
1.21%
9.42%
4.61%
2.83%
10.17%
6.20%
3.38%
0.75%
7.44%
2.57% 2.92%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Haryana: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Haryana
Overview
Haryana Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
12.28%
Rural
Headcount Ratio Intensity MPI
14.86% 44.38% 0.066
Haryana: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.4%
MPI (HxA)
0.055
Haryana
Urban
Headcount Ratio Intensity MPI
8.16% 44.48% 0.036
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.8%
Child & Adolescent Mortality: 1.8%
Maternal Health: 14.4%
Years of Schooling: 14.1%
School Attendance: 8.6%
Cooking Fuel: 8.9%
Sanitation: 5.4%
Drinking Water: 3.0%
Electricity: 0.7%
Housing: 6.5%
Assets: 2.2%
Bank Account: 2.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Haryana State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 92 93
HaryanaResults
0.008 to 0.054 0.055 to 0.100 0.101 to 0.147 0.148 to 0.194 0.195 to 0.241 0.242 to 0.287 0.288 to 0.335
1.99%
2.47%
4.47%
5.96%
6.40%
6.42%
6.76%
7.16%
7.92%
8.24%
9.27%
9.96%
10.68%
11.02%
11.59%
13.14%
13.09%
13.72%
14.58%
26.98%
63.18%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Ambala
Panchkula
Yamunanagar
Jhajjar
Karnal
Kurukshetra
Mahendragarh
Sonipat
Kaithal
Panipat
Jind
Hisar
Gurgaon
Fatehabad
Rewari
Faridabad
Bhiwani
Rohtak
Sirsa
Palwal
Mewat
Headcount Ratio (% of population who are multidimensionally poor)
Haryana: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Haryana.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Haryana
Multidimensional Poverty Index Score (District-wise)
Districts of Haryana are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.008 to 0.054 0.055 to 0.100 0.101 to 0.147 0.148 to 0.194 0.195 to 0.241 0.242 to 0.287 0.288 to 0.335
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 94 95
HaryanaResults
Multidimensional Poverty in Haryana
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Haryana
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Haryana are as per the 2011 Census of India Dis tricts of Haryana are as per the 2011 Census of India
Districts of Haryana Headcount Ratio Intensity MPI
Ambala 1.99% 39.52% 0.008
Bhiwani 13.14% 39.59% 0.052
Faridabad 13.09% 43.99% 0.058
Fatehabad 11.02% 41.37% 0.046
Gurgaon 10.68% 41.96% 0.045
Hisar 9.96% 39.85% 0.040
Jhajjar 5.96% 39.45% 0.023
Jind 9.27% 39.47% 0.037
Kaithal 7.92% 41.58% 0.033
Karnal 6.40% 42.92% 0.027
Kurukshetra 6.42% 42.22% 0.027
Mahendragarh 6.76% 38.15% 0.026
Mewat 63.18% 53.03% 0.335
Palwal 26.98% 46.68% 0.126
Panchkula 2.47% 40.83% 0.010
Panipat 8.24% 43.30% 0.036
Rewari 11.59% 39.31% 0.046
Rohtak 13.72% 41.93% 0.058
Sirsa 14.58% 41.10% 0.060
Sonipat 7.16% 39.49% 0.028
Yamunanagar 4.47% 43.11% 0.019
Rural Urban
Districts of Haryana
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ambala 2.72% 39.24% 0.011 1.10% 40.38% 0.004
Bhiwani 13.64% 39.61% 0.054 10.99% 39.51% 0.043
Faridabad 24.41% 47.72% 0.116 11.18% 42.62% 0.048
Fatehabad 12.38% 41.93% 0.052 5.66% 36.58% 0.021
Gurgaon 12.69% 38.74% 0.049 10.04% 43.26% 0.043
Hisar 13.40% 39.44% 0.053 2.11% 45.79% 0.010
Jhajjar 6.52% 38.82% 0.025 4.54% 41.73% 0.019
Jind 10.50% 38.66% 0.041 4.85% 45.80% 0.022
Kaithal 9.54% 41.22% 0.039 3.25% 44.61% 0.014
Karnal 7.27% 40.85% 0.030 4.41% 50.64% 0.022
Kurukshetra 7.00% 41.69% 0.029 5.01% 44.03% 0.022
Mahendragarh 7.54% 38.36% 0.029 2.62% 34.92% 0.009
Mewat 65.04% 52.78% 0.343 54.70% 54.40% 0.298
Palwal 30.72% 47.54% 0.146 17.31% 42.75% 0.074
Panchkula 5.61% 41.72% 0.023 0.45% 33.77% 0.002
Panipat 8.96% 44.47% 0.040 7.52% 41.94% 0.032
Rewari 12.06% 38.45% 0.046 10.62% 41.37% 0.044
Rohtak 16.83% 40.44% 0.068 9.18% 45.91% 0.042
Sirsa 18.29% 41.09% 0.075 4.40% 41.23% 0.018
Sonipat 8.79% 38.41% 0.034 3.83% 44.53% 0.017
Yamunanagar 5.89% 44.00% 0.026 2.27% 39.54% 0.009
INDIA MPI BASELINE REPORT 96 97
Himachal PradeshResults
Himachal Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
27.2%
1.7%
17.4%
3.8%
7.5%
0.9%1.1%
67.9%
53.1%
27.8%
18.3%
7.9%
5.0%
0.5%0.5%
29.3%
24.0%
2.7%
2.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
6.79%
0.59%
5.73%
1.47%
0.43%
7.14%
4.82%
1.39%
0.24%
5.24%
2.16%
0.65%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Himachal Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Himachal Pradesh
Overview
Himachal Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
7.62%
Rural
Headcount Ratio Intensity MPI
8.24% 39.28% 0.032
Himachal Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
39.43%
MPI (HxA)
0.03
Himachal Pradesh
Urban
Headcount Ratio Intensity MPI
1.46% 48.24% 0.007
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 37.7%
Child & Adolescent Mortality: 1.6%
Maternal Health: 15.9%
Years of Schooling: 8.2%
School Attendance: 2.4%
Cooking Fuel: 11.3%
Sanitation: 7.6%
Drinking Water: 2.2%
Electricity: 0.4%
Housing: 8.3%
Assets: 3.4%
Bank Account: 1.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Himachal Pradesh State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 98 99
Himachal PradeshResults
0.017 to 0.020 0.021 to 0.024 0.025 to 0.029 0.030 to 0.033 0.034 to 0.037 0.038 to 0.042 0.043 to 0.047
4.60%
5.10%
5.12%
5.88%
7.54%
7.5%
7.7%
8.3%
8.97%
9.24%
10.88%
11.27%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
Hamirpur
Kinnaur
Una
Kangra
Shimla
Bilaspur
Lahul & Spiti
Mandi
Kullu
Solan
Sirmaur
Chamba
Headcount Ratio (% of population who are multidimensionally poor)
Himachal Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Himachal Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Himachal Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Himachal Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.017 to 0.020 0.021 to 0.024 0.025 to 0.029 0.030 to 0.033 0.034 to 0.037 0.038 to 0.042 0.043 to 0.047
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 100 101
Himachal PradeshResults
Multidimensional Poverty in Himachal Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Himachal Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Himachal Pradesh are as per the 2011 Census of India Dis tricts of Himachal Pradesh are as per the 2011 Census of India
Districts of Himachal Pradesh Headcount Ratio Intensity MPI
Bilaspur 7.54% 36.62% 0.028
Chamba 11.27% 41.25% 0.046
Hamirpur 4.60% 36.36% 0.017
Kangra 5.88% 37.40% 0.022
Kinnaur 5.10% 38.60% 0.020
Kullu 8.97% 38.98% 0.035
Lahul & Spiti 7.72% 38.38% 0.030
Mandi 8.35% 39.09% 0.033
Shimla 7.46% 40.07% 0.030
Sirmaur 10.88% 43.14% 0.047
Solan 9.24% 40.41% 0.037
Una 5.12% 38.83% 0.020
Rural Urban
Districts of Himachal Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bilaspur 7.86% 36.34% 0.029 1.97% 55.95% 0.011
Chamba 11.93% 41.24% 0.049 2.17% 41.67% 0.009
Hamirpur 4.92% 36.36% 0.018 0.41% 35.71% 0.001
Kangra 6.09% 37.40% 0.023 0.00% 0.000
Kinnaur 5.10% 38.60% 0.020 - - -
Kullu 9.10% 38.76% 0.035 7.16% 42.60% 0.030
Lahul & Spiti 7.72% 38.38% 0.030 - - -
Mandi 8.86% 39.17% 0.035 1.98% 34.52% 0.007
Shimla 9.49% 40.07% 0.038 0.00% - 0.000
Sirmaur 12.31% 42.94% 0.053 0.84% 64.29% 0.005
Solan 10.61% 39.37% 0.042 3.21% 55.46% 0.018
Una 5.51% 38.83% 0.021 0.00% - 0.000
INDIA MPI BASELINE REPORT 102 103
JharkhandResults
Jharkhand: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
48.0%
3.3%
33.1%
18.3%
8.2%
31.1%
61.8%
21.4%
9.0%
82.1%
68.1%
75.4%
43.3%
18.8%
5.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
34.42%
2.74%
26.51%
16.46%
7.17%
41.25%
39.40%
17.47%
13.58%
35.93%
15.54%
6.63%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Jharkhand: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Jharkhand
Overview
Jharkhand Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
42.16%
Rural
Headcount Ratio Intensity MPI
50.93% 48.27% 0.246
Jharkhand: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.91%
MPI (HxA)
0.202
Jharkhand
Urban
Headcount Ratio Intensity MPI
15.26% 44.24% 0.067
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.4%
Child & Adolescent Mortality: 1.1%
Maternal Health: 10.9%
Years of Schooling: 13.6%
School Attendance: 5.9%
Cooking Fuel: 9.7%
Sanitation: 9.3%
Drinking Water: 4.1%
Electricity: 3.2%
Housing: 8.5%
Assets: 3.7%
Bank Account: 1.6%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Jharkhand State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 104 105
JharkhandResults
0.110 to 0.138 0.139 to 0.168 0.169 to 0.197 0.198 to 0.226 0.227 to 0.255 0.256 to 0.285 0.286 to 0.315
23.99%
27.70%
28.57%
29.49%
29.80%
32.68%
35.75%
41.79%
45.4%
46.7%
47.4%
45.54%
47.88%
48.65%
49.98%
50.56%
51.81%
52.71%
52.93%
53.26%
55.93%
57.60%
60.66%
60.74%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Purbi Singhbhum
Ranchi
Dhanbad
Bokaro
Ramgarh
Kodarma
Hazaribagh
Saraikela-Kharsawan
Lohardaga
Palamu
Gumla
Deoghar
Giridih
Khunti
Simdega
Jamtara
Godda
Latehar
Dumka
Garhwa
Sahibganj
Pashchimi Singhbhum
Pakur
Chatra
Headcount Ratio (% of population
Jharkhand: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Jharkhand.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Jharkhand
Multidimensional Poverty Index Score (District-wise)
Districts of Jharkhand are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.110 to 0.138 0.139 to 0.168 0.169 to 0.197 0.198 to 0.226 0.227 to 0.255 0.256 to 0.285 0.286 to 0.315
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 106 107
JharkhandResults
Multidimensional Poverty in Jharkhand
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Jharkhand
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Jharkhand are as per the 2011 Census of India Dis tricts of Jharkhand are as per the 2011 Census of India
Districts of Jharkhand Headcount Ratio Intensity MPI
Bokaro 29.49% 44.16% 0.130
Chatra 60.74% 50.40% 0.306
Deoghar 47.40% 46.71% 0.221
Dhanbad 28.57% 43.84% 0.125
Dumka 52.93% 48.33% 0.256
Garhwa 53.26% 48.44% 0.258
Giridih 47.88% 47.89% 0.229
Godda 51.81% 49.02% 0.254
Gumla 46.70% 47.10% 0.220
Hazaribagh 35.75% 43.70% 0.156
Jamtara 50.56% 47.54% 0.240
Khunti 48.65% 47.27% 0.230
Kodarma 32.68% 44.69% 0.146
Latehar 52.71% 50.55% 0.266
Lohardaga 45.37% 47.08% 0.214
Pakur 60.66% 51.90% 0.315
Palamu 45.54% 51.07% 0.233
Pashchimi Singhbhum 57.60% 53.90% 0.310
Purbi Singhbhum 23.99% 45.94% 0.110
Ramgarh 29.80% 44.23% 0.132
Ranchi 27.70% 43.72% 0.121
Sahibganj 55.93% 52.49% 0.294
Saraikela-Kharsawan 41.79% 46.33% 0.194
Simdega 49.98% 47.34% 0.237
Rural Urban
Districts of Jharkhand
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bokaro 43.46% 43.72% 0.190 15.47% 45.41% 0.070
Chatra 63.10% 50.75% 0.320 25.52% 37.28% 0.095
Deoghar 54.97% 46.95% 0.258 16.17% 43.27% 0.070
Dhanbad 40.45% 43.55% 0.176 20.13% 44.27% 0.089
Dumka 56.23% 48.35% 0.272 7.56% 45.58% 0.034
Garhwa 54.39% 48.65% 0.265 30.15% 40.57% 0.122
Giridih 51.21% 48.16% 0.247 16.20% 39.92% 0.065
Godda 54.15% 48.99% 0.265 11.58% 51.66% 0.060
Gumla 49.04% 47.28% 0.232 14.13% 38.42% 0.054
Hazaribagh 39.78% 43.88% 0.175 15.62% 41.45% 0.065
Jamtara 54.63% 47.65% 0.260 12.98% 43.33% 0.056
Khunti 50.98% 47.58% 0.243 22.11% 38.95% 0.086
Kodarma 37.13% 45.50% 0.169 17.13% 38.58% 0.066
Latehar 54.71% 50.63% 0.277 23.31% 47.76% 0.111
Lohardaga 50.29% 47.16% 0.237 6.57% 42.33% 0.028
Pakur 61.73% 52.05% 0.321 48.76% 49.88% 0.243
Palamu 51.26% 51.28% 0.263 6.33% 39.39% 0.025
Pashchimi Singhbhum 64.13% 54.18% 0.347 17.65% 47.67% 0.084
Purbi Singhbhum 42.86% 46.29% 0.198 8.51% 44.51% 0.038
Ramgarh 37.12% 43.24% 0.161 20.40% 46.55% 0.095
Ranchi 41.65% 44.01% 0.183 8.15% 41.60% 0.034
Sahibganj 59.62% 53.10% 0.317 32.15% 45.23% 0.145
Saraikela-Kharsawan 52.21% 46.17% 0.241 18.41% 47.33% 0.087
Simdega 52.38% 47.52% 0.249 27.14% 43.99% 0.119
INDIA MPI BASELINE REPORT 108 109
KarnatakaResults
Karnataka: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
33.6%
1.3%
12.4%
8.7%
10.0%
3.5%
2.2%
45.5%
21.9%
43.1%
25.2%
14.5%
6.9%
1.7%
0.9%
37.3%
36.4%
8.8%
5.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
10.08%
0.71%
5.35% 5.45%
2.36%
11.58%
11.04%
3.46%
0.97%
9.43%
4.99%
3.39%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Karnataka: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Karnataka
Overview
Karnataka Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
13.16%
Rural
Headcount Ratio Intensity MPI
19.01% 42.79% 0.081
Karnataka: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
42.7%
MPI (HxA)
0.056
Karnataka
Urban
Headcount Ratio Intensity MPI
5.07% 42.23% 0.021
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 29.9%
Child & Adolescent Mortality: 1.1%
Maternal Health: 7.9%
Years of Schooling: 16.1%
School Attendance: 7.0%
Cooking Fuel: 9.8%
Sanitation: 9.4%
Drinking Water: 2.9%
Electricity: 0.8%
Housing: 8.0%
Assets: 4.2%
Bank Account: 2.9%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Karnataka State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 110 111
KarnatakaResults
0.009 to 0.035 0.036 to 0.062 0.063 to 0.088 0.089 to 0.115 0.116 to 0.142 0.143 to 0.168 0.169 to 0.196
2.31%
6.62%
6.64%
6.69%
7.79%
8.39%
8.77%
8.74%
9.65%
10.30%
10.32%
11.19%
11.71%
12.26%
12.72%
13.21%
14.00%
15.16%
15.61%
15.79%
18.91%
19.42%
20.23%
20.27%
21.8%
22.4%
23.4%
24.6%
32.19%
41.67%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%
Bangalore
Mandya
Hassan
Dakshina Kannada
Mysore
Bangalore Rural
Kodagu
Ramanagara
Dharwad
Kolar
Udupi
Chikmagalur
Davanagere
Belgaum
Shimoga
Uttara Kannada
Tumkur
Chikkaballapura
Haveri
Chitradurga
Chamrajnagar
Bidar
Bagalkot
Gadag
Gulbarga
Bijapur
Bellary
Koppal
Raichur
Yadgir
Headcount Ratio (% of population who are multidimensionally poor)
Karnataka: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Karnataka.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Karnataka
Multidimensional Poverty Index Score (District-wise)
Districts of Karnataka are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.009 to 0.035 0.036 to 0.062 0.063 to 0.088 0.089 to 0.115 0.116 to 0.142 0.143 to 0.168 0.169 to 0.196
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 112 113
KarnatakaResults
Multidimensional Poverty in Karnataka
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Karnataka
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Karnataka are as per the 2011 Census of India Districts of Karnataka are as per the 2011 Census of India
Districts of Karnataka Headcount Ratio Intensity MPI
Bagalkot 20.23% 43.39% 0.088
Bangalore 2.31% 40.30% 0.009
Bangalore Rural 8.39% 39.58% 0.033
Belgaum 12.26% 39.96% 0.049
Bellary 23.44% 46.51% 0.109
Bidar 19.42% 41.63% 0.081
Bijapur 22.40% 42.62% 0.095
Chamrajnagar 18.91% 41.93% 0.079
Chikkaballapura 15.16% 41.88% 0.064
Chikmagalur 11.19% 41.38% 0.046
Chitradurga 15.79% 41.19% 0.065
Dakshina Kannada 6.69% 40.32% 0.027
Davanagere 11.71% 42.53% 0.050
Dharwad 9.65% 40.27% 0.039
Gadag 20.27% 43.12% 0.087
Gulbarga 21.75% 44.34% 0.096
Hassan 6.64% 40.22% 0.027
Haveri 15.61% 41.07% 0.064
Kodagu 8.74% 43.92% 0.038
Kolar 10.30% 40.56% 0.042
Koppal 24.56% 42.69% 0.105
Mandya 6.62% 43.36% 0.029
Mysore 7.79% 41.16% 0.032
Raichur 32.19% 45.44% 0.146
Ramanagara 8.77% 38.37% 0.034
Shimoga 12.72% 41.13% 0.052
Tumkur 14.00% 41.23% 0.058
Udupi 10.32% 41.24% 0.043
Uttara Kannada 13.21% 42.64% 0.056
Yadgir 41.67% 46.99% 0.196
Rural Urban
Districts of Karnataka
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bagalkot 24.37% 43.78% 0.107 12.33% 41.92% 0.052
Bangalore 5.89% 33.97% 0.020 1.98% 42.01% 0.008
Bangalore Rural 9.10% 39.54% 0.036 6.32% 39.72% 0.025
Belgaum 15.41% 40.16% 0.062 4.35% 38.17% 0.017
Bellary 28.15% 48.14% 0.136 16.67% 42.56% 0.071
Bidar 25.04% 42.10% 0.105 5.81% 36.62% 0.021
Bijapur 27.52% 42.99% 0.118 7.08% 38.33% 0.027
Chamrajnagar 19.91% 42.29% 0.084 14.97% 40.05% 0.060
Chikkaballapura 19.16% 41.31% 0.079 3.67% 50.49% 0.019
Chikmagalur 12.48% 41.02% 0.051 5.55% 44.87% 0.025
Chitradurga 19.61% 41.64% 0.082 4.88% 35.95% 0.018
Dakshina Kannada 9.79% 40.07% 0.039 3.10% 41.22% 0.013
Davanagere 16.26% 42.93% 0.070 2.90% 38.19% 0.011
Dharwad 17.92% 40.44% 0.072 3.72% 39.66% 0.015
Gadag 23.98% 42.53% 0.102 14.94% 44.49% 0.066
Gulbarga 28.33% 44.32% 0.126 11.58% 44.43% 0.051
Hassan 8.68% 40.39% 0.035 0.77% 34.52% 0.003
Haveri 17.37% 40.98% 0.071 9.06% 41.70% 0.038
Kodagu 9.97% 43.94% 0.044 0.20% 35.71% 0.001
Kolar 11.86% 39.59% 0.047 7.69% 43.07% 0.033
Koppal 27.91% 42.31% 0.118 11.36% 46.40% 0.053
Mandya 8.04% 43.26% 0.035 1.02% 46.43% 0.005
Mysore 12.98% 41.25% 0.054 0.62% 38.38% 0.002
Raichur 40.50% 45.34% 0.184 12.47% 46.18% 0.058
Ramanagara 10.60% 38.67% 0.041 3.50% 35.75% 0.013
Shimoga 16.00% 41.50% 0.066 6.16% 39.22% 0.024
Tumkur 16.31% 40.75% 0.066 6.48% 45.17% 0.029
Udupi 12.20% 40.92% 0.050 5.20% 43.31% 0.023
Uttara Kannada 16.43% 43.10% 0.071 5.82% 39.67% 0.023
Yadgir 48.37% 47.08% 0.228 20.24% 46.29% 0.094
INDIA MPI BASELINE REPORT 114 115
KeralaResults
Kerala: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
15.3%
0.2%
1.7% 1.8%
2.9%
0.5%0.3%
43.9%
28.2%
1.9%
1.3%
5.9%
5.4%
0.7%0.4%
10.8%
16.7%
4.3%
3.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
0.56%
0.00% 0.15% 0.19% 0.22% 0.58% 0.30% 0.14% 0.20% 0.40% 0.32% 0.17%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Kerala: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Kerala
Overview
Kerala Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
0.71%
Rural
Headcount Ratio Intensity MPI
0.95% 39.81% 0.004
Kerala: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
39.02%
MPI (HxA)
0.003
Kerala
Urban
Headcount Ratio Intensity MPI
0.43% 37.06% 0.002
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 34.0%
Child & Adolescent Mortality: 0.1%
Maternal Health: 4.5%
Years of Schooling: 11.3%
School Attendance: 13.6%
Cooking Fuel: 10.0%
Sanitation: 5.1%
Drinking Water: 2.4%
Electricity: 3.5%
Housing: 6.9%
Assets: 5.6%
Bank Account: 3.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Kerala State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 116 117
KeralaResults
0.000 to 0.001 0.002 to 0.003 0.004 to 0.005 0.006 to 0.007 0.008 to 0.009 0.010 to 0.0110.012 to 0.014
0.10%
0.26%
0.33%
0.44%
0.62%
0.71%
0.72%
0.83%
1.00%
1.08%
1.11%
1.6%
3.48%
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0%
Kottayam
Ernakulam
Kozhikode
Thrissur
Kannur
Palakkad
Alappuzha
Kollam
Pathanamthitta
Kasaragod
Thiruvananthapuram
Malappuram
Idukki
Wayanad
Headcount Ratio (% of population who are multidimensionally poor)
0%
Kerala: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Kerala.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Kerala
Multidimensional Poverty Index Score (District-wise)
Districts of Kerala are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.000 to 0.001 0.002 to 0.003 0.004 to 0.005 0.006 to 0.007 0.008 to 0.009 0.010 to 0.0110.012 to 0.014
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 118 119
KeralaResults
Multidimensional Poverty in Kerala
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Kerala
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Kerala are as per the 2011 Census of India Districts of Kerala are as per the 2011 Census of India
Districts of Kerala Headcount Ratio Intensity MPI
Alappuzha 0.71% 38.12% 0.003
Ernakulam 0.10% 38.10% 0.000
Idukki 1.65% 37.53% 0.006
Kannur 0.44% 36.04% 0.002
Kasaragod 1.00% 51.38% 0.005
Kollam 0.72% 42.76% 0.003
Kottayam 0.00% - 0.000
Kozhikode 0.26% 37.31% 0.001
Malappuram 1.11% 36.64% 0.004
Palakkad 0.62% 37.04% 0.002
Pathanamthitta 0.83% 42.48% 0.004
Thiruvananthapuram 1.08% 37.40% 0.004
Thrissur 0.33% 37.12% 0.001
Wayanad 3.48% 40.94% 0.014
Rural Urban
Districts of Kerala
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Alappuzha 0.93% 38.14% 0.004 0.52% 38.10% 0.002
Ernakulam 0.32% 38.10% 0.001 0.00% - 0.000
Idukki 1.72% 37.53% 0.006 0.00% - 0.000
Kannur 0.32% 40.48% 0.001 0.50% 34.46% 0.002
Kasaragod 1.47% 53.15% 0.008 0.29% 38.10% 0.001
Kollam 1.14% 42.98% 0.005 0.26% 41.67% 0.001
Kottayam 0.00% - 0.000 0.00% - 0.000
Kozhikode 0.52% 37.95% 0.002 0.12% 35.71% 0.000
Malappuram 1.02% 38.66% 0.004 1.20% 34.84% 0.004
Palakkad 0.75% 36.54% 0.003 0.16% 45.24% 0.001
Pathanamthitta 0.92% 42.48% 0.004 0.00% - 0.000
Thiruvananthapuram 1.58% 35.73% 0.006 0.65% 40.90% 0.003
Thrissur 0.07% 35.71% 0.000 0.47% 37.22% 0.002
Wayanad 3.62% 40.94% 0.015 0.00% - 0.000
INDIA MPI BASELINE REPORT 120 121
Madhya PradeshResults
Madhya Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
45.5%
3.6%
29.4%
16.1%
8.4%
29.8%
64.4%
19.3%
11.1%
71.2%
59.9%
65.3%
34.9%
9.0%
1.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
29.05%
2.72%
20.87%
14.02%
7.34%
34.90%
33.21%
17.56%
6.47%
32.73%
13.65%
7.36%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Madhya Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Madhya Pradesh
Overview
Madhya Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
36.65%
Rural
Headcount Ratio Intensity MPI
45.96% 47.57% 0.219
Madhya Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.25%
MPI (HxA)
0.173
Madhya Pradesh
Urban
Headcount Ratio Intensity MPI
13.82% 44.62% 0.062
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.0%
Child & Adolescent Mortality: 1.3%
Maternal Health: 10.0%
Years of Schooling: 13.5%
School Attendance: 7.1%
Cooking Fuel: 9.6%
Sanitation: 9.1%
Drinking Water: 4.8%
Electricity: 1.8%
Housing: 9.0%
Assets: 3.8%
Bank Account: 2.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Madhya Pradesh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 122 123
Madhya PradeshResults
0.049 to 0.099 0.100 to 0.150 0.151 to 0.201 0.202 to 0.253 0.254 to 0.304 0.305 to 0.355 0.356 to 0.407
10.86%
12.91%
19.50%
22.38%
24.73%
26.80%
29.67%
29.80%
30.14%
30.55%
31.87%
32.50%
33.18%
33.27%
34.12%
33.11%
33.59%
34.31%
34.50%
34.52%
35.80%
36.99%
37.04%
39.94%
40.14%
40.47%
40.51%
41.70%
41.99%
42.55%
41.7%
42.5%
42.6%
42.8%
43.5%
45.6%
45.7%
46.1%
46.3%
47.2%
47.5%
48.1%
49.0%
49.8%
51.92%
52.68%
56.23%
61.60%
68.86%
71.31%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
Indore
Bhopal
Jabalpur
Gwalior
Hoshangabad
Sehore
Dewas
Ujjain
Chhindwara
Narsimhapur
Neemuch
Morena
Harda
Bhind
Mandsaur
Shajapur
Satna
Datia
Betul
Raisen
West Nimar
Burhanpur
Rewa
Katni
Balaghat
Sagar
Dhar
Ratlam
Anuppur
Rajgarh
East Nimar
Seoni
Panna
Ashoknagar
Shahdol
Umaria
Guna
Shivpuri
Damoh
Vidisha
Tikamgarh
Mandla
Chhatarpur
Sheopur
Singrauli
Sidhi
Dindori
Barwani
Jhabua
Alirajpur
Headcount Ratio (% of population who are multidimensionally poor)
Madhya Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Madhya Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Madhya Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Madhya Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.049 to 0.099 0.100 to 0.150 0.151 to 0.201 0.202 to 0.253 0.254 to 0.304 0.305 to 0.355 0.356 to 0.407
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 124 125
Madhya PradeshResults
Multidimensional Poverty in Madhya Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Madhya Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Madhya Pradesh are as per the 2011 Census of India Dis tricts of Madhya Pradesh are as per the 2011 Census of India
Districts of Madhya Pradesh Headcount Ratio Intensity MPI
Alirajpur 71.31% 57.06% 0.407
Anuppur 41.70% 45.25% 0.189
Ashoknagar 42.78% 47.19% 0.202
Balaghat 40.14% 43.59% 0.175
Barwani 61.60% 57.27% 0.353
Betul 34.50% 46.90% 0.162
Bhind 33.18% 44.06% 0.146
Bhopal 12.91% 45.29% 0.058
Burhanpur 36.99% 52.20% 0.193
Chhatarpur 48.95% 48.35% 0.237
Chhindwara 30.14% 46.01% 0.139
Damoh 46.31% 46.31% 0.214
Datia 34.31% 44.23% 0.152
Dewas 29.67% 46.42% 0.138
Dhar 40.51% 49.29% 0.200
Dindori 56.23% 47.28% 0.266
East Nimar 42.53% 47.59% 0.202
Guna 45.67% 47.31% 0.216
Gwalior 22.38% 44.26% 0.099
Harda 33.11% 46.57% 0.154
Hoshangabad 24.73% 44.05% 0.109
Indore 10.86% 45.22% 0.049
Jabalpur 19.50% 45.39% 0.089
Jhabua 68.86% 55.97% 0.385
Katni 39.94% 45.25% 0.181
Mandla 48.09% 47.20% 0.227
Mandsaur 33.27% 45.15% 0.150
Morena 32.50% 44.83% 0.146
Narsimhapur 30.55% 44.63% 0.136
Neemuch 31.87% 44.94% 0.143
Panna 42.63% 47.93% 0.204
Raisen 34.52% 44.76% 0.155
Rajgarh 41.99% 45.91% 0.193
Ratlam 41.67% 48.50% 0.202
Rewa 37.04% 44.72% 0.166
Sagar 40.47% 44.53% 0.180
Satna 34.12% 43.96% 0.150
Sehore 26.80% 46.46% 0.125
Seoni 42.55% 44.50% 0.189
Shahdol 43.47% 46.41% 0.202
Shajapur 33.59% 45.72% 0.154
Sheopur 49.83% 49.56% 0.247
Shivpuri 46.09% 46.43% 0.214
Sidhi 52.68% 48.18% 0.254
Singrauli 51.92% 50.76% 0.264
Tikamgarh 47.52% 45.89% 0.218
Ujjain 29.80% 45.70% 0.136
Umaria 45.58% 46.06% 0.210
Vidisha 47.19% 48.64% 0.230
West Nimar 35.80% 49.16% 0.176
Rural Urban
Districts of Madhya Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Alirajpur 75.85% 57.26% 0.434 15.27% 44.80% 0.068
Anuppur 49.91% 45.49% 0.227 14.44% 42.46% 0.061
Ashoknagar 46.07% 47.48% 0.219 26.93% 44.80% 0.121
Balaghat 44.01% 43.71% 0.192 14.25% 41.12% 0.059
Barwani 70.72% 57.79% 0.409 14.24% 44.00% 0.063
Betul 41.59% 46.96% 0.195 3.89% 44.13% 0.017
Bhind 37.35% 44.46% 0.166 24.45% 42.78% 0.105
Bhopal 30.30% 45.66% 0.138 8.64% 44.97% 0.039
Burhanpur 49.48% 53.73% 0.266 16.29% 44.51% 0.073
Chhatarpur 57.12% 48.27% 0.276 19.05% 49.23% 0.094
Chhindwara 37.14% 46.31% 0.172 8.73% 42.21% 0.037
Damoh 54.26% 46.87% 0.254 20.29% 41.41% 0.084
Datia 41.62% 43.97% 0.183 8.60% 48.75% 0.042
Dewas 36.35% 45.98% 0.167 13.19% 49.47% 0.065
Dhar 48.00% 49.86% 0.239 14.27% 42.60% 0.061
Dindori 58.15% 47.17% 0.274 16.11% 55.19% 0.089
East Nimar 50.95% 47.94% 0.244 9.82% 40.43% 0.040
Guna 56.12% 47.56% 0.267 15.13% 44.60% 0.067
Gwalior 39.84% 45.25% 0.180 13.03% 42.64% 0.056
Harda 39.82% 47.12% 0.188 10.64% 39.71% 0.042
Hoshangabad 32.50% 44.41% 0.144 7.02% 40.30% 0.028
Indore 20.39% 47.69% 0.097 8.25% 43.53% 0.036
Jabalpur 33.72% 45.34% 0.153 8.65% 45.55% 0.039
Jhabua 72.38% 56.22% 0.407 34.57% 50.95% 0.176
Katni 45.70% 45.49% 0.208 10.69% 39.95% 0.043
Mandla 53.38% 47.41% 0.253 11.32% 40.48% 0.046
Mandsaur 38.65% 45.42% 0.176 16.14% 43.14% 0.070
Morena 38.97% 44.69% 0.174 16.98% 45.58% 0.077
Narsimhapur 36.35% 44.82% 0.163 9.76% 42.02% 0.041
Neemuch 37.30% 45.49% 0.170 18.49% 42.21% 0.078
Panna 47.17% 48.26% 0.228 18.49% 43.41% 0.080
Raisen 41.31% 45.10% 0.186 14.30% 41.87% 0.060
Rajgarh 48.71% 46.16% 0.225 14.07% 42.25% 0.059
Ratlam 51.83% 48.50% 0.251 20.12% 48.50% 0.098
Rewa 40.75% 45.10% 0.184 18.12% 40.41% 0.073
Sagar 44.82% 44.62% 0.200 29.25% 44.16% 0.129
Satna 40.66% 44.15% 0.180 9.64% 40.84% 0.039
Sehore 31.28% 46.57% 0.146 8.80% 45.02% 0.040
Seoni 46.39% 44.58% 0.207 14.99% 42.62% 0.064
Shahdol 48.90% 46.47% 0.227 17.43% 45.69% 0.080
Shajapur 39.29% 45.85% 0.180 11.36% 43.96% 0.050
Sheopur 53.22% 49.28% 0.262 30.07% 52.37% 0.157
Shivpuri 53.76% 46.78% 0.252 14.11% 40.84% 0.058
Sidhi 54.92% 48.40% 0.266 28.18% 43.50% 0.123
Singrauli 58.92% 50.83% 0.299 22.05% 49.96% 0.110
Tikamgarh 50.13% 45.27% 0.227 33.25% 51.04% 0.170
Ujjain 40.59% 45.57% 0.185 13.33% 46.33% 0.062
Umaria 50.70% 46.25% 0.234 22.44% 44.12% 0.099
Vidisha 51.78% 48.87% 0.253 34.46% 47.71% 0.164
West Nimar 39.87% 49.11% 0.196 14.61% 49.79% 0.073
INDIA MPI BASELINE REPORT 126 127
MaharashtraResults
Maharashtra: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
36.1%
1.4%
15.9%
6.5%
14.0%
4.2%
2.4%
39.5%
20.8%
48.0%
28.0%
13.9%
9.1%
6.6%
2.2%
27.9%
23.1%
10.3%
5.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
12.39%
0.83%
7.13%
4.27%
2.96%
12.46% 12.50%
5.27%
3.14%
10.12%
6.72%
3.80%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Maharashtra: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Maharashtra
Overview
Maharashtra Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
14.85%
Rural
Headcount Ratio Intensity MPI
22.83% 43.99% 0.1
Maharashtra: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.78%
MPI (HxA)
0.065
Maharashtra
Urban
Headcount Ratio Intensity MPI
5.55% 42.76% 0.024
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.7%
Child & Adolescent Mortality: 1.1%
Maternal Health: 9.1%
Years of Schooling: 10.9%
School Attendance: 7.6%
Cooking Fuel: 9.1%
Sanitation: 9.1%
Drinking Water: 3.9%
Electricity: 2.3%
Housing: 7.4%
Assets: 4.9%
Bank Account: 2.8%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Maharashtra State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 128 129
MaharashtraResults
0.014 to 0.0510.052 to 0.0890.090 to 0.127 0.128 to 0.165 0.166 to 0.203 0.204 to 0.241 0.242 to 0.280
3.59%
4.65%
5.29%
6.72%
8.19%
8.82%
10.17%
10.18%
10.19%
11.02%
12.24%
12.60%
13.38%
14.86%
15.24%
15.39%
15.45%
17.65%
17.84%
17.90%
18.22%
18.31%
18.47%
18.60%
18.75%
20.58%
22.53%
22.66%
23.39%
23.54%
27.48%
28.05%
29.41%
33.23%
52.12%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Mumbai
Mumbai Suburban
Pune
Nagpur
Bhandara
Wardha
Kolhapur
Sangli
Raigarh
Satara
Amravati
Solapur
Akola
Aurangabad
Thane
Sindhudurg
Ahmadnagar
Chandrapur
Osmanabad
Latur
Buldana
Nashik
Ratnagiri
Jalgaon
Gondiya
Garhchiroli
Washim
Bid
Parbhani
Yavatmal
Nanded
Hingoli
Jalna
Dhule
Nandurbar
Headcount Ratio (% of population who are multidimensionally poor)
Maharashtra: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Maharashtra.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Maharashtra
Multidimensional Poverty Index Score (District-wise)
Districts of Maharashtra are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.014 to 0.0510.052 to 0.0890.090 to 0.127 0.128 to 0.165 0.166 to 0.203 0.204 to 0.241 0.242 to 0.280
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 130 131
MaharashtraResults
Multidimensional Poverty in Maharashtra
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Maharashtra
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Maharashtra are as per the 2011 Census of India Districts of Maharashtra are as per the 2011 Census of India
Districts of Maharashtra Headcount Ratio Intensity MPI
Ahmadnagar 15.45% 43.67% 0.067
Akola 13.38% 40.05% 0.054
Amravati 12.24% 41.90% 0.051
Aurangabad 14.86% 42.85% 0.064
Bhandara 8.19% 38.79% 0.032
Bid 22.66% 43.15% 0.098
Buldana 18.22% 43.60% 0.079
Chandrapur 17.65% 43.00% 0.076
Dhule 33.23% 50.18% 0.167
Garhchiroli 20.58% 41.66% 0.086
Gondiya 18.75% 39.31% 0.074
Hingoli 28.05% 42.27% 0.119
Jalgaon 18.60% 45.58% 0.085
Jalna 29.41% 42.50% 0.125
Kolhapur 10.17% 40.38% 0.041
Latur 17.90% 41.30% 0.074
Mumbai 3.59% 39.73% 0.014
Mumbai Suburban 4.65% 42.97% 0.020
Nagpur 6.72% 38.02% 0.026
Nanded 27.48% 41.43% 0.114
Nandurbar 52.12% 53.78% 0.280
Nashik 18.31% 45.45% 0.083
Osmanabad 17.84% 41.75% 0.074
Parbhani 23.39% 40.59% 0.095
Pune 5.29% 39.45% 0.021
Raigarh 10.19% 45.55% 0.046
Ratnagiri 18.47% 40.77% 0.075
Sangli 10.18% 40.67% 0.041
Satara 11.02% 40.37% 0.045
Sindhudurg 15.39% 39.86% 0.061
Solapur 12.60% 42.62% 0.054
Thane 15.24% 46.94% 0.072
Wardha 8.82% 40.48% 0.036
Washim 22.53% 41.70% 0.094
Yavatmal 23.54% 43.56% 0.103
Rural Urban
Districts of Maharashtra
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ahmadnagar 18.57% 43.20% 0.080 3.38% 53.63% 0.018
Akola 17.03% 40.11% 0.068 6.83% 39.74% 0.027
Amravati 16.92% 42.30% 0.072 3.72% 38.59% 0.014
Aurangabad 21.41% 42.60% 0.091 6.67% 43.86% 0.029
Bhandara 11.22% 38.81% 0.044 0.96% 38.12% 0.004
Bid 23.97% 42.94% 0.103 18.03% 44.14% 0.080
Buldana 20.38% 43.93% 0.090 9.90% 40.97% 0.041
Chandrapur 25.06% 42.94% 0.108 2.57% 44.18% 0.011
Dhule 39.46% 50.53% 0.199 10.47% 45.42% 0.048
Garhchiroli 23.06% 41.70% 0.096 4.01% 40.04% 0.016
Gondiya 20.76% 39.33% 0.082 7.93% 39.02% 0.031
Hingoli 31.50% 42.37% 0.133 4.12% 36.84% 0.015
Jalgaon 21.73% 46.11% 0.100 9.72% 42.18% 0.041
Jalna 33.13% 42.13% 0.140 12.38% 47.01% 0.058
Kolhapur 13.97% 40.47% 0.057 1.69% 38.66% 0.007
Latur 20.17% 40.93% 0.083 10.03% 43.87% 0.044
Mumbai - - - 3.59% 39.73% 0.014
Mumbai Suburban - - - 4.65% 42.97% 0.020
Nagpur 11.26% 39.22% 0.044 4.83% 36.87% 0.018
Nanded 34.48% 41.53% 0.143 7.35% 40.07% 0.029
Nandurbar 55.92% 54.22% 0.303 20.68% 44.03% 0.091
Nashik 29.33% 44.39% 0.130 8.24% 48.87% 0.040
Osmanabad 18.99% 41.97% 0.080 10.16% 38.96% 0.040
Parbhani 27.84% 40.43% 0.113 12.84% 41.41% 0.053
Pune 8.03% 41.28% 0.033 3.62% 36.96% 0.013
Raigarh 18.39% 45.71% 0.084 2.53% 44.46% 0.011
Ratnagiri 21.38% 40.88% 0.087 2.32% 35.37% 0.008
Sangli 12.95% 40.51% 0.052 2.34% 43.20% 0.010
Satara 11.39% 40.49% 0.046 9.55% 39.81% 0.038
Sindhudurg 17.52% 39.86% 0.070 0.00% - 0.000
Solapur 15.92% 42.55% 0.068 5.80% 43.06% 0.025
Thane 50.10% 48.08% 0.241 5.52% 44.06% 0.024
Wardha 9.38% 40.64% 0.038 7.70% 40.08% 0.031
Washim 25.01% 41.97% 0.105 10.28% 38.34% 0.039
Yavatmal 27.32% 43.90% 0.120 7.09% 37.90% 0.027
INDIA MPI BASELINE REPORT 132 133
ManipurResults
Manipur: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
23.6%
1.8%
17.7%
5.4%
13.9%
2.4%2.5%
58.9%
28.7%
47.7%
35.1%
60.9%
28.5%
7.3%
1.9%
81.5%
76.8%
21.5%
4.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
13.41%
0.97%
10.14%
4.65%
1.77%
16.28%
11.68%
13.59%
3.49%
17.26%
6.91%
8.93%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Manipur: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Manipur
Overview
Manipur Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
17.89%
Rural
Headcount Ratio Intensity MPI
22.95% 45.07% 0.103
Manipur: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.44%
MPI (HxA)
0.08
Manipur
Urban
Headcount Ratio Intensity MPI
9.9% 42.17% 0.042
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.1%
Child & Adolescent Mortality: 1.0%
Maternal Health: 10.6%
Years of Schooling: 9.8%
School Attendance: 3.7%
Cooking Fuel: 9.8%
Sanitation: 7.0%
Drinking Water: 8.1%
Electricity: 2.1%
Housing: 10.3%
Assets: 4.1%
Bank Account: 5.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the
NFHS-5 Manipur State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 134 135
ManipurResults
0.034 to 0.054 0.055 to 0.075 0.076 to 0.096 0.097 to 0.116 0.117 to 0.137 0.138 to 0.158 0.159 to 0.180
8.47%
14.47%
14.75%
17.74%
21.2%
27.09%
28.52%
33.58%
37.66%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
Imphal West
Imphal East
Bishnupur
Thoubal
Churachandpur
Chandel
Ukhrul
Senapati
Tamenglong
Headcount Ratio (% of population who are multidimensionally poor)
Manipur: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Manipur.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Manipur
Multidimensional Poverty Index Score (District-wise)
Districts of Manipur are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.034 to 0.054 0.055 to 0.075 0.076 to 0.096 0.097 to 0.116 0.117 to 0.137 0.138 to 0.158 0.159 to 0.180
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 136 137
ManipurResults
Multidimensional Poverty in Manipur
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Manipur
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Manipur are as per the 2011 Census of India Districts of Manipur are as per the 2011 Census of India
Districts of Manipur Headcount Ratio Intensity MPI
Bishnupur 14.75% 40.98% 0.060
Chandel 27.09% 45.63% 0.124
Churachandpur 21.24% 47.32% 0.101
Imphal East 14.47% 44.21% 0.064
Imphal West 8.47% 40.05% 0.034
Senapati 33.58% 45.78% 0.154
Tamenglong 37.66% 47.79% 0.180
Thoubal 17.74% 42.66% 0.076
Ukhrul 28.52% 46.71% 0.133
Rural Urban
Districts of Manipur
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bishnupur 15.14% 39.06% 0.059 14.37% 42.93% 0.062
Chandel 28.98% 46.16% 0.134 17.43% 41.10% 0.072
Churachandpur 22.82% 47.44% 0.108 3.31% 38.10% 0.013
Imphal East 17.00% 44.32% 0.075 11.20% 44.01% 0.049
Imphal West 12.66% 40.49% 0.051 6.00% 39.49% 0.024
Senapati 34.76% 45.82% 0.159 3.83% 36.29% 0.014
Tamenglong 42.88% 48.32% 0.207 14.67% 41.06% 0.060
Thoubal 20.32% 42.57% 0.086 13.95% 42.86% 0.060
Ukhrul 33.28% 47.04% 0.157 7.39% 40.16% 0.030
INDIA MPI BASELINE REPORT 138 139
MeghalayaResults
Meghalaya: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
37.0%
3.1%
31.7%
19.7%
29.9%
6.2%
5.4%
77.1%
69.2%
38.6%
17.1%
33.5%
23.6%
8.2%8.1%
50.4%
55.9%
19.9%
9.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
23.84%
2.11%
22.48%
16.68%
5.34%
31.82%
18.58%
13.65%
6.43%
23.33%
19.41%
12.94%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Meghalaya: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Meghalaya
Overview
Meghalaya Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
32.67%
