राष्ट्रीय बहुआयामी गरीबी सूचकांक 2023

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INDIA
A PROGRESS REVIEW 2023
NATIONAL MULTIDIMENSIONAL
POVERTY INDEX
NITI Aayog

Copyright @ NITI Aayog, 2023
NITI Aayog
Government of India
Sansad Marg, New Delhi – 110001
Cover & Report Design by: Think Inc Studio
Source of Maps: Census of India 2011 & Political Map of
India 10th Edition (Survey of India) and DHS Program
Spatial Data Repository (DHS 2020).

INDIA
A PROGRESS REVIEW 2023
NATIONAL
MULTIDIMENSIONAL
POVERTY INDEX
NITI Aayog, 2023

The Sustainable Development Goals (SDGs) represent a universal aspiration that unites all
nations in their collective endeavour to foster an equitable and inclusive future. India has
wholeheartedly embraced the SDGs, leaving no stone unturned in its successful realization. At
the core of India’s priorities, lies SDG target 1.2, with its powerful mission to reduce poverty in
all its forms by at least half by 2030. In this resolute pursuit, we have made remarkable
progress, including the development of an indigenized index to monitor and address
multidimensional poverty at the sub-national and district levels. This report, National
Multidimensional Poverty Index (MPI): A Progress Review 2023 (based on NFHS-5) is a
significant update to its baseline and reaffirms India’s commitment to achieving this vital target
well before 2030.

Similar to its baseline edition launched in 2021, the second national MPI uses the latest
household microdata of the all-India National Family Health Survey (NFHS), sourced by the
International Institute for Population Sciences in coordination with the Ministry of Health and
Family Welfare. The MPI measures simultaneous deprivations across the three dimensions of
health and nutrition, education, and standard of living. It also retains the robust Alkire-Foster
methodology developed by our technical partners, the Oxford Poverty and Human
Development Initiative (OPHI) and United Nations Development Programme (UNDP). The
report offers a detailed analysis of the headcount ratio and intensity of multidimensional
poverty at the State/UT and district levels. Additionally, this time, it captures the changes in
multidimensional poverty between the survey periods of NFHS-4 (2015-16) and NFHS-5
(2019-21).
MESSAGE
SUMAN BERY
Vice Chairperson
National Institution for Transforming India
ii

iii
The Sustainable Development Goals (SDGs) represent a universal aspiration that unites all
nations in their collective endeavour to foster an equitable and inclusive future. India has
wholeheartedly embraced the SDGs, leaving no stone unturned in its successful realization. At
the core of India’s priorities, lies SDG target 1.2, with its powerful mission to reduce poverty in
all its forms by at least half by 2030. In this resolute pursuit, we have made remarkable
progress, including the development of an indigenized index to monitor and address
multidimensional poverty at the sub-national and district levels. This report, National
Multidimensional Poverty Index (MPI): A Progress Review 2023 (based on NFHS-5) is a
significant update to its baseline and reaffirms India’s commitment to achieving this vital target
well before 2030.

Similar to its baseline edition launched in 2021, the second national MPI uses the latest
household microdata of the all-India National Family Health Survey (NFHS), sourced by the
International Institute for Population Sciences in coordination with the Ministry of Health and
Family Welfare. The MPI measures simultaneous deprivations across the three dimensions of
health and nutrition, education, and standard of living. It also retains the robust Alkire-Foster
methodology developed by our technical partners, the Oxford Poverty and Human
Development Initiative (OPHI) and United Nations Development Programme (UNDP). The
report offers a detailed analysis of the headcount ratio and intensity of multidimensional
poverty at the State/UT and district levels. Additionally, this time, it captures the changes in
multidimensional poverty between the survey periods of NFHS-4 (2015-16) and NFHS-5
(2019-21).
I am happy to note that between NFHS-4 and NFHS-5, all States and UTs have made
commendable progress. India’s multi-sectoral approach in addressing poverty has been
evident in the reduction of multidimensionally poor people to nearly half, accounting for 14.96
percent, and the improved MPI score highlighted in this edition. I am certain that the national
MPI will continue to be a vital policy tool to monitor multidimensional poverty in the country. It
will facilitate data-driven decision making, formulation of sectoral policies, and targeted
interventions which contribute towards ensuring that “no one is left behind”. With our own
national MPI, India is poised to gain a deeper understanding of poverty’s complexities and
forge solutions that ensure inclusivity for all. The district-wise estimation of the national MPI will
also prioritise reaching out to the furthest behind first through focused efforts on specific
indicators and dimensions. The results and findings of the index provide valuable insights for
both policymakers and the wider community.
The year 2023, which is also the year of India’s G20 presidency marks a crucial midpoint in our
collective journey towards achieving the SDGs. Home to one-sixth of all humanity, India is
cognizant of its role and responsibility in driving inclusive development. We have made
remarkable progress in ensuring access to essential services such as housing, electricity,
sanitation, and cooking fuel through our flagship programmes. We have also prioritised social
protection measures to safeguard the most vulnerable sections of society. By leveraging our
strengths, including a high demographic dividend and a swiftly recovering economy, we can
confidently make the vision of a developed India, Viksit Bharat@2047 a reality.
I congratulate Ms. Shoko Noda, Resident Representative, UNDP India and her team; Shri
B.V.R. Subrahmanyam, CEO, NITI Aayog who has encouraged the SDG team at NITI Aayog
to develop the second edition of India’s Multidimensional Poverty Index: A Progress Review
2023 [based on NFHS-5] and Dr. Yogesh Suri, Senior Adviser for leading the SDG team in
bringing out this edition. My compliments and sincere thanks to the officials of State
Governments, UTs, Central Ministries and Dr. Sabina Alkire, OPHI whose efforts have resulted
in the compilation of this report.
SUMAN BERY
17 July, 2023
New Delhi,
India