Rural
Headcount Ratio Intensity MPI
38.6% 48.37% 0.187
Meghalaya: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
48.06%
MPI (HxA)
0.157
Meghalaya
Urban
Headcount Ratio Intensity MPI
8.62% 42.47% 0.037
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 25.3%
Child & Adolescent Mortality: 1.1%
Maternal Health: 11.9%
Years of Schooling: 17.7%
School Attendance: 5.7%
Cooking Fuel: 9.7%
Sanitation: 5.6%
Drinking Water: 4.1%
Electricity: 2.0%
Housing: 7.1%
Assets: 5.9%
Bank Account: 3.9%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Meghalaya State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 140 141
MeghalayaResults
0.047 to 0.074 0.075 to 0.102 0.103 to 0.129 0.130 to 0.157 0.158 to 0.185 0.186 to 0.212 0.213 to 0.241
11.27%
23.68%
27.33%
39.78%
41.78%
46.16%
46.31%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
South Garo Hills
East Khasi Hills
West Garo Hills
West Khasi Hills
East Garo Hills
Jaintia Hills
Ri Bhoi
Headcount Ratio (% of population who are multidimensionally poor)
Meghalaya: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Meghalaya.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Meghalaya
Multidimensional Poverty Index Score (District-wise)
Districts of Meghalaya are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.047 to 0.074 0.075 to 0.102 0.103 to 0.129 0.130 to 0.157 0.158 to 0.185 0.186 to 0.212 0.213 to 0.241
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 142 143
MeghalayaResults
Multidimensional Poverty in Meghalaya
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Meghalaya
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Meghalaya are as per the 2011 Census of India Districts of Meghalaya are as per the 2011 Census of India
Districts of Meghalaya Headcount Ratio Intensity MPI
East Garo Hills 41.78% 47.85% 0.200
East Khasi Hills 23.68% 46.22% 0.109
Jaintia Hills 46.16% 52.24% 0.241
Ri Bhoi 46.31% 49.83% 0.231
South Garo Hills 11.27% 42.05% 0.047
West Garo Hills 27.33% 46.72% 0.128
West Khasi Hills 39.78% 46.61% 0.185
Rural Urban
Districts of Meghalaya
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
East Garo Hills 44.63% 48.42% 0.216 24.46% 41.61% 0.102
East Khasi Hills 39.27% 46.77% 0.184 4.52% 40.32% 0.018
Jaintia Hills 49.79% 52.42% 0.261 8.65% 42.02% 0.036
Ri Bhoi 47.85% 50.05% 0.240 27.96% 45.29% 0.127
South Garo Hills 12.12% 42.04% 0.051 1.32% 42.86% 0.006
West Garo Hills 29.44% 46.84% 0.138 3.52% 35.90% 0.013
West Khasi Hills 41.64% 46.63% 0.194 26.43% 46.34% 0.122
INDIA MPI BASELINE REPORT 144 145
MizoramResults
Mizoram: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
21.4%
2.3%
16.1%
7.9%
24.2%
13.9%
3.8%
2.5%
32.2%
16.8%
15.8%
4.7%
9.5%
4.7%
4.1%
1.8%
5.8%
3.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
6.20%
0.63%
5.98%
5.46%
2.32%
8.68%
5.70%
2.80% 2.98%
7.60%
6.65%
2.68%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Mizoram: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Mizoram
Overview
Mizoram Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
9.8%
Rural
Headcount Ratio Intensity MPI
20.48% 47.93% 0.098
Mizoram: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.4%
MPI (HxA)
0.046
Mizoram
Urban
Headcount Ratio Intensity MPI
1.42% 41.4% 0.006
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 22.2%
Child & Adolescent Mortality: 1.1%
Maternal Health: 10.7%
Years of Schooling: 19.6%
School Attendance: 8.3%
Cooking Fuel: 8.9%
Sanitation: 5.8%
Drinking Water: 2.9%
Electricity: 3.1%
Housing: 7.8%
Assets: 6.8%
Bank Account: 2.7%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Mizoram State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 146 147
MizoramResults
0.007 to 0.028 0.029 to 0.050 0.051 to 0.072 0.073 to 0.094 0.095 to 0.116 0.117 to 0.138 0.139 to 0.161
1.76%
3.45%
8.69%
10.12%
10.16%
12.74%
25.29%
30.50%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Aizawl
Serchhip
Kolasib
Champhai
Lunglei
Saiha
Mamit
Lawangtlai
Headcount Ratio (% of population who are multidimensionally poor)
Mizoram: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Mizoram.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Mizoram
Multidimensional Poverty Index Score (District-wise)
Districts of Mizoram are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.007 to 0.028 0.029 to 0.050 0.051 to 0.072 0.073 to 0.094 0.095 to 0.116 0.117 to 0.138 0.139 to 0.161
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 148 149
MizoramResults
Multidimensional Poverty in Mizoram
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Mizoram
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Mizoram are as per the 2011 Census of India Districts of Mizoram are as per the 2011 Census of India
Districts of Mizoram Headcount Ratio Intensity MPI
Aizawl 1.76% 39.01% 0.007
Champhai 10.12% 39.82% 0.040
Kolasib 8.69% 48.34% 0.042
Lawangtlai 30.50% 52.71% 0.161
Lunglei 10.16% 43.92% 0.045
Mamit 25.29% 50.58% 0.128
Saiha 12.74% 42.25% 0.054
Serchhip 3.45% 40.31% 0.014
Rural Urban
Districts of Mizoram
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Aizawl 6.70% 38.45% 0.026 0.52% 40.84% 0.002
Champhai 15.10% 39.88% 0.060 3.03% 39.36% 0.012
Kolasib 19.96% 50.67% 0.101 3.60% 42.52% 0.015
Lawangtlai 37.27% 53.06% 0.198 3.27% 36.57% 0.012
Lunglei 16.53% 44.02% 0.073 1.54% 42.46% 0.007
Mamit 29.91% 50.74% 0.152 4.97% 46.46% 0.023
Saiha 21.81% 42.00% 0.092 2.27% 44.98% 0.010
Serchhip 6.20% 40.63% 0.025 1.84% 39.69% 0.007
INDIA MPI BASELINE REPORT 150 151
NagalandResults
Nagaland: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
24.5%
2.1%
33.1%
13.6%
33.9%
4.8%
5.4%
69.3%
56.9%
23.3%
12.3%
21.2%
10.7%
3.3%
1.5%
71.0%
66.4%
28.7%
7.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
17.16%
1.38%
18.34%
11.28%
3.67%
23.97%
8.71%
6.90%
2.50%
23.98%
16.67%
15.82%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Nagaland: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Nagaland
Overview
Nagaland Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
25.23%
Rural
Headcount Ratio Intensity MPI
32.8% 46.67% 0.153
Nagaland: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
46.33%
MPI (HxA)
0.117
Nagaland
Urban
Headcount Ratio Intensity MPI
10.75% 44.37% 0.048
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 24.5%
Child & Adolescent Mortality: 1.0%
Maternal Health: 13.1%
Years of Schooling: 16.1%
School Attendance: 5.2%
Cooking Fuel: 9.8%
Sanitation: 3.6%
Drinking Water: 2.8%
Electricity: 1.0%
Housing: 9.8%
Assets: 6.8%
Bank Account: 6.4%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Nagaland State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 152 153
NagalandResults
0.033 to 0.059 0.060 to 0.086 0.087 to 0.114 0.115 to 0.141 0.142 to 0.169 0.170 to 0.196 0.197 to 0.224
8.14%
11.18%
15.35%
17.33%
23.71%
24.6%
27.2%
33.88%
37.33%
38.62%
45.56%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
Mokokchung
Kohima
Wokha
Dimapur
Zunheboto
Peren
Phek
Longleng
Kiphire
Tuensang
Mon
Headcount Ratio (% of population who are multidimensionally poor)
Nagaland: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Nagaland.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Nagaland
Multidimensional Poverty Index Score (District-wise)
Districts of Nagaland are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.033 to 0.059 0.060 to 0.086 0.087 to 0.114 0.115 to 0.141 0.142 to 0.169 0.170 to 0.196 0.197 to 0.224
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 154 155
NagalandResults
Multidimensional Poverty in Nagaland
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Nagaland
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Nagaland are as per the 2011 Census of India Districts of Nagaland are as per the 2011 Census of India
Districts of Nagaland Headcount Ratio Intensity MPI
Mon 45.56% 49.25% 0.224
Dimapur 17.33% 49.52% 0.086
Kiphire 37.33% 44.37% 0.166
Kohima 11.18% 41.48% 0.046
Longleng 33.88% 44.65% 0.151
Mokokchung 8.14% 39.96% 0.033
Peren 24.58% 46.76% 0.115
Phek 27.25% 42.59% 0.116
Tuensang 38.62% 46.59% 0.180
Wokha 15.35% 42.48% 0.065
Zunheboto 23.71% 42.88% 0.102
Rural Urban
Districts of Nagaland
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Mon 51.68% 49.95% 0.258 18.10% 40.30% 0.073
Dimapur 23.31% 50.14% 0.117 12.08% 48.45% 0.059
Kiphire 44.46% 44.51% 0.198 19.13% 43.54% 0.083
Kohima 18.86% 41.92% 0.079 5.60% 40.39% 0.023
Longleng 36.62% 44.89% 0.164 21.46% 42.85% 0.092
Mokokchung 11.19% 40.17% 0.045 2.49% 38.19% 0.009
Peren 28.26% 47.61% 0.135 14.23% 42.02% 0.060
Phek 29.18% 42.59% 0.124 14.37% 42.67% 0.061
Tuensang 46.06% 46.74% 0.215 13.59% 44.92% 0.061
Wokha 23.37% 42.76% 0.100 2.57% 38.36% 0.010
Zunheboto 26.84% 43.18% 0.116 13.80% 41.06% 0.057
INDIA MPI BASELINE REPORT 156 157
OdishaResults
Odisha: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
37.3%
2.2%
19.5%
16.7%
5.0%
21.0%
55.8%
19.2%
10.9%
80.9%
65.3%
70.4%
39.5%
13.4%
3.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
22.41%
1.51%
12.77%
13.77%
4.32%
28.77%
27.14%
9.89%
8.93%
24.87%
13.29%
6.49%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Odisha: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Odisha
Overview
Odisha Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
29.35%
Rural
Headcount Ratio Intensity MPI
32.66% 46.45% 0.152
Odisha: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
46.42%
MPI (HxA)
0.136
Odisha
Urban
Headcount Ratio Intensity MPI
12.33% 46.12% 0.057
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.4%
Child & Adolescent Mortality: 0.9%
Maternal Health: 7.8%
Years of Schooling: 16.8%
School Attendance: 5.3%
Cooking Fuel: 10.1%
Sanitation: 9.5%
Drinking Water: 3.5%
Electricity: 3.1%
Housing: 8.7%
Assets: 4.6%
Bank Account: 2.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Odisha State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 158 159
OdishaResults
0.046 to 0.083 0.084 to 0.120 0.121 to 0.158 0.159 to 0.196 0.197 to 0.233 0.234 to 0.271 0.272 to 0.310
11.64%
11.83%
14.97%
15.49%
18.62%
20.49%
20.75%
21.67%
21.88%
24.42%
24.53%
24.57%
24.75%
24.90%
28.05%
27.49%
28.43%
30.08%
33.03%
37.1%
38.0%
38.8%
41.78%
44.75%
44.90%
47.28%
48.14%
51.14%
58.71%
59.32%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Puri
Jagatsinghapur
Cuttack
Khordha
Jharsuguda
Nayagarh
Jajapur
Kendrapara
Ganjam
Baleshwar
Sambalpur
Anugul
Sundargarh
Bargarh
Balangir
Subarnapur
Bhadrak
Dhenkanal
Bauda
Debagarh
Nuapada
Gajapati
Kendujhar
Kandhamal
Mayurbhanj
Kalahandi
Rayagada
Koraput
Malkangiri
Nabarangapur
Headcount Ratio (% of population who are multidimensionally poor)
Odisha: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Odisha.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Odisha
Multidimensional Poverty Index Score (District-wise)
Districts of Odisha are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.046 to 0.083 0.084 to 0.120 0.121 to 0.158 0.159 to 0.196 0.197 to 0.233 0.234 to 0.271 0.272 to 0.310
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 160 161
OdishaResults
Multidimensional Poverty in Odisha
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Odisha
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Odisha are as per the 2011 Census of India Dis tricts of Odisha are as per the 2011 Census of India
Districts of Odisha Headcount Ratio Intensity MPI
Anugul 24.57% 43.44% 0.107
Balangir 27.49% 45.13% 0.124
Baleshwar 24.42% 44.85% 0.110
Bargarh 24.90% 42.95% 0.107
Bauda 33.03% 43.89% 0.145
Bhadrak 28.43% 43.37% 0.123
Cuttack 14.97% 43.20% 0.065
Debagarh 37.10% 47.61% 0.177
Dhenkanal 30.08% 44.55% 0.134
Gajapati 38.76% 47.24% 0.183
Ganjam 21.88% 44.93% 0.098
Jagatsinghapur 11.83% 41.40% 0.049
Jajapur 20.75% 44.12% 0.092
Jharsuguda 18.62% 42.70% 0.080
Kalahandi 47.28% 47.86% 0.226
Kandhamal 44.75% 46.99% 0.210
Kendrapara 21.67% 42.19% 0.091
Kendujhar 41.78% 50.24% 0.210
Khordha 15.49% 44.75% 0.069
Koraput 51.14% 51.77% 0.265
Malkangiri 58.71% 52.73% 0.310
Mayurbhanj 44.90% 46.89% 0.211
Nabarangapur 59.32% 50.87% 0.302
Nayagarh 20.49% 44.42% 0.091
Nuapada 37.98% 45.62% 0.173
Puri 11.64% 39.64% 0.046
Rayagada 48.14% 50.80% 0.245
Sambalpur 24.53% 43.08% 0.106
Subarnapur 28.05% 41.47% 0.116
Sundargarh 24.75% 45.29% 0.112
Rural Urban
Districts of Odisha
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anugul 26.11% 43.58% 0.114 17.42% 42.48% 0.074
Balangir 29.60% 45.29% 0.134 10.80% 41.71% 0.045
Baleshwar 25.84% 44.61% 0.115 11.65% 49.62% 0.058
Bargarh 25.50% 42.97% 0.110 18.72% 42.70% 0.080
Bauda 33.82% 43.87% 0.148 11.21% 44.97% 0.050
Bhadrak 29.76% 43.46% 0.129 19.77% 42.49% 0.084
Cuttack 17.50% 41.75% 0.073 7.58% 52.99% 0.040
Debagarh 38.79% 47.64% 0.185 10.89% 46.08% 0.050
Dhenkanal 33.40% 44.62% 0.149 2.30% 35.71% 0.008
Gajapati 42.90% 47.42% 0.203 8.84% 41.11% 0.036
Ganjam 26.18% 44.85% 0.117 5.89% 46.31% 0.027
Jagatsinghapur 11.70% 41.04% 0.048 13.13% 44.87% 0.059
Jajapur 20.39% 43.23% 0.088 24.68% 52.18% 0.129
Jharsuguda 24.25% 42.27% 0.103 9.97% 44.31% 0.044
Kalahandi 50.24% 47.79% 0.240 7.70% 53.93% 0.042
Kandhamal 46.88% 47.18% 0.221 20.72% 42.27% 0.088
Kendrapara 22.27% 42.31% 0.094 8.73% 35.73% 0.031
Kendujhar 46.87% 50.38% 0.236 15.97% 48.24% 0.077
Khordha 17.91% 43.01% 0.077 13.34% 46.82% 0.062
Koraput 56.92% 51.98% 0.296 15.33% 46.85% 0.072
Malkangiri 61.25% 53.02% 0.325 27.98% 44.97% 0.126
Mayurbhanj 47.91% 46.85% 0.224 8.10% 49.90% 0.040
Nabarangapur 62.43% 50.97% 0.318 21.51% 47.21% 0.102
Nayagarh 21.73% 44.40% 0.096 5.78% 45.35% 0.026
Nuapada 39.12% 45.73% 0.179 20.53% 42.51% 0.087
Puri 12.86% 39.73% 0.051 5.49% 38.64% 0.021
Rayagada 54.42% 51.01% 0.278 14.78% 46.81% 0.069
Sambalpur 28.79% 43.29% 0.125 14.24% 42.05% 0.060
Subarnapur 29.18% 41.30% 0.121 14.04% 45.75% 0.064
Sundargarh 29.86% 44.98% 0.134 14.66% 46.55% 0.068
INDIA MPI BASELINE REPORT 162 163
PunjabResults
Punjab: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
22.1%
1.4%
12.7%
7.3%
2.6%
1.9%
19.3%
1.7%
3.7%
36.4%
23.3%
17.3%
13.4%
0.4%0.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
4.41%
0.50%
3.09% 3.41%
1.41%
4.23%
3.02%
0.34% 0.22%
3.32%
0.60% 1.03%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Punjab: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Punjab
Overview
Punjab Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
5.59%
Rural
Headcount Ratio Intensity MPI
6.4% 43.2% 0.028
Punjab: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.75%
MPI (HxA)
0.024
Punjab
Urban
Headcount Ratio Intensity MPI
4.32% 45.02% 0.019
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.1%
Child & Adolescent Mortality: 1.7%
Maternal Health: 10.5%
Years of Schooling: 23.2%
School Attendance: 9.6%
Cooking Fuel: 8.2%
Sanitation: 5.9%
Drinking Water: 0.7%
Electricity: 0.4%
Housing: 6.5%
Assets: 1.2%
Bank Account: 2.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Punjab State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 164 165
PunjabResults
0.009 to 0.012 0.013 to 0.017 0.018 to 0.022 0.023 to 0.027 0.028 to 0.031 0.032 to 0.036 0.037 to 0.042
2.01%
2.96%
3.31%
3.49%
3.56%
3.75%
3.83%
4.49%
5.11%
5.1%
5.2%
5.6%
5.8%
7.24%
7.42%
7.71%
8.01%
8.31%
9.42%
9.99%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%
Rupnagar
Faridkot
Jalandhar
Fatehgarh Sahib
Sangrur
Patiala
Ludhiana
Hoshiarpur
Sahibzada Ajit Singh Nagar
Gurdaspur
Kapurthala
Bathinda
Barnala
Shaheed Bhagat Singh Nagar
Amritsar
Muktsar
Moga
Tarn Taran
Firozpur
Mansa
Headcount Ratio (% of population who are multidimensionally poor)
Punjab: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Punjab.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Punjab
Multidimensional Poverty Index Score (District-wise)
Districts of Punjab are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.009 to 0.012 0.013 to 0.017 0.018 to 0.022 0.023 to 0.027 0.028 to 0.031 0.032 to 0.036 0.037 to 0.042
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 166 167
PunjabResults
Multidimensional Poverty in Punjab
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Punjab
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Punjab are as per the 2011 Census of India Districts of Punjab are as per the 2011 Census of India
Districts of Punjab Headcount Ratio Intensity MPI
Amritsar 7.42% 44.92% 0.033
Barnala 5.81% 43.39% 0.025
Bathinda 5.62% 44.01% 0.025
Faridkot 2.96% 42.69% 0.013
Fatehgarh Sahib 3.49% 43.61% 0.015
Firozpur 9.42% 43.30% 0.041
Gurdaspur 5.11% 43.67% 0.022
Hoshiarpur 4.49% 44.97% 0.020
Jalandhar 3.31% 39.09% 0.013
Kapurthala 5.19% 47.76% 0.025
Ludhiana 3.83% 45.35% 0.017
Mansa 9.99% 41.91% 0.042
Moga 8.01% 42.41% 0.034
Muktsar 7.71% 44.55% 0.034
Patiala 3.75% 41.82% 0.016
Rupnagar 2.01% 42.76% 0.009
Sahibzada Ajit Singh Nagar 5.05% 48.75% 0.025
Sangrur 3.56% 39.56% 0.014
Shaheed Bhagat Singh Nagar 7.24% 43.05% 0.031
Tarn Taran 8.31% 45.30% 0.038
Rural Urban
Districts of Punjab
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Amritsar 12.23% 46.71% 0.057 3.33% 39.32% 0.013
Barnala 6.29% 42.51% 0.027 4.80% 45.79% 0.022
Bathinda 4.61% 46.66% 0.022 6.87% 41.80% 0.029
Faridkot 2.87% 42.27% 0.012 3.20% 43.79% 0.014
Fatehgarh Sahib 3.22% 41.12% 0.013 4.08% 47.99% 0.020
Firozpur 10.31% 42.41% 0.044 7.11% 46.64% 0.033
Gurdaspur 5.96% 41.46% 0.025 3.20% 52.94% 0.017
Hoshiarpur 4.27% 46.46% 0.020 5.30% 40.47% 0.021
Jalandhar 3.03% 37.79% 0.011 3.54% 40.04% 0.014
Kapurthala 3.50% 40.51% 0.014 8.11% 53.17% 0.043
Ludhiana 2.85% 40.89% 0.012 4.44% 47.13% 0.021
Mansa 9.72% 41.81% 0.041 10.91% 42.18% 0.046
Moga 9.70% 42.26% 0.041 2.41% 44.37% 0.011
Muktsar 10.79% 44.86% 0.048 2.01% 41.50% 0.008
Patiala 5.51% 41.46% 0.023 1.72% 43.17% 0.007
Rupnagar 1.43% 42.72% 0.006 3.42% 42.80% 0.015
Sahibzada Ajit Singh Nagar 4.93% 42.70% 0.021 5.16% 53.68% 0.028
Sangrur 3.60% 39.13% 0.014 3.39% 41.32% 0.014
Shaheed Bhagat Singh Nagar 7.53% 41.55% 0.031 6.69% 46.35% 0.031
Tarn Taran 9.40% 45.82% 0.043 2.49% 34.82% 0.009
INDIA MPI BASELINE REPORT 168 169
RajasthanResults
Rajasthan: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
42.6%
3.0%
26.3%
17.1%
8.5%
26.8%
35.5%
20.5%
4.0%
69.9%
58.6%
53.9%
28.9%
8.7%
1.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
23.25%
2.09%
17.08%
13.37%
7.24%
27.69%
24.77%
13.11%
6.60%
18.71%
13.27%
2.22%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Rajasthan: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Rajasthan
Overview
Rajasthan Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
29.46%
Rural
Headcount Ratio Intensity MPI
35.22% 47.7% 0.168
Rajasthan: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.44%
MPI (HxA)
0.14
Rajasthan
Urban
Headcount Ratio Intensity MPI
11.52% 44.99% 0.052
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.7%
Child & Adolescent Mortality: 1.2%
Maternal Health: 10.2%
Years of Schooling: 15.9%
School Attendance: 8.6%
Cooking Fuel: 9.4%
Sanitation: 8.4%
Drinking Water: 4.5%
Electricity: 2.3%
Housing: 6.4%
Assets: 4.5%
Bank Account: 0.8%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Rajasthan State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 170 171
RajasthanResults
0.056 to 0.089 0.090 to 0.123 0.124 to 0.158 0.159 to 0.192 0.193 to 0.226 0.227 to 0.260 0.261 to 0.295
12.80%
13.30%
14.67%
15.48%
18.43%
18.51%
19.43%
22.31%
23.49%
24.02%
24.56%
25.23%
27.26%
27.69%
28.02%
28.32%
29.70%
29.96%
32.50%
32.74%
33.25%
33.43%
39.8%
39.9%
40.83%
41.19%
42.08%
44.69%
47.86%
50.97%
52.54%
53.06%
56.13%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Jhunjhunun
Kota
Sikar
Jaipur
Ganganagar
Ajmer
Hanumangarh
Nagaur
Churu
Bikaner
Pali
Tonk
Dausa
Bhilwara
Chittaurgarh
Jodhpur
Rajsamand
Alwar
Baran
Jhalawar
Sawai Madhopur
Bundi
Dhaulpur
Karauli
Bharatpur
Jalor
Sirohi
Dungarpur
Udaipur
Banswara
Pratapgarh
Jaisalmer
Barmer
Headcount Ratio (% of population who are multidimensionally poor)
Rajasthan: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Rajasthan.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Rajasthan
Multidimensional Poverty Index Score (District-wise)
Districts of Rajasthan are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.056 to 0.089 0.090 to 0.123 0.124 to 0.158 0.159 to 0.192 0.193 to 0.226 0.227 to 0.260 0.261 to 0.295
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 172 173
RajasthanResults
Multidimensional Poverty in Rajasthan
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Rajasthan
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Rajasthan are as per the 2011 Census of India Dis tricts of Rajasthan are as per the 2011 Census of India
Districts of Rajasthan Headcount Ratio Intensity MPI
Ajmer 18.51% 44.60% 0.083
Alwar 29.96% 45.16% 0.135
Banswara 50.97% 49.96% 0.255
Baran 32.50% 44.47% 0.145
Barmer 56.13% 52.63% 0.295
Bharatpur 40.83% 48.09% 0.196
Bhilwara 27.69% 46.49% 0.129
Bikaner 24.02% 46.97% 0.113
Bundi 33.43% 46.22% 0.155
Chittaurgarh 28.02% 47.30% 0.133
Churu 23.49% 44.22% 0.104
Dausa 27.26% 42.93% 0.117
Dhaulpur 39.82% 46.08% 0.183
Dungarpur 44.69% 49.32% 0.220
Ganganagar 18.43% 42.07% 0.078
Hanumangarh 19.43% 45.45% 0.088
Jaipur 15.48% 42.01% 0.065
Jaisalmer 53.06% 53.22% 0.282
Jalor 41.19% 49.97% 0.206
Jhalawar 32.74% 47.04% 0.154
Jhunjhunun 12.80% 43.62% 0.056
Jodhpur 28.32% 48.50% 0.137
Karauli 39.92% 45.92% 0.183
Kota 13.30% 45.23% 0.060
Nagaur 22.31% 46.53% 0.104
Pali 24.56% 46.16% 0.113
Pratapgarh 52.54% 50.28% 0.264
Rajsamand 29.70% 46.01% 0.137
Sawai Madhopur 33.25% 45.70% 0.152
Sikar 14.67% 43.29% 0.064
Sirohi 42.08% 50.51% 0.213
Tonk 25.23% 42.82% 0.108
Udaipur 47.86% 52.44% 0.251
Rural Urban
Districts of Rajasthan
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ajmer 25.93% 44.41% 0.115 6.07% 45.92% 0.028
Alwar 34.05% 45.26% 0.154 6.93% 42.38% 0.029
Banswara 53.51% 50.16% 0.268 16.55% 41.49% 0.069
Baran 35.91% 44.26% 0.159 19.32% 45.97% 0.089
Barmer 59.01% 52.84% 0.312 14.85% 40.50% 0.060
Bharatpur 44.26% 48.81% 0.216 27.53% 43.59% 0.120
Bhilwara 32.42% 46.05% 0.149 12.42% 50.16% 0.062
Bikaner 32.70% 47.38% 0.155 8.71% 44.21% 0.038
Bundi 37.84% 46.25% 0.175 16.89% 45.97% 0.078
Chittaurgarh 33.16% 47.52% 0.158 5.18% 41.00% 0.021
Churu 26.59% 44.22% 0.118 15.00% 44.23% 0.066
Dausa 29.81% 42.90% 0.128 10.20% 43.54% 0.044
Dhaulpur 44.12% 45.84% 0.202 23.36% 47.76% 0.112
Dungarpur 46.84% 49.40% 0.231 6.24% 38.76% 0.024
Ganganagar 21.33% 41.78% 0.089 10.68% 43.63% 0.047
Hanumangarh 19.66% 44.67% 0.088 18.57% 48.48% 0.090
Jaipur 21.20% 42.51% 0.090 9.49% 40.86% 0.039
Jaisalmer 57.23% 53.22% 0.305 17.17% 53.40% 0.092
Jalor 44.18% 49.99% 0.221 4.83% 47.43% 0.023
Jhalawar 37.13% 47.35% 0.176 9.02% 40.20% 0.036
Jhunjhunun 12.05% 41.48% 0.050 14.90% 48.50% 0.072
Jodhpur 37.72% 48.73% 0.184 10.14% 46.84% 0.047
Karauli 44.15% 45.76% 0.202 16.66% 48.19% 0.080
Kota 21.64% 45.46% 0.098 8.17% 44.85% 0.037
Nagaur 24.65% 46.97% 0.116 13.46% 43.51% 0.059
Pali 29.14% 46.59% 0.136 9.03% 41.40% 0.037
Pratapgarh 55.92% 50.33% 0.281 7.50% 45.36% 0.034
Rajsamand 33.99% 46.27% 0.157 4.60% 34.82% 0.016
Sawai Madhopur 35.11% 45.20% 0.159 25.66% 48.53% 0.125
Sikar 15.31% 42.07% 0.064 12.83% 47.47% 0.061
Sirohi 46.55% 50.33% 0.234 25.87% 51.70% 0.134
Tonk 29.75% 42.92% 0.128 12.04% 42.10% 0.051
Udaipur 57.23% 52.54% 0.301 2.12% 39.38% 0.008
INDIA MPI BASELINE REPORT 174 175
SikkimResults
Sikkim: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
13.3%
1.0%
5.4%
8.2%
9.5%
1.4%1.8%
42.2%
23.4%
10.4%
12.7%
2.3%
7.4%
0.6%0.7%
26.7%
26.2%
8.4%
6.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
2.87%
0.25%
1.75%
2.48%
0.36%
2.89%
1.13%
0.19% 0.07%
2.30%
1.84%
1.10%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Sikkim: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Sikkim
Overview
Sikkim Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
3.82%
Rural
Headcount Ratio Intensity MPI
4.25% 41.15% 0.018
Sikkim: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
41.2%
MPI (HxA)
0.016
Sikkim
Urban
Headcount Ratio Intensity MPI
2.8% 41.36% 0.012
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 30.4%
Child & Adolescent Mortality: 1.3%
Maternal Health: 9.3%
Years of Schooling: 26.3%
School Attendance: 3.8%
Cooking Fuel: 8.8%
Sanitation: 3.4%
Drinking Water: 0.6%
Electricity: 0.2%
Housing: 7.0%
Assets: 5.6%
Bank Account: 3.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Sikkim State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 176 177
SikkimResults
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
2.74%
3.90%
4.47%
4.66%
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%
South Sikkim
East Sikkim
North Sikkim
West Sikkim
Headcount Ratio (% of population who are multidimensionally poor)
Sikkim: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Sikkim.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.020
0.016
0.019
0.011
Sikkim
Multidimensional Poverty Index Score (District-wise)
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Sikkim
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
East Sikkim 4.88% 40.37% 0.020 2.92% 41.94% 0.012
North Sikkim 4.47% 41.66% 0.019 4.46% 41.15% 0.018
South Sikkim 2.89% 40.49% 0.012 1.98% 35.93% 0.007
West Sikkim 4.78% 42.38% 0.020 1.85% 42.86% 0.008
Multidimensional Poverty in Sikkim
Districts of Sikkim Headcount Ratio Intensity MPI
East Sikkim 3.90% 40.96% 0.016
North Sikkim 4.47% 41.57% 0.019
South Sikkim 2.74% 39.94% 0.011
West Sikkim 4.66% 42.39% 0.020
Districts of Sikkim are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.Districts of Sikkim are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 178 179
Tamil NaduResults
Tamil Nadu: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
24.8%
1.1%
6.7% 6.6%
1.0%
12.6%
20.2%
3.4%
6.4%
24.1%
17.1%
47.6%
27.4%
1.0%0.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
3.64%
0.30%
1.70%
2.27%
0.45%
3.63%
4.53%
1.21%
0.43%
2.64%
1.35% 1.48%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Tamil Nadu: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Tamil Nadu
Overview
Tamil Nadu Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
4.89%
Rural
Headcount Ratio Intensity MPI
7.32% 40.21% 0.029
Tamil Nadu: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
39.97%
MPI (HxA)
0.02
Tamil Nadu
Urban
Headcount Ratio Intensity MPI
2.49% 39.29% 0.01
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.1%
Child & Adolescent Mortality: 1.3%
Maternal Health: 7.2%
Years of Schooling: 19.3%
School Attendance: 3.9%
Cooking Fuel: 8.8%
Sanitation: 11.0%
Drinking Water: 3.0%
Electricity: 1.0%
Housing: 6.4%
Assets: 3.3%
Bank Account: 3.6%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Tamil Nadu State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 180 181
Tamil NaduResults
0.004 to 0.009 0.010 to 0.015 0.016 to 0.021 0.022 to 0.027 0.028 to 0.033 0.034 to 0.039 0.040 to 0.046
0.96%
1.52%
2.03%
2.29%
2.53%
2.73%
2.73%
3.02%
3.11%
3.73%
3.80%
4.16%
4.60%
4.76%
5.11%
5.26%
5.5%
5.9%
6.1%
6.3%
6.8%
6.56%
7.21%
7.23%
7.61%
8.18%
8.23%
8.64%
8.71%
9.18%
9.35%
11.71%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
Chennai
Kanniyakumari
The Nilgiris
Coimbatore
Thiruvallur
Namakkal
Erode
Kancheepuram
Tiruppur
Tiruchirappalli
Vellore
Dindigul
Karur
Theni
Krishnagiri
Dharmapuri
Tirunelveli
Tiruvannamalai
Madurai
Cuddalore
Salem
Thiruvarur
Ramanathapuram
Thanjavur
Perambalur
Nagappattinam
Thoothukkudi
Sivaganga
Ariyalur
Virudunagar
Viluppuram
Pudukkottai
Headcount Ratio (% of population who are multidimensionally poor)
Tamil Nadu: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Tamil Nadu.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Tamil Nadu
Multidimensional Poverty Index Score (District-wise)
Districts of Tamil Nadu are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.004 to 0.009 0.010 to 0.015 0.016 to 0.021 0.022 to 0.027 0.028 to 0.033 0.034 to 0.039 0.040 to 0.046
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 182 183
Tamil NaduResults
Multidimensional Poverty in Tamil Nadu
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Tamil Nadu
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Tamil Nadu are as per the 2011 Census of India Districts of Tamil Nadu are as per the 2011 Census of India
Districts of Tamil Nadu Headcount Ratio Intensity MPI
Ariyalur 8.71% 38.73% 0.034
Chennai 0.96% 41.79% 0.004
Coimbatore 2.29% 40.14% 0.009
Cuddalore 6.29% 39.61% 0.025
Dharmapuri 5.26% 39.68% 0.021
Dindigul 4.16% 38.87% 0.016
Erode 2.73% 42.54% 0.012
Kancheepuram 3.02% 39.03% 0.012
Kanniyakumari 1.52% 35.81% 0.005
Karur 4.60% 39.68% 0.018
Krishnagiri 5.11% 41.22% 0.021
Madurai 6.15% 39.28% 0.024
Nagappattinam 8.18% 40.35% 0.033
Namakkal 2.73% 41.11% 0.011
Perambalur 7.61% 39.81% 0.030
Pudukkottai 11.71% 39.18% 0.046
Ramanathapuram 7.21% 40.95% 0.030
Salem 6.56% 44.81% 0.029
Sivaganga 8.64% 38.50% 0.033
Thanjavur 7.23% 38.16% 0.028
The Nilgiris 2.03% 39.01% 0.008
Theni 4.76% 39.91% 0.019
Thiruvallur 2.53% 39.11% 0.010
Thiruvarur 6.79% 40.41% 0.027
Thoothukkudi 8.23% 40.42% 0.033
Tiruchirappalli 3.73% 38.20% 0.014
Tirunelveli 5.52% 40.35% 0.022
Tiruppur 3.11% 38.99% 0.012
Tiruvannamalai 5.92% 40.45% 0.024
Vellore 3.80% 37.30% 0.014
Viluppuram 9.35% 40.53% 0.038
Virudunagar 9.18% 39.45% 0.036
Rural Urban
Districts of Tamil Nadu
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Ariyalur 8.69% 38.68% 0.034 8.86% 39.07% 0.035
Chennai - - - 0.96% 41.79% 0.004
Coimbatore 3.73% 38.92% 0.015 1.81% 40.99% 0.007
Cuddalore 7.07% 39.45% 0.028 4.73% 40.07% 0.019
Dharmapuri 5.63% 40.25% 0.023 3.98% 36.86% 0.015
Dindigul 5.57% 39.25% 0.022 1.88% 37.04% 0.007
Erode 4.62% 41.67% 0.019 1.07% 45.82% 0.005
Kancheepuram 3.76% 37.09% 0.014 2.63% 40.49% 0.011
Kanniyakumari 3.04% 35.85% 0.011 1.20% 35.79% 0.004
Karur 6.00% 40.18% 0.024 2.65% 38.10% 0.010
Krishnagiri 6.48% 41.35% 0.027 0.70% 37.37% 0.003
Madurai 10.09% 40.65% 0.041 4.08% 37.51% 0.015
Nagappattinam 9.36% 40.35% 0.038 3.98% 40.38% 0.016
Namakkal 2.70% 41.64% 0.011 2.77% 40.38% 0.011
Perambalur 8.89% 39.80% 0.035 1.14% 40.41% 0.005
Pudukkottai 14.52% 39.29% 0.057 1.62% 35.71% 0.006
Ramanathapuram 8.68% 40.58% 0.035 3.89% 42.86% 0.017
Salem 10.42% 46.02% 0.048 2.56% 39.73% 0.010
Sivaganga 11.24% 38.33% 0.043 2.08% 40.83% 0.008
Thanjavur 8.69% 38.17% 0.033 4.47% 38.11% 0.017
The Nilgiris 2.40% 41.81% 0.010 1.76% 36.36% 0.006
Theni 5.54% 40.03% 0.022 4.32% 39.82% 0.017
Thiruvallur 5.37% 40.18% 0.022 1.04% 36.19% 0.004
Thiruvarur 7.63% 39.99% 0.031 3.35% 44.33% 0.015
Thoothukkudi 12.75% 40.39% 0.051 3.93% 40.54% 0.016
Tiruchirappalli 5.15% 37.41% 0.019 2.24% 40.14% 0.009
Tirunelveli 6.92% 41.40% 0.029 4.01% 38.39% 0.015
Tiruppur 3.30% 38.31% 0.013 3.01% 39.40% 0.012
Tiruvannamalai 6.89% 40.52% 0.028 1.75% 39.22% 0.007
Vellore 4.70% 37.05% 0.017 2.88% 37.72% 0.011
Viluppuram 9.81% 40.81% 0.040 7.01% 38.49% 0.027
Virudunagar 13.20% 39.98% 0.053 5.02% 38.00% 0.019
INDIA MPI BASELINE REPORT 184 185
TelanganaResults
Telangana: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
31.1%
1.4%
10.9%
15.8%
12.8%
2.1%2.1%
31.7%
7.8%
49.3%
23.8%
27.8%
3.2%
1.2%
0.4%
25.5%
19.7%
7.5%
2.9%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
10.15%
0.78%
5.10%
8.50%
1.14%
10.49%
12.22%
4.45%
0.87%
8.32%
5.99%
2.73%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Telangana: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Telangana
Overview
Telangana Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
13.74%
Rural
Headcount Ratio Intensity MPI
20.35% 43.23% 0.088
Telangana: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.2%
MPI (HxA)
0.059
Telangana
Urban
Headcount Ratio Intensity MPI
5.1% 43.01% 0.022
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.5%
Child & Adolescent Mortality: 1.1%
Maternal Health: 7.2%
Years of Schooling: 23.9%
School Attendance: 3.2%
Cooking Fuel: 8.4%
Sanitation: 9.8%
Drinking Water: 3.6%
Electricity: 0.7%
Housing: 6.7%
Assets: 4.8%
Bank Account: 2.2%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Telangana State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 186 187
TelanganaResults
0.018 to 0.032 0.033 to 0.048 0.049 to 0.063 0.064 to 0.079 0.080 to 0.094 0.095 to 0.110 0.111 to 0.126
4.27%
5.83%
9.20%
12.45%
13.75%
15.3%
17.9%
21.44%
26.11%
27.43%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Hyderabad
Rangareddy
Karimnagar
Warangal
Khammam
Nalgonda
Medak
Nizamabad
Mahbubnagar
Adilabad
Headcount Ratio (% of population who are multidimensionally poor)
Telangana: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Telangana.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Telangana
Multidimensional Poverty Index Score (District-wise)
Districts of Telangana are as per the 2011 Census of India (erstwhile Andhra Pradesh). The colour represents the MPI score of a district. The co- lour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.018 to 0.032 0.033 to 0.048 0.049 to 0.063 0.064 to 0.079 0.080 to 0.094 0.095 to 0.110 0.111 to 0.126
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 188 189
TelanganaResults
Multidimensional Poverty in Telangana
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Telangana
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Telangana are as per the 2011 Census of India (erstwhile Andhra Pradesh) Dis tricts of Telangana are as per the 2011 Census of India (erstwhile Andhra Pradesh)
Districts of Telangana Headcount Ratio Intensity MPI
Adilabad 27.43% 46.03% 0.126
Hyderabad 4.27% 41.01% 0.018
Karimnagar 9.20% 41.10% 0.038
Khammam 13.75% 42.26% 0.058
Mahbubnagar 26.11% 43.54% 0.114
Medak 17.87% 42.68% 0.076
Nalgonda 15.34% 43.81% 0.067
Nizamabad 21.44% 44.51% 0.095
Rangareddy 5.83% 41.84% 0.024
Warangal 12.45% 40.50% 0.050
Rural Urban
Districts of Telangana
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Adilabad 32.68% 46.09% 0.151 12.58% 45.55% 0.057
Hyderabad - - - 4.27% 41.01% 0.018
Karimnagar 11.81% 40.19% 0.047 4.27% 45.84% 0.020
Khammam 17.47% 42.55% 0.074 2.15% 35.00% 0.008
Mahbubnagar 29.42% 43.55% 0.128 1.43% 42.86% 0.006
Medak 20.61% 42.73% 0.088 10.48% 42.39% 0.044
Nalgonda 16.01% 42.82% 0.069 12.71% 48.73% 0.062
Nizamabad 24.71% 44.61% 0.110 11.91% 43.85% 0.052
Rangareddy 12.05% 42.61% 0.051 3.29% 40.68% 0.013
Warangal 16.47% 40.41% 0.067 2.54% 41.92% 0.011
INDIA MPI BASELINE REPORT 190 191
TripuraResults
Tripura: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
28.0%
1.3%
13.5%
10.8%
18.8%
2.2%
2.7%
65.8%
56.2%
36.4%
26.4%
16.3%
13.9%
7.2%
1.8%
74.7%
67.0%
3.6%
3.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
12.01%
0.88%
7.79% 8.10%
1.67%
15.50%
11.08%
7.26%
4.30%
16.19%
9.42%
2.20%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Tripura: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Tripura
Overview
Tripura Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
16.65%
Rural
Headcount Ratio Intensity MPI
20.93% 45.34% 0.095
Tripura: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
45.02%
MPI (HxA)
0.075
Tripura
Urban
Headcount Ratio Intensity MPI
5.6% 41.96% 0.024
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 26.7%
Child & Adolescent Mortality: 1.0%
Maternal Health: 8.7%
Years of Schooling: 18.0%
School Attendance: 3.7%
Cooking Fuel: 9.8%
Sanitation: 7.0%
Drinking Water: 4.6%
Electricity: 2.7%
Housing: 10.3%
Assets: 6.0%
Bank Account: 1.4%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Tripura State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 192 193
TripuraResults
0.039 to 0.053 0.054 to 0.068 0.069 to 0.084 0.085 to 0.099 0.100 to 0.114 0.115 to 0.129 0.130 to 0.146
9.03%
17.03%
26.23%
30.65%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
West Tripura
South Tripura
Dhalai
North Tripura
Headcount Ratio (% of population who are multidimensionally poor)
Tripura: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Tripura.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Tripura
Multidimensional Poverty Index Score (District-wise)
Districts of Tripura are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.039 to 0.053 0.054 to 0.068 0.069 to 0.084 0.085 to 0.099 0.100 to 0.114 0.115 to 0.129 0.130 to 0.146
Multidimensional Poverty Index
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Tripura
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Dhalai 27.90% 47.33% 0.132 12.82% 46.55% 0.060
North Tripura 35.74% 47.77% 0.171 7.46% 41.35% 0.031
South Tripura 19.01% 41.84% 0.080 6.08% 45.12% 0.027
West Tripura 12.04% 43.91% 0.053 4.81% 40.73% 0.020
Multidimensional Poverty in Tripura
Districts of Tripura Headcount Ratio Intensity MPI
Dhalai 26.23% 47.29% 0.124
North Tripura 30.65% 47.49% 0.146
South Tripura 17.03% 42.02% 0.072
West Tripura 9.03% 43.20% 0.039
Districts of Tripura are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 194 195
Uttar PradeshResults
Uttar Pradesh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
44.5%
5.0%
35.4%
17.5%
11.9%
5.4%
67.5%
12.4%
4.9%
68.9%
50.5%
63.7%
31.2%
27.4%
9.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
30.48%
3.82%
25.29%
15.08%
9.97%
34.26%
31.80%
2.37%
18.35%
33.43%
8.87%
3.34%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Uttar Pradesh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Uttar Pradesh
Overview
Uttar Pradesh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
37.79%
Rural
Headcount Ratio Intensity MPI
44.32% 47.67% 0.211
Uttar Pradesh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
47.6%
MPI (HxA)
0.18
Uttar Pradesh
Urban
Headcount Ratio Intensity MPI
18.07% 47.06% 0.085
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 28.2%
Child & Adolescent Mortality: 1.8%
Maternal Health: 11.7%
Years of Schooling: 14.0%
School Attendance: 9.2%
Cooking Fuel: 9.1%
Sanitation: 8.4%
Drinking Water: 0.6%
Electricity: 4.9%
Housing: 8.9%
Assets: 2.3%
Bank Account: 0.9%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Uttar Pradesh State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 196 197
Uttar PradeshResults
0.054 to 0.104 0.105 to 0.155 0.156 to 0.206 0.207 to 0.257 0.258 to 0.309 0.310 to 0.360 0.361 to 0.412
0.0%
Lucknow
Kanpur Nagar
Gautam Buddha Nagar
Ghaziabad
Jhansi
Baghpat
Meerut
Varanasi
Gorakhpur
Etawah
Jalaun
Saharanpur
Bijnor
Auraiya
Muzaffarnagar
Hamirpur
Mainpuri
Deoria
Firozabad
Mahamaya Nagar
Mau
Azamgarh
Allahabad
Bulandshahr
Agra
Rae Bareli
Ambedkar Nagar
Jyotiba Phule Nagar
Mahoba
Mathura
Lalitpur
Sultanpur
Moradabad
Pratapgarh
Ballia
Aligarh
Chandauli
Kanpur Dehat
Etah
Bareilly
Faizabad
Rampur
Farrukhabad
Banda
Jaunpur
Unnao
Ghazipur
Bh
adohi
Fatehpur
Mirzapur
Kushinagar
Basti
Pilibhit
Kannauj
Sant Kabir Nagar
Bara Banki
Kansiram Nagar
Sonbhadra
Maharajganj
Shahjahanpur
Hardoi
Chitrakoot
Kaushambi
Sitapur
Budaun
Siddharth Nagar
Gonda
Kheri
Balrampur
Bahraich
Shrawasti
12.16%
14.34%
17.08%
17.47%
20.27%
21.08%
21.11%
26.03%
26.26%
27.44%
27.67%
28.52%
29.78%
29.82%
29.85%
30.92%
31.32%
31.36%
32.01%
32.47%
32.70%
32.77%
32.77%
32.88%
34.13%
35.29%
33.59%
34.10%
34.84%
35.33%
35.98%
36.34%
36.86%
36.94%
37.11%
37.40%
37.91%
37.98%
38.47%
38.60%
38.73%
38.89%
39.18%
40.29%
40.78%
40.79%
41.04%
42.19%
42.66%
42.73%
42.94%
43.26%
43.50%
43.79%
43.3%
44.8%
47.8%
48.5%
49.1%
50.5%
51.2%
52.9%
56.06%
56.83%
57.10%
57.24%
59.26%
59.95%
69.45%
71.88%
74.3
8%
10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
Headcount Ratio (% of population who are multidimensionally poor)
Uttar Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Uttar Pradesh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Uttar Pradesh
Multidimensional Poverty Index Score (District-wise)
Districts of Uttar Pradesh are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.054 to 0.104 0.105 to 0.155 0.156 to 0.206 0.207 to 0.257 0.258 to 0.309 0.310 to 0.360 0.361 to 0.412
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 198 199
Uttar PradeshResults
Multidimensional Poverty in Uttar Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Uttar Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Uttar Pradesh are as per the 2011 Census of India Dis tricts of Uttar Pradesh are as per the 2011 Census of India
Districts of Uttar Pradesh Headcount Ratio Intensity MPI
Agra 33.59% 46.93% 0.158
Aligarh 37.40% 46.51% 0.174
Allahabad 32.77% 46.56% 0.153
Ambedkar Nagar 34.13% 43.52% 0.149
Auraiya 29.82% 44.92% 0.134
Azamgarh 32.77% 43.44% 0.142
Baghpat 21.08% 47.03% 0.099
Bahraich 71.88% 54.40% 0.391
Ballia 37.11% 45.12% 0.167
Balrampur 69.45% 53.77% 0.373
Banda 40.29% 46.12% 0.186
Bara Banki 44.77% 49.41% 0.221
Bareilly 38.60% 49.26% 0.190
Basti 43.26% 46.04% 0.199
Bhadohi 42.19% 45.53% 0.192
Bijnor 29.78% 45.10% 0.134
Budaun 57.10% 52.22% 0.298
Bulandshahr 32.88% 46.61% 0.153
Chandauli 37.91% 44.46% 0.169
Chitrakoot 52.86% 48.30% 0.255
Deoria 31.36% 45.43% 0.142
Etah 38.47% 45.61% 0.175
Etawah 27.44% 42.43% 0.116
Faizabad 38.73% 45.84% 0.178
Farrukhabad 39.18% 48.04% 0.188
Fatehpur 42.66% 47.32% 0.202
Firozabad 32.01% 46.68% 0.149
Gautam Buddha Nagar 17.08% 43.50% 0.074
Ghaziabad 17.47% 45.16% 0.079
Ghazipur 41.04% 44.95% 0.184
Gonda 59.26% 50.81% 0.301
Gorakhpur 26.26% 46.05% 0.121
Hamirpur 30.92% 44.12% 0.136
Hardoi 51.16% 48.14% 0.246
Jalaun 27.67% 43.80% 0.121
Jaunpur 40.78% 44.05% 0.180
Districts of Uttar Pradesh Headcount Ratio Intensity MPI
Jhansi 20.27% 44.28% 0.090
Jyotiba Phule Nagar 34.84% 48.24% 0.168
Kannauj 43.50% 47.26% 0.206
Kanpur Dehat 37.98% 43.87% 0.167
Kanpur Nagar 14.34% 44.09% 0.063
Kansiram Nagar 47.81% 49.57% 0.237
Kaushambi 56.06% 51.89% 0.291
Kheri 59.95% 51.32% 0.308
Kushinagar 42.94% 46.05% 0.198
Lalitpur 35.98% 44.31% 0.159
Lucknow 12.16% 44.57% 0.054
Mahamaya Nagar 32.47% 43.99% 0.143
Maharajganj 49.12% 45.88% 0.225
Mahoba 35.29% 43.76% 0.154
Mainpuri 31.32% 44.16% 0.138
Mathura 35.33% 44.40% 0.157
Mau 32.70% 44.82% 0.147
Meerut 21.11% 45.59% 0.096
Mirzapur 42.73% 47.11% 0.201
Moradabad 36.86% 48.05% 0.177
Muzaffarnagar 29.85% 47.79% 0.143
Pilibhit 43.26% 47.96% 0.207
Pratapgarh 36.94% 45.00% 0.166
Rae Bareli 34.10% 46.87% 0.160
Rampur 38.89% 49.66% 0.193
Saharanpur 28.52% 48.90% 0.139
Sant Kabir Nagar 43.79% 46.77% 0.205
Shahjahanpur 50.52% 48.90% 0.247
Shrawasti 74.38% 55.35% 0.412
Siddharth Nagar 57.24% 50.01% 0.286
Sitapur 56.83% 49.70% 0.282
Sonbhadra 48.46% 50.15% 0.243
Sultanpur 36.34% 47.34% 0.172
Unnao 40.79% 47.16% 0.192
Varanasi 26.03% 44.69% 0.116
INDIA MPI BASELINE REPORT 200 201
Uttar PradeshResults
Multidimensional Poverty in Uttar Pradesh
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Uttar Pradesh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Rural Urban
Districts of Uttar Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Agra 43.50% 47.16% 0.205 22.93% 46.45% 0.107
Aligarh 44.82% 45.91% 0.206 22.72% 48.84% 0.111
Allahabad 39.89% 46.61% 0.186 13.07% 46.18% 0.060
Ambedkar Nagar 35.47% 43.31% 0.154 24.38% 45.79% 0.112
Auraiya 33.01% 44.30% 0.146 15.10% 51.12% 0.077
Azamgarh 36.21% 43.16% 0.156 5.94% 56.93% 0.034
Baghpat 22.51% 46.62% 0.105 16.63% 48.75% 0.081
Bahraich 74.52% 54.29% 0.405 43.15% 56.45% 0.244
Ballia 37.73% 45.41% 0.171 31.60% 42.03% 0.133
Balrampur 72.06% 54.00% 0.389 40.85% 49.37% 0.202
Banda 44.20% 45.40% 0.201 22.54% 52.53% 0.118
Bara Banki 48.96% 49.39% 0.242 15.66% 49.78% 0.078
Bareilly 46.42% 49.40% 0.229 26.21% 48.86% 0.128
Basti 46.28% 46.00% 0.213 4.97% 50.51% 0.025
Bhadohi 45.50% 45.32% 0.206 26.01% 47.29% 0.123
Bijnor 32.73% 44.55% 0.146 20.47% 47.91% 0.098
Budaun 66.39% 52.40% 0.348 15.46% 48.66% 0.075
Bulandshahr 34.98% 45.26% 0.158 26.97% 51.53% 0.139
Chandauli 42.03% 44.23% 0.186 16.60% 47.54% 0.079
Chitrakoot 55.31% 47.53% 0.263 38.39% 54.83% 0.210
Deoria 33.57% 45.53% 0.153 17.88% 44.26% 0.079
Etah 43.43% 45.61% 0.198 13.16% 45.63% 0.060
Etawah 30.73% 42.85% 0.132 17.81% 40.33% 0.072
Faizabad 41.91% 45.63% 0.191 18.18% 49.04% 0.089
Farrukhabad 45.59% 48.01% 0.219 16.04% 48.37% 0.078
Fatehpur 44.39% 47.09% 0.209 27.45% 50.63% 0.139
Firozabad 37.10% 45.27% 0.168 23.53% 50.38% 0.119
Gautam Buddha Nagar 24.26% 43.03% 0.104 13.37% 43.94% 0.059
Ghaziabad 29.40% 45.61% 0.134 12.81% 44.76% 0.057
Ghazipur 43.30% 45.02% 0.195 15.27% 42.77% 0.065
Gonda 61.95% 50.90% 0.315 22.08% 47.36% 0.105
Gorakhpur 31.48% 45.96% 0.145 4.18% 48.89% 0.020
Hamirpur 36.33% 44.11% 0.160 9.03% 44.22% 0.040
Hardoi 56.27% 48.52% 0.273 25.01% 43.85% 0.110
Jalaun 34.86% 43.14% 0.150 9.09% 50.39% 0.046
Jaunpur 41.81% 44.12% 0.184 28.12% 42.73% 0.120
Rural Urban
Districts of Uttar Pradesh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Jhansi 29.26% 44.51% 0.130 8.53% 43.25% 0.037
Jyotiba Phule Nagar 39.76% 48.12% 0.191 20.18% 48.98% 0.099
Kannauj 46.40% 47.27% 0.219 28.48% 47.21% 0.134
Kanpur Dehat 39.40% 43.82% 0.173 22.97% 44.88% 0.103
Kanpur Nagar 31.67% 44.06% 0.140 6.85% 44.16% 0.030
Kansiram Nagar 54.81% 49.60% 0.272 21.96% 49.26% 0.108
Kaushambi 56.16% 52.32% 0.294 55.20% 47.82% 0.264
Kheri 61.53% 51.65% 0.318 47.16% 47.74% 0.225
Kushinagar 44.94% 46.18% 0.208 18.85% 42.18% 0.079
Lalitpur 42.49% 44.53% 0.189 10.75% 40.84% 0.044
Lucknow 25.38% 45.36% 0.115 4.71% 42.16% 0.020
Mahamaya Nagar 36.70% 44.00% 0.161 17.88% 43.95% 0.079
Maharajganj 51.37% 45.83% 0.235 17.76% 47.96% 0.085
Mahoba 40.99% 44.21% 0.181 16.74% 40.17% 0.067
Mainpuri 34.15% 43.68% 0.149 18.11% 48.44% 0.088
Mathura 37.56% 43.74% 0.164 31.53% 45.73% 0.144
Mau 32.55% 43.89% 0.143 33.14% 47.55% 0.158
Meerut 23.27% 43.47% 0.101 20.10% 46.73% 0.094
Mirzapur 45.61% 47.60% 0.217 28.16% 43.05% 0.121
Moradabad 45.50% 48.61% 0.221 20.45% 45.67% 0.093
Muzaffarnagar 31.96% 47.16% 0.151 25.00% 49.65% 0.124
Pilibhit 48.88% 48.02% 0.235 15.96% 47.17% 0.075
Pratapgarh 39.26% 45.01% 0.177 0.83% 35.71% 0.003
Rae Bareli 36.42% 46.90% 0.171 6.53% 45.00% 0.029
Rampur 44.74% 49.85% 0.223 21.42% 48.52% 0.104
Saharanpur 32.38% 49.39% 0.160 19.62% 47.06% 0.092
Sant Kabir Nagar 45.25% 46.66% 0.211 29.97% 48.40% 0.145
Shahjahanpur 57.44% 49.02% 0.282 20.97% 47.47% 0.100
Shrawasti 75.65% 55.35% 0.419 38.63% 55.76% 0.215
Siddharth Nagar 59.37% 50.09% 0.297 21.42% 46.34% 0.099
Sitapur 60.84% 49.93% 0.304 28.75% 46.43% 0.133
Sonbhadra 55.25% 50.01% 0.276 9.68% 54.59% 0.053
Sultanpur 38.04% 47.39% 0.180 9.61% 44.47% 0.043
Unnao 45.29% 47.29% 0.214 14.81% 44.97% 0.067
Varanasi 34.78% 44.06% 0.153 15.85% 46.29% 0.073
Districts of Uttar
Pradesh are as per the 2011 Census of India Districts of Uttar Pradesh are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 202 203
UttarakhandResults
Uttarakhand: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
32.9%
2.6%
28.6%
9.8%
4.4%
9.0%
35.6%
13.8%
6.9%
52.1%
40.8%
34.1%
21.2%
2.2%
0.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
14.66%
1.63%
13.06%
6.74%
3.18%
15.80%
11.22%
3.21%
1.39%
12.35%
6.21%
3.23%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Uttarakhand: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Uttarakhand
Overview
Uttarakhand Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
17.72%
Rural
Headcount Ratio Intensity MPI
21.94% 43.78% 0.096
Uttarakhand: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.37%
MPI (HxA)
0.079
Uttarakhand
Urban
Headcount Ratio Intensity MPI
9.89% 46.82% 0.046
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.1%
Child & Adolescent Mortality: 1.7%
Maternal Health: 13.8%
Years of Schooling: 14.3%
School Attendance: 6.7%
Cooking Fuel: 9.6%
Sanitation: 6.8%
Drinking Water: 1.9%
Electricity: 0.8%
Housing: 7.5%
Assets: 3.8%
Bank Account: 2.0%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Uttarakhand State Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 204 205
UttarakhandResults
0.031 to 0.043 0.044 to 0.055 0.056 to 0.067 0.068 to 0.079 0.080 to 0.092 0.093 to 0.104 0.105 to 0.117
6.88%
11.93%
13.41%
13.91%
13.96%
16.8%
19.5%
19.99%
22.41%
25.65%
23.20%
24.28%
24.76%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Dehradun
Garhwal
Nainital
Rudraprayag
Pithoragarh
Chamoli
Tehri Garhwal
Bageshwar
Champawat
Udham Singh Nagar
Uttarkashi
Haridwar
Almora
Headcount Ratio (% of population who are multidimensionally poor)
Uttarakhand: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Uttarakhand.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
Uttarakhand
Multidimensional Poverty Index Score (District-wise)
Districts of Uttarakhand are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.031 to 0.043 0.044 to 0.055 0.056 to 0.067 0.068 to 0.079 0.080 to 0.092 0.093 to 0.104 0.105 to 0.117
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 206 207
UttarakhandResults
Multidimensional Poverty in Uttarakhand
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Uttarakhand
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Uttarakhand are as per the 2011 Census of India Districts of Uttarakhand are as per the 2011 Census of India
Districts of Uttarakhand Headcount Ratio Intensity MPI
Almora 25.65% 40.34% 0.103
Bageshwar 19.99% 41.08% 0.082
Chamoli 16.78% 41.32% 0.069
Champawat 22.41% 44.77% 0.100
Dehradun 6.88% 45.42% 0.031
Garhwal 11.93% 40.26% 0.048
Haridwar 24.76% 47.26% 0.117
Nainital 13.41% 43.74% 0.059
Pithoragarh 13.96% 41.06% 0.057
Rudraprayag 13.91% 40.28% 0.056
Tehri Garhwal 19.53% 40.64% 0.079
Udham Singh Nagar 23.20% 45.62% 0.106
Uttarkashi 24.28% 44.51% 0.108
Rural Urban
Districts of Uttarakhand
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Almora 27.71% 40.38% 0.112 2.98% 36.41% 0.011
Bageshwar 20.62% 41.06% 0.085 3.46% 44.05% 0.015
Chamoli 20.08% 41.32% 0.083 0.00% - 0.000
Champawat 22.68% 44.23% 0.100 20.90% 48.11% 0.101
Dehradun 12.29% 44.64% 0.055 3.64% 46.99% 0.017
Garhwal 13.85% 40.06% 0.055 1.86% 48.00% 0.009
Haridwar 29.55% 47.08% 0.139 17.98% 47.67% 0.086
Nainital 16.45% 43.15% 0.071 9.18% 45.20% 0.041
Pithoragarh 16.86% 41.06% 0.069 0.00% - 0.000
Rudraprayag 14.91% 40.28% 0.060 0.00% - 0.000
Tehri Garhwal 23.72% 40.64% 0.096 0.00% - 0.000
Udham Singh Nagar 26.68% 45.39% 0.121 17.62% 46.16% 0.081
Uttarkashi 26.13% 44.48% 0.116 1.34% 51.19% 0.007
INDIA MPI BASELINE REPORT 208 209
West BengalResults
West Bengal: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
33.6%
1.5%
14.4%
15.9%
14.1%
3.8%
2.7%
73.0%
61.6%
48.0%
32.0%
11.5%
5.1%
5.7%
2.5%
54.3%
47.2%
13.8%
4.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
16.22%
1.02%
9.44%
11.25%
2.82%
20.81%
16.93%
4.35%
3.74%
18.81%
8.66%
7.14%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
West Bengal: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in West Bengal
Overview
West Bengal Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
21.43%
Rural
Headcount Ratio Intensity MPI
25.8% 45.39% 0.117
West Bengal: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
45.49%
MPI (HxA)
0.097
West Bengal
Urban
Headcount Ratio Intensity MPI
11.67% 46% 0.054
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 27.7%
Child & Adolescent Mortality: 0.9%
Maternal Health: 8.1%
Years of Schooling: 19.2%
School Attendance: 4.8%
Cooking Fuel: 10.2%
Sanitation: 8.3%
Drinking Water: 2.1%
Electricity: 1.8%
Housing: 9.2%
Assets: 4.2%
Bank Account: 3.5%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 West Bengal State Report (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of LivingNFHS-5: Education
INDIA MPI BASELINE REPORT 210 211
West BengalResults
0.013 to 0.044 0.045 to 0.075 0.076 to 0.107 0.108 to 0.139 0.140 to 0.171 0.172 to 0.203 0.204 to 0.236
2.80%
9.80%
11.34%
11.41%
12.84%
14.19%
14.93%
20.33%
22.02%
22.28%
22.42%
23.82%
27.2%
27.4%
27.6%
28.3%
35.70%
42.84%
49.69%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Kolkata
North 24 Parganas
Nadia
Darjeeling
Howrah
Purba Medinipur
Hugli
Barddhaman
Jalpaiguri
Koch Bihar
Dakshin Dinajpur
Pashchim Medinipur
Murshidabad
Bankura
Birbhum
South 24 Parganas
Maldah
Uttar Dinajpur
Puruliya
Headcount Ratio (% of population who are multidimensionally poor)
West Bengal: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of West Bengal.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
West Bengal
Multidimensional Poverty Index Score (District-wise)
Districts of West Bengal are as per the 2011 Census of India. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
0.013 to 0.044 0.045 to 0.075 0.076 to 0.107 0.108 to 0.139 0.140 to 0.171 0.172 to 0.203 0.204 to 0.236
Multidimensional Poverty Index
INDIA MPI BASELINE REPORT 212 213
West BengalResults
Multidimensional Poverty in West Bengal
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in West Bengal
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of West Bengal are as per the 2011 Census of India Districts of West Bengal are as per the 2011 Census of India
Districts of West Bengal Headcount Ratio Intensity MPI
Bankura 27.42% 44.58% 0.122
Barddhaman 20.33% 47.06% 0.096
Birbhum 27.61% 45.60% 0.126
Dakshin Dinajpur 22.42% 44.18% 0.099
Darjeeling 11.41% 44.97% 0.051
Howrah 12.84% 45.12% 0.058
Hugli 14.93% 44.23% 0.066
Jalpaiguri 22.02% 45.90% 0.101
Koch Bihar 22.28% 45.13% 0.101
Kolkata 2.80% 45.56% 0.013
Maldah 35.70% 45.66% 0.163
Murshidabad 27.23% 45.96% 0.125
Nadia 11.34% 42.60% 0.048
North 24 Parganas 9.80% 41.51% 0.041
Pashchim Medinipur 23.82% 43.50% 0.104
Purba Medinipur 14.19% 42.68% 0.061
Puruliya 49.69% 47.44% 0.236
South 24 Parganas 28.27% 45.67% 0.129
Uttar Dinajpur 42.84% 49.79% 0.213
Rural Urban
Districts of West Bengal
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Bankura 29.38% 44.79% 0.132 7.34% 36.00% 0.026
Barddhaman 21.92% 48.56% 0.106 18.38% 44.86% 0.082
Birbhum 30.08% 45.76% 0.138 12.97% 43.41% 0.056
Dakshin Dinajpur 24.89% 44.45% 0.111 5.13% 35.03% 0.018
Darjeeling 15.24% 45.60% 0.069 4.93% 41.69% 0.021
Howrah 14.34% 45.55% 0.065 11.88% 44.79% 0.053
Hugli 17.23% 43.21% 0.074 10.92% 47.00% 0.051
Jalpaiguri 27.88% 46.12% 0.129 3.80% 40.68% 0.015
Koch Bihar 23.98% 45.36% 0.109 7.26% 38.51% 0.028
Kolkata - - - 2.80% 45.56% 0.013
Maldah 37.53% 45.79% 0.172 24.36% 44.42% 0.108
Murshidabad 27.50% 45.03% 0.124 26.33% 49.19% 0.130
Nadia 14.08% 42.39% 0.060 3.92% 44.73% 0.018
North 24 Parganas 13.86% 40.24% 0.056 6.52% 43.70% 0.028
Pashchim Medinipur 24.91% 43.54% 0.108 14.85% 42.91% 0.064
Purba Medinipur 14.74% 43.06% 0.063 9.95% 38.36% 0.038
Puruliya 49.76% 46.04% 0.229 49.30% 55.58% 0.274
South 24 Parganas 31.75% 45.76% 0.145 16.70% 45.12% 0.075
Uttar Dinajpur 46.23% 49.72% 0.230 19.24% 50.92% 0.098
INDIA MPI BASELINE REPORT 214 215
Andaman & Nicobar IslandsResults
Andaman & Nicobar Islands: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
22.0%
0.8%
5.1% 4.9%
0.9%
6.2%
33.6%
7.1%
1.6%
24.5%
20.2%
24.4%
12.0%
2.7%2.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
3.41%
0.31%
1.02%
1.95%
0.27%
3.15% 2.99%
1.18% 1.55%
3.51%
2.15%
0.11%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Andaman & Nicobar Islands: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Andaman & Nicobar Islands
Overview
Andaman & Nicobar Islands Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
4.3%
Rural
Headcount Ratio Intensity MPI
6.76% 40.86% 0.028
Andaman & Nicobar Islands: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
40.56%
MPI (HxA)
0.017
Andaman & Nicobar Islands
Urban
Headcount Ratio Intensity MPI
0.97% 37.76% 0.004
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 32.6%
Child & Adolescent Mortality: 1.5%
Maternal Health: 4.9%
Years of Schooling: 18.6%
School Attendance: 2.6%
Cooking Fuel: 8.6%
Sanitation: 8.2%
Drinking Water: 3.2%
Electricity: 4.2%
Housing: 9.6%
Assets: 5.9%
Bank Account: 0.3%
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Andaman & Nicobar Islands UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 216 217
Andaman & Nicobar IslandsResults
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Andaman & Nicobar Islands: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.009
0.019
0.039
Andaman & Nicobar Islands
Multidimensional Poverty Index Score (District-wise)
Districts of Andaman & Nicobar Islands are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union
Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves
from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with
the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Andaman & Nicobar Islands.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
2.20%
5.28%
9.36%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%
South Andaman
Nicobar
North & Middle Andaman
Headcount Ratio (% of population who are multidimensionally poor)
District-wise Headcount Ratio, Intensity and MPI Score
Rural Urban
Districts of Andaman & Nicobar Islands
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Nicobar 5.28% 36.80% 0.019 - - -
North & Middle Andaman 9.47% 41.94% 0.040 6.33% 35.71% 0.023
South Andaman 4.44% 39.88% 0.018 0.85% 38.10% 0.003
Multidimensional Poverty in Andaman & Nicobar Islands
Districts of Andaman & Nicobar Islands Headcount Ratio Intensity MPI
Nicobar 5.28% 36.80% 0.019
North & Middle Andaman 9.36% 41.79% 0.039
South Andaman 2.20% 39.45% 0.009
Multidimensional Poverty Index
Districts of Andaman & Nicobar Islands are as per the 2011 Census of India
INDIA MPI BASELINE REPORT 218 219
ChandigarhResults
4.90%
0.52%
3.20% 3.23%
1.46%
3.26%
4.84%
1.30%
0.48%
2.48%
1.20% 0.81%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Chandigarh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
Chandigarh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
INDIA MPI
A snapshot of multidimensional poverty in Chandigarh
Overview
Chandigarh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
5.97%
Rural
Headcount Ratio Intensity MPI
18.56% 47.88% 0.089
Chandigarh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.39%
MPI (HxA)
0.026
Chandigarh
Urban
Headcount Ratio Intensity MPI
5.45% 42.76% 0.023
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.5%
Child & Adolescent Mortality: 1.7%
Maternal Health: 10.3%
Years of Schooling: 20.7%
School Attendance: 9.4%
Cooking Fuel: 6.0%
Sanitation: 8.9%
Drinking Water: 2.4%
Electricity: 0.9%
Housing: 4.6%
Assets: 2.2%
Bank Account: 1.5%
23.1%
1.2%
11.0%
5.8%
1.8%
2.4%
6.4%
2.7%
4.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
4.9%
4.2%
19.0%
15.0%
0.5%0.1%
0.026
Chandigarh
Multidimensional Poverty Index Score (District-wise)
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Districts of Chandigarh are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Chandigarh UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 220 221
Dadra & Nagar HaveliResultsRESULT
A snapshot of multidimensional poverty in Dadra & Nagar Haveli
Overview
Dadra & Nagar Haveli Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
27.36%
Rural
Headcount Ratio Intensity MPI
44.67% 44.82% 0.2
Dadra & Nagar Haveli: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.57%
MPI (HxA)
0.122
Dadra & Nagar Haveli
Urban
Headcount Ratio Intensity MPI
4.89% 41.67% 0.02
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 33.1%
Child & Adolescent Mortality: 0.9%
Maternal Health: 5.8%
Years of Schooling: 9.5%
School Attendance: 9.0%
Cooking Fuel: 9.4%
Sanitation: 10.3%
Drinking Water: 4.0%
Electricity: 0.8%
Housing: 9.9%
Assets: 4.9%
Bank Account: 2.5%
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territory of Dadra & Nagar Haveli & Daman & Diu have been computed separately for their erstwhile regions.
Dadra & Nagar Haveli: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
Dadra & Nagar Haveli: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
45.0%
2.0%
13.4%
7.8% 7.7%
26.9%
57.1%
20.8%
10.9%
46.6%
25.7%
68.1%
36.9%
2.7%
0.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
24.22%
1.28%
8.46%
6.93% 6.56%
24.07%
26.29%
10.17%
2.12%
25.46%
12.60%
6.38%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the
NFHS-5 Dadra & Nagar Haveli District Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 222 223
Daman & DiuResultsINDIA MPI
A snapshot of multidimensional poverty in Daman & Diu
Overview
Daman & Diu Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
6.82%
Rural
Headcount Ratio Intensity MPI
5.19% 41.79% 0.022
Daman & Diu: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.18%
MPI (HxA)
0.03
Daman & Diu
Urban
Headcount Ratio Intensity MPI
7.41% 44.79% 0.033
x =
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 29.9%
Child & Adolescent Mortality: 0.9%
Maternal Health: 10.8%
Years of Schooling: 17.7%
School Attendance: 17.1%
Cooking Fuel: 2.3%
Sanitation: 7.2%
Drinking Water: 2.3%
Electricity: 0.0%
Housing: 1.7%
Assets: 5.0%
Bank Account: 5.1%
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territory of Dadra & Nagar
Haveli & Daman & Diu have been computed separately for their erstwhile regions.
Daman & Diu: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
20.9%
0.9%
14.7%
7.6%
4.7%
11.7%
8.1%
14.7%
12.5%
9.2%
7.9%
14.7%
34.8%
32.9%
10.3%
0.0%
0.5%
0.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
5.40%
0.33%
3.90%
3.20% 3.09%
1.44%
4.55%
1.42%
0.00%
1.08%
3.19% 3.25%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Daman & Diu: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
DOWNLOAD DATA
Note on comparison: The striped and dotted bars denote the provisional estimates of the uncensored headcount ratio based on the data avail- able in the NFHS-5 Daman District Factsheet and the NFHS-5 Diu District Factsheet (2019-20), respectively.
Dimension
Health Education Standard of Living (Daman & Diu)
NFHS-5: Standard of Living (Daman)
NFHS-5: Standard of Living (Diu)
INDIA MPI BASELINE REPORT 224 225
Daman & DiuResults
Daman & Diu: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
Multidimensional Poverty Index
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Daman & Diu.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
4.34%
7.46%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Diu
Daman
Headcount Ratio (% of population who are multidimensionally poor)
27.36%
Dadra & Nagar
Haveli
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Daman & Diu
Districts of Daman & Diu Headcount Ratio Intensity MPI
Daman 7.46% 45.16% 0.034
Diu 4.34% 37.64% 0.016
Rural Urban
Districts of Daman & Diu
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Daman 4.95% 44.77% 0.022 8.04% 45.21% 0.036
Diu 5.51% 38.37% 0.021 2.77% 35.72% 0.010
Districts of Daman & Diu are as per the 2011 Census of India
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.034
0.016
0.122
Dadra & Nagar Haveli, & Daman & Diu
Multidimensional Poverty Index Score (District-wise)
Districts of Dadra & Nagar Haveli, & Daman & Diu are as per the 2011 Census of India. Due to there being a relatively lower number of districts,
all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour
moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents
areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
INDIA MPI BASELINE REPORT 226 227
DelhiResultsINDIA MPI
A snapshot of multidimensional poverty in Delhi
Overview
Delhi Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
4.79%
Rural
Headcount Ratio Intensity MPI
3.41% 38.28% 0.013
Delhi: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
43.99%
MPI (HxA)
0.021
Delhi
Urban
Headcount Ratio Intensity MPI
4.8% 44.02% 0.021
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 31.0%
Child & Adolescent Mortality: 2.8%
Maternal Health: 13.2%
Years of Schooling: 20.1%
School Attendance: 9.1%
Cooking Fuel: 1.4%
Sanitation: 8.1%
Drinking Water: 3.8%
Electricity: 0.1%
Housing: 3.3%
Assets: 4.1%
Bank Account: 2.9%
Delhi: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
23.4%
1.9%
15.2%
5.9%
2.6%
23.7%
10.8%
5.5%
8.4%
2.2%
1.1%
26.7%
18.9%
0.3%0.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
3.92%
0.71%
3.34%
2.54%
1.15%
0.62%
3.58%
1.66%
0.06%
1.48% 1.80%
1.28%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Delhi: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Delhi UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 228 229
DelhiResults
2.29%
2.41%
2.86%
3.84%
4.26%
4.28%
6.06%
6.98%
7.35%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%
West Delhi
South West
North Delhi
Central Delhi
New Delhi
East Delhi
South Delhi
North West
North East
Headcount Ratio (% of population who are multidimensionally poor)
Delhi: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Delhi.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.010
0.018
0.018
0.025
0.011
0.031
0.012
0.033
0.017
Delhi
Multidimensional Poverty Index Score (District-wise)
Districts of Delhi are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
INDIA MPI BASELINE REPORT 230 231
DelhiResults
Multidimensional Poverty in Delhi
District-wise Headcount Ratio, Intensity and MPI Score
Districts of Delhi are as per the 2011 Census of India
Districts of Delhi Headcount Ratio Intensity MPI
Central Delhi 3.84% 43.78% 0.017
East Delhi 4.28% 41.77% 0.018
New Delhi 4.26% 43.27% 0.018
North Delhi 2.86% 41.15% 0.012
North East 7.35% 42.79% 0.031
North West 6.98% 47.21% 0.033
South Delhi 6.06% 41.76% 0.025
South West 2.41% 44.27% 0.011
West Delhi 2.29% 44.66% 0.010
Multidimensional Poverty in Delhi
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Districts of Delhi are as per the 2011 Census of India
Rural Urban
Districts of Delhi
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Central Delhi - - - 3.84% 43.78% 0.017
East Delhi - - - 4.28% 41.77% 0.018
New Delhi - - - 4.26% 43.27% 0.018
North Delhi 10.45% 34.86% 0.036 2.73% 41.55% 0.011
North East - - - 7.35% 42.79% 0.031
North West 0.62% 44.05% 0.003 7.08% 47.21% 0.033
South Delhi - - - 6.06% 41.76% 0.025
South West 4.03% 39.29% 0.016 2.38% 44.46% 0.011
West Delhi - - - 2.29% 44.66% 0.010
INDIA MPI BASELINE REPORT 232 233
Jammu & Kashmir, & LadakhResultsINDIA MPI
A snapshot of multidimensional poverty in Jammu & Kashmir, & Ladakh
Overview
Jammu & Kashmir, & Ladakh Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
12.58%
Rural
Headcount Ratio Intensity MPI
16.39% 44.27% 0.073
Jammu & Kashmir, & Ladakh: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
44.11%
MPI (HxA)
0.055
Jammu & Kashmir, & Ladakh
Urban
Headcount Ratio Intensity MPI
3.5% 42.32% 0.015
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 29.2%
Child & Adolescent Mortality: 1.3%
Maternal Health: 9.2%
Years of Schooling: 13.4%
School Attendance: 7.5%
Cooking Fuel: 9.7%
Sanitation: 9.1%
Drinking Water: 4.3%
Electricity: 1.4%
Housing: 8.1%
Assets: 5.7%
Bank Account: 1.2%
Note on data representation: As the data period for the NFHS-4 is 2015-16, the estimates for the present Union Territories of Jammu & Kash- mir, and Ladakh have been computed for their combined geographical region.
Jammu & Kashmir, & Ladakh: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
Jammu & Kashmir, & Ladakh: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
25.9%
1.8%
12.7%
6.8%
16.1%
3.7%
2.8%
14.2%
10.3%
29.6%
25.4%
3.9%
3.0%
45.2%
32.7%
23.7%
47.0%
24.3%
57.7%
2.8%
0.7%0.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
9.70%
0.85%
6.10%
4.47%
2.49%
11.25%
10.62%
4.97%
1.67%
9.44%
6.65%
1.43%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
DOWNLOAD DATA
Note on comparison: The striped and dotted bars denote the provisional estimates of the uncensored headcount ratio based on the data avail- able in the NFHS-5 Jammu & Kashmir UT Report and the Ladakh UT Factsheet (2019-20) respectively.
Dimension
Health Education Standard of Living (Jammu & Kashmir, & Ladakh)
NFHS-5: Education (Jammu & Kashmir) NFHS-5: Standard of Living (Jammu & Kashmir)
NFHS-5: Standard of Living (Ladakh)
INDIA MPI BASELINE REPORT 234 235
Jammu & Kashmir, & LadakhResults
1.51%
3.79%
6.51%
6.84%
6.97%
7.06%
7.43%
7.82%
8.36%
9.67%
11.07%
13.08%
16.08%
21.92%
24.27%
24.29%
26.83%
27.52%
28.92%
35.26%
Srinagar
Pulwama
Shupiyan
Badgam
Jammu
Baramula
Kulgam
Ganderbal
Anantnag
Samba
Bandipore
Kathua
Kupwara
Reasi
Punch
Kishtwar
Udhampur
Rajouri
Doda
Ramban
5.36%
19.4%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Leh (Ladakh)
Kargil
Headcount Ratio (% of population who are multidimensionally poor)
Jammu & Kashmir Ladakh
Jammu & Kashmir, & Ladakh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Jammu & Kashmir, & Ladakh.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Multidimensional Poverty Index
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.006
0.050
0.030
0.030
0.041
0.034
0.016
0.106
0.039
0.028
0.079
0.067
0.097
0.033
0.021
0.057
0.136
0.163
0.111
0.123
0.117
Jammu & Kashmir, & Ladakh
Multidimensional Poverty Index Score (District-wise)
Districts of Jammu & Kashmir, & Ladakh are as per the Political Map of India 10
th
Edition (Survey of India). Due to there being a relatively lower
number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
INDIA MPI BASELINE REPORT 236 237
Jammu & Kashmir, & LadakhResults
Multidimensional Poverty in Jammu & Kashmir, & Ladakh
District-wise Headcount Ratio, Intensity and MPI Score
Districts of Jammu & Kashmir Headcount Ratio Intensity MPI
Anantnag 8.36% 46.54% 0.039
Badgam 6.84% 41.74% 0.029
Bandipore 11.07% 45.41% 0.050
Baramula 7.06% 43.09% 0.030
Doda 28.92% 47.10% 0.136
Ganderbal 7.82% 43.18% 0.034
Jammu 6.97% 43.33% 0.030
Kathua 13.08% 43.48% 0.057
Kishtwar 24.29% 45.52% 0.111
Kulgam 7.43% 43.79% 0.033
Kupwara 16.08% 41.57% 0.067
Pulwama 3.79% 42.07% 0.016
Punch 24.27% 43.48% 0.106
Rajouri 27.52% 44.68% 0.123
Ramban 35.26% 46.29% 0.163
Reasi 21.92% 44.45% 0.097
Samba 9.67% 42.77% 0.041
Shupiyan 6.51% 43.32% 0.028
Srinagar 1.51% 39.64% 0.006
Udhampur 26.83% 43.59% 0.117
Districts of Ladakh Headcount Ratio Intensity MPI
Kargil 19.43% 40.74% 0.079
Leh (Ladakh) 5.36% 38.83% 0.021
Multidimensional Poverty in Jammu & Kashmir, & Ladakh
Urban and Rural Headcount Ratio, Intensity and MPI Score for each District
Rural Urban
Districts of Ladakh
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Kargil 23.42% 40.76% 0.095 3.46% 40.22% 0.014
Leh (Ladakh) 6.72% 37.93% 0.025 2.72% 43.14% 0.012
Rural Urban
Districts of Jammu & Kashmir
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Anantnag 8.76% 44.65% 0.039 7.54% 50.93% 0.038
Badgam 7.99% 41.79% 0.033 1.63% 40.65% 0.007
Bandipore 12.58% 45.85% 0.058 6.27% 42.61% 0.027
Baramula 8.38% 42.81% 0.036 2.37% 46.71% 0.011
Doda 30.57% 47.14% 0.144 1.56% 35.71% 0.006
Ganderbal 8.43% 44.19% 0.037 5.99% 38.91% 0.023
Jammu 9.28% 45.51% 0.042 4.54% 38.64% 0.018
Kathua 14.75% 43.44% 0.064 0.87% 47.62% 0.004
Kishtwar 25.81% 45.55% 0.118 1.23% 35.71% 0.004
Kulgam 9.20% 43.75% 0.040 0.39% 47.62% 0.002
Kupwara 18.99% 41.59% 0.079 4.98% 41.24% 0.021
Pulwama 3.91% 42.25% 0.017 1.46% 33.33% 0.005
Punch 25.68% 43.55% 0.112 4.70% 37.86% 0.018
Rajouri 28.84% 44.78% 0.129 8.93% 40.11% 0.036
Ramban 36.49% 46.34% 0.169 5.23% 38.10% 0.020
Reasi 22.39% 44.45% 0.100 16.78% 44.50% 0.075
Samba 10.05% 42.70% 0.043 2.89% 47.39% 0.014
Shupiyan 6.81% 43.32% 0.030 1.91% 42.86% 0.008
Srinagar 7.92% 50.36% 0.040 1.42% 38.86% 0.006
Udhampur 29.73% 43.71% 0.130 10.05% 41.37% 0.042
Districts of
Jammu & Kashmir, & Ladakh are as per the Political Map of India 10th Edition (Survey of India) Districts of Jammu & Kashmir, & Ladakh are as per the Political Map of India 10th Edition (Survey of India)
INDIA MPI BASELINE REPORT 238 239
LakshadweepResultsINDIA MPI
A snapshot of multidimensional poverty in Lakshadweep
Overview
Lakshadweep Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
1.82%
Rural
Headcount Ratio Intensity MPI
1.16% 42.86% 0.005
Lakshadweep: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
36.15%
MPI (HxA)
0.007
Lakshadweep
Urban
Headcount Ratio Intensity MPI
2% 35.09% 0.007
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 46.1%
Child & Adolescent Mortality: 6.6%
Maternal Health: 10.9%
Years of Schooling: 0.0%
School Attendance: 16.2%
Cooking Fuel: 8.0%
Sanitation: 1.0%
Drinking Water: 4.1%
Electricity: 0.0%
Housing: 2.3%
Assets: 1.0%
Bank Account: 3.8%
Lakshadweep: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
1.82%
0.53% 0.86%
0.00%
0.64%
1.11%
0.14%
0.57%
0.00% 0.32% 0.14% 0.52%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Lakshadweep: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
31.5%
2.0%
6.5%
0.9%
1.4%
9.3%
1.5%
1.0%
5.6%
58.2%
40.6%
0.4%0.2% 0.0%0.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
0.007
Lakshadweep
Multidimensional Poverty Index Score (District-wise)
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
Districts of Lakshadweep are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI scores represented by a colour.