iv
India has been making continuous strides in achieving the global Sustainable Development
Goals, by embracing the goals and targets and integrating them into its national development
agenda. Sustainable development requires sustained action over time. The country has been
consistently putting efforts in implementing sustainable solutions for the world’s greatest
challenges ranging from poverty to climate change, thus creating a way for a sustainable and
resilient future for generations to come.
Eradicating poverty by 2030 is a pivotal goal of the Agenda for Sustainable Development.
Target 1.2 specifically aims at reducing at least half the proportion of men, women and children
of all ages living in poverty in all dimensions. Developed under the Government of India’s
Global Indices for Reforms and Growth (GIRG) mandate, India’s National Multidimensional
Poverty Index (MPI) is the first-of-its-kind index which estimates multiple and simultaneous
deprivations at a household level across the three macro dimensions of health, education and
living standards. Accordingly, this index rigorously measures national and sub-national
performance to facilitate policy actions. The headcount ratio and intensity of multidimensional
poverty estimates have also been provided for all districts in the country which is its unique
feature.
Based on the National Family Heath Survey 5 (2019-21), this edition of the national MPI
represents India’s progress in reducing multidimensional poverty between NFHS-4 (2015-16)
and NFHS-5 (2019-21).
B.V.R. SUBRAHMANYAM
Chief Executive Officer
National Institution for Transforming India
MESSAGE

v
India has been making continuous strides in achieving the global Sustainable Development
Goals, by embracing the goals and targets and integrating them into its national development
agenda. Sustainable development requires sustained action over time. The country has been
consistently putting efforts in implementing sustainable solutions for the world’s greatest
challenges ranging from poverty to climate change, thus creating a way for a sustainable and
resilient future for generations to come.
Eradicating poverty by 2030 is a pivotal goal of the Agenda for Sustainable Development.
Target 1.2 specifically aims at reducing at least half the proportion of men, women and children
of all ages living in poverty in all dimensions. Developed under the Government of India’s
Global Indices for Reforms and Growth (GIRG) mandate, India’s National Multidimensional
Poverty Index (MPI) is the first-of-its-kind index which estimates multiple and simultaneous
deprivations at a household level across the three macro dimensions of health, education and
living standards. Accordingly, this index rigorously measures national and sub-national
performance to facilitate policy actions. The headcount ratio and intensity of multidimensional
poverty estimates have also been provided for all districts in the country which is its unique
feature.
Based on the National Family Heath Survey 5 (2019-21), this edition of the national MPI
represents India’s progress in reducing multidimensional poverty between NFHS-4 (2015-16)
and NFHS-5 (2019-21).
I am glad to note that during this period, the share of India’s population who are
multidimensionally poor has declined from 24.85% to 14.96%. This dramatic progress is a
testament to our Prime Minister, Shri Narendra Modiji ’s vision and commitment to eradicating
poverty as reflected in his statement that, “This nation, our government, our systems, they are
all for the poor. Our aim is to empower the poor to fight poverty.”
I may add that under the GIRG initiative, reform areas and actions formulated based on the
insights from national MPI baseline report are being implemented by Union Ministries and
States/UTs. The insights from this second edition of national MPI report may be utilized to
prepare additional reform areas and actions to further accelerate efforts to improve the lives
of our people. I appreciate Union Ministries and States/UTs for their consistent efforts in this
endeavour.
I congratulate the SDG team at NITI Aayog and also compliment our technical partners, the
United Nations Development Programme (UNDP) and the Oxford Poverty and Human
Development Initiative (OPHI) for their support in bringing out the report. I request States/UTs
and district administration to rigorously examine the report and take appropriate action to
improve these indicators, which will significantly help upgrading the lives of people in their
respective areas.
B.V.R SUBRAHMANYAM
17 July, 2023
New Delhi,
India