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Lakshadweep UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 240 241
PuducherryResults
A snapshot of multidimensional poverty in Puducherry
Overview
Puducherry Headcount Ratio, Intensity and MPI
Headcount Ratio (H)
1.72%
Rural
Headcount Ratio Intensity MPI
3.33% 36.74% 0.012
Puducherry: Indicator-wise Contribution to the MPI
Percentage contribution of each indicator to the MPI score
Intensity (A)
38.54%
MPI (HxA)
0.007
Puducherry
Urban
Headcount Ratio Intensity MPI
0.99% 41.28% 0.004
x =
Note on the data period: The NFHS 4 (2015-16) precedes the full roll out of flagship schemes of Pradhan Mantri Awas Yojana (PMAY), Jal Jeevan
Mission (JJM), Swachh Bharat Mission (SBM), Pradhan Mantri Sahaj Bijli Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala Yojana (PMUY),
and the Pradhan Mantri Jan Dhan Yojana (PMJDY).
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Electricity
Housing
Assets
Bank Account
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health Education Standard of Living
Nutrition: 33.4%
Child & Adolescent Mortality: 3.6%
Maternal Health: 5.5%
Years of Schooling: 22.3%
School Attendance: 0.2%
Cooking Fuel: 9.7%
Sanitation: 10.2%
Drinking Water: 0.8%
Electricity: 0.8%
Housing: 7.9%
Assets: 3.0%
Bank Account: 2.6%
RESULT
Puducherry: Uncensored Headcount Ratio
Percentage of total population who are deprived in each indicator
21.9%
0.7%
4.1%
3.3%
1.2%
5.5%
17.6%
1.6%
5.3%
13.5%
7.7%
35.1%
15.1%
0.2%0.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Nutrition Chil d &
Adol escen t
Mortality
Maternal Heal th Years of
Sc hooli ng
Sc hool
Attend ance
Cooking Fue l Sanit ation Drinking Water Elect ricit y Housin g Assets Bank Accoun t
% of population deprived
1.33%
0.28% 0.44%
0.89%
0.01%
1.35% 1.42%
0.11% 0.11%
1.10%
0.42% 0.37%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Nutrition Child &
Adolescent
Mortality
Maternal Health Years of
Schooling
School
Attendance
Cooking Fuel Sanitation Drinking Water Electricity Housing Assets Bank Account
% of population MPI poor & deprived
Puducherry: Censored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
DOWNLOAD DATA
Note on comparison: The striped bars denote the provisional estimates of the uncensored headcount ratio based on the data available in the NFHS-5 Puducherry UT Factsheet (2019-20).
Dimension Health Education Standard of Living NFHS-5: Standard of Living
INDIA MPI BASELINE REPORT 242 243
PuducherryResults
Puducherry: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
The size of the bar represents the percentage of population who are multidimensionally poor in each district of Puducherry.
The colour of the bar represents the MPI score of the district. The colour moves from green, through yellow, to red as the MPI score increases.
Green represents areas with the lowest MPI scores while red represents areas with the highest MPI scores. The legend provides the range of MPI
scores represented by a colour.
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.08%
1.30%
3.13%
5.18%
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
Mahe
Puducherry
Karaikal
Yanam
Headcount Ratio (% of population who are multidimensionally poor)
Multidimensional Poverty Index
Districts of Puducherry Headcount Ratio Intensity MPI
Karaikal 3.13% 35.97% 0.011
Mahe 0.08% 35.71% 0.000
Puducherry 1.30% 39.28% 0.005
Yanam 5.18% 41.38% 0.021
District-wise Headcount Ratio, Intensity and MPI Score
Multidimensional Poverty in Puducherry
Rural Urban
Districts of Puducherry
Headcount
Ratio
Intensity MPI
Headcount
Ratio
Intensity MPI
Karaikal 5.49% 36.17% 0.020 0.91% 34.86% 0.003
Mahe - - - 0.08% 35.71% 0.000
Puducherry 2.59% 37.15% 0.010 0.74% 42.52% 0.003
Yanam - - - 5.18% 41.38% 0.021
Districts of
Puducherry are as per the 2011 Census of India
0.000 to 0.023 0.024 to 0.046 0.047 to 0.069 0.070 to 0.092 0.093 to 0.116 0.117 to 0.139 0.140 to 0.163
0.000
0.005
0.011
0.021
Puducherry
Multidimensional Poverty Index Score (District-wise)
Districts of Puducherry are as per the 2011 Census of India. Due to there being a relatively lower number of districts, all Union Territories and the
States of Sikkim and Goa share the same colour scale. The colour represents the MPI score of a district. The colour moves from green, through
yellow, to red as the MPI score increases. Green represents areas with the lowest MPI scores while red represents areas with the highest MPI
scores. The legend provides the range of MPI scores represented by a colour.
INDIA MpI BASelINe reporT
ResultsSeCTIoN
244 245
SECTION
IV
TECHNICAL NOTES
&
DATA TABLES
INDIA MPI 246 247
Technical Notes Appendix
A.1 Policy for treatment of Missing Values
Any individual (and in extension household) for whom data for all indicators, and data for all constituents of an
indicator is not present, is not considered in the estimation sample of the national MPI and its disaggregation. It is
classified as a dropped observation.
For example, if an individual has data for eleven indicators of the national MPI but the information for one indicator is missing, that individual will not be considered in the estimation for the national MPI. Another example would be, supposing that in the indicator for drinking water, an individual has information for the type of drinking water source but information for round-trip time to the drinking water source is missing, then the individual is not considered in the estimation of the national MPI. Similarly, in the case of the indicator for sanitation, if the information for type of sanitation facility is available for an individual but the information for whether the facility is shared or exclusive is not available, then the individual is not considered for the estimation of the national MPI.
The exception to this policy is the maternal health indicator - the specific policy for which has been detailed sub-
sequently in this section.
A.2 Policy for the indicator on Bank Accounts
If an individual, when asked if they have a bank account, has replied that they “don’t know”, they are considered to
be deprived in the indicator for Bank Accounts.
In the case of the indicator for bank accounts, the NFHS-4 reports that 90.3% of individuals have stated that they
have a bank account, 9.5% stated that they do not have bank account and 0.1% of individuals responded “don’t
know” when asked if they have a bank account. For the national MPI, the 0.1% of individuals who responded with
“don’t know” (2,969 unweighted observations) have been treated as deprived in the indicator for bank accounts.
The rationale behind this is the assumption that if an individual is unaware of their ownership status for a bank ac-
count, then it may be considered analogous to them not having a bank account to begin with.
A
ANNEXURE
TECHNICAL NOTES
ESTIMATION DETAILS
A1
However, this assumption was not made discounting the possibility that there might be cases where the individual
has chosen to not disclose the information to the survey enumerator or the person responsible for the operation of
the bank account was not present in the household at the time of the survey. In such cases, the relatively low weight
assigned to the bank account indicator acts as a moderator, i.e., well-off individuals who have responded “don’t
know” to the bank account indicator will not be affected as they will need to be deprived in a substantial number
of other indicators to be considered as multidimensionally poor and on the other hand individuals who are already
multidimensionally poor by virtue of other indicators will be retained in the final estimation sample.
A.3 Policy for the indicator on Maternal Health
The indicator for maternal health is a composite of 2 discrete datapoints – the number of antenatal care visits a
woman received during her last pregnancy and the type of assistance (if any) that she received during the birth of
her last child. In order for her to be considered as deprived in the indicator for maternal health, she has to have a)
received less than 4 antenatal care visits (deprived in antenatal care) or b) not received assistance from a skilled
healthcare provider during childbirth (deprived in assisted delivery). In order to be deprived in the indicator for
maternal health, a woman must be deprived in either antenatal care or assisted delivery.
If the information for both antenatal care and assisted delivery are missing, then, adhering to the policy for treat-
ment of missing values, the woman for whom the information is missing, is not included in the estimation of the
national MPI.
The conundrum however arises, when the information for either antenatal care (or assisted delivery) is present, but
the information for assisted delivery (or antenatal care) is missing. Therefore, there are 9 possible scenarios which
may occur during the determination of the maternal health indicator.
The decision regarding the deprivation status of the maternal health indicator is fairly straightforward for outcomes
1 through 6 and outcome number 9. The problem lies with outcomes 7 and 8, where a woman is not deprived in
Antenatal Care but the information for Assisted Delivery is missing and vice versa. This is because the indicator for
which the information is missing may take a value of deprived or not deprived thereby determining the status of the
maternal health indicator as a whole. Thus, for observations falling in outcomes 7 and 8, it becomes impossible to
determine the actual deprivation status of the maternal health. A total number of 6,087 unweighted observations
fall in outcome 8 and there are no observations in outcome 7.
Outcome
Number
Deprived in Antenatal Care Deprived in Assisted Delivery Deprivation Status in Maternal Health
1 No No No
2 Ye s No Ye s
3 No Ye s Ye s
4 Ye s Ye s Ye s
5 Ye s Information Missing Ye s
6 Information Missing Ye s Ye s
7 No Information Missing ?
8 Information Missing No ?
9 Information Missing Information Missing
Obs. is dropped from estimation
sample
INDIA MPI BASELINE REPORT 248 249
Technical NotesAppendix
If the policy for treatment of missing values is to be applied, then these 6,087 indicators would be dropped from
the final estimation sample. This however would risk further reducing an already restricted sample (women who
have had at least one childbirth in the 5 years preceding the survey) of observations eligible for the maternal health
indicator.
Therefore, an exception to the policy for treatment of missing indicators has been made for the maternal health
indicator in order to retain the 6,087 observations in the final MPI estimation sample. In its place a different policy
has been utilized, the policy is as follows:
For individuals where the information for either antenatal care or assisted delivery is missing while the information
for the other is present and takes a value of “not deprived”, the individual is included in the estimation sample of
the national M
PI only if their deprivation score is higher than the second order cutoff and irrespective of the value
taken by the indicator on maternal health.
A.3.1 There are four steps involved to implement this policy, they have been outlined in the
following paragraphs.
4.4.2.i Step 1
Identify the number of observations for which either antenatal care or assisted delivery is not deprived while the
information for the other is missing. There are 6,087 observations which are not deprived in assisted delivery and
whose information for antenatal care is missing. There are no observations which are not deprived in antenatal care
and for whom the information on assisted delivery is missing.
4.4.2.ii Step 2
Within the 6,087 observations determine the ones where the deprivation score is above the second order cut-off
(i.e. c≥��) for 2 specific scenarios:
Scenario 1: Assume 6,087 observations are not deprived in maternal health and compute the deprivation scores for
them. Identify the observations for whom the deprivation score is above 33.33%. Scenario 1 yields the following
results,
4,908 observations are not multidimensionally poor, 1,071 are multidimensionally poor and 108 have missing values
in other indicators and have been dropped from the sample.
Scenario 2: Assume 6,087 observations are deprived in maternal health and compute the deprivation scores for
them. Identify the observations for whom the deprivation score is above 33.33%. Scenario 2 yields the following
results,
MPI Poor Freq. Percent
No 4,908 80.63
Ye s 1,071 17.59
Missing 108 1.77
Total 6,087 100.00
MPI Poor Freq. Percent
No 3,767 61.89
Ye s 2,212 36.34
Missing 108 1.77
Total 6,087 100.00
3,767 observations are not multidimensionally poor, 2,212 are multidimensionally poor and 108 have missing values in other indicators and have been dropped from the sample.
4.4.2.iii Step 3
Identify observations whose deprivation status remain unchanged across both scenario’s 1 and 2. That is, we identify the observations for whom the deprivation score remains above or below 33.33% irrespective of the value taken by the maternal health indicator.
4,838 (3,767 not deprived and 1,071 deprived) observations remain common across both the scenarios i.e. their
deprivation status (c
��
≥�� or c
��
<��) remains unchanged irrespective of the value taken by the maternal health indicator.
4.4.2.iv Step 4
Of the 6,087 identified ambiguous observations, it can be determined with absolute certainty that 4,838 obser-
vations will remain multidimensionally poor or not regardless of the value taken by the maternal health indicator.
Therefore, these 4,838 observations will be retained in the estimation sample of the national MPI.
A.4 Sample size
The national MPI utilizes 2,699,110 unweighted observations from the NFHS 4 as its estimation sample. This sample
size consists of de jure household members for whom the data for all twelve indicators of the national MPI is pres-
ent. Therefore, from the 2,869,043 unweighted observations present in the NFHS-4 microdata, 67,085 (2.34%) ob-
servations belonging to non-usual household members have been dropped. A further 102,848 observations (3.58%)
were dropped due to them missing the data for one or more component indicators of the national MPI.
The national MPI thus uses 94.08% of the total unweighted sample of the NFHS-4 for estimation. In comparison, the
global MPI uses 94.2% of the total unweighted sample of the NFHS-4 for estimation.
A.5 Micro-data Extraction, Treatment, and Visualization
The micro-data for the NFHS 4 was obtained from the official repository of the Demographic and Health Surveys
Program. The estimation of India's national MPI, its indicators, and related estimates was done utilizing the Birth
Recode (IABR74FL), Individual Recode (IAIR74FL), Men's Recode (IAMR74FL), and Person's Recode (IAPR74FL).
Extraction of data, adjustments for survey design and application of sample weights was completed adhering to
the procedures stated in the Standard Recode Manual for DHS-6. As the reporting of data was done up to the level
of urban and rural areas within a district, the occurrence of singleton sampling units within a strata was inevitable,
particularly in designated rural areas of primarily urban districts. For such occurrences, the standard errors have not
been reported and has been replaced with "*".
The processing of the data and computation of point estimates and estimate variance was carried out in STATA 16
(MP) and STATA 17 (MP). The final point estimates and standard errors were exported to Microsoft Excel for visuali-
sation. The choropleth maps were constructed in Tableau and QGIS using Shapefiles obtained from Survey of India.
INDIA MPI BASELINE REPORT 250 251
Index of tablesAppendix
• Alkir. Multidimensional Poverty Measures as Policy Tools. In V. Beck, H. Hahn, & R. Lepenies, Dimensions of
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8), 476-487.
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and Analysis. Oxford: Oxford University Press.
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Methodological Note 47.
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Development Goals at Midpoint: Where do we stand and where do we need to go? European Report on Development.
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Multidimensional Indexes. In V. Beck, H. Hahn, & R. Lepenies, Dimensions of Poverty: Measurement, Epistemic Injustices,
Activism (p. 308). Springer.
• Chakravarty, S. R. (2009). Inequality, Polarization, and Poverty. New York: Springer.
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Measure in the Resource Space): An Illustration Using Indian Data. In V. Beck, H. Hahn, & R. Lepenies, Dimensions of Poverty: Measurement, Epistemic Injustices, Activism (p. 229). Springer.
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Godinot, X., & Walker, R. (2020). Poverty in All Its Forms: Determining the Dimensions of Poverty Through Merg-
ing Knowledge. In H. H. Valentin Beck, Dimensions of Poverty: Measurement, Epistemic Injustices, Activism (p. 264). Springer.
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Greve, B. (2020). Poverty: The Basics. New York: Routledge.
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Bangladesh. Journal of Asian Economics.
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Evidence Using a Multi-Dimensional Measure of Financial Inclusion. Springer, 149(2), 613-639.
• Ministry of Health and Family Welfare. (2017). National Family Health Survey (NFHS-4). Government of India.
• OPHI, U. &. (2019). How to Build a National Multidimensional Poverty Index (MPI): Using the MPI to inform the SDGs.
New York: UNDP.
• Rowntree, B. S. (1901). Poverty: A study of town life. London: Macmillan and Company.
• Sen, A. (1979). Equality of What? The Tanner Lecture on Human Values.
• Sen, A. (1987). The Standard of Living. Cambridge : Cambridge University Press.
• Sen, A. (1999). Commodities and capabilities. Oxford: Oxford University Press.
• Townsend, P. (1979). Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living. Har-
mondsworth: Allen Lane.
• UNDP. (2010). Human Development Report 2010. New York: Palgrave Macmillan.
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oped by the United Nations Inter-agency Group for Child Mortality Estimation. New York City (NY): UNICEF.
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Washington, DC: World Bank.
Page No.
State/UT
Table 1- State/UT-Wise: Headcount Ratio, Intensity, MPI 252
Table 2- State/UT-wise: Uncensored Headcount Ratio 253
Table 3- State/UT-wise: Uncensored Headcount Ratio (Rural) 254
T
able 4- State/UT-wise: Uncensored Headcount Ratio (Urban)
255
T
able 5- State/UT-wise: Censored Headcount Ratio
256
T
able 6- State/UT-wise: Censored Headcount Ratio (Rural)
257
Table 7- State/UT-wise: Censored Headcount Ratio (Urban) 258
T
able 8- State/UT-wise: Indicator Contribution to the MPI Score
259
Table 9- State/UT-wise: Indicator Contribution to the MPI Score (Rural) 260
T
able 10- State/UT-wise: Indicator Contribution to the MPI Score (Urban)
261
Districts
Table 11- District-wise: Uncensored Headcount Ratio
262
T
able 12- District-wise: Uncensored Headcount Ratio (Rural)
277
Table 13- District-wise: Uncensored Headcount Ratio (Urban) 292
Table 14- District-wise: Censored Headcount Ratio 307
Table 15- District-wise: Censored Headcount Ratio (Rural) 322
T
able 16- District-wise: Censored Headcount Ratio (Urban)
337
Table 17- District-wise: Indicator Contribution to the MPI Score 352
Table 18- District-wise: Indicator Contribution to the MPI Score (Rural) 367
Table 19- District-wise: Indicator Contribution to the MPI Score (Urban) 382
Standard Errors
Table 20- Standard Errors: State/UT-wise - Headcount Ratio, Intensity, MPI 397
Table 21- Standard Errors: State/UT-wise - Uncensored Headcount Ratio 398
Table 22- Standard Errors: State/UT-wise - Censored Headcount Ratio 399
Table 23- Standard Errors: District-wise - Headcount Ratio, Intensity, MPI 400
REFERENCES INDEX OF TABLES
INDIA MpI BASelINe reporT State/UTData Tables
252 253
Headcount Ratio, Intensity, MPIAllRuralUrban
State/UT
Headcount
Ratio
IntensityMPI
Headcount
Ratio
IntensityMPI
Headcount
Ratio
IntensityMPI
State
Andhra Pradesh12.31%43.23%0.05315.37%43.28%0.0674.91%42.83%0.021
Arunachal Pradesh24.27%47.26%0.11529.23%47.60%0.1398.15%43.18%0.035
Assam32.67%47.89%0.15636.16%48.07%0.1749.97%43.63%0.044
Bihar51.91%51.02%0.26556.01%51.14%0.28623.91%49.00%0.117
Chhattisgarh29.91%44.64%0.13435.73%44.83%0.16010.20%42.41%0.043
Goa3.76%40.16%0.0154.44%39.30%0.0173.34%40.84%0.014
Gujarat18.60%45.00%0.08427.40%45.12%0.1246.59%44.34%0.029
Haryana12.28%44.40%0.05514.86%44.38%0.0668.16%44.48%0.036
Himachal Pradesh7.62%39.43%0.0308.24%39.28%0.0321.46%48.24%0.007
Jharkhand42.16%47.91%0.20250.93%48.27%0.24615.26%44.24%0.067
Karnataka13.16%42.70%0.05619.01%42.79%0.0815.07%42.23%0.021
Kerala0.71%39.02%0.0030.95%39.81%0.0040.43%37.06%0.002
Madhya Pradesh36.65%47.25%0.17345.96%47.57%0.21913.82%44.62%0.062
Maharashtra14.85%43.78%0.06522.83%43.99%0.1005.55%42.76%0.024
Manipur17.89%44.44%0.08022.95%45.07%0.1039.90%42.17%0.042
Meghalaya32.67%48.06%0.15738.60%48.37%0.1878.62%42.47%0.037
Mizoram9.80%47.40%0.04620.48%47.93%0.0981.42%41.40%0.006
Nagaland25.23%46.33%0.11732.80%46.67%0.15310.75%44.37%0.048
Odisha29.35%46.42%0.13632.66%46.45%0.15212.33%46.12%0.057
Punjab5.59%43.75%0.0246.40%43.20%0.0284.32%45.02%0.019
Rajasthan29.46%47.44%0.14035.22%47.70%0.16811.52%44.99%0.052
Sikkim3.82%41.20%0.0164.25%41.15%0.0182.80%41.36%0.012
Tamil Nadu4.89%39.97%0.0207.32%40.21%0.0292.49%39.29%0.010
Telangana13.74%43.20%0.05920.35%43.23%0.0885.10%43.01%0.022
Tripura16.65%45.02%0.07520.93%45.34%0.0955.60%41.96%0.024
Uttar Pradesh37.79%47.60%0.18044.32%47.67%0.21118.07%47.06%0.085
Uttarakhand17.72%44.37%0.07921.94%43.78%0.0969.89%46.82%0.046
West Bengal21.43%45.49%0.09725.80%45.39%0.11711.67%46.00%0.054
Union Territory
Andaman & Nicobar Islands4.30%40.56%0.0176.76%40.86%0.0280.97%37.76%0.004
Chandigarh5.97%43.39%0.02618.56%47.88%0.0895.45%42.76%0.023
Dadra & Nagar Haveli27.36%44.57%0.12244.67%44.82%0.2004.89%41.67%0.020
Daman & Diu6.82%44.18%0.0305.19%41.79%0.0227.41%44.79%0.033
Delhi4.79%43.99%0.0213.41%38.28%0.0134.80%44.02%0.021
Jammu & Kashmir & Ladakh12.58%44.11%0.05516.39%44.27%0.0733.50%42.32%0.015
Lakshadweep1.82%36.15%0.0071.16%42.86%0.0052.00%35.09%0.007
Puducherry1.72%38.54%0.0073.33%36.74%0.0120.99%41.28%0.004
India25.01%47.13%0.11832.75%47.38%0.1558.81%45.25%0.040
T
able 1- STATE/UT-WISE: HEADCOUNT RATIO, INTENSITY, MPI
Uncensored Headcount RatioHealthEducationStandard of Living
State/UTNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh26.38%1.82%9.66%16.90%2.34%37.90%46.38%32.53%0.77%17.55%10.96%4.73%
Arunachal Pradesh21.04%1.97%28.34%17.77%8.15%57.79%38.94%14.95%11.83%76.15%23.36%15.40%
Assam39.67%2.90%25.44%16.19%6.55%77.12%51.25%17.71%21.78%75.90%19.95%15.40%
Bihar51.88%4.58%45.62%26.27%12.52%82.92%73.61%2.34%39.86%73.73%24.32%26.00%
Chhattisgarh43.02%3.32%24.70%13.47%5.38%78.04%65.39%18.30%3.64%63.31%14.92%5.74%
Goa24.65%0.57%7.14%4.70%0.96%14.91%21.43%4.11%0.18%16.16%2.97%4.02%
Gujarat41.37%2.21%14.77%9.83%6.68%48.79%37.16%12.51%3.75%24.25%13.60%9.41%
Haryana32.34%2.17%23.86%7.10%3.82%51.24%19.26%12.67%1.06%24.27%4.65%8.17%
Himachal Pradesh27.18%1.66%17.42%3.78%0.89%67.90%27.75%7.92%0.49%29.30%7.52%2.69%
Jharkhand47.99%3.32%33.07%18.32%8.19%82.14%75.38%31.06%18.79%61.78%21.37%8.98%
Karnataka33.56%1.34%12.36%8.70%3.54%45.54%43.11%14.51%1.71%37.30%10.05%8.83%
Kerala15.29%0.19%1.73%1.78%0.54%43.89%1.86%5.91%0.74%10.76%2.94%4.32%
Madhya Pradesh45.49%3.60%29.39%16.09%8.39%71.25%65.25%29.83%8.96%64.38%19.31%11.15%
Maharashtra36.09%1.42%15.95%6.54%4.20%39.49%47.97%13.89%6.59%27.90%13.97%10.35%
Manipur23.56%1.80%17.66%5.36%2.36%58.93%47.68%60.89%7.31%81.49%13.93%21.54%
Meghalaya37.05%3.10%31.70%19.71%6.15%77.08%38.61%33.52%8.18%50.40%29.88%19.91%
Mizoram21.37%2.30%16.11%7.93%3.75%32.18%15.82%9.48%4.08%24.17%13.95%5.81%
Nagaland24.50%2.07%33.06%13.63%4.81%69.28%23.28%21.23%3.25%70.98%33.91%28.66%
Odisha37.26%2.23%19.50%16.66%4.95%80.94%70.43%20.97%13.37%55.81%19.22%10.94%
Punjab22.11%1.39%12.70%7.29%2.59%36.40%17.30%1.93%0.39%19.30%1.72%3.71%
Rajasthan42.62%2.95%26.33%17.10%8.48%69.94%53.95%26.77%8.73%35.54%20.49%4.03%
Sikkim13.32%1.00%5.42%8.20%1.42%42.20%10.42%2.34%0.65%26.71%9.52%8.38%
Tamil Nadu24.76%1.15%6.70%6.61%1.03%24.07%47.57%12.61%0.97%20.18%3.38%6.35%
Telangana31.10%1.38%10.87%15.84%2.10%31.66%49.27%27.81%1.23%25.53%12.79%7.46%
Tripura28.02%1.28%13.49%10.79%2.19%65.84%36.39%16.31%7.18%74.66%18.76%3.63%
Uttar Pradesh44.47%4.97%35.45%17.52%11.91%68.85%63.69%5.38%27.43%67.52%12.44%4.88%
Uttarakhand32.85%2.58%28.56%9.79%4.37%52.07%34.11%9.03%2.17%35.59%13.84%6.90%
West Bengal33.62%1.50%14.38%15.86%3.83%73.02%47.99%11.53%5.75%54.26%14.11%13.81%
Union Territory
Andaman & Nicobar Islands22.05%0.83%5.11%4.87%0.92%24.53%24.38%6.20%2.72%33.61%7
.10%1.57%
Chandigarh23.11%1.16%11.05%5.83%1.76%4.85%19.04%2.35%0.48%6.40%2.71%3.97%
Dadra & Nagar Haveli45.00%2.01%13.44%7.76%7.73%46.62%68.11%26.95%2.74%57.06%20.81%10.92%
Daman & Diu20.92%0.90%14.69%7.56%4.72%9.24%34.80%11.67%0.03%8.08%14.74%12.52%
Delhi23.40%1.91%15.19%5.95%2.65%2.21%26.70%23.75%0.28%10.79%5.54%8.36%
Jammu & Kashmir & Ladakh25.89%1.85%12.72%6.84%3.72%45.21%47.04%14.16%2.78%29.62%16.12%3.95%
Lakshadweep31.47%1.96%6.50%0.95%1.43%58.15%0.44%9.31%0.05%1.54%1.02%5.62%
Puducherry21.87%0.66%4.13%3.29%1.21%13.51%35.06%5.49%0.24%17.59%1.65%5.35%
India37.60%2.69%22.59%13.88%6.40%58.48%51.97% 14.60%12.16%45.65%13.97%9.66%
T
able 2- STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT State/UTData Tables
254 255
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
State/UT (Rural)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh29.49%2.10%10.58%19.82%2.52%49.42%56.18%32.74%0.91%22.30%13.48%4.08%
Arunachal Pradesh22.53%2.20%30.44%21.05%8.66%71.47%42.86%17.73%15.24%85.41%28.33%18.11%
Assam41.67%3.13%27.39%17.75%7.23%85.39%53.55%18.64%24.48%81.85%21.71%16.60%
Bihar53.69%4.80%47.38%28.13%13.03%89.55%78.10%2.30%44.01%79.94%25.66%27.45%
Chhattisgarh46.32%3.66%27.30%15.48%5.93%92.60%74.80%21.33%4.56%75.53%17.85%6.20%
Goa31.95%0.39%9.31%3.47%1.18%28.46%17.74%6.54%0.11%26.81%4.04%4.96%
Gujarat48.81%2.76%18.04%12.73%8.23%74.69%53.90%15.79%5.55%37.96%19.21%10.42%
Haryana35.70%2.28%26.34%7.18%4.09%73.03%21.37%12.47%1.50%33.01%5.63%7.48%
Himachal Pradesh27.94%1.68%18.16%3.90%0.85%73.63%28.53%7.90%0.47%31.59%7.81%2.70%
Jharkhand52.11%3.83%37.29%21.80%9.55%93.57%86.78%34.66%23.97%75.78%25.43%10.49%
Karnataka37.53%1.66%13.49%11.90%4.18%67.69%57.50%16.90%2.34%50.51%14.37%10.27%
Kerala15.65%0.16%1.51%2.05%0.58%50.35%2.39%7.00%1.05%13.00%3.78%4.54%
Madhya Pradesh49.17%4.02%32.63%19.15%9.90%90.19%78.92%37.57%11.92%79.14%24.75%12.79%
Maharashtra42.64%1.49%17.94%8.58%5.11%66.77%54.94%22.82%8.51%45.12%20.01%10.68%
Manipur25.74%2.20%22.91%7.08%2.94%72.39%46.63%64.14%9.56%88.85%18.66%25.08%
Meghalaya39.24%3.62%36.88%23.37%7.21%89.69%40.87%37.73%9.90%56.27%35.19%22.19%
Mizoram25.99%3.26%26.23%15.31%5.99%65.59%25.96%13.47%8.96%38.36%26.58%9.68%
Nagaland26.64%2.60%38.08%18.14%5.49%86.52%20.45%21.39%4.73%82.23%45.95%38.42%
Odisha39.60%2.43%20.61%18.19%5.20%89.30%76.34%22.95%14.95%61.97%21.45%11.47%
Punjab23.73%1.53%13.19%8.21%2.48%53.46%19.81%2.52%0.37%27.16%1.70%3.50%
Rajasthan45.49%3.30%28.82%19.46%9.52%85.57%62.84%31.63%11.10%43.62%25.13%4.11%
Sikkim13.83%1.20%5.10%8.98%1.46%58.85%5.33%3.02%0.44%33.57%11.73%7.97%
Tamil Nadu29.28%1.45%7.43%8.95%1.26%38.63%65.28%10.02%1.31%26.21%4.94%6.78%
Telangana36.26%1.55%11.90%21.31%2.01%50.49%60.70%33.61%1.85%38.44%18.39%6.96%
Tripura29.86%1.55%15.70%13.01%2.80%79.54%38.49%21.30%9.63%86.18%23.28%4.29%
Uttar Pradesh47.98%5.52%38.85%18.86%12.34%83.99%74.83%4.48%34.84%80.28%13.90%4.89%
Uttarakhand34.98%2.67%31.05%10.13%4.32%71.59%38.95%13.03%2.99%48.75%18.55%6.95%
West Bengal37.23%1.78%16.13%18.34%4.06%89.07%52.03%11.37%7.18%67.31%16.79%14.76%
Union Territory
Andaman & Nicobar Islands24.49%1.04%6.49%6.86%1.12%41.05%33.19%10.77%4.45%50
.83%10.96%1.68%
Chandigarh51.55%0.00%4.12%18.56%7.22%22.68%69.07%0.00%0.00%15.46%1.03%1.03%
Dadra & Nagar Haveli58.04%3.18%16.18%11.62%10.98%77.12%88.34%29.71%4.48%84.02%29.54%14.76%
Daman & Diu25.52%0.59%16.20%8.73%4.36%25.66%28.59%13.39%0.12%7.88%9.41%10.63%
Delhi26.93%0.00%22.29%2.58%0.00%24.80%11.78%35.79%0.00%6.45%6.49%6.09%
Jammu & Kashmir & Ladakh29.19%2.11%15.81%7.47%4.43%60.43%53.04%18.81%3.80%37.65%20.79%4.35%
Lakshadweep34.33%1.93%6.95%2.31%1.55%73.64%0.77%7.68%0.00%2.60%0.90%10.72%
Puducherry23.28%0.82%5.79%3.51%1.43%28.29%53.67%2.00%0.60%31.86%2.91%3.36%
India42.36%3.20%26.49%16.98%7.52%77.41%62.88%16.44%16.80%59.52%17.82%10.74%
Uncensored Headcount Ratio: UrbanHealthEducationStandard of Living
State/UT (Urban)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh18.86%1.16%7.44%9.84%1.93%10.06%22.69%32.04%0.44%6.06%4.86%6.30%
Arunachal Pradesh16.19%1.19%21.50%7.08%6.47%13.27%26.15%5.91%0.74%45.99%7.16%6.58%
Assam26.67%1.41%12.74%6.03%2.15%23.32%36.34%11.65%4.21%37.17%8.46%7.59%
Bihar39.52%3.13%33.56%13.56%9.07%37.67%42.94%2.63%11.50%31.29%15.18%16.12%
Chhattisgarh31.84%2.19%15.91%6.65%3.52%28.71%33.50%8.06%0.52%21.96%5.02%4.19%
Goa20.26%0.68%5.84%5.43%0.83%6.77%23.64%2.65%0.23%9.76%2.32%3.46%
Gujarat31.21%1.46%10.32%5.86%4.57%13.45%14.31%8.03%1.29%5.53%5.94%8.04%
Haryana26.99%1.99%19.91%6.97%3.39%16.47%15.91%12.99%0.35%10.32%3.07%9.27%
Himachal Pradesh19.67%1.43%10.06%2.54%1.28%11.46%20.04%8.08%0.69%6.73%4.68%2.63%
Jharkhand35.34%1.75%20.15%7.67%3.99%47.11%40.41%20.01%2.92%18.86%8.94%4.35%
Karnataka28.07%0.91%10.80%4.27%2.65%14.85%23.18%11.21%0.83%19.00%4.06%6.82%
Kerala14.89%0.22%1.98%1.47%0.50%36.58%1.24%4.67%0.38%8.23%1.99%4.08%
Madhya Pradesh36.47%2.57%21.44%8.56%4.67%24.83%31.75%10.86%1.68%28.22%5.99%7.11%
Maharashtra28.44%1.34%13.62%4.17%3.15%7.66%39.83%3.46%4.35%7.81%6.92%9.96%
Manipur20.12%1.18%9.36%2.65%1.45%37.65%49.33%55.75%3.77%69.86%6.45%15.93%
Meghalaya28.18%0.96%10.68%4.87%1.84%25.95%29.44%16.48%1.17%26.62%8.37%10.68%
Mizoram17.74%1.55%8.17%2.14%2.00%5.95%7.87%6.35%0.25%13.04%4.03%2.76%
Nagaland20.42%1.07%23.47%5.00%3.49%36.31%28.71%20.93%0.42%49.46%10.87%10.02%
Odisha25.17%1.19%13.74%8.75%3.65%37.90%39.98%10.80%5.23%24.06%7.73%8.25%
Punjab19.62%1.18%11.95%5.86%2.75%10.01%13.42%1.02%0.40%7.16%1.75%4.04%
Rajasthan33.69%1.87%18.58%9.72%5.25%21.30%26.26%11.65%1.34%10.41%6.06%3.78%
Sikkim12.14%0.53%6.16%6.40%1.32%3.58%22.22%0.76%1.15%10.79%4.42%9.31%
Tamil Nadu20.30%0.85%5.97%4.29%0.79%9.68%30.05%15.18%0.63%14.22%1.84%5.93%
Telangana24.36%1.17%9.53%8.69%2.22%7.05%34.33%20.24%0.42%8.66%5.46%8.10%
Tripura23.28%0.57%7.79%5.08%0.61%30.58%30.98%3.47%0.87%45.00%7.13%1.93%
Uttar Pradesh33.87%3.30%25.18%13.44%10.61%23.11%30.06%8.13%5.03%28.99%8.04%4.83%
Uttarakhand28.91%2.40%23.95%9.15%4.48%15.84%25.13%1.61%0.64%11.18%5.08%6.80%
West Bengal25.58%0.86%10.48%10.33%3.32%37.18%38.95%11.88%2.56%25.14%8.13%11.70%
Union Territory
Andaman & Nicobar Islands18.74%0.55%3.23%2.19%0.64%2.13%12.44%0.00%0
.36%10.25%1.87%1.41%
Chandigarh21.94%1.21%11.33%5.30%1.53%4.12%16.97%2.45%0.50%6.02%2.78%4.09%
Dadra & Nagar Haveli28.08%0.48%9.88%2.75%3.51%7.04%41.85%23.37%0.47%22.08%9.49%5.93%
Daman & Diu19.24%1.01%14.13%7.13%4.85%3.24%37.07%11.04%0.00%8.15%16.68%13.21%
Delhi23.37%1.93%15.13%5.97%2.67%2.02%26.82%23.65%0.28%10.83%5.53%8.38%
Jammu & Kashmir & Ladakh18.06%1.24%5.37%5.33%2.04%9.00%32.79%3.10%0.34%10.52%5.04%2.99%
Lakshadweep30.69%1.97%6.38%0.58%1.39%53.95%0.35%9.75%0.06%1.25%1.05%4.23%
Puducherry21.24%0.59%3.39%3.20%1.12%6.86%26.69%7.05%0.08%11.18%1.08%6.24%
India27.63%1.62%14.41%7.38%4.04%18.84%29.12%10.74%2.44%16.59%5.91%7.41%
T
able 3- STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL) Table 4- STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (URBAN)
INDIA MpI BASelINe reporT State/UTData Tables
256 257
Censored Headcount RatioHealthEducationStandard of Living
State/UTNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh9.22%0.87%4.76%7.72%1.47%9.72%10.50%4.39%0.60%5.69%4.82%1.74%
Arunachal Pradesh13.80%1.19%14.86%13.48%5.90%21.29%16.65%6.19%7.15%23.29%12.85%9.23%
Assam25.46%2.18%17.78%14.26%5.64%31.64%24.43%8.25%14.71%31.39%13.90%10.51%
Bihar41.60%3.92%36.51%24.70%11.62%50.20%46.57%1.64%28.79%47.10%18.69%19.60%
Chhattisgarh24.04%2.25%16.96%10.91%4.31%29.14%26.64%10.19%2.78%26.78%10.43%3.40%
Goa2.96%0.20%1.39%2.24%0.59%2.06%2.81%0.30%0.00%1.83%0.86%0.79%
Gujarat15.42%1.11%8.74%6.68%4.79%17.26%15.51%4.74%2.90%11.41%8.20%4.36%
Haryana10.41%1.21%9.42%4.61%2.83%10.17%6.20%3.38%0.75%7.44%2.57%2.92%
Himachal Pradesh6.79%0.59%5.73%1.47%0.43%7.14%4.82%1.39%0.24%5.24%2.16%0.65%
Jharkhand34.42%2.74%26.51%16.46%7.17%41.25%39.40%17.47%13.58%35.93%15.54%6.63%
Karnataka10.08%0.71%5.35%5.45%2.36%11.58%11.04%3.46%0.97%9.43%4.99%3.39%
Kerala0.56%0.00%0.15%0.19%0.22%0.58%0.30%0.14%0.20%0.40%0.32%0.17%
Madhya Pradesh29.05%2.72%20.87%14.02%7.34%34.90%33.21%17.56%6.47%32.73%13.65%7.36%
Maharashtra12.39%0.83%7.13%4.27%2.96%12.46%12.50%5.27%3.14%10.12%6.72%3.80%
Manipur13.41%0.97%10.14%4.65%1.77%16.28%11.68%13.59%3.49%17.26%6.91%8.93%
Meghalaya23.84%2.11%22.48%16.68%5.34%31.82%18.58%13.65%6.43%23.33%19.41%12.94%
Mizoram6.20%0.63%5.98%5.46%2.32%8.68%5.70%2.80%2.98%7.60%6.65%2.68%
Nagaland17.16%1.38%18.34%11.28%3.67%23.97%8.71%6.90%2.50%23.98%16.67%15.82%
Odisha22.41%1.51%12.77%13.77%4.32%28.77%27.14%9.89%8.93%24.87%13.29%6.49%
Punjab4.41%0.50%3.09%3.41%1.41%4.23%3.02%0.34%0.22%3.32%0.60%1.03%
Rajasthan23.25%2.09%17.08%13.37%7.24%27.69%24.77%13.11%6.60%18.71%13.27%2.22%
Sikkim2.87%0.25%1.75%2.48%0.36%2.89%1.13%0.19%0.07%2.30%1.84%1.10%
Tamil Nadu3.64%0.30%1.70%2.27%0.45%3.63%4.53%1.21%0.43%2.64%1.35%1.48%
Telangana10.15%0.78%5.10%8.50%1.14%10.49%12.22%4.45%0.87%8.32%5.99%2.73%
Tripura12.01%0.88%7.79%8.10%1.67%15.50%11.08%7.26%4.30%16.19%9.42%2.20%
Uttar Pradesh30.48%3.82%25.29%15.08%9.97%34.26%31.80%2.37%18.35%33.43%8.87%3.34%
Uttarakhand14.66%1.63%13.06%6.74%3.18%15.80%11.22%3.21%1.39%12.35%6.21%3.23%
West Bengal16.22%1.02%9.44%11.25%2.82%20.81%16.93%4.35%3.74%18.81%8.66%7.14%
Union Territory
Andaman & Nicobar Islands3.41%0.31%1.02%1.95%0.27%3.15%2.99%1.18%1.55%3.51%2.15%0
.11%
Chandigarh4.90%0.52%3.20%3.23%1.46%3.26%4.84%1.30%0.48%2.48%1.20%0.81%
Dadra & Nagar Haveli24.22%1.28%8.46%6.93%6.56%24.07%26.29%10.17%2.12%25.46%12.60%6.38%
Daman & Diu5.40%0.33%3.90%3.20%3.09%1.44%4.55%1.42%0.00%1.08%3.19%3.25%
Delhi3.92%0.71%3.34%2.54%1.15%0.62%3.58%1.66%0.06%1.48%1.80%1.28%
Jammu & Kashmir & Ladakh9.70%0.85%6.10%4.47%2.49%11.25%10.62%4.97%1.67%9.44%6.65%1.43%
Lakshadweep1.82%0.53%0.86%0.00%0.64%1.11%0.14%0.57%0.00%0.32%0.14%0.52%
Puducherry1.33%0.28%0.44%0.89%0.01%1.35%1.42%0.11%0.11%1.10%0.42%0.37%
India19.90%1.88%14.71%10.71%5.23%23.13%21.32%5.53%8.29%20.56%8.87%5.37%
Censored Headcount Ratio: RuralHealthEducationStandard of Living
State/UT (Rural)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh11.29%1.09%6.03%9.56%1.68%12.80%13.57%5.71%0.74%7.51%6.28%1.83%
Arunachal Pradesh16.34%1.50%17.71%16.44%6.86%26.65%20.27%7.71%9.20%28.42%16.08%11.48%
Assam28.14%2.40%19.76%15.82%6.31%35.47%27.05%9.14%16.59%34.95%15.47%11.63%
Bihar44.94%4.21%39.32%26.69%12.29%54.70%50.70%1.75%31.89%51.58%19.99%21.01%
Chhattisgarh28.60%2.68%20.31%12.91%4.99%35.41%32.25%12.51%3.50%32.78%12.85%4.09%
Goa3.82%0.12%1.52%1.56%0.44%3.68%3.24%0.79%0.00%3.44%1.36%0.94%
Gujarat22.73%1.67%12.98%9.17%6.41%26.76%24.03%7.51%4.43%18.47%12.64%6.00%
Haryana12.64%1.42%11.61%4.90%3.29%13.93%7.26%4.28%1.06%9.97%3.18%3.09%
Himachal Pradesh7.34%0.62%6.19%1.56%0.43%7.83%5.18%1.46%0.25%5.73%2.33%0.68%
Jharkhand41.42%3.33%31.97%19.99%8.68%50.18%48.17%21.12%17.49%44.94%19.18%8.08%
Karnataka14.44%0.98%7.48%7.79%3.09%17.59%16.41%5.29%1.38%14.28%7.50%4.96%
Kerala0.72%0.01%0.17%0.26%0.25%0.84%0.47%0.22%0.35%0.62%0.54%0.25%
Madhya Pradesh36.18%3.22%26.07%17.31%9.02%45.21%42.65%23.08%8.65%42.47%17.95%9.10%
Maharashtra18.74%0.94%10.55%6.28%4.18%21.48%19.49%9.22%5.16%17.22%10.89%5.12%
Manipur16.88%1.21%14.09%6.25%2.30%21.33%14.24%17.50%4.78%22.28%9.82%11.43%
Meghalaya27.99%2.54%26.94%19.96%6.31%38.01%21.94%16.33%7.81%27.76%23.48%15.24%
Mizoram12.82%1.26%12.55%11.54%4.80%18.70%12.22%6.15%6.68%16.25%14.31%5.68%
Nagaland21.65%1.83%23.60%15.31%4.75%32.16%10.11%8.42%3.68%31.44%23.31%21.84%
Odisha25.04%1.68%14.21%15.23%4.62%32.21%30.35%11.11%9.99%27.95%14.93%7.05%
Punjab5.18%0.54%3.50%3.74%1.24%5.45%3.53%0.41%0.16%4.31%0.59%1.02%
Rajasthan27.62%2.48%20.33%15.75%8.33%34.28%30.28%16.29%8.46%23.50%16.59%2.50%
Sikkim3.04%0.35%1.88%2.65%0.33%3.99%0.85%0.27%0.05%2.91%2.39%1.32%
Tamil Nadu5.32%0.46%2.39%3.49%0.56%6.01%6.99%1.75%0.71%4.17%2.23%2.20%
Telangana14.87%1.00%7.41%12.50%1.36%16.63%18.34%6.91%1.25%13.28%9.38%3.70%
Tripura14.76%1.16%9.82%10.14%2.27%19.82%13.90%9.84%5.85%20.61%12.17%2.83%
Uttar Pradesh35.94%4.39%29.93%16.77%10.75%41.98%38.83%2.61%23.31%40.85%10.28%3.62%
Uttarakhand18.32%1.71%16.18%7.01%3.21%20.81%14.15%4.61%1.93%17.20%8.33%3.48%
West Bengal19.45%1.27%11.34%13.44%3.13%25.49%20.69%4.90%4.57%23.42%10.49%8.27%
Union Territory
Andaman & Nicobar Islands5.22%0.53%1.77%2.77%0.46%5.37%4.94%2.05%2.48%5.7
4%3.63%0.19%
Chandigarh17.53%0.00%4.12%14.43%7.22%16.49%18.56%0.00%0.00%5.15%1.03%1.03%
Dadra & Nagar Haveli39.75%2.08%13.36%11.09%10.11%41.77%42.99%16.31%3.76%43.32%21.36%10.61%
Daman & Diu3.43%0.00%2.47%3.31%1.61%2.58%3.44%0.39%0.00%0.91%2.29%2.44%
Delhi3.12%0.00%3.12%0.29%0.00%2.39%2.68%1.25%0.00%0.00%2.46%1.25%
Jammu & Kashmir & Ladakh12.63%1.04%8.23%5.40%3.17%15.21%14.07%6.90%2.24%12.83%8.90%1.84%
Lakshadweep1.16%0.00%0.00%0.00%1.16%1.16%0.00%1.16%0.00%0.00%0.00%0.00%
Puducherry2.60%0.36%1.12%0.94%0.00%3.20%3.17%0.12%0.19%2.36%0.75%0.96%
India26.01%2.40%19.33%13.75%6.49%31.47%28.60%7.40%11.64%28.09%11.86%6.84%
T
able 5- STATE/UT-WISE: CENSORED HEADCOUNT RATIO Table 6- STATE/UT-WISE: CENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT State/UTData Tables
258 259
Censored Headcount Ratio: UrbanHealthEducationStandard of Living
State/UT (Urban)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh4.21%0.36%1.71%3.29%0.94%2.25%3.09%1.19%0.27%1.30%1.30%1.52%
Arunachal Pradesh5.53%0.18%5.60%3.83%2.79%3.84%4.88%1.24%0.50%6.59%2.33%1.90%
Assam8.02%0.78%4.92%4.15%1.28%6.76%7.40%2.47%2.49%8.27%3.69%3.26%
Bihar18.83%1.91%17.32%11.15%7.09%19.45%18.36%0.90%7.59%16.51%9.88%9.97%
Chhattisgarh8.59%0.80%5.62%4.15%2.02%7.89%7.64%2.32%0.33%6.48%2.21%1.08%
Goa2.44%0.24%1.31%2.65%0.67%1.08%2.55%0.00%0.00%0.87%0.56%0.70%
Gujarat5.44%0.34%2.95%3.28%2.58%4.31%3.88%0.98%0.81%1.78%2.13%2.12%
Haryana6.86%0.87%5.92%4.14%2.10%4.17%4.52%1.94%0.24%3.41%1.59%2.64%
Himachal Pradesh1.37%0.27%1.16%0.65%0.42%0.26%1.24%0.72%0.15%0.43%0.55%0.41%
Jharkhand12.95%0.95%9.76%5.62%2.55%13.87%12.50%6.27%1.59%8.30%4.36%2.17%
Karnataka4.04%0.34%2.41%2.20%1.33%3.24%3.61%0.92%0.41%2.71%1.51%1.21%
Kerala0.38%0.00%0.12%0.10%0.20%0.28%0.10%0.05%0.03%0.14%0.08%0.08%
Madhya Pradesh11.57%1.51%8.12%5.95%3.24%9.63%10.09%4.04%1.15%8.86%3.10%3.10%
Maharashtra4.97%0.70%3.12%1.92%1.54%1.93%4.33%0.65%0.77%1.84%1.85%2.25%
Manipur7.91%0.59%3.90%2.14%0.93%8.30%7.64%7.41%1.44%9.34%2.32%4.97%
Meghalaya7.01%0.35%4.42%3.37%1.40%6.75%4.97%2.77%0.87%5.39%2.92%3.62%
Mizoram1.00%0.14%0.83%0.69%0.37%0.82%0.57%0.17%0.08%0.80%0.63%0.32%
Nagaland8.57%0.54%8.28%3.57%1.62%8.33%6.05%3.99%0.25%9.71%3.98%4.31%
Odisha8.89%0.64%5.39%6.27%2.77%11.07%10.60%3.58%3.44%8.98%4.85%3.62%
Punjab3.23%0.45%2.45%2.89%1.67%2.35%2.22%0.25%0.31%1.79%0.61%1.03%
Rajasthan9.65%0.90%6.98%5.96%3.86%7.17%7.64%3.23%0.84%3.80%2.93%1.32%
Sikkim2.48%0.00%1.46%2.09%0.43%0.33%1.78%0.00%0.13%0.90%0.56%0.57%
Tamil Nadu1.99%0.15%1.01%1.06%0.35%1.28%2.10%0.68%0.15%1.13%0.48%0.78%
Telangana3.99%0.50%2.08%3.27%0.85%2.48%4.21%1.23%0.36%1.84%1.55%1.47%
Tripura4.94%0.17%2.58%2.84%0.15%4.37%3.82%0.60%0.33%4.81%2.32%0.57%
Uttar Pradesh13.99%2.08%11.29%9.96%7.63%10.95%10.56%1.63%3.35%11.02%4.62%2.50%
Uttarakhand7.87%1.47%7.26%6.25%3.13%6.49%5.77%0.61%0.39%3.36%2.27%2.76%
West Bengal9.00%0.46%5.20%6.36%2.12%10.36%8.54%3.14%1.89%8.51%4.58%4.62%
Union Territory
Andaman & Nicobar Islands0.97%0.00%0.00%0.83%0.00%0.14%0.35%0.00%0
.28%0.48%0.14%0.00%
Chandigarh4.38%0.54%3.17%2.76%1.22%2.71%4.28%1.35%0.50%2.37%1.21%0.80%
Dadra & Nagar Haveli4.05%0.25%2.11%1.53%1.95%1.10%4.63%2.19%0.00%2.27%1.24%0.90%
Daman & Diu6.12%0.45%4.42%3.16%3.64%1.02%4.96%1.80%0.00%1.13%3.52%3.54%
Delhi3.93%0.72%3.35%2.56%1.16%0.61%3.59%1.66%0.06%1.49%1.80%1.28%
Jammu & Kashmir & Ladakh2.75%0.41%1.01%2.24%0.88%1.83%2.44%0.40%0.29%1.36%1.29%0.46%
Lakshadweep2.00%0.67%1.09%0.00%0.50%1.09%0.17%0.41%0.00%0.41%0.17%0.66%
Puducherry0.75%0.25%0.13%0.86%0.01%0.52%0.63%0.10%0.07%0.53%0.27%0.10%
India7.10%0.80%5.04%4.35%2.58%5.68%6.09%1.61%1.29%4.80%2.61%2.29%
Indicator ContributionHealthEducationStandard of Living
State/UTNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh28.88%1.37%7.46%24.19%4.59%8.69%9.40%3.92%0.54%5.09%4.31%1.56%
Arunachal Pradesh20.05%0.87%10.80%19.58%8.58%8.84%6.91%2.57%2.97%9.67%5.33%3.83%
Assam27.12%1.16%9.47%15.20%6.00%9.63%7.44%2.51%4.48%9.56%4.23%3.20%
Bihar26.19%1.23%11.49%15.55%7.32%9.03%8.38%0.29%5.18%8.47%3.36%3.52%
Chhattisgarh30.01%1.40%10.59%13.62%5.38%10.39%9.50%3.63%0.99%9.55%3.72%1.21%
Goa32.65%1.09%7.68%24.78%6.50%6.49%8.87%0.94%0.00%5.79%2.71%2.50%
Gujarat30.71%1.10%8.70%13.30%9.55%9.82%8.83%2.70%1.65%6.49%4.66%2.48%
Haryana31.83%1.85%14.39%14.08%8.65%8.88%5.42%2.95%0.65%6.50%2.24%2.55%
Himachal Pradesh37.65%1.63%15.88%8.16%2.38%11.31%7.64%2.20%0.38%8.30%3.43%1.04%
Jharkhand28.40%1.13%10.94%13.58%5.92%9.73%9.29%4.12%3.20%8.47%3.66%1.56%
Karnataka29.88%1.05%7.94%16.15%6.98%9.81%9.35%2.93%0.82%7.99%4.22%2.87%
Kerala33.98%0.14%4.48%11.28%13.61%10.01%5.15%2.42%3.49%6.87%5.60%2.97%
Madhya Pradesh27.96%1.31%10.04%13.49%7.07%9.60%9.13%4.83%1.78%9.00%3.75%2.02%
Maharashtra31.74%1.06%9.13%10.94%7.60%9.12%9.15%3.86%2.30%7.41%4.92%2.78%
Manipur28.10%1.01%10.63%9.75%3.71%9.75%6.99%8.14%2.09%10.34%4.14%5.35%
Meghalaya25.30%1.12%11.93%17.70%5.67%9.65%5.64%4.14%1.95%7.08%5.89%3.93%
Mizoram22.23%1.13%10.73%19.59%8.31%8.90%5.84%2.87%3.06%7.79%6.81%2.75%
Nagaland24.47%0.99%13.08%16.08%5.24%9.77%3.55%2.81%1.02%9.77%6.79%6.45%
Odisha27.41%0.92%7.81%16.85%5.29%10.06%9.48%3.46%3.12%8.69%4.64%2.27%
Punjab30.09%1.72%10.53%23.22%9.60%8.25%5.87%0.67%0.43%6.47%1.16%2.00%
Rajasthan27.73%1.25%10.19%15.95%8.64%9.44%8.44%4.47%2.25%6.38%4.52%0.76%
Sikkim30.44%1.31%9.28%26.33%3.79%8.76%3.42%0.57%0.22%6.98%5.58%3.33%
Tamil Nadu31.06%1.29%7.23%19.34%3.86%8.84%11.03%2.95%1.05%6.43%3.29%3.62%
Telangana28.51%1.10%7.16%23.86%3.19%8.42%9.80%3.57%0.69%6.68%4.80%2.19%
Tripura26.72%0.98%8.66%18.01%3.72%9.85%7.04%4.61%2.74%10.29%5.98%1.40%
Uttar Pradesh28.25%1.77%11.72%13.97%9.24%9.07%8.42%0.63%4.86%8.85%2.35%0.88%
Uttarakhand31.07%1.72%13.83%14.29%6.75%9.57%6.79%1.94%0.84%7.48%3.76%1.95%
West Bengal27.73%0.87%8.07%19.23%4.82%10.16%8.27%2.13%1.83%9.19%4.23%3.49%
Union Territory
Andaman & Nicobar Islands32.60%1.46%4.87%18.61%2.55%8.59%8.16%3.21%4.22%9
.57%5.86%0.30%
Chandigarh31.53%1.67%10.30%20.74%9.38%5.99%8.90%2.38%0.88%4.55%2.20%1.48%
Dadra & Nagar Haveli33.09%0.88%5.78%9.47%8.96%9.40%10.27%3.97%0.83%9.94%4.92%2.49%
Daman & Diu29.87%0.90%10.78%17.72%17.12%2.27%7.20%2.25%0.00%1.70%5.05%5.14%
Delhi31.03%2.82%13.22%20.12%9.11%1.40%8.09%3.75%0.14%3.34%4.08%2.89%
Jammu & Kashmir & Ladakh29.15%1.28%9.16%13.43%7.48%9.66%9.12%4.27%1.43%8.10%5.71%1.23%
Lakshadweep46.10%6.64%10.88%0.00%16.21%8.00%0.98%4.12%0.00%2.32%0.98%3.77%
Puducherry33.36%3.56%5.54%22.33%0.22%9.73%10.18%0.76%0.77%7.89%3.03%2.63%
India28.14%1.33% 10.40%15.14%7.39%9.34%8.61%2.23%3.35%8.31%3.58%2.17%
T
able 7- STATE/UT-WISE: CENSORED HEADCOUNT RATIO (URBAN) Table 8- STATE/UT-WISE: INDICATOR CONTRIBUTION TO THE MPI SCORE
INDIA MpI BASelINe reporT State/UTData Tables
260 261
Indicator Contribution: RuralHealthEducationStandard of Living
State/UT (Rural)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh28.28%1.36%7.55%23.94%4.21%9.16%9.71%4.08%0.53%5.37%4.49%1.31%
Arunachal Pradesh19.57%0.90%10.61%19.69%8.22%9.12%6.94%2.64%3.15%9.73%5.50%3.93%
Assam26.98%1.15%9.47%15.17%6.05%9.72%7.41%2.50%4.55%9.58%4.24%3.19%
Bihar26.15%1.23%11.44%15.53%7.15%9.09%8.43%0.29%5.30%8.58%3.32%3.49%
Chhattisgarh29.76%1.39%10.57%13.43%5.19%10.53%9.59%3.72%1.04%9.74%3.82%1.22%
Goa36.42%0.56%7.25%14.87%4.23%10.02%8.85%2.16%0.00%9.38%3.70%2.56%
Gujarat30.65%1.13%8.75%12.36%8.65%10.31%9.26%2.89%1.71%7.11%4.87%2.31%
Haryana31.95%1.80%14.68%12.39%8.31%10.06%5.24%3.09%0.77%7.20%2.30%2.23%
Himachal Pradesh37.77%1.59%15.93%8.00%2.22%11.52%7.62%2.14%0.37%8.42%3.42%1.00%
Jharkhand28.08%1.13%10.84%13.55%5.88%9.72%9.33%4.09%3.39%8.70%3.72%1.57%
Karnataka29.58%1.00%7.66%15.96%6.34%10.30%9.61%3.10%0.81%8.36%4.39%2.91%
Kerala31.98%0.19%3.73%11.49%11.04%10.56%5.94%2.79%4.42%7.84%6.84%3.18%
Madhya Pradesh27.58%1.23%9.94%13.20%6.87%9.85%9.29%5.03%1.88%9.25%3.91%1.98%
Maharashtra31.10%0.78%8.76%10.42%6.94%10.19%9.24%4.37%2.45%8.16%5.16%2.43%
Manipur27.21%0.97%11.35%10.07%3.71%9.82%6.56%8.06%2.20%10.26%4.52%5.26%
Meghalaya24.98%1.14%12.02%17.82%5.63%9.70%5.60%4.17%1.99%7.08%5.99%3.89%
Mizoram21.76%1.07%10.65%19.58%8.14%9.07%5.93%2.98%3.24%7.88%6.94%2.76%
Nagaland23.58%0.99%12.85%16.67%5.17%10.00%3.14%2.62%1.15%9.78%7.25%6.79%
Odisha27.51%0.92%7.80%16.74%5.08%10.11%9.53%3.49%3.14%8.78%4.69%2.21%
Punjab31.21%1.61%10.55%22.53%7.46%9.38%6.08%0.70%0.28%7.42%1.01%1.76%
Rajasthan27.41%1.23%10.08%15.63%8.27%9.72%8.58%4.62%2.40%6.66%4.70%0.71%
Sikkim28.94%1.68%8.93%25.26%3.12%10.87%2.31%0.73%0.13%7.92%6.52%3.60%
Tamil Nadu30.10%1.30%6.76%19.76%3.17%9.72%11.30%2.83%1.15%6.75%3.60%3.56%
Telangana28.16%0.95%7.02%23.68%2.57%9.00%9.93%3.74%0.68%7.19%5.08%2.00%
Tripura25.92%1.02%8.62%17.80%3.98%9.94%6.97%4.94%2.93%10.34%6.11%1.42%
Uttar Pradesh28.36%1.73%11.80%13.23%8.48%9.46%8.75%0.59%5.25%9.21%2.32%0.82%
Uttarakhand31.78%1.48%14.04%12.17%5.57%10.32%7.01%2.29%0.96%8.52%4.13%1.73%
West Bengal27.68%0.90%8.07%19.13%4.45%10.36%8.41%1.99%1.86%9.52%4.27%3.36%
Union Territory
Andaman & Nicobar Islands31.48%1.60%5.35%16.73%2.80%9.25%8.52%3.53%4.27%9
.89%6.25%0.33%
Chandigarh32.87%0.00%3.87%27.07%13.54%8.84%9.94%0.00%0.00%2.76%0.55%0.55%
Dadra & Nagar Haveli33.09%0.87%5.56%9.23%8.41%9.94%10.22%3.88%0.89%10.30%5.08%2.52%
Daman & Diu26.33%0.00%9.47%25.39%12.37%5.66%7.54%0.85%0.00%2.01%5.02%5.36%
Delhi39.81%0.00%19.91%3.73%0.00%8.71%9.78%4.56%0.00%0.00%8.95%4.56%
Jammu & Kashmir & Ladakh29.00%1.19%9.45%12.41%7.28%9.98%9.23%4.52%1.47%8.42%5.84%1.20%
Lakshadweep38.89%0.00%0.00%0.00%38.89%11.11%0.00%11.11%0.00%0.00%0.00%0.00%
Puducherry35.33%2.45%7.64%12.81%0.00%12.43%12.34%0.45%0.73%9.19%2.92%3.72%
India27.94%1.29%10.38%14.77%6.98%9.66%8.78%2.27%3.57%8.62%3.64%2.10%
Indicator Contribution: UrbanHealthEducationStandard of Living
State/UT (Urban)Nutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
State
Andhra Pradesh33.43%1.44%6.77%26.10%7.50%5.11%7.01%2.69%0.62%2.94%2.95%3.44%
Arunachal Pradesh26.20%0.42%13.27%18.13%13.19%5.20%6.60%1.68%0.67%8.92%3.15%2.57%
Assam30.71%1.49%9.42%15.89%4.89%7.40%8.10%2.71%2.73%9.05%4.04%3.57%
Bihar26.79%1.36%12.31%15.86%10.09%7.90%7.46%0.36%3.09%6.71%4.02%4.05%
Chhattisgarh33.09%1.53%10.82%16.00%7.79%8.68%8.41%2.56%0.36%7.14%2.43%1.19%
Goa29.76%1.49%8.01%32.40%8.23%3.78%8.88%0.00%0.00%3.03%1.95%2.45%
Gujarat31.05%0.97%8.42%18.71%14.74%7.02%6.33%1.59%1.32%2.91%3.48%3.46%
Haryana31.50%2.00%13.58%19.00%9.63%5.47%5.93%2.55%0.31%4.48%2.09%3.46%
Himachal Pradesh32.47%3.24%13.66%15.39%9.85%1.74%8.36%4.86%1.04%2.93%3.70%2.77%
Jharkhand31.98%1.18%12.05%13.88%6.30%9.79%8.82%4.43%1.12%5.85%3.08%1.53%
Karnataka31.46%1.34%9.39%17.15%10.38%7.22%8.02%2.05%0.92%6.03%3.35%2.70%
Kerala39.37%0.00%6.50%10.74%20.50%8.52%3.02%1.44%1.00%4.26%2.26%2.39%
Madhya Pradesh31.26%2.04%10.97%16.08%8.77%7.44%7.79%3.12%0.89%6.85%2.40%2.40%
Maharashtra34.91%2.45%10.98%13.49%10.84%3.87%8.69%1.31%1.55%3.70%3.71%4.51%
Manipur31.58%1.17%7.79%8.53%3.71%9.47%8.71%8.44%1.64%10.65%2.64%5.67%
Meghalaya31.91%0.79%10.06%15.34%6.38%8.78%6.47%3.61%1.13%7.02%3.80%4.71%
Mizoram28.42%2.05%11.78%19.73%10.47%6.65%4.64%1.36%0.65%6.53%5.12%2.59%
Nagaland29.93%0.95%14.46%12.47%5.66%8.31%6.04%3.98%0.25%9.68%3.97%4.30%
Odisha26.04%0.94%7.90%18.37%8.12%9.27%8.88%3.00%2.88%7.52%4.07%3.03%
Punjab27.62%1.94%10.47%24.73%14.30%5.75%5.43%0.60%0.77%4.37%1.49%2.53%
Rajasthan31.03%1.45%11.21%19.17%12.40%6.58%7.02%2.97%0.77%3.49%2.69%1.21%
Sikkim35.71%0.00%10.51%30.08%6.15%1.37%7.30%0.00%0.54%3.68%2.30%2.36%
Tamil Nadu33.91%1.28%8.64%18.10%5.94%6.21%10.23%3.32%0.75%5.48%2.34%3.79%
Telangana30.35%1.91%7.91%24.83%6.46%5.38%9.15%2.67%0.78%4.00%3.37%3.20%
Tripura35.02%0.59%9.14%20.15%1.07%8.85%7.73%1.21%0.67%9.73%4.70%1.15%
Uttar Pradesh27.42%2.04%11.06%19.53%14.96%6.13%5.91%0.91%1.88%6.17%2.58%1.40%
Uttarakhand28.31%2.64%13.05%22.48%11.26%6.68%5.93%0.62%0.40%3.46%2.34%2.83%
West Bengal27.93%0.71%8.08%19.75%6.59%9.19%7.57%2.78%1.68%7.55%4.06%4.10%
Union Territory
Andaman & Nicobar Islands44.14%0.00%0.00%37.90%0.00%1.78%4.50%0.00%3.66%6.23%1.78%0.0
0%
Chandigarh31.32%1.93%11.31%19.74%8.72%5.54%8.73%2.76%1.02%4.83%2.46%1.63%
Dadra & Nagar Haveli33.13%1.01%8.62%12.49%15.94%2.57%10.82%5.13%0.00%5.31%2.89%2.09%
Daman & Diu30.72%1.12%11.09%15.88%18.26%1.46%7.11%2.59%0.00%1.63%5.06%5.08%
Delhi30.98%2.84%13.19%20.20%9.16%1.36%8.09%3.75%0.14%3.35%4.05%2.88%
Jammu & Kashmir & Ladakh30.97%2.29%5.69%25.25%9.85%5.88%7.84%1.28%0.94%4.38%4.16%1.47%
Lakshadweep47.49%7.92%12.98%0.00%11.84%7.40%1.16%2.77%0.00%2.77%1.16%4.49%
Puducherry30.72%5.04%2.72%35.17%0.51%6.10%7.28%1.18%0.82%6.14%3.18%1.16%
India29.70%1.67%10.53%18.17% 10.80%6.79%7.27%1.92%1.54%5.74%3.12%2.74%
T
able 10- STATE/UT-WISE: INDICATOR CONTRIBUTION TO THE MPI SCORE (URBAN)Table 9- STATE/UT-WISE: INDICATOR CONTRIBUTION TO THE MPI SCORE (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
262 263
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Andhra Pradesh
Anantapur33.87%2.20%10.29%13.99%1.71%33.88%53.96%43.07%0.59%30.34%10.26%4.67%
Chittoor28.91%0.60%12.54%12.66%1.32%38.05%59.70%38.08%0.73%9.90%9.69%3.05%
East Godavari22.26%1.48%10.07%16.32%1.06%49.71%42.30%22.29%0.29%14.07%6.88%5.72%
Guntur16.25%1.50%8.20%19.56%1.43%18.26%34.80%32.38%0.50%17.46%11.95%6.01%
Krishna21.50%0.95%5.28%16.19%1.07%27.20%36.14%35.85%0.66%22.13%8.04%3.88%
Kurnool37.46%2.88%15.54%18.82%6.79%37.87%48.17%24.54%0.11%29.00%9.46%4.11%
Prakasam27.83%3.38%10.89%22.69%3.86%33.27%45.69%49.15%1.22%12.89%13.01%5.18%
SPSR Nellore23.19%1.29%6.00%15.98%2.71%42.83%46.78%36.17%1.79%11.73%10.17%6.97%
Srikakulam29.72%2.63%8.95%13.55%1.56%62.95%66.66%26.98%1.85%15.29%16.07%2.25%
Visakhapatnam25.93%1.99%10.33%15.22%2.96%46.97%47.00%21.42%1.00%14.41%15.97%4.69%
Vizianagaram35.38%1.67%9.98%19.64%1.07%52.52%69.73%20.70%1.13%17.16%17.57%5.01%
West Godavari19.07%1.72%7.05%19.27%2.02%37.46%38.91%30.44%0.85%18.37%10.47%5.57%
Y.S.R. Kadapa29.77%2.04%9.66%16.24%2.95%24.52%29.53%46.98%0.36%9.61%8.28%3.73%
Arunachal Pradesh
Anjaw11.78%1.51%28.62%19.76%4.87%79.70%56.77%14.31%10.25%85.95%32.81%7.42%
Changlang20.14%2.03%24.20%17.67%11.17%77.55%43.83%33.20%22.76%81.53%16.79%11.74%
Dibang Valley17.42%0.93%23.47%18.46%3.98%62.95%20.46%1.56%8.22%72.02%28.42%9.17%
East Kameng27.87%2.23%45.35%20.40%12.45%63.14%63.51%20.75%48.81%87.39%54.87%34.79%
East Siang13.48%0.28%20.02%9.82%4.61%59.68%26.63%2.00%4.07%73.30%8.44%3.40%
Kurung Kumey29.20%2.62%40.50%29.65%9.68%60.80%54.15%7.17%8.14%91.80%41.80%29.10%
Lohit28.43%4.26%28.82%22.47%10.31%78.52%37.60%9.78%20.75%81.90%21.35%16.63%
Lower Dibang Valley19.67%2.42%26.84%19.96%6.79%59.78%31.17%5.71%28.63%75.94%10.79%14.63%
Lower Subansiri16.54%0.82%19.91%17.08%6.11%35.09%27.37%2.45%1.19%74.01%14.69%21.31%
Papum Pare19.24%1.63%29.26%9.93%8.65%16.31%29.75%10.47%1.46%51.76%9.56%10.37%
Tawang12.10%1.94%32.17%40.75%6.95%55.32%43.37%6.25%3.43%65.79%33.90%26.74%
Tirap24.17%2.99%25.97%16.77%9.08%81.59%46.58%35.73%1.73%85.02%34.09%5.75%
Upper Siang15.09%0.34%18.41%15.12%4.47%79.97%44.02%10.87%6.84%83.39%26.13%10.54%
Upper Subansiri23.57%0.72%33.67%21.86%5.35%59.10%38.42%12.85%11.58%88.28%36.96%25.73%
West Kameng19.10%0.89%28.91%19.09%7.08%40.17%40.77%5.85%1.35%61.81%22.70%17.21%
West Siang14.33%0.97%22.58%10.81%2.97%53.30%25.23%12.65%4.87%80
.05%20.98%13.74%
Assam
Baksa32.81%2.43%23.76%10.26%2.41%85.37%42.42%18.23%16.86%81.64%9.76%10.99%
Barpeta44.84%2.78%29.85%17.29%6.03%79.56%63.95%2.13%27.56%82.97%18.85%21.59%
Bongaigaon37.60%1.83%36.12%16.85%6.06%75.01%53.21%25.22%10.87%74.03%13.58%15.27%
Cachar46.65%4.05%23.40%14.78%8.19%73.16%62.27%46.16%33.67%72.85%33.48%10.30%
Chirang37.14%2.24%30.64%15.75%3.09%83.13%67.04%29.83%27.87%82.65%13.24%17.67%
Darrang44.35%3.04%30.41%23.16%10.66%84.13%53.93%3.05%24.11%81.53%15.31%20.21%
Dhemaji33.59%3.02%23.92%8.21%5.08%90.32%56.42%8.47%36.34%87.48%18.70%20.13%
Dhubri47.54%2.36%37.89%27.31%13.27%85.40%66.68%10.64%29.16%85.37%32.27%35.79%
Dibrugarh40.83%1.44%17.38%16.84%4.84%72.23%40.12%1.31%23.62%70.20%19.00%14.97%
Dima Hasao30.40%3.82%30.09%14.32%5.63%76.28%38.85%54.47%20.68%73.29%45.91%13.92%
Goalpara43.57%2.74%33.13%22.59%10.45%81.86%53.19%13.38%30.37%75.14%24.27%18.53%
Golaghat36.63%2.43%15.18%10.69%3.17%85.55%36.54%6.10%11.48%80.98%12.77%12.14%
Hailakandi47.35%4.19%33.59%17.02%10.32%87.52%63.83%57.72%44.25%86.58%43.96%7.41%
Jorhat38.59%1.82%10.44%7.54%4.66%70.33%34.92%13.01%12.73%63.51%10.62%9.75%
Kamrup33.95%1.85%23.89%12.80%4.93%63.18%45.24%6.77%9.94%74.05%14.60%23.16%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Assam
Kamrup Metropolitan25.22%1.46%15.08%8.85%1.68%21.52%36.33%21.69%4.98%36.33%9.25%9.66%
Karbi Anglong31.96%4.27%32.54%19.99%7.82%87.98%56.52%48.73%17.85%88.19%19.82%12.95%
Karimganj49.58%5.55%38.08%13.53%8.22%78.46%60.01%44.55%27.26%71.39%39.83%8.44%
Kokrajhar31.81%2.76%27.44%18.49%4.62%83.21%59.44%23.26%25.09%77.86%17.58%16.68%
Lakhimpur32.70%2.31%19.36%9.08%4.46%83.82%51.62%26.22%24.20%78.82%18.33%11.43%
Marigaon43.42%5.39%28.87%18.53%7.03%83.67%58.03%2.69%21.52%85.22%19.70%16.45%
Nagaon41.11%3.61%28.17%15.42%4.51%81.96%52.83%8.72%16.19%79.07%17.38%8.76%
Nalbari31.85%2.19%20.88%9.08%3.00%66.17%46.45%1.87%16.59%75.25%10.13%10.46%
Sivasagar42.07%3.08%12.51%13.38%6.49%74.78%42.09%5.22%19.15%72.60%18.20%12.94%
Sonitpur32.98%2.29%21.34%16.73%5.18%77.55%37.50%29.52%20.75%69.82%10.67%8.69%
Tinsukia45.42%2.75%18.52%27.16%12.45%73.60%48.84%4.12%23.09%69.80%24.67%25.80%
Udalguri36.50%2.43%27.21%17.00%4.95%86.83%44.53%18.87%14.33%83.41%8.53%11.66%
Bihar
Araria58.06%7.48%46.80%42.27%19.07%94.86%86.28%0.53%50.16%89.42%26.80%37.60%
Arwal55.28%4.23%45.55%16.08%7.63%90.98%77.36%0.75%51.87%77.43%30.88%16.25%
Aurangabad47.96%3.56%38.83%13.61%8.54%84.52%72.48%1.70%39.97%68.67%21.63%18.10%
Banka55.04%4.35%42.92%28.26%9.85%91.43%84.25%8.95%52.35%77.93%26.04%28.22%
Begusarai51.07%5.53%45.22%28.57%11.05%84.32%64.26%2.09%35.20%73.71%27.33%18.59%
Bhagalpur48.28%3.46%43.48%27.09%11.21%79.08%66.14%3.37%30.28%67.36%27.77%22.01%
Bhojpur49.34%3.96%41.54%12.37%7.23%80.69%72.98%0.25%33.35%65.14%18.54%18.18%
Buxar48.45%5.96%41.62%13.24%3.84%77.55%70.63%0.25%33.22%70.28%19.41%13.62%
Darbhanga52.70%3.05%49.42%30.66%13.43%83.79%72.29%0.46%39.78%83.17%27.30%34.12%
Gaya62.84%7.67%50.55%20.88%9.81%84.32%71.46%4.99%26.58%67.98%28.37%22.39%
Gopalganj43.53%3.88%41.28%17.33%8.12%79.46%71.57%1.47%42.20%70.74%13.30%32.32%
Jamui61.05%5.43%55.52%29.53%12.13%90.67%84.90%20.89%36.86%77.68%29.89%21.76%
Jehanabad49.73%4.09%40.80%21.86%10.66%81.44%68.77%1.61%30.00%58.62%34.76%18.31%
Kaimur49.07%4.62%43.01%16.04%9.04%86.47%78.03%5.38%25.26%70.71%22.94%15.70%
Katihar50.30%4.41%45.59%38.10%20.02%90.12%77.25%1.19%66.98%82.13%21.75%34.31%
Khagaria53.82%5.78%50.06%35.28%15.95%87.78%69.17%1.29%45.48%78.51%37.11%29.61%
Kishanganj55.70%3.19%47.74%42.08%20.19%94.41%82.67%1.17%46.45%82.27%22.84%42.42%
Lakhisar
ai49.70%5.24%46.77%23.76%9.30%86.27%61.30%7.19%18.04%61.20%33.70%17.99%
Madhepura56.78%4.69%51.09%40.46%18.89%91.90%85.74%0.17%46.08%88.50%28.36%27.07%
Madhubani54.09%3.93%45.23%27.04%11.71%88.05%79.67%1.10%45.67%85.18%26.90%30.21%
Munger47.53%4.02%39.90%19.38%6.95%74.90%65.45%9.90%31.70%59.39%23.23%17.74%
Muzaffarpur49.74%3.07%40.85%24.80%11.42%80.13%71.13%0.58%26.70%79.62%18.94%24.95%
Nalanda52.55%4.22%42.48%23.15%14.69%78.13%69.00%3.50%30.17%52.62%28.18%20.13%
Nawada54.33%2.67%46.40%22.52%12.42%80.64%69.84%2.26%42.95%59.78%43.76%24.82%
Pashchim Champaran48.25%5.07%44.86%32.36%18.47%81.28%78.53%4.24%51.96%82.96%20.60%44.41%
Patna42.86%3.28%37.56%14.62%7.04%50.40%48.61%1.97%13.05%38.07%16.51%10.83%
Purba Champaran52.81%4.29%53.93%34.91%21.37%84.94%76.86%0.57%56.90%80.24%24.68%34.50%
Purnia60.35%7.22%49.70%38.21%16.05%92.82%85.08%0.17%49.52%86.05%26.46%30.07%
Rohtas50.66%5.30%45.15%10.10%4.28%83.74%72.80%1.34%18.55%61.49%21.94%11.82%
Saharsa57.27%5.97%51.54%35.09%19.58%89.19%83.38%0.80% 40.60%85.67%31.58%30.22%
Samastipur47.42%3.04%46.41%29.80%13.33%87.28%80.44%1.80%58.74%85.39%29.13%37.34%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIOTable 11- DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
264 265
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Bihar
Saran45.00%3.82%42.97%16.94%9.40%84.62%73.07%1.91%38.67%67.42%17.01%29.63%
Sheikhpura59.67%5.75%50.72%23.88%9.89%84.41%66.19%8.11%19.17%61.25%28.26%17.99%
Sheohar54.50%6.80%49.02%38.66%14.22%87.83%76.04%1.16%38.69%88.68%30.60%27.35%
Sitamarhi55.80%5.33%48.14%37.07%16.71%87.48%78.22%0.83%59.67%87.20%28.27%28.84%
Siwan45.20%4.85%42.40%12.89%8.54%78.05%74.69%1.74%45.07%65.02%13.72%17.38%
Supaul60.95%3.58%53.07%35.54%15.35%94.45%83.67%0.58%39.27%88.00%25.85%26.63%
Vaishali53.88%5.48%44.19%21.74%8.60%78.32%67.75%2.54%34.81%74.45%17.18%19.44%
Chhattisgarh
Bastar56.50%4.19%27.79%23.87%9.65%88.71%81.37%16.88%12.66%84.51%29.75%6.20%
Bijapur47.01%3.33%33.32%19.13%4.89%90.51%77.97%12.37%7.39%85.73%20.64%4.68%
Bilaspur36.30%3.40%28.35%10.19%5.28%74.56%60.73%17.46%0.46%61.59%8.19%4.29%
Dantewada57.96%6.23%26.31%35.33%22.36%85.34%77.12%17.34%12.56%84.26%42.43%8.92%
Dhamtari45.76%2.49%14.43%8.37%1.88%80.33%43.49%14.07%1.35%50.74%11.05%2.15%
Durg39.98%2.66%22.47%7.59%3.14%64.77%53.06%11.97%0.51%46.84%6.88%4.35%
Janjgir-Champa39.58%2.30%22.77%9.96%5.97%81.17%68.42%9.47%1.51%53.85%10.82%4.57%
Jashpur43.22%4.39%32.34%16.56%7.46%92.72%83.56%32.10%15.49%89.24%33.32%10.37%
Kabeerdham48.40%3.50%32.10%21.35%8.67%90.39%75.99%14.92%3.69%71.80%17.07%6.88%
Korba45.20%4.41%27.76%13.48%4.07%74.00%64.96%28.83%5.43%58.86%16.11%5.14%
Koriya40.70%3.24%30.12%17.72%4.26%80.70%74.71%41.01%9.04%75.78%23.38%7.00%
Mahasamund47.56%2.44%21.23%12.96%4.71%87.30%75.80%13.17%1.04%68.61%11.36%4.58%
Narayanpur53.56%3.53%35.01%27.72%12.07%90.31%85.37%17.15%21.65%88.62%28.74%7.39%
North Bastar Kanker47.41%3.64%18.69%10.77%3.83%86.65%61.10%9.08%3.61%76.56%18.42%4.91%
Raigarh38.56%2.78%21.97%13.37%3.92%81.02%71.53%14.45%3.90%71.91%16.44%9.51%
Raipur40.52%3.37%19.58%11.14%4.66%66.12%58.73%16.20%1.28%43.06%8.38%6.54%
Rajnandgaon43.40%3.41%26.38%6.25%1.57%83.33%53.06%15.53%1.04%66.96%4.87%1.42%
Surguja45.58%3.52%29.22%22.99%7.32%87.91%79.93%37.08%6.16%87.11%31.55%9.00%
Goa
North Goa26.37%0.53%6.88%3.83%0.41%15.84%18.78%3.77%0.31%18.95%3.51%4.04%
South Goa22.18%0.63%7.51%5.95%1.75%13.58%25.24%4.60%0.00%12.16%2.18%3.99%
Gujarat
Ahmadabad32.17%2.49%2.67%5.63%3.30%14.47%10.99%4.86%0.73%6.04%6.21%4.35%
Amreli28.89%1.24%15.67%10.22%4.35%49
.07%24.72%6.41%0.85%25.92%5.90%8.57%
Anand51.22%3.83%8.70%5.12%4.76%59.83%33.69%4.65%2.24%23.12%10.55%8.99%
Banas Kantha52.03%3.39%22.94%13.68%13.75%72.17%60.86%7.80%11.38%24.76%28.73%7.13%
Bharuch44.42%3.32%9.52%10.26%6.11%49.52%31.07%21.96%2.74%28.75%12.72%8.88%
Bhavnagar38.41%1.58%18.41%12.25%4.72%58.38%37.90%8.66%0.68%34.28%7.70%12.38%
Dang62.81%3.10%32.42%20.73%9.19%92.21%81.54%49.49%11.71%89.59%35.73%18.30%
Dohad59.23%5.41%27.11%17.43%12.73%86.60%80.12%36.21%20.51%71.46%36.11%14.92%
Gandhinagar44.37%2.30%6.81%11.12%6.65%48.77%39.45%4.81%4.94%17.04%11.62%7.52%
Jamnagar32.27%1.97%10.24%13.29%8.55%40.16%32.30%13.29%1.81%6.59%7.98%10.16%
Junagadh28.80%2.07%9.97%8.93%6.20%54.12%25.82%5.66%0.61%14.02%7.18%4.50%
Kachchh41.22%3.42%20.21%22.24%17.74%62.20%41.38%23.99%4.32%7.78%20.23%14.09%
Kheda52.28%3.26%21.72%6.18%6.63%72.30%48.00%8.02%3.94%39.75%19.19%18.53%
Mahesana41.27%1.63%12.44%4.49%3.87%43.55%34.23%8.81%2.71%13.73%8.00%3.61%
Narmada59.28%2.28%24.09%11.40%6.29%86.43%66.22%6.25%6.39%67.01%27.42%5.78%
Navsari42.13%1.27%2.87%6.40%2.27%47.13%30.42%34.64%1.22%40.11%7.02%5.02%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Gujarat
Panch Mahals59.69%1.79%25.56%7.23%7.48%78.76%64.61%24.17%13.55%62.47%29.80%18.24%
Patan48.09%3.00%16.84%13.29%6.97%68.12%44.34%6.64%5.89%23.43%20.27%8.01%
Porbandar29.12%0.73%15.97%9.56%2.47%49.92%25.59%18.69%0.52%9.20%7.00%7.93%
Rajkot31.46%1.32%11.33%8.04%5.72%34.64%28.10%11.40%1.59%7.55%4.57%7.09%
Sabar Kantha51.21%1.98%19.17%7.55%5.53%71.71%55.09%11.23%4.39%31.39%23.04%7.73%
Surat34.12%0.51%15.16%6.34%5.31%15.33%21.43%6.63%1.30%12.85%8.17%11.27%
Surendranagar43.26%2.22%18.93%17.65%10.01%64.55%53.69%23.12%4.02%17.95%14.13%9.25%
Tapi56.67%0.98%12.04%11.34%3.88%76.69%60.04%15.97%6.31%74.86%18.34%9.76%
Vadodara41.36%2.58%20.36%13.00%7.81%46.94%39.62%9.05%2.34%26.73%14.17%9.80%
Valsad38.42%1.87%16.08%10.81%5.17%43.45%39.31%37.31%1.22%35.77%15.32%13.69%
Haryana
Ambala26.49%1.15%12.58%2.67%1.07%33.60%9.05%1.05%0.15%12.71%0.85%2.44%
Bhiwani36.66%3.10%23.05%5.18%1.72%69.87%16.71%19.66%0.70%39.80%4.80%4.44%
Faridabad27.31%3.33%27.83%8.84%5.59%18.77%18.77%50.07%0.61%12.48%3.50%16.66%
Fatehabad37.03%1.66%23.30%9.49%2.73%63.88%11.61%2.23%0.25%29.96%2.29%6.62%
Gurgaon29.86%1.13%25.21%8.30%4.20%21.98%29.22%2.43%0.24%11.23%7.84%11.60%
Hisar29.62%2.37%23.80%5.95%2.24%63.70%13.77%16.56%0.84%24.20%2.60%5.36%
Jhajjar22.97%0.80%20.91%2.60%2.09%58.98%12.46%11.40%0.67%13.03%4.29%6.54%
Jind33.26%2.25%19.26%4.56%1.32%65.46%14.69%18.60%0.49%30.88%1.96%5.18%
Kaithal33.07%2.60%18.34%6.05%0.95% 60.40%20.83%7.53%0.13%26.81%2.38%2.45%
Karnal31.28%1.74%16.39%6.30%0.47%47.49%11.67%1.52%0.19%22.80%2.69%2.79%
Kurukshetra24.49%0.59%12.70%6.78%0.88%43.56%12.87%0.48%0.13%22.15%1.29%3.28%
Mahendragarh29.73%1.78%18.97%1.85%0.41%66.77%24.78%8.06%1.24%18.78%3.74%6.04%
Mewat61.16%9.04%64.83%26.74%31.65%84.17%49.24%33.34%9.74%69.42%24.48%22.01%
Palwal37.13%3.03%39.91%11.39%11.27%70.16%31.09%14.68%5.14%39.12%12.48%22.16%
Panchkula17.59%0.66%8.62%2.75%1.05%25.23%14.02%0.51%0.34%7.60%0.92%2.01%
Panipat27.66%1.12%20.73%8.00%3.05%38.42%8.20%1.20%0.00%15.63%1.78%1.82%
Rewari36.01%1.77%28.65%2.79%1.11%63.27%28.56%8.26%1.02%18.56%7.54%17.01%
Rohtak32.15%2.01%25.12%7.44%1.51%58.32%21.16%18.06%0.60%21.61%4.01%11.56%
Sirsa41.71%2.73%24.63%8.97%1.96%59.94%18.86%2.