SHOKO NODA
Resident Representative
UNDP India
I congratulate the Government of India and NITI Aayog on the release of India’s National
Multidimensional Poverty Index: A Progress Review 2023 (MPI). This index is an important tool
that enables the country to track its progress towards the Sustainable Development Goals
(SDGs), particularly SDG target 1.2, that aims to reduce poverty in all its dimensions.
The national MPI report outlines the remarkable progress made by India in nearly halving
multidimensional poverty between 2015-2016 and 2019-2021, highlighting the country’s
unwavering commitment to achieving the SDGs and its determined efforts to address poverty
and improve the lives of its citizens. It is commendable that India’s rural areas and its poorest
states have shown the fastest decline.
These achievements demonstrate the transformative power of India’s multisectoral approach to
poverty reduction, evident in large investments in improving people’s access to sanitation,
cooking fuel, and electricity. Additionally, India’s focus on achieving universal coverage in
education, nutrition, water, and housing has played an important role in driving these positive
outcomes.
As we stand at the midpoint of the 2030 Agenda, global progress is being threatened by multiple
intersecting crises. It is crucial to generate and use high-quality evidence to closely monitor
progress, assess gaps, and swiftly address emerging challenges. This Progress Review of
India’s national MPI builds upon the excellent foundation laid by India’s Baseline National MPI
report published in 2021.
MESSAGE
vi

I congratulate the Government of India and NITI Aayog on the release of India’s National
Multidimensional Poverty Index: A Progress Review 2023 (MPI). This index is an important tool
that enables the country to track its progress towards the Sustainable Development Goals
(SDGs), particularly SDG target 1.2, that aims to reduce poverty in all its dimensions.
The national MPI report outlines the remarkable progress made by India in nearly halving
multidimensional poverty between 2015-2016 and 2019-2021, highlighting the country’s
unwavering commitment to achieving the SDGs and its determined efforts to address poverty
and improve the lives of its citizens. It is commendable that India’s rural areas and its poorest
states have shown the fastest decline.
These achievements demonstrate the transformative power of India’s multisectoral approach to
poverty reduction, evident in large investments in improving people’s access to sanitation,
cooking fuel, and electricity. Additionally, India’s focus on achieving universal coverage in
education, nutrition, water, and housing has played an important role in driving these positive
outcomes.
As we stand at the midpoint of the 2030 Agenda, global progress is being threatened by multiple
intersecting crises. It is crucial to generate and use high-quality evidence to closely monitor
progress, assess gaps, and swiftly address emerging challenges. This Progress Review of
India’s national MPI builds upon the excellent foundation laid by India’s Baseline National MPI
report published in 2021.
The granular data presented in this report will not only allow policymakers, State Governments,
and district officials to monitor progress, but also empower them to understand the extent,
source, and complexity of deprivations among those that remain in multidimensional poverty. It
gives them the power to design targeted policies and programmes, ensuring that public
resources flow where they can have the greatest impact.
I am confident that when complemented with monetary poverty measures, the national MPI will
enable policymakers to reflect on, and effectively respond to the comprehensiveness and
complexity of poverty in the country. It will also inform public dialogue and serve as a valuable
resource for citizens and civil society to engage on these issues.
It has been a pleasure to collaborate with NITI Aayog and the Oxford Poverty and Human
Development Initiative (OPHI) in this endeavour. I would like to express my gratitude to Shri
Suman Bery, Vice Chairperson, NITI Aayog, for his visionary leadership in guiding this report. I
also extend my appreciation to Shri B.V.R. Subrahmanyam, CEO, NITI Aayog, for his
continuous encouragement and to Dr. Yogesh Suri, Senior Adviser, NITI Aayog for his
commitment in driving the publication of this report. Additionally, I am grateful to Dr. Sabina
Alkire and her team at OPHI for their technical support in this exercise.
UNDP remains steadfast in its partnership with the Government of India on our collective
journey to eradicate poverty and accelerate the achievement of the SDGs.
SHOKO NODA
17 July, 2023
New Delhi,
India
vii

It has been an honour to collaborate on India’s National Multidimensional Poverty Index: A
Progress Review 2023 under the leadership of NITI Aayog, Government of India. Building on
the Baseline Report of India’s National MPI, this report measures and monitors progress on
achieving target 1.2 of the Sustainable Development Goals on multidimensional poverty.