90%0.70%37.13%3.24%4.35%
Sonipat29.97%0.51%23.56%3.60%1.69%54.79%17.45%6.91%0.43%15.58%2.47%5.09%
Yamunanagar30.29%0.35%11.84%4.81%2.09%38.71%18.54%0.46%0.13%23.68%1.82%5.57%
Himachal Pradesh
Bilaspur28.01%0.67%18.59%3.34%0.88%84.15%26.82%20.84%0.12%16.15%7.62%3.72%
Chamba30.03%3.11%23.59%6.83%1.35%78.48%22.29%12.33%1.63%38.56%12.69%1.94%
Hamirpur25.35%1.51%15.71%2.38%0.38%78.80%16.50%4.18%0.33%25.93%4.28%1.85%
Kangra28.19%1.67%11.52%2.93%0.15%70.94%32.04%4.65%0.00%28.85%3.70%4.10%
Kinnaur19.99%1.68%15.81%7.36%1.10%32.95%31.36%8.03%0.64%39.04%8.13%2.11%
Kullu22.71%1.03%17.67%3.97%2.02%64.61%31.73%7.43%0.48%49.99%9.36%1.55%
Lahul & Spiti15.04%1.43%13.66%11.23%0.75%43.46%44.89%4.36%1.95%69.68%8.56%1.79%
Mandi25.90%1.16%26.34%4.54%0.45%80.38%26.30%5.58%0.31%35.20%10.62%1.65%
Shimla26.22%1.58%13.53%2.84%1.32%38.84%26.44%6.06%0.53%33.81%9.08%3.35%
Sirmaur34.01%2.74%17.02%5.56%1.33%66.14%29.81%13.45%0.84%21.90%13.63%1.54%
Solan28.54%1.97%17.52%3.13%1.10%55.70%28.52%14.55%1.21%15.40%4.85%1.95%
Una23.68%1.32%17.87%3.05%1.61%71.52%29.12%1.95%0.22%21.44%4.19%3.44%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
266 267
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Jharkhand
Bokaro44.89%2.44%25.63%10.07%4.40%75.16%64.38%29.40%11.54%38.71%12.78%6.66%
Chatra59.19%4.03%48.03%23.26%9.74%90.95%84.89%38.75%56.40%77.77%25.26%10.76%
Deoghar53.27%4.84%39.27%17.01%6.89%81.68%79.11%24.83%18.83%63.66%17.60%5.90%
Dhanbad43.45%2.03%25.90%10.82%4.19%81.45%71.20%36.75%3.61%32.92%13.13%5.73%
Dumka52.18%3.54%32.57%27.70%7.32%91.13%87.89%24.03%29.17%85.15%26.85%12.58%
Garhwa50.51%5.13%43.59%16.04%10.66%94.88%88.90%19.27%48.29%85.08%29.41%19.27%
Giridih54.23%4.19%38.18%22.77%12.51%84.95%80.39%41.66%8.15%60.05%22.26%6.19%
Godda49.66%3.85%34.45%30.27%12.69%94.29%82.51%27.60%19.69%74.58%26.77%5.51%
Gumla45.52%1.79%33.43%14.45%8.09%90.12%83.29%58.09%21.52%85.33%20.17%7.75%
Hazaribagh48.98%3.68%30.73%12.10%5.98%82.96%71.75%38.12%3.08%48.53%16.72%2.80%
Jamtara55.65%3.25%39.34%25.03%9.13%96.65%85.23%16.27%15.90%73.06%20.98%7.83%
Khunti45.94%1.67%29.56%19.54%6.63%92.60%84.50%54.47%31.43%86.32%26.83%7.52%
Kodarma51.90%2.41%24.56%14.65%6.48%70.88%71.69%27.13%8.22%40.53%15.54%2.22%
Latehar44.81%2.67%37.32%25.69%12.20%95.18%87.45%31.67%43.84%87.63%29.97%20.76%
Lohardaga48.64%1.42%34.21%16.81%10.29%85.43%80.14%37.10%17.08%80.62%19.56%9.76%
Pakur56.04%2.95%38.90%38.26%19.28%94.08%86.53%20.18%22.57%81.97%30.98%6.65%
Palamu45.78%3.52%36.71%19.88%10.10%83.41%80.81%17.39%35.97%77.30%33.92%17.67%
Pashchimi Singhbhum53.01%6.88%41.39%28.59%16.37%89.82%83.93%39.50%30.98%83.00%38.52%20.06%
Purbi Singhbhum35.26%1.21%21.97%10.97%4.36%59.86%55.66%19.53%7.34%43.66%13.03%7.39%
Ramgarh44.44%2.41%26.58%10.75%3.95%82.63%59.88%40.13%3.83%35.86%14.13%6.79%
Ranchi41.83%2.39%24.29%9.93%2.88%61.14%61.22%30.39%6.20%45.46%12.41%5.77%
Sahibganj51.66%5.55%42.77%36.06%15.28%88.94%73.13%31.07%25.48%74.56%36.32%12.42%
Saraikela-Kharsawan50.49%3.81%34.89%15.17%4.45%78.57%74.59%28.34%13.94%60.43%18.05%7.80%
Simdega43.87%4.06%36.42%16.91%8.44%94.40%90.83%43.22%43.87%88.33%24.39%10.68%
Karnataka
Bagalkot45.63%2.32%12.83%10.67%4.45%66.52%77.09%10.81%2.27%53.58%13.68%12.14%
Bangalore23.87%0.47%14.95%2.44%2.30%3.96%13.49%8.02%0.42%5.42%2.15%6.39%
Bangalore Rural31.73%1.21%11.76%6.70%1.68%31.32%22.41%27.90%1.24%41.99%8.59%7.77%
Belgaum37.14%1.63%10.22%7.06%1.54%52.08%56.23%5.97%1.15%37.59%8.66%5.42%
Bellary45.00%3.45%15.89%16.46%7.93%52.73%61.26%9
.04%2.09%45.84%13.57%13.97%
Bidar40.33%1.94%11.60%8.66%2.76%67.68%72.09%13.99%1.97%47.04%15.66%9.30%
Bijapur34.48%1.28%12.84%12.28%6.33%71.40%77.57%25.44%2.98%49.99%17.51%13.04%
Chamrajnagar35.92%2.07%10.61%16.86%3.13%60.67%63.67%11.71%4.58%44.19%15.13%17.64%
Chikkaballapura38.03%1.25%7.55%10.12%2.96%49.33%50.40%25.01%2.14%41.69%15.39%9.01%
Chikmagalur27.24%0.68%14.64%8.77%2.42%50.91%36.58%21.94%2.78%49.69%9.58%16.28%
Chitradurga31.48%1.78%9.66%7.67%3.12%60.86%56.41%18.11%2.74%42.35%15.21%9.52%
Dakshina Kannada34.51%0.55%12.25%4.17%1.92%49.06%7.93%16.02%2.15%29.89%6.95%8.05%
Davanagere38.76%1.86%6.65%8.78%3.61%47.60%37.18%6.61%1.18%40.91%8.14%7.41%
Dharwad32.09%1.62%12.06%5.40%1.12%45.12%36.96%6.50%0.97%54.15%9.93%4.17%
Gadag30.49%1.49%10.38%10.57%7.30%76.02%69.54%25.69%2.09%54.77%18.88%9.44%
Gulbarga45.37%0.84%12.85%12.35%6.49%63.27%70.82%8.56%1.32%51.33%11.37%12.48%
Hassan25.49%1.43%6.80%9.65%1.73%43.20%33.15%12.62%2.45%44.95%8.58%7.19%
Haveri34.55%0.90%13.83%10.08%3.54%65.91%44.12%14.90%2.62%55.37%12.23%8.07%
Kodagu31.75%0.99%8.58%8.00%2.76%53.03%15.74%19.67%3.01%32.94%10.93%7.76%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Karnataka
Kolar35.29%1.54%10.59%6.84%1.65%39.13%39.56%35.83%0.86%29.75%8.15%5.84%
Koppal45.93%1.96%19.99%11.85%5.15%68.63%52.78%15.40%1.34%52.96%10.86%6.30%
Mandya25.53%0.77%8.65%7.38%3.08%42.35%41.99%7.33%1.15%36.38%7.61%9.10%
Mysore26.04%0.60%9.62%7.27%1.37%31.01%34.86%6.15%2.73%32.57%10.57%10.59%
Raichur39.52%1.91%18.19%21.17%8.16%72.56%72.38%21.15%2.04%50.23%19.78%14.45%
Ramanagara28.32%0.90%11.37%10.31%1.92%40.75%36.16%13.50%1.83%39.68%8.13%5.23%
Shimoga32.00%2.19%10.96%7.22%3.19%38.32%26.86%20.62%3.50%45.30%8.02%10.87%
Tumkur29.23%2.11%11.82%8.62%3.57%57.83%47.26%27.45%1.58%52.10%12.34%4.60%
Udupi33.98%0.84%8.80%10.48%2.08%57.57%9.87%36.57%1.43%28.83%5.93%7.13%
Uttara Kannada40.23%1.03%12.29%6.47%2.28%52.58%33.06%31.15%2.45%36.72%10.76%11.36%
Yadgir46.85%1.80%21.65%28.54%16.63%79.11%81.46%22.55%1.83%57.29%23.15%13.70%
Kerala
Alappuzha14.92%0.39%0.95%1.38%0.49%37.03%3.01%9.11%0.42%16.79%1.84%5.99%
Ernakulam8.96%0.24%1.76%1.00%0.10%20.34%0.73%2.97%0.02%9.63%1.47%4.32%
Idukki13.92%0.28%0.64%3.16%0.00%54.45%5.30%7.65%0.64%17.02%2.25%5.72%
Kannur17.19%0.23%3.63%2.18%0.52%44.46%1.05%7.92%1.33%7.72%3.33%3.65%
Kasaragod19.23%0.24%1.59%3.45%1.24%46.56%2.15%7.57%0.90%22.22%3.91%3.93%
Kollam11.83%0.11%1.50%1.01%0.75%39.91%1.91%4.92%1.86%8.45%3.55%7.61%
Kottayam10.58%0.13%0.98%0.98%0.00%38.65%0.89%5.93%0.35%7.69%1.66%2.10%
Kozhikode17.23%0.39%0.84%1.74%0.26%50.95%0.53%3.23%0.62%13.28%2.96%3.49%
Malappuram21.03%0.15%2.94%1.53%1.24%56.15%1.48%6.41%0.56%6.87%4.48%4.70%
Palakkad19.68%0.00%0.63%1.97%0.10%50.26%3.09%6.33%0.75%12.72%2.90%2.50%
Pathanamthitta11.54%0.00%0.53%1.96%1.22%42.86%2.98%7.18%0.77%9.44%1.77%7.04%
Thiruvananthapuram13.06%0.18%2.09%2.45%0.64%47.86%2.99%6.54%0.60%12.25%3.75%3.03%
Thrissur13.79%0.09%1.82%1.51%0.28%33.22%0.33%4.77%0.20%3.91%0.83%4.08%
Wayanad20.11%0.52%3.83%3.63%1.20%67.72%4.10%7.36%3.63%23.23%8.80%5.48%
Madhya Pradesh
Alirajpur63.37%6.72%54.00%46.85%42.03%90.13%84.68%22.84%7.08%84.80%27.93%13.88%
Anuppur43.15%3.36%28.20%12.68%4.76%83.88%79.90%48.72%20.16%76.11%24.40%6.07%
Ashoknagar49.57%2.38%30.57%18.07%10.04%85.20%78.73%31.58%11.39%84.47%18.49%8.35%
Balaghat54.68%3.19%26.31%10.81%3.46%84.37%71.
03%42.89%10.12%78.83%16.62%9.63%
Barwani65.20%4.84%44.78%36.47%31.58%80.52%80.73%25.27%10.18%75.41%33.78%14.16%
Betul42.69%3.38%27.71%14.47%4.73%74.59%69.03%32.00%6.38%70.43%23.47%8.90%
Bhind48.22%3.73%32.41%7.68%7.14%83.10%67.54%15.00%11.88%74.63%12.67%9.46%
Bhopal33.34%2.77%15.56%9.16%5.36%22.86%36.82%11.24%1.45%23.93%5.61%4.70%
Burhanpur47.49%3.53%32.56%24.74%13.82%54.60%59.27%16.74%9.22%53.66%21.81%12.69%
Chhatarpur46.94%5.96%38.12%19.17%12.04%86.70%85.47%47.75%20.18%74.72%21.43%16.56%
Chhindwara42.66%2.49%23.64%12.76%2.92%75.07%66.12%32.98%9.23%64.30%23.79%9.30%
Damoh42.11%4.82%32.12%15.52%7.93%86.27%76.86%53.31%12.38%82.33%29.30%21.61%
Datia44.96%5.25%28.39%10.06%4.20%80.16%64.10%24.74%9.47%88.10%15.53%6.73%
Dewas48.57%4.73%27.43%15.81%8.40%64.50%49.61%23.37%1.13%50.82%10.44%4.99%
Dhar51.21%4.76%33.95%23.56%10.88%68.28%64.56%23.77%4.08%55.10%18.07%10.07%
Dindori50.63%3.43%31.94%20.39%4.07%95.52%92.31%50.04%24.71%89.97%54.77%13.38%
East Nimar52.32%3.14%32.38%23.48%9.80%72.52%63.40%32.36%8.05%57.34%18.08%7.11%
Guna54.05%3.99%35.92%19.58%10.43%77.31%72.04%34.75%5.86%73.26%16.52%7.19%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
268 269
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Madhya Pradesh
Gwalior41.34%4.11%25.21%9.53%6.49%44.05%41.43%9.29%3.78%48.36%9.85%6.48%
Harda40.84%3.58%33.42%19.09%7.55%69.80%48.21%23.07%3.07%59.27%15.63%5.71%
Hoshangabad40.06%2.24%27.44%10.66%3.34%66.56%52.52%18.33%4.97%53.54%14.55%4.78%
Indore36.54%0.96%10.76%10.44%4.20%16.07%24.59%11.44%0.72%15.20%2.65%6.09%
Jabalpur37.70%2.44%19.91%8.80%6.89%50.17%49.02%16.51%4.20%40.33%15.13%5.24%
Jhabua57.92%6.17%42.63%41.44%34.48%92.16%87.28%36.19%10.88%82.42%42.25%29.50%
Katni42.17%4.84%27.62%11.79%4.42%82.63%77.93%31.59%15.74%76.16%25.45%12.51%
Mandla46.67%3.37%26.68%18.78%5.38%85.63%85.27%51.81%21.68%81.35%39.99%6.95%
Mandsaur48.57%2.95%31.00%15.13%4.93%71.19%68.27%37.08%1.83%60.11%10.77%17.93%
Morena44.30%3.93%30.33%9.93%8.63%77.49%61.96%16.81%11.04%72.41%13.63%5.59%
Narsimhapur39.68%2.30%25.25%13.11%1.97%79.10%56.27%16.08%6.40%70.68%26.97%17.74%
Neemuch45.47%2.68%26.48%17.20%7.14%64.54%66.39%30.77%1.90%55.72%10.57%15.67%
Panna40.40%4.00%33.65%18.17%9.23%88.20%79.05%38.56%18.15%81.99%23.47%15.96%
Raisen47.39%2.94%29.43%13.66%6.33%75.11%59.37%21.78%5.94%70.08%17.93%5.06%
Rajgarh48.98%3.29%28.71%18.44%6.68%81.45%79.66%56.69%4.11%65.59%18.92%7.22%
Ratlam49.40%2.53%28.18%21.86%10.09%66.39%64.65%22.08%5.33%57.78%25.05%29.85%
Rewa38.96%5.23%33.91%11.00%7.58%87.28%72.55%24.53%11.32%83.20%22.59%13.28%
Sagar41.29%3.32%37.07%12.46%6.06%83.50%72.11%38.90%14.23%79.43%22.57%12.44%
Satna36.93%4.20%29.12%12.34%5.28%78.62%67.55%21.17%9.30%78.74%21.45%7.94%
Sehore42.39%3.32%26.06%16.36%6.94%76.33%51.15%30.27%1.70%56.74%9.35%5.41%
Seoni46.92%1.99%23.51%13.48%4.01%83.19%76.24%44.63%16.11%77.75%27.10%12.85%
Shahdol41.47%3.87%28.86%15.31%6.79%85.90%83.10%50.96%21.40%80.55%26.76%12.87%
Shajapur47.40%2.55%19.27%18.80%10.39%75.32%62.69%39.70%2.78%54.89%12.05%9.42%
Sheopur54.35%4.22%32.38%24.35%11.64%85.23%83.40%28.56%16.47%80.33%28.86%10.03%
Shivpuri49.07%3.91%33.35%14.43%6.50%80.17%76.27%58.52%12.27%80.43%21.18%7.76%
Sidhi47.89%4.98%41.57%14.85%8.93%92.99%88.70%47.03%21.72%88.30%32.75%19.12%
Singrauli46.21%6.10%43.45%18.70%11.43%85.29%86.07%53.88%29.43%82.69%29.71%10.64%
Tikamgarh49.87%4.39%33.97%14.26%7.81%86.80%85.47%44.09%13.28%71.69%23.14%19.35%
Ujjain45.45%2.68%29.46%16.79%7.11%54.45%46.29%23.80%2.38%44.72%10
.29%16.68%
Umaria44.93%3.50%33.05%16.18%7.50%88.34%81.54%42.22%19.36%81.62%27.09%7.67%
Vidisha46.01%5.19%37.32%20.44%11.21%82.32%75.74%25.62%14.28%80.89%29.68%24.21%
West Nimar52.11%2.64%30.38%22.15%10.31%69.24%67.11%13.75%1.91%51.13%16.24%15.45%
Maharashtra
Ahmadnagar37.36%0.87%20.36%4.95%5.32%40.72%48.18%16.36%5.78%35.28%13.84%21.84%
Akola39.82%1.42%14.26%3.13%2.58%54.78%50.51%8.12%4.81%35.18%15.32%7.36%
Amravati36.10%0.97%11.47%5.17%0.81%49.18%33.31%9.14%6.24%37.94%15.01%8.71%
Aurangabad38.24%2.21%18.96%5.67%3.80%44.62%52.02%15.41%2.87%23.87%12.29%9.51%
Bhandara37.45%1.39%6.69%4.55%0.42%50.47%29.54%18.55%2.71%38.03%8.30%1.88%
Bid39.58%1.96%19.23%9.91%6.97%71.87%59.40%21.51%8.32%29.60%19.57%11.70%
Buldana40.99%0.88%14.69%7.65%3.46%67.20%53.58%18.58%3.29%39.84%15.29%5.47%
Chandrapur42.38%1.13%9.53%6.67%1.43%53.69%45.96%24.39%7.74%34.47%18.55%4.14%
Dhule45.55%1.48%25.98%15.86%13.49%62.97%71.00%9.22%13.22%50.46%23.43%18.04%
Garhchiroli38.17%0.76%13.76%10.68%2.59%77.23%67.65%18.68%5.87%52.56%17.65%7.27%
Gondiya46.04%1.20%12.46%5.25%0.44%75.81%41.80%27.60%3.04%72.53%12.23%4.59%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Maharashtra
Hingoli46.34%2.57%18.99%9.50%5.27%76.30%56.89%25.96%14.05%44.32%23.88%6.66%
Jalgaon31.67%2.08%20.63%10.99%5.70%46.58%55.54%6.62%7.68%30.72%16.01%12.39%
Jalna45.79%1.79%19.46%11.34%4.22%79.11%62.30%41.51%12.31%36.56%23.21%8.28%
Kolhapur34.63%1.31%17.01%4.22%3.00%35.80%32.53%6.18%2.45%38.64%9.50%12.99%
Latur40.78%1.01%16.00%5.32%2.64%65.44%56.30%33.02%5.22%32.44%20.35%9.31%
Mumbai22.53%0.96%7.86%4.18%2.51%1.57%60.33%0.83%0.52%2.40%11.72%11.58%
Mumbai Suburban24.89%1.81%13.34%5.14%3.46%2.44%75.10%0.87%7.43%2.93%9.04%14.70%
Nagpur34.60%0.97%9.21%4.30%1.23%23.00%27.72%6.88%1.65%24.02%6.72%6.41%
Nanded49.69%2.82%21.85%7.58%4.20%67.69%55.27%25.68%9.29%40.93%26.65%10.83%
Nandurbar56.51%1.66%25.46%28.36%22.39%77.96%75.03%25.74%26.89%76.33%45.43%17.99%
Nashik43.36%2.27%24.99%5.68%6.43%33.64%47.83%16.15%7.06%29.14%13.81%7.37%
Osmanabad37.16%1.47%14.44%7.71%1.41%63.08%71.55%32.55%8.89%35.74%15.91%8.62%
Parbhani49.26%1.01%18.64%6.85%4.54%74.63%64.55%16.19%7.28%32.45%16.96%8.12%
Pune27.24%1.12%12.28%4.69%1.89%15.51%36.87%6.65%3.97%10.07%7.86%8.29%
Raigarh35.61%1.01%13.45%6.64%2.33%23.66%27.07%8.94%5.60%16.46%8.96%13.56%
Ratnagiri35.42%0.63%12.97%6.59%4.03%66.21%33.47%17.50%3.33%53.29%22.11%19.83%
Sangli31.66%1.03%15.05%3.48%4.14%36.88%27.17%7.08%7.56%31.22%8.58%10.13%
Satara38.47%0.91%16.73%4.69%3.32%45.22%36.18%9.13%5.18%33.22%10.24%11.42%
Sindhudurg34.83%1.05%13.30%4.63%2.45%65.30%21.92%27.07%1.19%42.93%18.91%17.31%
Solapur35.04%1.41%13.40%7.03%3.16%48.06%47.03%21.15%8.45%24.73%10.40%5.71%
Thane30.76%1.16%15.00%6.37%6.08%20.12%37.22%14.14%8.77%17.17%12.14%9.58%
Wardha36.46%1.49%8.16%6.37%1.63%40.29%41.04%7.05%3.81%30.90%13.02%3.68%
Washim43.18%1.31%20.81%6.76%4.43%64.96%56.53%19.19%4.27%46.35%18.85%4.30%
Yavatmal44.97%1.14%17.06%7.21%4.92%64.61%56.32%24.02%9.07%48.48%22.22%9.67%
Manipur
Bishnupur25.12%1.49%8.15%3.03%1.99%62.00%59.79%61.56%3.64%89.89%6.11%19.26%
Chandel24.95%2.54%31.46%11.41%4.18%81.87%37.28%62.38%6.50%89.05%23.53%21.03%
Churachandpur27.71%3.03%22.14%10.32%2.88%58.16%32.47%49.82%8.89%80.89%24.00%20.19%
Imphal East23.02%1.33%10.46%4.42%2.50%49.62%53.20%71.21%2.95%77.16%8.62%17.99%
Imphal West17.23%1.09%11.18%1.68%0.60%38.59%50.31%48.31%2.22%70
.56%3.98%19.40%
Senapati30.50%3.11%38.46%10.57%4.89%82.13%39.57%65.89%7.36%89.11%23.53%28.70%
Tamenglong30.03%3.44%40.54%15.33%4.62%87.06%36.61%67.08%12.62%90.44%44.22%32.14%
Thoubal24.73%1.71%14.60%3.57%2.63%64.78%51.46%67.77%14.20%89.64%8.24%20.87%
Ukhrul25.70%1.99%33.73%6.79%2.10%89.63%37.51%59.34%22.31%85.55%44.48%34.01%
Meghalaya
East Garo Hills31.84%1.56%39.65%20.62%11.03%93.55%46.73%59.34%13.13%84.43%29.10%21.37%
East Khasi Hills38.68%2.65%21.72%14.25%2.91%52.71%32.61%17.91%3.96%32.21%25.58%22.36%
Jaintia Hills49.02%4.89%42.79%31.69%10.91%78.27%42.47%32.70%13.48%31.41%45.75%32.88%
Ri Bhoi44.83%5.20%43.97%32.36%8.89%89.75%41.60%32.43%13.48%53.23%46.12%27.60%
South Garo Hills22.00%3.45%15.63%5.96%2.62%90.16%13.25%27.03%0.92%54.16%7.51%2.07%
West Garo Hills27.64%2.39%26.78%14.96%5.07%82.84%55.20%45.87%6.60%68.08%20.07%6.22%
West Khasi Hills43.01%3.38%42.73%24.43%5.67%93.13%19.37%27.99%9.96%46.45%39.44%25.67%
Mizoram
Aizawl16.98%1.80%8.31%2.02%2.23%12.43%7.87%7.23%0.24%10.72%3.59%3.19%
Champhai24.48%2.37%21.97%8.26%2.86%45.79%12.38%4.00%0.10%28.41%12.01%4.07%
Kolasib22.05%2.47%15.13%8.82%3.68%29.05%12.44%3.75%0.94%27.28%13.26%4.98%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
270 271
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Mizoram
Lawangtlai29.52%2.99%28.53%23.30%10.68%53.95%33.42%25.68%24.92%47.94%40.11%12.56%
Lunglei20.27%2.38%17.89%9.20%2.80%45.06%20.88%6.20%0.77%29.80%15.38%7.54%
Mamit25.25%2.49%24.23%17.13%6.74%59.68%33.72%21.09%14.42%49.29%33.31%12.32%
Saiha29.80%3.91%25.89%6.70%3.84%37.87%19.54%8.09%0.57%23.01%18.68%6.31%
Serchhip21.01%2.48%13.98%4.27%1.27%35.44%7.94%5.01%0.26%15.37%8.35%1.51%
Nagaland
Mon35.64%1.75%49.71%23.34%9.34%94.61%17.64%27.87%13.44%91.32%62.98%51.16%
Dimapur22.18%1.60%27.10%13.02%5.15%34.36%31.72%22.31%1.58%54.24%10.28%21.31%
Kiphire32.95%3.70%40.55%15.08%5.47%89.66%18.63%15.84%1.74%79.09%53.41%42.36%
Kohima18.57%1.30%21.11%6.34%1.91%46.67%29.45%22.06%0.70% 44.00%16.15%10.57%
Longleng27.53%1.43%36.08%11.93%4.96%95.97%26.78%60.65%2.55%86.74%50.96%34.05%
Mokokchung15.88%1.09%15.23%7.38%1.60%59.97%9.23%17.50%0.51%67.55%13.85%18.41%
Peren23.21%3.25%39.13%14.39%3.09%84.19%24.89%40.95%2.25%79.08%31.96%22.13%
Phek20.13%3.68%34.95%12.60%3.98%91.59%15.34%5.33%1.45%91.23%53.04%28.79%
Tuensang33.51%4.01%48.04%18.24%4.96%91.79%26.21%7.56%2.67%84.13%49.07%37.42%
Wokha15.96%1.53%24.73%9.21%2.49%61.40%24.69%32.91%1.90%49.65%23.53%18.51%
Zunheboto21.13%0.66%28.22%11.12%5.26%86.73%18.02%16.28%0.37%83.54%40.52%33.61%
Odisha
Anugul33.80%3.95%15.50%11.47%2.73%78.62%64.15%30.10%11.63%41.02%17.20%7.43%
Balangir44.84%2.19%10.63%16.15%4.01%90.57%85.50%22.09%15.62%65.84%16.75%6.08%
Baleshwar37.66%1.09%23.43%11.12%3.82%86.32%61.76%10.07%9.55%69.26%12.37%13.07%
Bargarh40.46%2.45%13.01%11.26%2.86%86.41%72.08%19.01%15.13%62.18%14.23%7.29%
Bauda42.36%2.80%15.77%15.57%3.04%88.36%83.30%24.27%11.24%72.69%20.24%7.37%
Bhadrak39.11%1.57%24.06%9.97%3.36%89.25%75.92%9.78%11.46%75.15%14.10%23.25%
Cuttack25.74%1.28%18.90%7.36%2.18%66.97%60.43%11.61%7.21%33.80%10.00%13.17%
Debagarh42.75%2.56%23.77%19.80%5.01%93.67%67.72%22.01%17.52%67.30%31.08%16.51%
Dhenkanal35.61%1.66%15.82%14.00%3.12%79.58%66.62%48.45%10.94%51.33%19.34%13.87%
Gajapati36.64%3.48%25.61%26.87%9.30%82.77%62.21%38.62%11.40%45.93%39.70%7.90%
Ganjam28.15%2.14%16.94%20.09%3.80%65.10%58.50%17.83%8.66%28.10%15.95%5.79%
Jagatsinghapur25.18%1.77%15.11%6.46%0.81%86.79%67.52%8.99%6.56%39.99%12.13%7.45%
Jajapur37.40%2.06%23.27%9.10%2.68%83.78%68.41%16.62%6.
09%46.58%12.28%10.71%
Jharsuguda38.75%1.55%12.11%8.70%1.65%71.90%59.49%18.46%7.35%53.61%8.10%5.10%
Kalahandi41.41%1.83%21.33%28.84%8.47%92.52%85.40%27.27%31.17%75.93%27.94%16.98%
Kandhamal42.69%3.57%27.12%19.50%5.43%93.88%83.95%52.94%20.93%63.14%45.09%6.69%
Kendrapara33.19%1.69%20.51%8.69%1.70%87.72%73.45%11.93%6.62%58.51%16.17%6.76%
Kendujhar44.14%3.26%30.18%20.51%7.26%82.36%78.48%25.69%23.54%69.82%28.70%16.17%
Khordha22.51%1.02%16.72%11.36%3.80%55.25%52.48%22.63%3.32%28.12%9.53%8.64%
Koraput47.60%2.56%24.91%38.82%15.58%81.10%82.22%20.06%22.62%59.13%39.72%16.35%
Malkangiri58.90%6.94%28.08%43.56%17.12%94.63%83.26%19.56%11.53%76.07%35.56%9.26%
Mayurbhanj43.20%1.83%20.87%21.83%5.52%89.72%82.67%27.28%25.43%80.50%30.03%19.62%
Nabarangapur53.72%4.06%33.56%34.15%15.40%90.73%83.31%26.92%29.89%80.22%33.74%19.31%
Nayagarh25.31%1.77%19.48%16.21%2.74%78.38%67.10%28.52%5.93%39.59%14.29%10.04%
Nuapada49.05%2.52%16.59%21.37%7.34%90.56%80.02%18.59%16.83%71.33%17.54%5.85%
Puri22.94%1.61%15.43%5.88%1.23%79.80%59.61%15.30%4.04%34.29%7.57%6.11%
Rayagada46.62%5.50%25.96%36.80%12.98%83.60%76.90%16.26%19.18%59.51%36.58%15.80%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Odisha
Sambalpur40.90%1.56%11.42%14.73%2.51%74.24%65.98%16.98%10.33%62.14%13.77%4.01%
Subarnapur43.04%2.03%14.17%11.83%1.28%85.30%82.59%17.64%9.16%64.12%17.77%6.63%
Sundargarh40.96%2.09%13.07%11.81%3.85%75.77%63.11%21.48%13.75%60.64%13.46%5.47%
Punjab
Amritsar20.38%1.14%9.69%8.47%4.67%28.99%20.63%0.11%0.65%22.66%1.58%4.49%
Barnala21.79%2.16%13.08%10.73%1.72%49.50%13.50%1.00%0.05%21.38%1.32%3.79%
Bathinda19.29%1.21%12.20%9.52%3.36%39.57%14.05%6.08%0.45%19.50%2.24%4.38%
Faridkot22.24%0.60%6.98%6.64%4.00%36.67%13.96%6.96%0.66%25.51%0.80%1.55%
Fatehgarh Sahib22.08%1.98%12.45%5.80%1.08%32.85%14.96%0.24%0.06%9.71%1.14%1.26%
Firozpur23.81%1.61%11.44%12.54%4.53%51.17%21.92%8.08%0.39%41.52%1.97%3.30%
Gurdaspur20.02%1.63%15.66%4.56%1.03%31.90%24.58%0.70%0.31%18.21%1.90%3.11%
Hoshiarpur24.71%1.39%9.71%3.92%1.58%39.40%25.27%0.39%0.39%14.49%1.31%2.99%
Jalandhar19.74%1.43%11.52%3.57%0.94%23.34%10.96%0.15%0.25%11.43%0.38%3.83%
Kapurthala19.49%1.20%8.44%7.32%2.07%25.75%17.95%1.33%3.31%11.96%2.69%3.54%
Ludhiana21.11%0.89%10.18%6.05%3.24%22.86%15.37%0.20%0.05%9.82%2.41%4.66%
Mansa30.27%0.88%13.52%12.78%2.15%61.60%24.02%5.16%0.29%32.02%3.05%2.10%
Moga22.62%2.25%13.50%11.51%3.17%47.44%12.89%3.21%0.34%33.67%1.14%5.12%
Muktsar25.93%2.41%14.95%11.26%2.73%40.97%19.14%3.87%0.27%22.52%2.44%3.15%
Patiala19.09%1.55%20.03%4.73%1.26%35.16%11.15%0.98%0.08%14.40%1.78%3.42%
Rupnagar21.86%1.65%8.61%3.87%0.82%35.30%18.39%0.30%0.00%7.66%0.65%1.38%
Sahibzada Ajit Singh Nagar22.42%1.80%8.60%4.97%3.46%24.49%20.39%0.82%0.66%10.31%2.46%3.55%
Sangrur23.86%1.86%8.52%5.16%1.31%49.11%17.71%0.39%0.27%15.45%1.14%2.82%
Shaheed Bhagat Singh Nagar26.83%0.66%17.72%7.87%2.12%51.49%11.52%1.40%0.25%20.51%1.66%5.89%
Tarn Taran24.32%1.82%21.46%11.25%3.73%46.63%20.01%0.42%0.41%27.10%1.76%3.93%
Rajasthan
Ajmer41.69%2.07%20.42%13.27%5.21%52.66%30.22%30.52%1.21%11.71%8.53%1.16%
Alwar45.89%3.68%36.24%11.69%6.97%80.12%60.63%15.40%2.58%39.30%11.94%6.13%
Banswara50.98%2.16%24.66%23.63%15.91%87.98%79.37%28.59%31.05%76.14%49.79%2.29%
Baran45.22%2.03%21.55%15.33%8.22%75.47%66.41%26.39%6.62%57.92%21.47%2.25%
Barmer44.71%4.28%41.71%34.79%20.03%86.28%79.27%42.80%33.33%55.15%48.80%5.50%
Bharatpur45.29%4.00%40.70%14.27%12.73%85.37%65.59%47
.94%8.92%55.22%22.32%7.42%
Bhilwara36.37%2.27%21.94%20.73%8.35%73.94%65.65%35.85%2.96%23.93%15.07%2.48%
Bikaner40.17%2.57%26.57%18.03%9.53%63.22%34.17%15.75%8.88%23.56%15.61%3.31%
Bundi45.04%2.35%23.58%19.40%6.11%78.26%71.58%23.53%7.37%41.67%20.78%3.03%
Chittaurgarh36.60%2.05%24.10%21.81%7.27%75.25%68.18%24.82%3.89%40.03%12.56%3.68%
Churu40.32%1.88%33.51%16.46%5.56%70.79%23.77%21.38%4.47%21.61%22.10%7.48%
Dausa43.31%3.96%29.83%11.10%2.05%85.09%63.56%20.57%9.21%37.73%22.40%5.29%
Dhaulpur51.37%4.80%34.64%15.56%9.73%81.23%68.89%19.98%7.59%45.38%23.14%2.03%
Dungarpur54.65%2.31%24.20%22.01%8.99%86.08%68.18%32.89%21.75%70.78%43.55%2.15%
Ganganagar34.35%2.55%18.38%13.18%2.84%62.55%24.23%8.41%5.86%44.82%7.56%3.55%
Hanumangarh36.57%2.64%25.20%14.73%5.09%75.74%43.52%5.24%5.07%48.49%11.00%3.83%
Jaipur38.74%2.79%20.83%7.14%3.63%49.69%41.10%15.18%1.77%19.59%9.61%4.22%
Jaisalmer42.84%4.19%41.10%37.49%23.47%85.02%62.56%52.83%24.05%44.65%37.72%8.26%
Jalor51.69%3.36%32.48%24.73%14.06%74.91%60.58%30.70%16.86%38.23%36.93%3.91%
Jhalawar42.08%2.38%21.49%19.23%7.92%73.49%62.50%39.15%3.79%54.00%24.01%1.42%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
272 273
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Rajasthan
Jhunjhunun36.03%2.73%21.14%8.96%4.30%49.42%33.05%16.68%3.73%22.97%12.49%4.53%
Jodhpur39.28%2.88%25.29%19.51%12.09%57.83%47.51%30.85%8.76%21.49%16.78%3.47%
Karauli49.90%5.32%32.42%16.27%6.82%86.95%77.59%32.79%4.82%53.66%32.30%6.80%
Kota37.26%1.23%14.20%10.83%3.99%40.92%47.66%11.04%1.36%24.04%9.41%2.33%
Nagaur39.30%1.27%21.98%16.58%6.11%76.28%44.85%38.16%9.41%18.07%18.10%5.09%
Pali43.20%3.75%21.25%18.05%7.63%58.69%48.85%30.91%4.38%19.58%11.28%2.25%
Pratapgarh49.93%3.41%28.36%26.97%13.40%86.94%84.09%48.43%24.35%76.07%37.04%3.72%
Rajsamand41.26%3.32%25.31%19.14%6.28%77.12%67.98%32.89%3.82%22.30%22.51%2.42%
Sawai Madhopur45.13%4.71%30.54%13.76%8.33%83.46%63.14%31.10%13.65%41.41%24.89%5.73%
Sikar36.03%3.07%24.97%10.01%5.66%56.44%33.77%13.73%3.27%14.39%10.85%3.57%
Sirohi50.03%5.10%30.34%25.37%15.32%63.89%61.63%26.43%14.47%35.04%30.16%8.34%
Tonk43.00%1.79%20.23%14.48%5.64%79.07%64.52%32.24%2.01%31.53%14.97%1.15%
Udaipur53.49%3.74%25.75%28.43%15.14%77.51%71.18%39.74%17.49%54.06%35.41%4.15%
Sikkim
East Sikkim13.74%0.91%7.35%8.09%1.66%29.41%15.07%1.82%0.98%24.07%8.95%13.06%
North Sikkim10.86%0.45%5.11%11.78%1.56%52.23%9.63%2.20%0.27%33.57%12.77%6.41%
South Sikkim10.33%0.55%2.86%6.63%1.03%48.59%5.15%0.53%0.20%20.37%6.23%3.35%
West Sikkim16.26%1.82%3.99%9.34%1.27%61.32%6.04%5.55%0.49%37.98%13.68%3.94%
Tamil Nadu
Ariyalur28.02%1.03%5.60%10.86%0.55%50.41%71.61%7.50%1.33%32.58%4.01%8.98%
Chennai17.62%0.26%6.35%1.71%1.13%1.66%17.16%21.28%0.32%5.84%0.44%4.18%
Coimbatore23.24%0.42%3.58%4.71%1.04%11.84%41.86%6.31%1.13%14.96%2.60%6.16%
Cuddalore30.95%1.71%5.08%6.29%0.85%45.94%60.18%7.33%0.77%36.06%4.05%5.92%
Dharmapuri29.13%1.01%3.90%8.01%1.37%22.10%62.62%10.87%1.07%14.82%2.74%7.21%
Dindigul26.44%1.12%3.66%6.60%1.11%32.26%58.44%6.07%2.37%20.17%4.65%5.04%
Erode19.30%0.34%6.16%8.45%0.86%8.54%37.18%3.76%1.11%16.82%2.75%4.06%
Kancheepuram19.44%1.60%6.95%3.10%1.17%11.84%32.89%22.19%0.12%15.61%1.35%3.12%
Kanniyakumari18.00%1.22%7.97%2.26%0.46%39.08%14.37%8.34%0.57%20.20%2.15%5.68%
Karur29.00%0.87%7.99%8.29%1.55%19.48%52.26%7.95%1.04%13.39%3.15%3.80%
Krishnagiri32.39%2.54%6.22%6.28%0.88%35.35%56.02%12.83%0.84%17.28%2.00%3.68%
Madurai19.54%0.96%11.92%6.17%0.76%19.16%43.59%26.42%0
.67%16.57%3.18%9.27%
Nagappattinam30.29%1.56%9.13%6.31%1.75%38.83%58.14%12.59%0.90%39.12%4.50%6.67%
Namakkal19.23%0.17%7.04%7.67%0.89%10.46%48.62%8.71%0.41%17.88%3.05%10.19%
Perambalur27.08%1.77%10.06%8.37%1.65%35.12%62.18%18.12%0.90%23.11%4.09%6.13%
Pudukkottai33.00%0.78%14.04%7.06%0.92%59.23%64.81%24.20%2.30%30.60%4.07%8.48%
Ramanathapuram26.89%1.00%13.19%6.16%2.32%39.93%52.32%30.43%1.57%24.02%5.47%9.80%
Salem22.85%0.67%9.96%9.96%1.45%20.05%53.54%10.37%1.06%16.15%4.01%6.73%
Sivaganga30.26%1.12%5.56%6.50%1.40%43.19%51.83%18.26%1.41%21.83%5.15%5.94%
Thanjavur28.00%1.68%4.77%5.32%0.96%51.45%53.44%4.47%1.85%39.48%5.11%5.66%
The Nilgiris24.95%0.31%4.16%3.53%0.14%23.23%36.16%7.73%2.36%19.09%4.61%4.83%
Theni20.35%0.82%6.59%7.70%1.63%20.42%54.45%6.62%2.50%18.42%6.18%10.70%
Thiruvallur23.28%1.04%2.04%4.86%0.70%9.81%33.99%17.92%0.72%11.18%2.20%5.63%
Thiruvarur30.32%1.65%6.11%4.92%0.62%54.80%54.40%4.79%2.28%46.45%3.88%5.34%
Thoothukkudi24.51%1.17%12.94%10.28%1.06%27.90%48.15%9.74%1.38%24.39%6.40%12.65%
Tiruchirappalli26.55%1.78%6.79%6.29%0.77%25.26%53.24%5.14%0.78%17.24%3.56%5.88%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Tamil Nadu
Tirunelveli27.74%1.70%7.25%9.54%1.61%21.99%54.46%8.68%0.94%27.59%5.73%8.73%
Tiruppur21.40%1.31%3.33%9.71%0.46%6.12%48.60%5.89%0.39%17.19%2.59%5.82%
Tiruvannamalai29.14%0.79%5.97%8.56%0.62%32.40%68.78%5.81%1.09%18.30%5.11%6.41%
Vellore28.68%1.90%3.06%6.20%0.37%19.36%45.43%19.78%0.46%14.36%2.84%5.19%
Viluppuram30.32%1.49%9.44%10.32%1.08%40.45%68.07%7.78%0.70%29.44%2.97%4.54%
Virudunagar21.65%1.33%10.06%11.89%2.22%21.32%63.89%20.07%1.23%30.08%6.78%15.52%
Telangana
Adilabad40.26%2.25%15.24%23.65%3.80%61.11%69.58%25.82%2.32%38.99%21.47%9.66%
Hyderabad23.48%1.40%7.51%8.78%2.69%3.51%25.89%7.75%0.00%9.05%3.63%9.24%
Karimnagar29.06%0.97%10.37%17.42%0.93%23.63%41.85%35.83%0.48%24.58%9.97%7.54%
Khammam28.14%1.02%6.75%18.05%1.80%28.55%49.96%26.17%2.53%22.73%14.14%7.21%
Mahbubnagar39.19%2.34%18.29%22.40%3.55%55.13%69.41%35.21%1.66%30.70%20.87%4.66%
Medak36.40%2.72%13.29%16.75%1.47%53.00%45.82%17.71%1.56%47.47%15.26%11.26%
Nalgonda30.17%0.69%10.48%22.15%1.59%37.92%50.24%54.80%2.09%21.30%16.21%5.67%
Nizamabad35.65%2.81%15.13%19.36%1.96%42.95%55.91%21.32%2.02%42.15%14.73%8.63%
Rangareddy28.51%0.53%9.80%8.25%1.87%14.63%50.28%26.38%0.35%11.54%7.63%5.78%
Warangal30.01%0.45%6.18%12.93%1.27%36.52%53.12%33.89%1.43%36.91%15.59%6.47%
Tripura
Dhalai28.89%1.32%15.31%17.14%3.20%78.93%49.70%39.55%11.27%87.02%30.87%3.40%
North Tripura38.52%2.93%21.52%15.63%3.94%77.48%51.40%40.64%13.64%72.94%30.01%4.73%
South Tripura27.03%0.52%13.72%10.69%1.91%73.33%35.81%13.35%7.10%85.02%18.97%3.65%
West Tripura24.21%1.00%9.86%7.67%1.44%54.93%28.11%3.57%3.86%67.71%11.82%3.24%
Uttar Pradesh
Agra40.81%5.23%33.41%14.22%12.76%56.66%56.40%33.21%4.74%82.05%9.33%4.05%
Aligarh45.12%7.29%35.73%14.37%14.97%62.18%58.92%4.05%13.63%79.88%11.67%5.43%
Allahabad42.30%4.18%37.28%11.48%7.58%64.60%66.75%10.95%19.20%59.28%7.20%4.18%
Ambedkar Nagar49.96%5.02%33.52%9.57%4.77%86.13%78.55%0.25%25.47%82.14%6.26%3.19%
Auraiya38.01%2.96%34.36%7.93%6.76%72.91%64.77%3.51%29.71%75.47%11.65%2.69%
Azamgarh44.81%3.86%38.63%9.48%5.31%78.35%75.01%0.58%14.72%75.25%9.06%4.57%
Baghpat37.58%2.68%24.09%11.16%9.93%60.80%33.77%1.17%7.12%60.16%11.38%3.32%
Bahraich62.12%6.65%53.71%41.43%26.33%84.77%86.04%0.97%66.52%85.24%23.19%22.46%
Ballia41.52%6.02%41.81%11.63%8.59%77
.01%73.73%1.67%29.27%75.82%11.39%3.69%
Balrampur58.81%6.13%52.52%40.91%31.13%90.17%85.34%1.42%60.33%76.65%13.84%20.41%
Banda35.16%3.38%31.38%17.91%12.28%85.14%71.43%9.15%31.24%86.92%17.74%1.93%
Bara Banki44.29%3.36%33.32%25.42%14.20%71.43%78.79%1.89%48.12%76.27%17.19%7.15%
Bareilly43.49%5.04%27.52%27.70%19.87%58.58%43.05%1.17%29.16%51.39%15.39%4.35%
Basti48.42%4.55%43.66%18.12%8.79%80.19%81.54%0.12%29.06%75.72%8.70%3.75%
Bhadohi52.87%6.70% 40.60%13.75%9.45%74.68%77.13%15.76%17.75%82.87%10.61%2.84%
Bijnor45.26%3.36%32.15%16.33%9.93%66.48%36.28%0.99%19.20%50.70%14.07%4.48%
Budaun52.10%7.73%44.52%29.75%28.94%79.90%68.28%0.59%45.57%74.23%22.77%5.45%
Bulandshahr43.18%5.45%37.02%16.11%11.05%69.46%49.68%0.74%10.49%68.25%12.35%6.02%
Chandauli48.22%3.81%41.64%11.63%5.34%78.13%67.76%11.39%26.42%66.21%8.36%3.27%
Chitrakoot52.06%6.12%39.94%19.13%14.68%89.27%84.31%21.67%30.39%89.82%25.09%2.34%
Deoria42.74%3.85%35.19%13.26%5.28%68.11%75.19%0.57%28.21%67.25%9.90%3.43%
Etah43.38%6.37%38.31%11.78%9.26%75.81%74.52%3.07%31.98%70.69%16.59%4.06%
Etawah41.08%3.78%33.88%6.72%5.36%73.89%69.50%3.26%7.80%64.23%9.51%4.72%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
274 275
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Uttar Pradesh
Faizabad47.61%5.18%36.28%14.46%8.55%74.97%74.44%1.46%31.60%73.66%10.13%4.28%
Farrukhabad44.07%7.58%40.83%15.83%10.91%77.63%69.47%0.91%37.57%69.18%15.54%5.98%
Fatehpur43.09%2.38%32.41%17.67%13.37%82.11%72.63%6.48%53.52%83.63%15.94%3.05%
Firozabad42.55%5.74%36.00%14.72%11.54%63.07%63.24%7.77%12.57%57.24%14.39%6.41%
Gautam Buddha Nagar30.63%4.42%27.50%7.84%4.82%28.76%40.19%31.11%1.10%43.22%8.65%4.52%
Ghaziabad33.85%3.85%28.11%10.72%7.74%24.83%30.84%14.92%2.57%39.08%10.03%3.96%
Ghazipur45.45%5.27%36.83%12.59%7.42%85.54%80.41%2.34%29.46%82.35%7.85%3.59%
Gonda56.46%7.87%51.16%27.70%16.92%79.85%89.20%0.14%58.55%76.44%12.50%9.10%
Gorakhpur37.88%4.58%32.45%14.66%5.09%58.43%63.48%0.68%22.57%59.48%9.04%5.51%
Hamirpur40.86%3.11%32.44%12.23%7.11%80.74%51.50%7.38%23.38%68.64%17.24%2.77%
Hardoi48.21%5.58%41.01%21.28%16.25%77.59%70.86%2.23%56.08%81.49%14.49%6.21%
Jalaun34.07%3.24%27.37%10.11%7.52%68.88%50.93%8.60%10.10%64.68%12.92%2.24%
Jaunpur50.02%6.69%40.77%9.34%7.91%78.46%74.97%5.52%19.33%81.43%6.24%2.52%
Jhansi35.06%3.13%21.36%11.00%5.37%58.58%51.69%12.02%9.00%43.90%11.95%2.46%
Jyotiba Phule Nagar49.12%5.03%31.61%18.33%13.70%70.90%47.17%0.64%23.28%54.64%18.27%3.37%
Kannauj46.25%6.51%41.17%18.11%11.26%81.03%76.31%5.15%28.51%72.38%17.08%6.18%
Kanpur Dehat41.50%3.38%32.78%10.09%6.81%83.06%70.30%7.37%54.36%84.31%12.98%2.40%
Kanpur Nagar27.13%1.72%18.74%7.22%5.72%29.90%35.19%4.82%14.84%32.04%5.41%2.89%
Kansiram Nagar48.56%8.99%44.71%24.01%14.96%81.71%77.29%0.78%43.37%64.11%14.75%6.55%
Kaushambi50.64%5.75%40.03%32.16%19.96%82.71%80.19%10.69%49.63%83.38%16.71%5.39%
Kheri54.20%6.75%44.49%32.03%23.84%84.17%74.02%1.46%57.15%85.36%21.65%4.91%
Kushinagar46.71%5.56%46.18%16.96%6.34%76.17%75.94%2.43%49.41%70.04%10.98%3.85%
Lalitpur41.38%4.01%24.82%14.09%6.08%82.11%77.86%18.66%19.81%74.81%22.37%2.34%
Lucknow29.03%2.26%13.64%8.70%4.66%25.29%34.38%1.68%6.46%30.15%4.63%3.13%
Mahamaya Nagar39.45%3.93%33.08%11.82%8.23%71.38%67.56%5.95%10.92%94.30%10.47%3.24%
Maharajganj51.40%4.75%43.32%23.04%8.54%80.85%79.17%1.59%43.52%72.96%8.30%4.50%
Mahoba46.29%2.36%28.39%13.87%6.44%84.61%63.87%21.25%17.59%63.04%17.53%2.08%
Mainpuri39.83%7.23%38.41%8.79%5.74%76.13%71.11%2.25%16.22%71.05%12.55%8.49%
Mathura43.50%5.88%34.68%10.93%10.17%69.06%55.70%27.26%3.46%88.53%9
.74%4.18%
Mau48.11%4.99%37.62%11.62%9.25%78.86%71.84%0.46%14.96%73.45%9.15%2.81%
Meerut35.21%4.21%26.44%15.86%14.13%39.02%30.57%0.81%4.04%21.64%8.40%6.29%
Mirzapur47.97%5.92%43.84%14.10%9.04%82.91%76.23%18.01%27.87%88.10%8.89%3.36%
Moradabad48.39%6.04%29.54%24.07%16.63%57.49%45.01%0.83%20.65%47.55%18.01%3.64%
Muzaffarnagar42.70%3.61%31.56%18.20%15.29%64.10%42.97%1.18%5.78%46.90%14.77%3.49%
Pilibhit49.48%4.32%33.33%19.34%17.69%71.61%59.92%0.52%47.21%73.85%17.06%2.22%
Pratapgarh43.73%3.45%32.21%15.97%5.73%76.33%83.47%8.88%26.16%74.16%8.62%4.79%
Rae Bareli40.26%3.06%27.22%18.87%9.25%77.75%81.24%3.19%25.98%81.23%11.29%3.17%
Rampur44.55%3.43%25.37%32.04%22.76%69.77%43.32%1.48%17.95%60.51%14.50%3.03%
Saharanpur44.14%5.16%29.42%19.97%12.40%65.30%45.50%3.04%5.44%47.99%13.82%3.51%
Sant Kabir Nagar50.38%5.73%41.05%17.14%8.58%82.41%83.06%0.35%33.11%81.33%13.02%5.52%
Shahjahanpur50.24%7.45%38.78%20.86%20.87%76.42%65.72%0.58%45.43%80.35%18.52%3.50%
Shrawasti59.14%9.37%58.70%44.88%30.77%90.63%89.55%3.59%70.50%92.43%21.98%12.84%
Siddharth Nagar56.66%6.26%52.55%29.62%17.61%80.30%84.51%0.55%33.86%76.28%12.27%9.63%
Uncensored Headcount RatioHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Uttar Pradesh
Sitapur52.08%6.39%40.61%23.77%16.81%81.58%82.58%1.85%69.63%84.48%20.20%4.38%
Sonbhadra47.44%4.57%38.34%22.19%12.79%83.23%78.31%21.85%47.10%78.77%17.48%3.38%
Sultanpur44.66%6.65%34.71%16.01%8.35%83.02%81.07%4.97%22.56%83.62%6.38%4.29%
Unnao43.51%4.17%36.50%17.75%10.21%77.05%69.33%3.21%51.63%73.68%12.58%4.81%
Varanasi41.60%3.49%31.03%9.81%7.64%52.71%49.87%5.34%10.24%65.11%5.32%3.87%
Uttarakhand
Almora39.37%2.32%30.67%6.14%0.19%74.02%33.89%25.16%2.65%74.57%26.50%4.59%
Bageshwar36.18%1.64%30.70%6.44%0.51%80.84%31.44%21.81%2.38%52.37%29.73%2.41%
Chamoli28.36%2.53%29.02%5.35%0.14%67.63%34.99%12.31%4.22%55.68%26.52%3.27%
Champawat32.62%3.18%30.93%8.73%2.47%72.53%39.85%13.76%7.16%58.32%28.89%3.93%
Dehradun28.91%1.56%20.45%6.68%1.81%16.96%23.18%0.94%0.51%11.88%5.72%2.92%
Garhwal26.03%1.18%21.19%5.01%0.85%65.05%31.90%17.64%1.74%45.24%15.31%5.88%
Haridwar37.91%4.29%36.53%16.58%10.22%59.83%40.42%1.56%2.02%29.45%9.26%10.57%
Nainital30.93%2.32%26.09%5.79%3.88%40.91%27.20%4.90%1.22%25.17%12.45%9.10%
Pithoragarh23.30%2.33%24.25%6.79%0.41%65.45%37.64%19.19%2.54%51.22%23.97%3.32%
Rudraprayag23.90%0.77%23.52%6.52%0.38%71.63%31.31%22.72%0.92%45.34%20.95%3.78%
Tehri Garhwal35.12%2.61%29.36%8.07%0.00%68.22%33.80%28.13%1.83%39.70%21.15%1.64%
Udham Singh Nagar36.54%2.87%33.59%14.57%8.88%51.85%39.69%2.59%3.07%40.45%7.79%13.03%
Uttarkashi33.03%2.80%26.45%7.06%1.72%75.32%53.14%29.49%6.42%51.63%34.69%3.17%
West Bengal
Bankura43.81%2.07%8.52%16.66%3.31%84.95%68.59%17.07%9.80%57.49%15.34%8.19%
Barddhaman34.95%0.79%8.36%17.45%2.75%71.31%51.13%11.86%4.89%44.78%14.89%12.85%
Birbhum41.42%0.76%8.70%22.56%3.23%83.26%68.64%10.92%3.63%65.88%21.41%11.53%
Dakshin Dinajpur32.69%2.01%11.72%17.93%4.22%88.37%52.72%2.68%5.38%78.99%14.68%9.33%
Darjeeling25.38%0.68%15.61%8.52%2.26%43.32%35.52%32.81%3.61%32.83%11.53%13.79%
Howrah30.06%1.99%11.02%11.03%4.73%55.75%39.74%9.60%2.05%29.96%8.97%13.40%
Hugli28.40%1.21%10.15%13.61%1.88%69.37%42.61%6.15%4.73%34.72%10.75%14.83%
Jalpaiguri35.67%0.92%13.86%15.57%3.19%73.38%48.23%17.56%9.23%54.43%18.29%25.61%
Koch Bihar35.73%2.06%13.92%18.78%3.32%86.54%47.04%4.14%9.51%79.35%16.40%16.42%
Kolkata16.53%1.21%1.39%6.97%2.60%8.78%53.70%6.06%0.13%5.98%5.24%10.35%
Maldah40.61%2.97%27.89%24.87%5.48%83.93%53.47%18.
05%4.04%76.04%15.85%12.12%
Murshidabad38.65%1.31%21.12%20.62%4.65%82.40%46.16%7.16%6.93%56.76%16.82%12.09%
Nadia23.68%1.07%7.56%16.39%1.22%71.87%29.58%11.10%3.68%49.77%8.19%7.09%
North 24 Parganas20.28%0.65%11.18%9.39%2.93%51.72%31.50%9.79%2.49%41.61%8.06%12.27%
Pashchim Medinipur42.72%1.48%10.17%14.12%2.65%86.33%56.39%10.54%3.55%70.43%13.76%9.68%
Purba Medinipur31.93%1.43%13.56%9.90%2.88% 90.06%29.67%9.69%2.12%66.02%8.99%10.12%
Puruliya62.78%3.17%20.07%20.91%4.85%93.01%87.65%41.20%17.95%69.90%22.70%24.22%
South 24 Parganas32.36%2.10%25.02%16.27%6.12%82.94%47.38%10.71%11.95%69.01%21.93%20.73%
Uttar Dinajpur41.32%2.79%37.21%29.15%13.29%89.88%67.43%2.92%9.24%75.07%21.75%24.41%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
276 277
Uncensored Headcount RatioHealthEducationStandard of Living
UT DistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Andaman
& Nicobar
Nicobar14.48%0.31%12.16%3.76%4.09%47.10%13.98%0.01%0.27%75.54%10.41%1.61%
North & Middle Andaman25.22%0.88%5.74%7.50%0.44%55.71%49.77%10.84%7.30%68.89%15.65%2.61%
South Andaman21.60%0.87%4.11%3.96%0.77%9.78%15.44%5.02%1.16%15.18%3.37%1.15%
Chandigarh23.11%1.16%11.05%5.83%1.76%4.85%19.04%2.35%0.48%6.40%2.71%3.97%
Dadra & Nagar Haveli45.00%2.01%13.44%7.76%7.73%46.62%68.11%26.95%2.74%57.06%20.81%10.92%
Daman
& Diu
Daman18.01%0.85%15.42%7.61%5.57%4.03%36.94%14.19%0.00%7.38%17.32%14.94%
Diu32.21%1.09%11.84%7.38%1.43%29.49%26.49%1.88%0.16%10.80%4.69%3.10%
Delhi
Central Delhi16.88%1.35%6.56%5.62%3.97%0.76%24.32%7.81%0.55%9.73%5.38%7.21%
East Delhi18.70%2.30%13.05%6.08%2.70%0.59%20.07%7.20%0.05%18.34%5.14%14.66%
New Delhi18.72%1.58%10.30%3.09%1.63%5.25%35.22%16.13%0.22%13.07%4.49%8.04%
North Delhi25.88%2.48%11.69%4.28%3.28%1.57%22.33%12.10%1.06%14.70%6.56%11.33%
North East27.75%2.16%22.40%7.71%6.38%1.03%25.88%6.49%0.00%16.54%4.41%9.21%
North West23.01%2.39%16.48%8.47%3.56%3.79%34.51%26.01%0.87%7.82%7.81%11.38%
South Delhi22.86%2.22%11.36%7.16%1.54%3.59%28.36%27.06%0.20%4.80%8.83%7.30%
South West26.56%1.01%19.35%3.80%0.65%2.26%31.00%50.48%0.02%14.07%2.72%5.94%
West Delhi18.45%1.66%8.53%3.05%1.47%0.67%13.27%10.34%0.00%5.81%3.68%3.70%
Jammu & Kashmir
Anantnag18.29%1.84%11.54%7.95%4.54%32.14%43.44%6.42%3.49%19.09%16.94%4.77%
Badgam21.50%2.00%3.78%8.70%3.93%41.73%48.04%4.95%0.95%17.69%9.17%1.54%
Bandipore21.13%2.31%9.54%9.70%6.24%48.80%61.59%11.03%4.87%20.54%12.94%4.18%
Baramula26.63%2.08%10.09%5.48%3.55%29.79%36.83%9.84%0.10%20.98%11.23%3.79%
Doda28.95%2.63%31.68%10.44%9.51%86.66%54.65%15.69%6.07%69.62%47.18%5.40%
Ganderbal23.20%2.74%8.01%6.98%3.46%33.55%41.55%6.54%0.25%14.85%9.85%3.68%
Jammu25.68%0.77%9.35%5.72%1.43%28.64%36.88%4.53%0.12%17.62%4.33%4.11%
Kathua30.89%2.18%7.63%5.40%2.28%62.32%58.40%20.40%0.36%37.67%15.40%3.79%
Kishtwar28.85%3.58%22.78%12.60%7.10%75.30%58.80%14.50%12.91%56.89%34.63%2.75%
Kulgam19.88%2.67%8.40%8.34%4.00%53.67%45.54%3.37%2.85%21.09%14.39%2.75%
Kupwara34.54%2.78%16.62%4.80%4.18%53.29%59.10%13.13%6.18%28.39%23.62%3.20%
Pulwama18.41%1.94%5.20%4.64%1.53%24.56%30.24%5.67%1.63%9.50%9.93%2.35%
Punch34.37%2.35%25.34%5.18%3.12%79
.00%44.90%33.02%3.40%52.34%33.28%5.03%
Rajouri36.77%2.04%27.64%7.19%5.02%68.77%60.92%40.82%5.94%45.16%28.27%9.25%
Ramban31.71%2.65%28.32%17.94%12.19%81.93%58.07%23.47%11.30%67.51%44.54%3.66%
Reasi32.53%1.65%27.94%5.46%3.22%84.58%55.11%32.42%9.82%57.31%21.68%3.61%
Samba25.49%1.03%14.05%4.37%2.16%51.66%54.26%7.70%0.67%25.13%12.80%6.06%
Shupiyan20.96%1.74%8.12%6.37%3.41%60.96%39.52%7.20%3.11%17.03%12.14%2.35%
Srinagar15.20%0.89%3.00%4.93%2.08%2.61%31.90%2.97%0.06%8.24%1.63%2.10%
Udhampur38.85%1.77%10.95%8.65%4.67%75.45%65.85%45.06%2.55%57.92%27.04%4.89%
Ladakh
Kargil35.76%2.85%18.19%5.36%3.24%55.96%82.79%18.20%2.48%85.68%10.73%2.15%
Leh (Ladakh)16.66%1.28%4.30%9.00%1.12%11.27%82.32%33.91%0.16%91.01%7.27%1.50%
Lakshadweep31.47%1.96%6.50%0.95%1.43%58.15%0.44%9.31%0.05%1.54%1.02%5.62%
Puducherry
Karaikal27.37%0.58%5.38%4.02%0.49%23.80%30.66%3.56%0.36%28.10%2.69%4.07%
Mahe22.92%0.09%8.25%0.79%0.00%11.93%1.78%10.13%0.18%7.73%0.96%2.75%
Puducherry20.43%0.68%3.48%2.95%1.43%11.71%37.82%5.74%0.21%16.30%1.30%5.78%
Yanam25.96%1.12%8.12%8.81%0.97%6.92%26.09%4.90%0.40%7.01%4.54%4.36%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Andhra Pradesh
Anantapur36.78%1.86%12.06%16.52%2.10%43.92%65.52%35.02%0.62%38.30%11.61%4.55%
Chittoor32.42%0.43%12.15%13.97%1.41%49.27%67.14%28.63%0.87%12.16%11.18%2.98%
East Godavari24.04%1.78%11.14%18.67%1.40%60.91%49.01%22.21%0.38%16.87%8.38%5.11%
Guntur16.48%1.51%6.19%23.15%1.04%26.79%43.86%39.86%0.71%23.95%15.28%5.13%
Krishna25.10%0.83%5.08%20.55%1.18%39.10%44.26%44.85%0.54%30.79%10.37%2.85%
Kurnool42.97%3.97%18.25%22.37%7.37%48.55%55.55%26.76%0.16%38.11%11.10%3.14%
Prakasam28.69%3.51%11.44%24.97%4.05%36.36%48.73%47.48%1.05%14.64%14.34%4.22%
SPSR Nellore24.98%1.56%7.76%16.39%2.16%51.11%56.82%35.83%2.23%14.46%11.74%5.52%
Srikakulam31.98%3.10%9.60%15.45%1.54%69.40%73.72%29.40%2.22%16.87%18.72%2.71%
Visakhapatnam36.32%3.19%16.43%22.12%4.07%80.55%74.79%27.33%1.23%22.17%27.35%4.04%
Vizianagaram39.86%1.92%11.41%21.85%1.00%65.62%82.31%18.68%1.44%21.20%20.77%4.35%
West Godavari20.88%2.12%7.98%19.98%1.73%45.06%43.80%31.34%0.85%21.65%11.22%4.99%
Y.S.R. Kadapa32.01%2.02%7.82%22.61%4.25%35.22%42.46%38.03%0.56%13.39%11.27%2.83%
Arunachal Pradesh
Anjaw11.29%1.41%28.05%19.90%4.71%82.07%56.96%14.80%10.60%86.99%33.65%7.62%
Changlang19.87%2.26%26.43%18.90%11.59%82.19%45.86%35.39%25.13%85.23%17.87%12.88%
Dibang Valley19.65%0.63%26.03%20.81%3.54%72.11%20.93%1.84%10.22%79.22%33.63%11.09%
East Kameng27.86%2.89%43.18%26.70%16.03%85.71%75.81%25.52%70.61%95.48%73.26%43.10%
East Siang13.83%0.41%21.83%11.82%5.09%76.82%29.82%2.91%5.92%83.12%9.81%4.14%
Kurung Kumey29.20%2.68%40.50%29.88%9.51%61.70%54.18%7.30%8.34%91.72%42.78%29.61%
Lohit30.67%4.58%31.35%25.54%10.89%89.51%39.70%12.08%24.39%88.10%24.49%18.61%
Lower Dibang Valley20.31%2.60%28.64%22.37%6.70%70.75%31.54%7.13%36.27%84.99%11.13%16.46%
Lower Subansiri16.89%0.92%19.75%17.78%6.59%37.91%29.19%2.73%1.26%75.92%15.87%21.05%
Papum Pare24.85%1.16%33.85%14.57%8.93%32.99%31.48%15.15%3.25%73.89%14.67%15.26%
Tawang12.87%2.14%34.49%43.74%7.62%60.75%46.25%6.78%3.77%69.72%36.64%28.89%
Tirap25.97%3.50%29.87%19.44%10.04%93.52%51.22%38.53%2.11%93.37%38.63%6.92%
Upper Siang15.36%0.41%19.45%16.98%4.59%93.15%49.53%12.74%8.22%91.64%30.88%11.58%
Upper Subansiri23.93%0.91%35.58%26.29%5.09%73.52%42.22%16.25%14.65%94.90%45.42%30.32%
West Kameng20.98%1.10%32.53%21.09%7.59%47.96%42.52%7.08%1.58%67.46%26.06%20.45%
West Siang16.58%1.29%25.85%11.85%2.69%69.02%30.07%15.
93%6.37%90.24%26.38%16.17%
Assam
Baksa32.77%2.45%23.76%10.33%2.44%86.12%42.45%18.37%17.00%82.31%9.63%10.97%
Barpeta45.30%2.96%31.78%18.34%6.08%83.55%64.70%1.83%29.14%85.91%19.41%22.25%
Bongaigaon39.37%2.15%37.38%17.98%6.39%84.48%57.85%26.25%11.63%82.05%14.40%17.03%
Cachar51.27%4.71%25.59%16.94%9.74%83.84%68.24%53.62%38.89%82.65%38.63%11.43%
Chirang36.90%2.42%31.15%15.45%3.26%85.48%69.57%31.32%29.17%84.46%13.40%18.03%
Darrang44.95%3.18%31.85%24.30%11.33%88.36%55.01%3.27%25.73%84.41%16.02%21.00%
Dhemaji34.38%3.27%25.03%8.55%5.49%92.62%56.74%9.04%38.06% 90.40%18.95%21.27%
Dhubri49.23%2.25%40.18%29.52%14.23%92.48%69.65%10.75%31.67%90.93%34.97%37.89%
Dibrugarh44.86%1.59%19.04%19.08%5.78%86.62%39.79%1.50%28.30%77.89%21.73%16.81%
Dima Hasao34.09%4.38%36.64%19.66%7.74%96.01%44.33%61.10%29.03%89.33%60.56%18.61%
Goalpara45.99%2.73%36.12%25.37%11.51%90.23%55.97%14.33%34.93%82.13%26.79%20.58%
Golaghat36.84%2.71%16.02%11.02%3.35%91.42%38.01%6.78%11.71%84.61%13.47%13.23%
Hailakandi48.39%4.26%34.94%17.98%11.06%92.18%66.77%61.35%47.21%89.95%46.05%7.81%
Jorhat40.18%2.27%12.05%8.32%5.00%81.36%37.54%15.17%15.03%73.09%11.88%11.74%
Kamrup33.79%1.79%23.37%13.22%5.04%65.34%44.61%6.06%10.38%74.42%14.71%23.47%
T
able 12- DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO
INDIA MpI BASelINe reporT DistrictData Tables
278 279
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Assam
Kamrup Metropolitan32.26%2.50%24.18%14.30%2.66%64.54%43.14%29.62%16.20%65.49%16.85%12.12%
Karbi Anglong33.41%4.25%33.95%21.12%8.33%91.53%57.66%49.59%19.42%90.38%20.88%13.79%
Karimganj52.11%5.80%39.34%14.63%8.99%84.26%63.30%48.22%29.20%75.70%41.65%8.91%
Kokrajhar33.08%2.93%28.44%18.93%4.90%86.69%60.85%24.19%26.36%81.75%18.31%17.35%
Lakhimpur34.44%2.57%20.26%9.33%4.31%88.77%52.00%28.07%26.79%81.68%19.01%12.05%
Marigaon44.36%5.71%30.03%19.65%7.16%87.32% 60.06%2.95%22.96%87.55%20.63%17.48%
Nagaon43.09%3.69%31.12%16.96%5.13%88.32%54.23%6.74%17.62%82.78%18.58%8.95%
Nalbari31.77%2.43%22.00%9.73%3.22%67.86%45.64%1.34%17.65%76.43%10.48%11.31%
Sivasagar43.59%3.38%13.09%14.12%7.05%80.70%42.21%5.73%20.88%75.46%19.02%13.25%
Sonitpur34.07%2.47%22.28%17.93%5.60%83.53%38.37%30.16%22.41%74.00%11.31%9.28%
Tinsukia50.96%2.84%20.41%32.71%14.14%88.17%53.23%4.84%28.52%79.74%28.96%30.33%
Udalguri36.19%2.57%27.88%17.94%5.06%90.37%44.78%18.96%15.12%85.79%8.97%11.76%
Bihar
Araria59.94%7.92%47.83%44.15%19.95%97.93%89.29%0.57%52.95%91.35%27.40%38.03%
Arwal56.30%4.57%46.50%16.37%7.79%93.01%77.72%0.81%55.03%79.05%32.35%16.49%
Aurangabad50.04%3.17%40.48%13.91%7.19%90.67%76.84%1.83%42.80%73.99%22.41%18.70%
Banka55.50%4.26%43.13%28.28%9.94%92.28%84.65%9.14%52.78%78.69%26.34%28.31%
Begusarai52.81%4.86%46.06%31.19%11.15%88.37%68.56%2.29%38.59%78.72%29.04%19.93%
Bhagalpur52.10%3.52%46.68%31.30%12.47%90.77%71.68%3.77%35.01%78.62%30.42%23.24%
Bhojpur51.20%4.79%43.63%12.27%7.52%87.28%77.42%0.30%37.84%72.20%20.11%18.69%
Buxar50.90%6.42%43.95%14.29%3.68%84.36%74.55%0.14%36.87%76.43%19.76%14.66%
Darbhanga54.37%3.09%50.81%32.69%13.82%88.32%74.83%0.51%42.21%87.69%28.24%34.75%
Gaya65.02%8.26%54.11%21.81%10.59%89.62%75.48%5.01%29.46%74.04%29.85%23.47%
Gopalganj43.14%3.76%41.10%16.62%7.24%80.52%71.59%1.58%42.29%70.29%12.49%31.39%
Jamui62.54%5.50%56.38%30.44%12.84%92.65%86.60%21.75%38.66%80.62%31.18%22.20%
Jehanabad50.97%4.47%43.59%23.91%11.38%90.86%73.55%1.86%34.05%66.09%38.24%20.07%
Kaimur50.89%4.48%44.20%16.19%9.09%91.10%80.24%5.73%26.78%74.68%23.34%16.36%
Katihar51.61%4.75%46.58%40.33%21.40%95.71%82.43%0.53%73.49%87.42%22.85%35.81%
Khagaria55.36%6.02%51.91%36.74%16.41%90.53%70.95%1.08%47.33%81.05%38.58%30.72%
Kishanganj56.40%3.16%48.29%43.22%20.86%97.19%83.88%1.1
4%47.82%84.23%23.85%42.40%
Lakhisarai50.64%6.00%47.04%24.60%9.40%89.90%62.65%7.78%20.00%65.20%35.99%19.69%
Madhepura58.29%4.92%52.01%41.98%19.38%95.30%87.68%0.18%48.09%91.10%28.64%28.24%
Madhubani54.78%3.57%45.10%27.58%11.98%89.41%80.61%1.11%46.80%86.24%26.92%30.59%
Munger48.80%4.94%42.56%22.04%7.46%85.68%72.21%12.14%39.02%71.36%26.64%17.57%
Muzaffarpur50.72%3.36%42.16%25.80%10.97%85.59%74.91%0.36%28.07%84.28%19.67%25.74%
Nalanda54.14%4.47%43.01%25.58%15.37%88.45%75.71%2.33%35.79%62.22%32.14%21.22%
Nawada53.58%2.90%46.67%24.57%13.18%86.86%75.27%2.56%48.41%65.58%47.80%27.01%
Pashchim Champaran50.05%5.41%47.36%36.89%18.11%92.48%87.40%3.26%60.02% 90.60%22.18%47.36%
Patna51.92%3.99%45.59%18.96%7.99%82.09%68.30%2.22%23.20%63.31%24.01%14.95%
Purba Champaran53.43%4.50%53.58%35.44%20.86%87.14%77.81%0.62%59.59%82.18%23.77%33.57%
Purnia62.74%6.90%53.22%41.89%17.60%97.65%88.00%0.00%54.08%90.55%28.43%32.65%
Rohtas51.98%5.51%46.52%10.33%4.06%90.04%76.82%1.54%19.67%67.92%22.05%11.90%
Saharsa59.23%6.41%53.12%36.27%20.32%92.89%85.71%0.86%42.64%88.58%32.97%31.55%
Samastipur47.87%3.17%47.37%29.99%13.79%89.31%82.07%1.80%60.53%87.19%29.14%38.03%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Bihar
Saran46.39%4.24%44.91%18.32%9.85%89.98%76.59%1.86%41.46%71.50%17.07%30.23%
Sheikhpura62.10%5.64%53.64%26.39%10.59%92.99%70.40%8.39%20.12%68.92%30.91%18.92%
Sheohar54.82%6.79%48.70%38.49%13.69%89.57%76.72%1.07%39.66%89.76%30.75%26.47%
Sitamarhi55.77%5.78%48.03%38.72%17.33%91.48%82.17%0.91%62.45%91.38%28.93%30.75%
Siwan46.05%4.94%42.73%12.80%8.28%80.92%76.07%1.50%46.67%66.75%13.85%16.78%
Supaul60.83%3.49%52.78%34.86%14.90%94.82%84.14%0.37%40.12%88.54%25.45%25.89%
Vaishali54.99%5.82%46.56%22.94%8.92%83.99%67.72%2.13%37.54%79.06%17.91%20.22%
Chhattisgarh
Bastar59.78%4.32%29.44%26.03%10.27%95.71%86.82%18.80%14.33%89.79%32.82%6.66%
Bijapur48.11%3.57%34.57%21.05%5.53%97.87%84.04%13.93%8.17%91.14%22.58%5.02%
Bilaspur38.87%4.26%32.37%11.39%5.65%89.97%72.58%22.76%0.59%75.19%9.53%4.21%
Dantewada59.81%6.93%29.91%39.82%25.49%95.33%84.87%18.15%14.55%91.44%48.47%10.01%
Dhamtari47.60%2.85%16.03%7.93%1.98%91.86%47.27%16.08%1.39%58.26%11.98%2.29%
Durg44.70%3.17%26.30%9.28%3.07%91.60%66.72%14.96%0.71%65.02%9.30%5.38%
Janjgir-Champa39.30%2.04%21.82%11.03%6.80%87.81%74.83%10.60%1.77%60.12%11.64%4.64%
Jashpur44.74%4.57%33.72%17.59%7.80%96.04%85.92%33.17%16.54%92.67%34.90%11.05%
Kabeerdham50.50%3.41%33.15%21.57%8.57%95.32%81.62%16.43%3.91%76.58%17.96%7.52%
Korba51.80%5.11%34.06%17.72%4.16%93.93%83.14%37.84%8.24%81.01%22.86%5.61%
Koriya46.25%3.69%33.09%21.21%5.18%95.16%86.80%49.11%12.63%92.29%28.84%7.27%
Mahasamund48.39%2.53%21.99%13.35%4.11%92.19%77.68%12.80%1.16%71.27%11.26%4.77%
Narayanpur55.80%3.67%37.66%30.92%13.71%97.17%92.40%19.68%25.01%94.02%32.12%8.13%
North Bastar Kanker50.19%3.60%19.08%11.84%4.28%94.38%64.56%9.72%4.02%84.01%20.11%5.30%
Raigarh41.21%3.23%23.67%14.88%4.43%90.03%77.85%16.32%4.54%78.98%18.36%10.12%
Raipur45.70%4.10%24.24%13.06%5.36%91.78%72.47%19.24%1.49%58.53%10.55%6.50%
Rajnandgaon45.29%3.07%26.75%6.09%1.35%93.99%56.56%17.18%1.26%76.22%4.99%1.38%
Surguja48.07%3.61%30.29%25.19%7.50%95.01%84.89%40.53%6.74%94.19%34.45%9.79%
Goa
North Goa34.84%0.65%8.26%3.02%0.00%28.54%18.83%6.29%0.18%29.13%4.63%6.15%
South Goa27.56%0.00%10.92%4.17%2.97%28.34%16.08%6.92%0.00%23.28%3.13%3.14%
Gujarat
Ahmadabad50.52%5.11%8.34%15.35%7.57%64.37%29.21%7.68%1.68%22.96%11.27%7.84%
Amreli28.75%1.00%13.77%10.92%4.93%58.50%30
.84%7.54%1.16%25.89%6.88%8.15%
Anand57.69%5.08%9.47%5.83%4.91%76.77%43.44%3.63%2.74%29.06%12.08%9.83%
Banas Kantha53.37%3.53%23.98%14.55%14.56%76.75%66.80%7.65%12.66%27.23%29.78%6.83%
Bharuch51.13%4.53%11.90%12.71%7.18%71.10%42.92%15.27%3.50%40.85%18.24%10.43%
Bhavnagar41.28%1.48%22.86%15.13%6.46%82.92%53.36%13.53%0.91%50.02%9.15%16.03%
Dang64.90%3.19%34.84%22.16%9.76%97.77%85.34%51.21%12.57%94.35%37.81%18.98%
Dohad61.70%6.01%28.63%18.79%13.29%93.78%85.84%40.15%22.79%77.33%39.61%13.92%
Gandhinagar49.05%3.21%6.90%8.85%3.65%63.43%53.17%4.29%2.73%18.71%13.09%9.29%
Jamnagar31.35%1.87%8.02%13.61%6.71%62.61%41.90%15.49%1.21%10.58%9.01%10.81%
Junagadh30.37%2.84%10.61%11.03%8.37%70.17%33.76%7.74%0.89%18.51%7.88%5.02%
Kachchh43.79%3.58%22.33%23.13%19.35%73.47%45.80%19.51%5.20%9.32%24.00%12.65%
Kheda53.42%3.01%20.13%4.86%5.58%77.90%55.42%8.41%3.08%43.60%22.02%16.91%
Mahesana46.79%1.91%14.21%5.76%5.28%55.86%39.39%9.17%3.36%17.37%10.49%3.92%
Narmada62.72%2.25%25.98%12.27%6.52%92.49%70.53%6.57%6.81%71.04%29.07%6.23%
Navsari48.92%1.73%1.61%7.18%1.94%64.70%36.46%38.00%1.77%55.55%8.36%4.76%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
280 281
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Gujarat
Panch Mahals65.50%2.09%28.83%8.05%8.59%91.72%75.72%28.33%15.88%71.50%34.27%19.36%
Patan50.38%2.78%15.82%15.32%7.47%79.38%50.66%6.42%6.53%27.30%23.37%7.57%
Porbandar34.02%0.78%15.81%12.65%3.97%76.00%35.05%27.28%0.99%14.01%11.55%8.56%
Rajkot32.37%0.87%8.44%9.59%4.50%63.02%41.43%13.24%1.30%14.61%6.80%6.94%
Sabar Kantha52.42%1.85%21.34%8.39%5.96%79.34%62.28%13.33%4.77%36.20%25.41%7.10%
Surat54.92%0.53%22.35%11.64%8.74%60.55%56.57%9.81%2.18%53.33%16.28%10.04%
Surendranagar49.32%3.09%24.32%22.82%13.59%81.81%68.35%27.45%5.59%23.43%18.67%11.53%
Tapi58.24%0.79%12.61%11.69%3.28%83.15%65.16%16.00%6.82%80.55%19.61%10.03%
Vadodara49.14%3.57%22.08%22.32%10.54%82.52%62.95%13.67%3.79%49.94%21.89%12.62%
Valsad43.41%1.75%19.99%14.34%5.66%61.17%51.32%42.42%1.79%50.42%20.59%14.92%
Haryana
Ambala28.13%0.75%13.94%3.45%0.68%52.94%13.23%1.68%0.27%18.21%1.29%0.84%
Bhiwani37.26%2.93%23.61%4.83%1.51%79.74%18.33%23.31%0.86%42.87%5.66%4.46%
Faridabad37.01%3.83%35.43%14.85%11.81%64.62%25.44%14.26%0.93%32.84%7.48%16.45%
Fatehabad39.18%2.08%25.49%9.53%3.05%76.62%13.24%2.68%0.32%37.43%2.50%6.74%
Gurgaon36.27%0.90%31.67%3.01%2.20%62.50%21.89%5.98%1.00%26.76%10.53%10.56%
Hisar33.49%2.19%29.64%6.72%2.26%82.08%15.09%21.66%0.95%31.86%3.06%5.66%
Jhajjar23.12%0.78%21.45%2.33%1.93%76.01%13.47%11.36%0.93%15.32%4.45%5.90%
Jind35.40%2.11%19.37%4.46%1.34%78.20%16.92%21.82%0.44%35.88%2.25%4.14%
Kaithal35.86%2.82%22.02%6.07%0.63%71.11%23.50%9.61%0.18%30.77%2.90%2.59%
Karnal36.55%1.89%17.66%6.38%0.38%62.07%13.78%2.19%0.24%29.55%2.81%2.66%
Kurukshetra27.18%0.55%15.01%5.86%0.71%53.03%14.87%0.67%0.00%27.67%1.18%3.68%
Mahendragarh31.42%1.88%18.92%2.09%0.49%74.27%26.66%8.61%1.47%20.97%4.37%6.19%
Mewat61.42%9.48%66.72%27.08%33.15%88.71%49.54%38.02%10.90%70.34%25.91%20.14%
Palwal37.58%3.00%40.92%12.52%13.00%84.12%36.92%18.79%6.79%44.92%14.61%25.56%
Panchkula23.08%1.35%14.03%4.25%1.60%58.36%30.29%1.21%0.41%14.97%2.28%2.67%
Panipat28.06%1.03%24.00%7.85%3.25%65.04%7.11%1.93%0.00%22.81%2.30%1.57%
Rewari35.67%1.96%28.60%2.46%0.50%78.70%28.90%10.38%1.15%21.85%8.33%19.04%
Rohtak35.52%1.88%28.94%5.31%1.24%79.27%22.59%30.12%0.76%29.52%5.01%11.42%
Sirsa43.84%2.91%29.78%10.24%2.28%78.96%21.44%2.