Using the National Family Health Survey (NFHS), this report showcases India’s 2019-21 MPI
results – plus, the progress in multidimensional poverty reduction between 2015-16 and
2019-21.
For the first time, this Progress Review provides the extent of multidimensional poverty
reduction by state and district, and shows how the indicator composition of poverty changed by
state. This high-resolution mapping of the overlapping deprivations of the poorest makes it a
powerful policy tool to benchmark progress in winning the race to end poverty in all its forms.
In line with 2030 Agenda, India’s national MPI reflects the interlinkages across 12 SDG-related
indicators at the level of households. Understanding how deprivations overlap in poor
households – and also how these indicators have progressed over time – is salient. It informs
the design of multipronged interventions that ‘break silos’ and address interlinked deprivations
together.
As a policy tool, the MPI data in this report can be utilized by actors at national, state and district
levels to accelerate multidimensional poverty reduction. This disaggregation is crucial,
especially in a country as diverse as India, because the patterns of deprivations vary across and
within states as well as over time. These data are vital to plan concretely how to reduce
deprivations efficiently.
DR. SABINA ALKIRE
Director
Oxford Poverty and Human Development Initiative
Department of International Development
University of Oxford
MESSAGE
viii

This Progress Review also provides precise methodological details and definitions which will
also be of interest to students, academics and analysts in India and abroad.
Our technical assistance reflects our strengthened partnership with UNDP India. I wish to thank
Shoko Noda and her team, especially Amee Misra and Ashulipi Singhal. I would like to
acknowledge the contributions of the OPHI team and Sourav Das for their support to this
technically rigorous project. Special thanks are also due to Sanyukta Samaddar, IAS, former
Adviser (SDGs) at NITI Aayog with Alen John, Sourav Das and Soumya Guha who
spearheaded the Baseline MPI report and its communication.
I am grateful to Shri Suman Bery, Vice Chairperson, NITI Aayog for his leadership and critically
important guidance extended to this nationally important project. I would also like to commend
Shri B.V.R. Subrahmanyam, CEO, NITI Aayog and his SDG team led by Dr. Yogesh Suri, Senior
Adviser, for their dedication and commitment in developing the MPI Report into a fully-fledged
monitoring tool.
The results published here present an accurate and technically rigorous estimation of
multidimensional poverty methodologies to the NFHS datasets.
DR. SABINA ALKIRE
17 July, 2023
New Delhi,
India
ix

x
As we reach the midway milestone in our journey towards achieving the Sustainable
Development Goals (SDGs) this year, NITI Aayog's unwavering commitment in overseeing the
progress of the 2030 Agenda is evident. With resolute dedication, NITI Aayog has undertaken
the crucial responsibility in implementing and monitoring the SDGs at both national and
sub-national levels right from its adoption. In the context of India's development, eradicating
poverty and hunger holds immense significance for sustainable progress, emphasizing the
need for a comprehensive understanding of poverty levels within the country.
Traditionally, poverty estimation relied solely on income or monetary measures. However, a new
approach has evolved to incorporate multiple dimensions and non-income factors. NITI Aayog
took a significant step in 2021 by releasing the first ever Multidimensional Poverty Index [MPI]
for India (based on NFHS 4). This initiative aims to improve India's position in globally accepted
indices, underscoring the importance of comprehensive poverty alleviation efforts. It serves as
a valuable complement to monetary poverty statistics by providing insights into "how many are
poor" and "how poor are the poor". It provides a holistic understanding of poverty by considering
dimensions such as health, education, and living standards.
This Progress Review of the national Multidimensional Poverty Index (based on NFHS-5)
provides comprehensive analysis, enabling a detailed examination of poverty trends across
States/UTs and districts. Comparing the poverty levels between the baseline report of 2021 and
this edition sheds light on changes in poverty from 2015-16 to 2019-21 across all States/UTs
and districts. It serves as a beneficial policy tool, providing a comprehensive understanding of
multidimensional poverty at the most granular level.
Utilizing the national MPI will empower policymakers with valuable insights into specific areas
and population groups that are most affected by poverty. We are hopeful that this knowledge will
enable the formulation of targeted strategies and interventions to uplift vulnerable segments of
society, thereby promoting inclusive and sustainable development.
DR. YOGESH SURI
Senior Adviser (SDGs)
National Institution for Transforming India
FOREWORD