95%0.95%44.11%4.34%4.67%
Sonipat30.43%0.40%25.05%2.80%0.95%70.54%18.34%7.49%0.64%20.85%2.73%4.61%
Yamunanagar31.93%0.14%10.68%6.01%2.43%58.86%25.63%0.30%0.00%34.85%2.87%4.48%
Himachal Pradesh
Bilaspur28.84%0.71%18.95%3.05%0.93%88.86%27.44%19.61%0.13%16.69%7.81%3.61%
Chamba30.09%3.06%24.51%7.29%1.16%83.33%22.68%12.87%1.75%41.17%13.46%2.08%
Hamirpur25.81%1.62%15.95%2.46%0.13%81.45%16.40%4.49%0.36%26.46%4.50%1.99%
Kangra28.97%1.72%11.71%2.62%0.15%72.87%32.20%4.82%0.00%29.53%3.83%3.96%
Kinnaur19.99%1.68%15.81%7.36%1.10%32.95%31.36%8.03%0.64%39.04%8.13%2.11%
Kullu23.37%1.11%18.64%3.61%1.81%68.65%30.98%7.38%0.51%52.41%9.19%1.45%
Lahul & Spiti15.04%1.43%13.66%11.23%0.75%43.46%44.89%4.36%1.95%69.68%8.56%1.79%
Mandi25.84%0.91%26.10%4.83%0.18%85.04%26.19%4.43%0.33%37.26%11.16%1.52%
Shimla27.03%2.01%16.85%3.35%1.68%48.99%29.89%7.71%0.67%42.25%9.98%3.63%
Sirmaur36.69%2.37%17.74%6.24%1.52%73.38%31.99%15.37%0.96%24.86%14.93%1.48%
Solan30.63%2.19%18.59%3.52%1.12%67.78%30.26%13.46%0.65%16.88%4.43%1.86%
Una24.27%1.42%18.94%3.18%1.73%74.99%29.87%2.10%0.24%22.92%4.36%3.67%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Jharkhand
Bokaro53.48%2.93%32.89%12.02%4.22%96.02%87.43%39.83%19.05%56.97%17.22%7.69%
Chatra59.37%4.30%49.16%24.61%10.23%93.68%89.00%40.01% 60.09%81.78%26.86%11.35%
Deoghar58.22%6.01%43.58%19.30%7.64%92.06%90.13%26.08%22.62%73.96%20.04%6.40%
Dhanbad50.18%2.34%32.48%13.12%3.62%93.98%86.86%41.27%6.09%46.37%14.26%7.61%
Dumka53.47%3.66%34.13%29.47%7.51%95.74%92.04%24.67%31.12%89.57%28.16%13.22%
Garhwa51.36%5.10%44.32%16.24%11.18%96.21%90.39%19.64%49.33%87.12%30.01%19.68%
Giridih56.23%4.32%39.29%24.49%12.90%90.13%86.97%44.45%8.63%65.44%23.68%6.67%
Godda51.23%4.07%35.18%31.02%12.64%96.19%85.23%27.43%20.62%77.63%27.92%5.51%
Gumla46.08%1.92%34.15%15.23%8.30%94.37%86.65%59.32%22.78%90.53%20.90%8.09%
Hazaribagh50.30%3.52%33.50%13.37%6.33%90.31%81.45%41.04%3.62%54.39%19.05%3.04%
Jamtara57.44%3.39%40.91%27.17%9.83%98.60%89.63%16.41%17.43%78.91%22.57%8.28%
Khunti46.30%1.62%30.24%20.95%7.17%96.60%88.20%55.13%34.18%91.81%28.77%8.05%
Kodarma53.77%2.86%26.90%17.92%6.92%81.40%81.01%29.81%9.84%47.38%18.08%2.40%
Latehar45.35%2.78%38.24%26.86%12.49%96.82%89.93%33.02%46.50%91.02%30.87%21.43%
Lohardaga51.32%1.31%35.63%18.48%11.59%93.56%86.78%39.28%18.57%89.32%21.19%10.24%
Pakur56.18%3.07%39.35%39.21%19.09%95.87%88.73%20.26%24.16%84.28%32.28%6.94%
Palamu47.74%3.82%39.37%21.66%11.58%92.65%88.41%19.41%41.06%86.19%37.56%19.79%
Pashchimi Singhbhum55.38%7.77%44.70%31.96%17.84%97.39%89.60%43.89%34.72%92.57%42.27%22.43%
Purbi Singhbhum44.06%1.32%29.25%17.11%6.19%95.22%90.55%29.99%14.39%81.60%21.28%11.74%
Ramgarh49.14%1.98%29.25%12.74%3.66%93.60%69.05%46.52%5.14%50.79%16.81%6.64%
Ranchi49.94%3.17%29.51%13.38%3.50%86.02%80.94%42.65%9.36%69.45%16.95%7.81%
Sahibganj52.41%5.90%44.53%39.84%17.38%93.62%75.38%27.77%28.01%79.57%38.92%13.61%
Saraikela-Kharsawan56.45%4.70%37.91%16.17%4.69%96.08%89.07%38.54%17.90%79.84%22.27%7.58%
Simdega44.55%4.12%37.14%17.45%9.14%98.47%94.94%45.95%47.33%93.47%25.33%11.12%
Karnataka
Bagalkot43.78%2.69%13.53%12.86%4.63%76.38%86.12%8.08%2.71%62.86%17.02%13.93%
Bangalore38.71%1.55%42.83%1.16%1.58%12.04%11.26%6.29%0.00%7.70%8.03%7.99%
Bangalore Rural33.62%1.17%11.88%6.64%1.77%37.97%23.62%20.49%1.35%46.38%9.55%7.57%
Belgaum37.79%2.06%11.70%9.09%1.74%67.39%64.43%7.89%1.48%43.62%10.23%5.13%
Bellary47.14%3.14%17.38%20.87%8.45%62.56%74.61%12.29%2.
97%55.13%18.44%17.81%
Bidar44.58%2.07%11.74%10.98%3.50%84.78%85.88%16.42%2.71%52.60%19.67%10.11%
Bijapur34.94%1.71%14.81%15.11%7.28%82.78%86.10%28.00%3.57%59.43%19.80%14.13%
Chamrajnagar34.48%2.59%8.92%19.07%2.30%66.27%70.60%12.14%4.59%44.10%17.20%19.33%
Chikkaballapura38.75%0.66%7.71%11.79%3.80%62.85%58.71%16.15%2.22%49.19%18.32%9.30%
Chikmagalur30.13%0.83%15.20%9.51%2.53%59.42%37.15%21.92%2.14%53.07%10.59%15.64%
Chitradurga32.91%2.40%9.85%9.42%3.41%80.13%68.89%21.94%3.70%52.22%19.36%11.11%
Dakshina Kannada39.77%1.03%15.02%4.64%1.56%68.10%9.12%20.47%1.72%35.83%6.97%8.13%
Davanagere41.90%2.33%7.89%11.49%4.12%66.85%50.10%9.42%1.53%50.76%10.70%8.92%
Dharwad35.98%2.18%13.75%7.61%1.66%80.52%50.76%12.71%1.81%79.97%16.60%3.47%
Gadag30.45%1.03%10.44%12.44%5.80%91.80%78.82%24.28%2.39%68.27%23.56%10.64%
Gulbarga47.18%0.31%13.31%14.41%7.98%87.96%89.73%10.17%1.91%59.91%13.93%15.25%
Hassan26.62%1.31%7.19%12.04%1.91%56.12%40.67%12.84%3.04%49.05%10.02%7.74%
Haveri36.65%1.15%13.49%11.46%3.67%74.45%49.67%17.56%2.88%61.94%13.63%8.01%
Kodagu34.09%1.14%8.89%8.62%3.15%59.41%15.38%21.35%3.44%34.28%12.23%8.38%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
282 283
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Karnataka
Kolar39.04%1.27%11.67%7.64%1.43%54.09%48.78%20.19%1.15%37.84%8.76%5.64%
Koppal49.80%2.24%21.91%12.91%5.90%79.48%59.21%17.33%0.70%55.51%12.86%6.81%
Mandya25.03%0.97%8.18%8.56%2.83%52.45%47.73%8.36%1.44%42.72%8.94%8.89%
Mysore30.79%0.62%10.92%11.17%1.72%50.62%50.19%7.79%3.90%45.30%16.73%13.02%
Raichur42.18%1.66%20.81%25.07%9.89%88.88%86.66%25.55%2.33%61.18%25.09%18.74%
Ramanagara30.24%0.54%11.54%11.66%2.11%49.35%40.84%12.60%2.38%44.69%9.71%6.09%
Shimoga37.58%1.99%10.17%8.97%3.86%52.91%30.95%27.76%4.07%54.93%9.49%9.95%
Tumkur31.28%2.48%13.33%9.77%2.83%72.58%57.25%20.32%1.72%57.12%14.76%4.45%
Udupi36.57%1.15%10.03%11.09%2.70%66.14%10.96%42.03%1.63%34.02%6.93%6.36%
Uttara Kannada43.34%0.91%12.51%7.16%2.54%62.87%38.07%38.62%3.23%42.20%14.12%12.37%
Yadgir45.41%2.36%23.91%32.64%20.46%90.25%90.19%25.04%2.14%59.54%28.44%17.50%
Kerala
Alappuzha14.55%0.44%1.47%1.24%0.45%38.60%4.87%12.02%0.34%15.05%2.08%5.90%
Ernakulam10.51%0.00%1.78%1.68%0.32%34.61%0.99%5.00%0.06%14.06%1.66%5.92%
Idukki14.15%0.29%0.66%3.24%0.00%55.64%5.52%7.97%0.67%17.73%2.34%5.57%
Kannur12.68%0.32%0.00%2.86%0.56%42.20%1.03%9.28%1.51%8.50%4.21%3.45%
Kasaragod18.50%0.00%1.39%3.70%0.70%52.73%2.84%10.26%1.26%29.95%3.86%4.84%
Kollam14.31%0.21%1.30%1.09%1.20%45.81%2.41%6.16%3.02%11.10%5.17%7.78%
Kottayam10.04%0.17%1.32%1.06%0.00%42.68%0.73%5.68%0.47%6.53%1.85%1.98%
Kozhikode19.75%0.00%1.71%1.79%0.00%58.40%0.61%5.25%1.11%19.13%3.55%3.31%
Malappuram20.82%0.17%3.21%1.65%1.14%58.01%1.19%5.76%0.91%8.57%5.46%5.06%
Palakkad20.21%0.00%0.81%2.04%0.13%54.65%2.89%6.97%0.77%13.04%3.51%2.73%
Pathanamthitta11.13%0.00%0.58%2.11%1.35%43.31%3.29%6.86%0.81%9.55%1.88%7.31%
Thiruvananthapuram12.28%0.20%2.22%2.85%0.72%58.36%3.81%6.89%0.69%14.03%6.12%4.25%
Thrissur13.72%0.26%0.00%1.71%0.40%41.11%0.42%7.05%0.46%6.63%1.46%1.78%
Wayanad20.39%0.54%3.98%3.61%1.24%69.10%4.26%7.20%3.77%23.56%8.93%5.61%
Madhya Pradesh
Alirajpur66.19%7.26%55.87%49.89%44.51%95.92%90.08%23.46%7.66%89.71%29.76%14.76%
Anuppur47.37%3.94%30.41%14.42%5.48%95.70%93.29%56.68%25.28%89.55%29.34%7.08%
Ashoknagar50.39%2.17%29.10%19.28%11.04%94.03%85.52%36.33%13.52%89.08%20.76%9.38%
Balaghat57.87%3.04%27.97%11.43%3.32%92.37%7
6.64%47.79%10.66%86.00%17.87%9.70%
Barwani70.75%5.48%49.64%41.51%37.05%91.56%90.05%29.61%11.87%85.00%38.17%15.70%
Betul46.86%4.16%30.76%16.90%5.48%88.73%79.85%38.57%7.79%82.59%28.01%9.90%
Bhind49.21%3.65%31.55%7.75%7.51%93.09%77.63%20.03%16.01%83.19%15.58%10.96%
Bhopal35.06%2.88%17.86%13.23%8.42%74.23%73.03%23.65%4.57%69.98%16.40%9.30%
Burhanpur52.80%3.97%36.99%32.49%18.01%75.62%68.89%23.99%13.90%67.34%29.57%15.65%
Chhatarpur50.41%5.82%40.96%21.94%12.95%97.50%94.43%54.39%24.73%84.30%25.09%18.34%
Chhindwara45.79%2.62%25.14%14.93%3.36%89.75%75.61%41.28%12.00%75.14%29.50%10.57%
Damoh46.27%4.23%35.01%19.13%9.62%97.57%87.51%58.26%15.22%91.52%34.21%22.72%
Datia48.39%6.29%32.89%11.38%4.66%95.92%73.33%30.05%12.08%95.71%18.04%6.88%
Dewas49.81%4.47%29.88%18.22%10.23%82.58%56.92%27.75%1.39%61.81%12.94%5.10%
Dhar55.04%5.09%34.98%27.37%13.71%83.02%73.61%28.87%5.23%67.39%21.83%11.27%
Dindori51.65%3.60%32.22%20.84%4.16%98.04%94.35%52.02%25.41%92.50%56.08%13.59%
East Nimar57.24%3.51%34.18%27.47%11.20%87.82%73.25%38.10%9.91%69.31%21.06%8.24%
Guna56.61%3.97%38.21%22.80%12.22%93.78%84.65%45.17%7.77%85.53%21.41%8.73%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Madhya Pradesh
Gwalior45.33%5.45%32.52%15.37%9.07%87.46%68.18%20.88%9.98%73.70%19.60%7.45%
Harda43.18%4.11%35.08%23.06%9.59%83.63%53.69%28.66%3.77%68.76%18.57%5.97%
Hoshangabad43.73%2.50%30.34%13.15%3.86%85.11%64.39%25.38%6.72%66.42%18.75%5.13%
Indore43.68%1.46%13.84%13.78%6.75%55.76%40.07%16.82%0.23%38.17%6.32%10.87%
Jabalpur44.37%3.40%24.26%12.09%7.22%89.07%76.21%26.08%7.84%69.98%27.40%6.48%
Jhabua58.05%6.28%43.96%44.14%36.59%95.49%91.03%38.62%11.77%86.32%44.65%30.91%
Katni45.57%5.62%29.65%12.98%4.95%94.43%86.69%36.03%18.71%86.65%28.59%12.81%
Mandla49.19%3.72%28.79%20.50%6.05%94.25%92.16%56.95%24.70%88.86%44.18%7.34%
Mandsaur50.15%2.50%29.01%17.27%5.27%86.55%77.17%43.34%2.38%67.06%12.92%17.91%
Morena46.72%4.33%29.01%10.31%9.61%92.12%77.43%22.23%15.01%80.89%17.33%5.60%
Narsimhapur42.99%2.49%27.26%15.13%2.52%92.09%64.65%19.47%7.27%81.56%31.54%19.04%
Neemuch48.69%3.13%27.25%20.49%6.08%76.94%75.61%37.87%2.12%59.79%11.95%17.91%
Panna41.50%4.76%34.72%20.66%9.74%95.25%86.40%43.66%21.47%88.00%26.90%16.37%
Raisen50.67%3.40%30.87%15.69%7.85%89.80%68.90%26.93%7.87%82.91%19.78%6.12%
Rajgarh49.80%3.61%29.74%20.06%7.28%93.84%89.14%65.02%5.04%73.41%22.80%8.22%
Ratlam52.55%2.43%29.12%25.28%12.16%86.03%80.76%28.12%6.16%72.75%33.27%31.14%
Rewa40.35%5.50%33.31%12.00%8.33%96.07%79.00%27.69%13.45%92.61%24.55%14.29%
Sagar41.07%3.62%37.86%14.51%5.46%95.99%79.92%43.65%17.30%89.95%26.99%13.61%
Satna40.28%4.23%30.46%14.34%6.47%93.08%77.81%24.78%11.36%88.01%25.29%8.42%
Sehore43.58%3.49%28.09%18.35%7.78%88.32%60.05%33.07%1.95%66.09%11.19%5.75%
Seoni48.69%2.02%24.27%14.67%4.42%91.90%82.30%47.53%18.24%85.12%29.62%14.26%
Shahdol43.83%4.46%30.88%17.15%7.11%93.36%91.19%56.62%24.26%87.17%30.07%13.55%
Shajapur48.75%2.41%20.94%20.70%11.59%88.82%71.81%46.95%3.39%63.97%12.87%10.39%
Sheopur53.13%4.44%33.89%24.95%10.74%91.79%87.99%32.12%19.20%83.90%31.21%9.83%
Shivpuri51.48%4.29%35.55%16.60%7.25%95.54%87.36%65.16%14.88%88.61%25.06%9.16%
Sidhi48.41%5.16%42.67%15.16%9.53%95.53%90.94%49.19%22.58%91.90%34.40%20.15%
Singrauli49.83%6.97%47.06%20.37%12.26%95.73%95.88%62.87%34.66%94.16%34.51%11.12%
Tikamgarh50.14%3.67%33.76%14.94%7.09%92.05%88.65%47.62%14.73%76.57%25.55%20.70%
Ujjain50.08%2.54%32.96%20.58%8.61%80.30%61.62%32.81%3.63%61.58%1
4.58%19.52%
Umaria46.62%4.02%36.09%16.91%8.48%96.10%89.59%45.41%21.01%88.63%30.44%8.26%
Vidisha47.08%6.21%39.65%21.66%10.61%93.16%83.03%27.55%16.18%86.16%35.33%25.12%
West Nimar54.71%2.64%32.12%23.78%11.27%78.12%75.73%15.73%2.09%59.05%17.30%15.81%
Maharashtra
Ahmadnagar38.74%0.67%18.97%5.76%5.46%48.75%54.12%18.80%6.76%40.78%15.52%22.50%
Akola43.67%0.91%14.75%3.70%1.23%76.39%58.79%11.47%5.25%44.93%20.51%6.94%
Amravati39.12%0.65%12.95%6.87%0.64%66.04%41.96%12.37%9.36%50.93%20.82%8.37%
Aurangabad41.31%1.90%22.48%6.05%4.53%66.76%63.70%22.77%4.62%36.57%16.75%6.88%
Bhandara39.48%1.97%7.34%5.43%0.60%67.30%32.58%22.53%3.80%49.00%10.66%2.34%
Bid38.43%1.43%17.92%10.09%7.27%80.47%63.37%27.04%8.84%34.81%21.36%9.57%
Buldana43.29%0.82%15.09%8.76%4.14%78.56%58.20%21.43%3.91%46.34%17.46%5.03%
Chandrapur47.76%1.42%12.28%8.75%1.83%72.16%60.12%32.80%10.82%46.92%25.44%4.34%
Dhule47.92%1.10%26.93%19.23%15.39%77.47%80.97%11.11%16.00%62.76%28.77%21.44%
Garhchiroli38.65%0.73%13.08%11.69%2.85%85.55%73.00%21.49%6.71%59.24%20.30%8.04%
Gondiya48.52%1.14%12.29%5.71%0.32%82.44%45.03%29.78%3.60%78.16%13.51%4.66%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
284 285
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Maharashtra
Hingoli47.61%2.67%19.81%10.59%5.52%85.83%61.90%29.22%15.92%49.51%26.48%6.97%
Jalgaon33.35%2.27%22.41%12.81%6.27%58.14%60.29%7.35%9.05%35.47%19.15%14.13%
Jalna47.08%2.07%20.28%11.53%3.92%90.61%65.98%49.32%13.47%41.56%25.89%6.75%
Kolhapur39.49%1.82%18.45%5.74%3.94%49.50%38.09%7.83%1.48%48.61%11.22%13.82%
Latur40.29%0.59%16.85%5.95%1.81%77.60%61.66%38.81%5.62%36.37%22.90%8.56%
Mumbai------------
Mumbai Suburban------------
Nagpur41.23%1.72%5.59%5.78%0.59%51.74%41.60%10.37%5.24%42.98%13.23%6.19%
Nanded54.29%3.22%26.24%9.55%4.59%85.90%64.96%31.03%10.27%52.90%31.87%11.39%
Nandurbar59.49%1.41%26.03%31.07%23.83%85.21%80.18%25.98%28.99%82.82%49.76%18.43%
Nashik46.96%1.86%24.43%7.90%5.22%63.58%59.76%28.24%12.30%46.27%19.45%8.34%
Osmanabad37.59%1.48%13.72%8.62%1.56%69.33%73.53%35.85%9.60%37.78%17.81%8.76%
Parbhani53.72%0.41%18.66%8.10%4.29% 90.06%74.47%17.70%8.51%39.69%20.02%7.48%
Pune32.75%1.83%14.34%5.35%2.44%38.66%35.75%15.20%4.48%21.42%14.05%6.78%
Raigarh42.74%1.59%14.93%11.64%3.64%43.94%45.87%16.08%4.84%31.81%16.77%17.77%
Ratnagiri36.29%0.75%14.07%7.55%4.52%77.29%37.92%19.90%2.68%62.32%25.72%21.74%
Sangli34.60%1.40%16.64%3.46%4.53%47.76%29.37%9.40%9.34%38.14%11.12%8.30%
Satara41.90%0.86%17.18%4.89%2.03%52.97%34.05%8.90%4.21%37.69%10.88%12.72%
Sindhudurg37.12%1.20%13.39%4.75%2.78%73.90%22.69%28.11%1.12%47.95%21.21%19.24%
Solapur35.31%1.56%13.40%8.12%2.69%66.29%52.32%29.07%11.10%33.09%13.06%5.49%
Thane60.51%2.18%25.28%11.64%19.27%77.49%70.76%56.39%16.71%63.80%31.80%13.44%
Wardha35.30%0.95%6.14%7.40%0.76%54.57%46.04%8.39%4.41%42.63%15.10%3.89%
Washim44.20%1.15%20.44%7.86%3.92%75.62%63.95%23.08%4.04%53.15%21.87%4.50%
Yavatmal46.26%1.29%16.98%8.33%5.31%77.08%64.88%28.18%9.85%55.57%25.59%10.12%
Manipur
Bishnupur25.20%1.35%9.42%3.16%1.08%69.58%56.25%60.80%2.60%94.37%5.30%19.90%
Chandel24.84%2.95%31.44%12.66%4.20%84.45%35.94%64.92%7.76%92.96%25.34%21.72%
Churachandpur28.64%2.93%23.59%11.10%2.94%60.59%32.56%48.35%9.68%82.19%25.52%21.78%
Imphal East23.79%1.47%12.09%4.78%2.99%64.85%55.60%75.01%3.04%88.18%9.67%20.48%
Imphal West18.41%1.73%16.41%1.88%0.65%56.63%53.89%60
.15%3.58%83.14%5.56%23.71%
Senapati31.07%3.10%39.60%10.91%5.09%84.81%39.83%64.95%7.66%90.27%24.28%29.73%
Tamenglong31.73%3.71%44.86%18.03%5.37%92.48%37.73%67.03%15.21%94.62%51.69%36.10%
Thoubal26.33%1.77%17.73%3.92%3.11%74.94%53.36%66.74%16.49%94.30%8.17%24.05%
Ukhrul27.39%2.36%37.12%7.86%2.57%95.48%36.36%64.60%26.68%86.43%49.01%38.84%
Meghalaya
East Garo Hills31.91%1.60%41.51%22.19%12.43%97.45%49.55%61.21%15.08%87.32%32.27%23.41%
East Khasi Hills49.57%4.14%34.65%23.69%4.37%85.71%32.25%21.07%7.04%41.36%41.69%31.13%
Jaintia Hills51.38%5.25%46.02%34.18%11.63%84.94%43.69%34.68%14.78%34.40%49.29%35.14%
Ri Bhoi45.23%5.46%44.74%33.77%8.96%90.48%43.47%33.89%13.65%54.30%46.81%27.90%
South Garo Hills23.25%3.75%16.51%6.47%2.73%92.05%13.26%24.94%1.00%56.08%8.15%2.16%
West Garo Hills27.44%2.47%28.19%16.24%5.25%88.36%58.00%49.10%7.18%71.09%21.63%6.71%
West Khasi Hills42.03%3.75%45.14%25.29%6.14%94.70%20.16%30.83%10.26%47.87%41.42%27.19%
Mizoram
Aizawl20.65%4.04%18.06%5.09%2.16%58.84%7.78%11.53%0.48%15.03%8.22%2.64%
Champhai29.18%2.76%27.63%10.65%3.48%65.09%17.94%5.54%0.11%32.23%16.86%4.35%
Kolasib26.26%2.83%28.59%17.44%7.40%60.08%19.48%5.06%1.64%41.83%23.36%11.09%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Mizoram
Lawangtlai30.11%3.47%32.14%28.87%12.90%64.71%39.98%21.11%30.99%58.29%48.52%15.25%
Lunglei22.48%2.89%24.65%14.23%4.10%71.43%31.81%9.58%1.34%37.37%23.61%12.36%
Mamit25.29%2.67%27.11%20.28%7.68%68.54%38.61%24.57%17.28%57.10%38.30%14.59%
Saiha34.02%4.58%33.48%10.23%5.62%67.85%22.81%10.40%0.68%34.47%29.18%10.06%
Serchhip26.28%2.64%21.21%5.94%1.20%67.20%9.38%7.62%0.30%12.20%12.19%1.97%
Nagaland
Mon36.94%2.14%52.12%27.40%11.16%98.00%17.44%29.61%16.09%95.57%73.45%60.78%
Dimapur23.47%2.04%29.26%17.51%6.07%52.77%26.70%28.05%3.03%62.86%11.93%28.42%
Kiphire35.50%4.64%43.03%19.90%6.70%95.90%11.94%13.55%1.99%83.21%64.22%53.83%
Kohima19.50%2.45%26.76%11.01%1.05%86.77%26.83%26.40%1.20%65.56%28.22%15.89%
Longleng28.91%1.32%37.38%13.58%5.16%98.69%25.20%58.02%2.98%90.13%55.30%37.85%
Mokokchung15.23%1.54%18.17%10.53%2.46%78.72%6.75%10.57%0.79%78.52%20.26%26.18%
Peren24.00%3.90%40.83%18.68%2.65%89.77%27.10%37.84%2.61%85.78%38.34%26.38%
Phek20.95%3.61%36.74%13.87%3.96%96.10%15.14%5.28%1.67%90.77%56.63%31.82%
Tuensang35.83%4.30%53.64%22.87%3.93%97.62%27.55%7.56%3.30%89.17%59.12%46.31%
Wokha19.32%2.25%29.09%14.26%1.30%81.22%25.20%37.33%3.09%68.05%34.57%28.25%
Zunheboto21.69%0.67%32.18%14.34%6.19%92.90%15.44%11.07%0.49%87.60%49.58%41.81%
Odisha
Anugul35.79%4.05%16.00%12.23%2.47%84.71%68.98%33.87%12.61%44.14%18.93%6.81%
Balangir47.14%2.38%10.56%17.39%4.37%94.81%88.57%22.91%17.39%70.61%18.04%6.26%
Baleshwar38.90%1.22%24.16%11.70%3.83%92.42%64.72%10.47%10.24%74.00%13.32%13.64%
Bargarh41.65%2.69%13.29%11.14%2.83%90.67%74.85%20.07%15.90%65.51%14.66%7.15%
Bauda42.83%2.90%16.02%15.78%3.08%89.91%84.16%25.08%11.64%74.12%20.81%7.48%
Bhadrak40.57%1.59%24.30%10.24%2.79%91.61%80.29%11.28%11.84%78.32%15.10%23.30%
Cuttack27.03%1.72%20.55%7.12%1.78%80.49%70.44%14.31%7.95%39.41%11.18%13.50%
Debagarh43.46%2.73%24.46%20.67%5.25%96.27%69.46%22.95%18.09%69.78%32.34%16.70%
Dhenkanal37.50%1.60%17.29%15.66%3.39%87.94%73.70%52.49%12.25%56.48%21.64%15.53%
Gajapati38.80%3.96%26.77%28.73%10.11%89.94%65.57%43.57%12.56%49.48%44.69%8.56%
Ganjam30.44%2.52%19.21%23.10%4.44%78.09%66.58%20.73%9.63%33.38%18.40%5.21%
Jagatsinghapur25.65%1.68%14.84%6.42%0.89%90.25%67.01%8.90%5.22%38.60%12.28%7.33%
Jajapur36.97%1.85%22.51%8.91%1.83%86.36%69
.63%16.98%5.87%47.75%12.55%10.60%
Jharsuguda44.74%1.98%12.57%10.42%1.23%87.87%69.85%18.00%10.30%73.67%10.87%5.23%
Kalahandi42.77%1.56%22.32%30.35%9.10%96.33%89.67%28.15%33.17%79.64%29.52%17.73%
Kandhamal44.02%3.82%27.87%20.50%5.91%95.68%85.51%54.07%21.80%65.53%47.75%6.98%
Kendrapara33.81%1.61%21.25%8.98%1.78%88.19%73.84%12.46%6.76%59.55%16.62%6.86%
Kendujhar46.14%3.73%31.84%23.47%8.43%91.28%85.98%28.12%25.34%77.00%32.16%17.95%
Khordha26.58%1.41%17.68%10.37%1.50%79.87%74.58%33.51%3.69%39.16%9.43%7.15%
Koraput51.11%2.70%26.44%42.72%17.48% 90.00%86.37%20.20%24.88%65.72%44.87%17.86%
Malkangiri60.63%7.21%29.22%45.91%18.21%96.19%83.60%19.11%11.56%77.64%37.69%9.49%
Mayurbhanj45.45%1.98%21.89%23.19%5.68%94.52%86.44%28.98%26.54%84.04%31.63%20.93%
Nabarangapur55.31%4.34%34.52%36.34%16.10%94.61%85.15%26.84%31.44%83.45%35.60%20.31%
Nayagarh25.46%1.83%20.06%16.98%2.85%81.64%69.12%29.98%6.07%42.01%15.11%10.29%
Nuapada49.68%2.52%17.15%22.17%7.31%93.70%82.06%19.39%17.59%74.18%18.29%5.61%
Puri23.47%1.68%15.55%6.30%1.30%85.83%62.76%17.11%4.65%36.34%8.73%6.50%
Rayagada50.01%6.34%28.32%41.38%14.38%92.51%83.11%17.55%22.10%65.11%40.97%16.50%
(CONTD.) DIST
RICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)(CONTD.) DISTRICT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
INDIA MpI BASelINe reporT DistrictData Tables
286 287
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Odisha
Sambalpur44.64%1.35%13.03%16.94%1.46% 90.00%76.18%22.88%13.81%76.76%16.54%4.29%
Subarnapur43.91%2.19%14.82%12.21%1.14%88.58%84.39%18.83%9.20%65.63%18.26%6.18%
Sundargarh45.52%2.32%13.71%13.38%3.85%93.29%74.44%24.51%18.96%79.89%16.20%4.89%
Punjab
Amritsar24.77%1.35%13.03%12.26%7.10%53.71%27.48%0.00%0.85%40.73%2.47%4.28%
Barnala22.82%2.35%13.04%11.50%1.11%60.59%13.48%1.41%0.00%25.67%0.90%2.75%
Bathinda19.57%1.00%11.57%9.43%4.12%61.01%18.91%6.59%0.63%29.09%1.95%4.92%
Faridkot22.23%0.68%5.35%6.88%4.91%45.36%14.40%7.52%0.89%29.41%0.79%1.15%
Fatehgarh Sahib23.64%2.04%12.98%5.18%0.63%44.27%14.40%0.05%0.00%12.87%1.11%0.85%
Firozpur23.18%2.03%10.43%14.31%4.49%63.23%25.24%9.63%0.54%52.59%2.17%3.16%
Gurdaspur23.18%1.55%15.35%4.84%0.61%42.07%28.44%0.62%0.00%23.84%2.29%2.96%
Hoshiarpur25.23%1.26%9.29%3.64%1.59%46.38%28.41%0.38%0.49%16.58%1.14%3.11%
Jalandhar19.21%1.11%11.06%3.16%0.38%40.64%14.52%0.34%0.54%14.79%0.44%2.12%
Kapurthala19.37%1.21%8.59%6.19%0.96%34.47%14.81%1.71%1.15%13.51%0.90%2.40%
Ludhiana21.81%1.16%8.50%4.89%1.34%51.97%8.49%0.53%0.13%18.78%1.21%4.69%
Mansa30.15%0.87%14.13%12.27%1.86%73.52%24.83%6.02%0.37%35.32%3.34%1.84%
Moga23.74%2.66%15.69%12.76%3.99%58.87%14.05%4.05%0.25%41.56%1.40%5.98%
Muktsar28.63%2.16%16.85%14.60%3.49%56.69%23.71%4.53%0.14%32.65%3.27%2.94%
Patiala20.75%2.77%22.37%7.26%1.50%60.87%15.14%0.82%0.15%23.16%2.01%4.63%
Rupnagar21.50%1.70%8.20%3.68%0.64%45.76%21.03%0.42%0.00%9.66%0.50%1.22%
Sahibzada Ajit Singh Nagar25.68%1.60%11.76%5.09%1.83%43.86%28.49%0.97%0.00%16.42%2.82%4.44%
Sangrur23.93%1.25%7.48%4.85%0.66%55.55%16.94%0.49%0.11%16.44%0.96%2.51%
Shaheed Bhagat Singh Nagar29.83%0.70%17.59%8.15%1.34%67.94%11.97%2.13%0.31%26.04%1.74%5.20%
Tarn Taran26.07%1.69%22.75%12.55%3.98%53.69%22.36%0.50%0.49%30.60%1.66%4.40%
Rajasthan
Ajmer46.73%2.81%24.88%16.85%6.02%78.76%39.86%44.20%1.65%15.58%11.84%1.00%
Alwar50.02%3.74%39.67%12.18%7.54%91.07%67.00%16.88%2.62%45.11%12.98%6.34%
Banswara52.86%2.15%25.50%24.98%16.48%92.68%83.68%29.41%32.83%80.58%52.48%2.41%
Baran47.10%1.96%20.92%16.88%8.68%85.75%71.96%29.36%7.70%64.99%23.81%1.26%
Barmer44.64%4.57%42.96%36.70%21.28%92.29%81.85%45.32%35.65%58.36%52.03%5.42%
Bharatpur46.56%4.59%43.57%15.74%14.15%94.20%70
.61%46.49%10.52%61.96%26.47%7.56%
Bhilwara38.96%2.71%22.69%23.22%9.22%88.33%76.46%43.17%2.96%29.79%16.67%2.19%
Bikaner42.18%2.89%32.00%22.30%11.57%90.13%43.87%23.26%13.57%35.01%22.68%3.18%
Bundi46.75%2.15%24.18%21.32%6.49%88.56%77.42%29.09%9.33%48.60%23.71%2.73%
Chittaurgarh38.45%1.96%24.72%25.29%8.91%88.19%75.34%29.35%4.71%47.05%14.50%4.00%
Churu42.53%1.88%33.82%16.74%4.99%88.25%24.93%21.21%5.48%25.32%27.05%7.70%
Dausa44.24%4.43%30.01%11.94%2.03%93.02%68.73%20.42%10.33%42.32%23.92%5.67%
Dhaulpur55.17%5.42%36.54%15.89%9.64%92.64%75.71%21.61%9.13%51.78%25.44%2.41%
Dungarpur55.79%2.44%25.36%22.87%9.38%89.60%70.68%33.93%22.97%74.25%45.83%2.28%
Ganganagar36.64%2.14%20.37%13.91%3.17%76.30%26.51%9.68%6.88%54.81%9.18%3.20%
Hanumangarh35.95%2.47%23.22%14.52%3.63%86.88%47.94%6.21%5.99%54.97%12.27%3.89%
Jaipur43.68%3.04%25.34%8.78%4.08%82.25%57.05%21.25%3.00%29.68%14.00%2.84%
Jaisalmer44.79%4.22%42.80%39.74%25.40%91.63%66.63%55.67%26.79%46.29%41.55%8.55%
Jalor53.21%3.46%33.99%26.31%14.94%80.33%63.25%31.36%18.06%40.91%39.46%4.15%
Jhalawar44.14%2.68%23.17%22.03%8.68%84.70%69.03%44.40%4.21%61.22%27.36%1.24%
Uncensored Headcount Ratio: RuralHealthEducationStandard of Living
StateDistrictNutrition
Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
Sanitation
Drinking
Water
Electricity Housing Assets
Bank
Account
Rajasthan
Jhunjhunun36.61%2.33%20.64%6.44%1.68%60.19%37.53%16.38%4.13%25.32%14.70%4.22%
Jodhpur42.14%3.91%29.31%23.06%14.76%83.31%65.37%45.28%13.01%29.44%23.41%3.71%
Karauli52.02%6.26%34.28%16.27%6.34%94.46%83.41%36.61%5.68%59.10%35.76%7.16%
Kota44.28%1.60%16.78%14.82%4.31%77.93%69.29%22.68%2.67%47.58%16.18%2.21%
Nagaur41.96%1.60%21.97%18.11%7.07%85.51%48.55%36.98%11.27%20.79%20.95%5.83%
Pali46.45%3.78%24.06%20.27%8.64%70.05%51.55%37.27%5.14%24.28%13.50%2.49%
Pratapgarh50.96%3.67%29.19%28.74%13.97%91.33%88.01%50.42%26.18%80.62%39.62%4.00%
Rajsamand43.99%3.31%26.39%21.96%7.35%86.94%75.81%37.69%4.47%24.18%25.98%2.66%
Sawai Madhopur45.33%4.86%31.80%13.24%6.90%92.45%69.81%30.50%15.85%47.08%26.96%5.82%
Sikar36.88%3.77%24.50%8.28%4.22%71.14%39.70%14.91%4.27%17.19%12.00%3.45%
Sirohi51.51%5.33%32.65%26.39%16.07%72.35%66.95%30.38%16.04%41.14%34.05%8.51%
Tonk41.64%2.31%20.63%17.01%5.45%93.05%73.34%41.64%2.70%40.81%18.76%1.15%
Udaipur58.50%4.37%29.98%33.91%18.24%92.22%82.64%47.46%21.08%64.18%41.87%4.71%
Sikkim
East Sikkim14.71%1.29%8.14%9.35%1.91%55.12%7.84%2.69%0.64%36.98%13.23%15.06%
North Sikkim10.52%0.54%5.48%11.84%1.45%60.89%7.19%2.64%0.32%38.60%14.35%7.41%
South Sikkim10.53%0.51%2.29%7.37%1.23%57.98%2.22%0.63%0.13%22.77%7.15%3.60%
West Sikkim16.66%1.90%4.17%9.51%1.16%63.64%5.00%5.79%0.51%38.97%13.83%4.03%
Tamil Nadu
Ariyalur27.33%1.04%5.57%11.03%0.00%50.42%73.84%7.55%1.20%31.17%4.15%8.84%
Chennai------------
Coimbatore29.65%0.86%1.21%5.83%1.51%25.31%60.94%6.42%1.28%20.09%3.72%7.27%
Cuddalore33.88%1.82%6.27%6.81%0.74%55.89%73.47%5.39%0.57%40.33%4.72%5.71%
Dharmapuri29.64%1.30%3.89%8.99%1.59%26.57%70.36%11.22%1.38%17.19%2.88%7.81%
Dindigul30.29%1.20%4.16%7.23%1.15%42.87%69.30%3.83%2.84%21.66%6.04%3.53%
Erode21.22%0.00%3.48%10.73%1.36%14.99%49.56%2.34%1.83%19.80%4.28%4.23%
Kancheepuram26.01%1.45%8.54%4.83%0.99%27.64%52.96%13.53%0.34%21.92%2.65%3.35%
Kanniyakumari21.20%0.00%8.85%2.73%1.37%46.70%16.93%2.92%0.47%23.69%1.07%1.76%
Karur34.80%0.71%9.50%9.51%2.12%28.81%66.16%9.90%1.46%15.48%3.55%2.91%
Krishnagiri35.20%2.72%6.42%7.21%1.15%46.09%68.47%10.06%0.99%21.12%2.62%4.34%
Madurai24.27%1.46%10.23%12.84%1.84%37.77%65.
93%27.19%0.48%22.41%6.83%10.72%
Nagappattinam34.83%1.79%9.10%7.31%1.58%46.74%67.30%9.83%1.06%44.03%5.29%6.82%
Namakkal17.26%0.26%6.28%8.13%0.97%12.69%52.14%3.00%0.43%20.01%3.55%11.52%
Perambalur29.11%2.01%10.82%9.54%1.08%40.83%69.03%16.68%1.04%25.71%4.83%6.27%
Pudukkottai34.97%1.