xi
This edition of the national MPI is a testament to the dedicated efforts of both the States/UTs and
Central Ministries who have actively supported and adopted this initiative. The SDG-MPI
workshops held across various States and UTs have provided significant momentum for the
preparation of this edition. It is important to acknowledge and appreciate their encouragement
and acceptance of the report, as without their valuable contribution, this achievement would not
have been possible.
We would like to thank Dr. Sabina Alkire, Director of the Oxford Poverty and Human
Development Initiative and the designer of the global MPI, along with her team, for their
invaluable technical advice and guidance throughout our journey. Their vast knowledge and
global experience in working with the MPI have greatly benefitted our efforts.
Furthermore, we extend our deep appreciation to Ms. Shoko Noda, Resident Representative of
UNDP India, as well as her team Amee Misra, Senior Economist, and Ashulipi Singhal for their
significant contributions in conducting the computations for the MPI and the preparation of the
report. We firmly believe that India's remarkable progress in reducing poverty by half will pave
the way for exponential advancements in achieving the SDGs.
We extend our thanks to Shri Suman Bery, Vice Chairperson, NITI Aayog, for his relentless
support and motivation. His dedicated commitment has been a driving force in our endeavor.
Furthermore, we express our sincere gratitude to Shri B.V.R. Subrahmanyam, CEO, NITI
Aayog, for his inspiration, encouragement, and support in advancing the adoption of the SDGs
in our country. His guidance and dedication have been instrumental in fostering a deep
understanding of this important initiative.
It is crucial to acknowledge the significant contributions made by the entire team of the SDG
Vertical at NITI Aayog: Rajesh Gupta, Sharmistha Sinha, Jyoti Khattar, Farha Anis, Sakshi
Gupta, Sneha Kuriakose and Ishita Aggarwal. They have consistently shouldered the
responsibility of conducting extensive computations and estimations for the Multidimensional
Poverty Index (MPI), demonstrating their unflinching dedication. We also extend our thanks to
Ms. Sanyukta Samaddar, Former Adviser (SDGs) at NITI Aayog and Shri Sourav Das for their
invaluable contribution in the preparation of the baseline MPI and initiating the work relating to
its second edition.
We truly hope that this policy tool acts as a strong catalyst in speeding up the achievement of
SDGs across the entire country. It is our core principle to ensure that no one is left behind, and
this tool aligns perfectly with that principle, benefiting everyone.
DR. YOGESH SURI
17 July, 2023
New Delhi,
India

EXECUTIVE SUMMARY
Overview
Home to one-sixth of humanity and to more young
minds than any other country, India plays a decisive
role in Agenda 2030. At the core of India's
development agenda is the elimination of poverty in all
its forms, ensuring that no individual is left behind.
Historically, poverty estimation has predominantly
relied on income as the sole indicator. However, the
Global Multidimensional Poverty Index (MPI), based
on the Alkire-Foster (AF) methodology, captures
overlapping deprivations in health, education, and
living standards. It complements income poverty
measurements because it measures and compares
deprivations directly. The global MPI Report is jointly
published by the Oxford Poverty and Human
Development Initiative (OPHI) and the United Nations
Development Programme (UNDP).
Government of India has acknowledged the
significance of the global MPI under the mandate of
the Global Indices for Reform and Action (GIRG)
initiative. The emphasis of the GIRG initiative is not
only to improve the country’s performance and ranking
in the global indices, but also to leverage the indices
as tools for driving systemic reforms and growth.
In this context, NITI Aayog, as the nodal agency for
MPI, has been responsible for constructing an
indigenized index for monitoring the performance of
States and Union Territories in addressing
multidimensional poverty. In order to institutionalize
xii
NATIONAL
MULTIDIMENSIONAL
POVERTY INDEX
A Progress Review 2023
this, NITI Aayog constituted an inter-ministerial MPI
Coordination Committee (MPICC) including Ministries
and departments pertaining to areas such as health,
education, nutrition, rural development, drinking water,
sanitation, electricity, and urban development, among
others. It also included experts from the Ministry of
Statistics and Programme Implementation (MoSPI)
and technical partners – OPHI and UNDP. The
composition of the MPICC drew from the
multidimensional nature of the indicators and
sub-indicators within the index. This brought forth
cross-sectoral perspectives on policies and
interventions needed to improve achievements at the
level of households.
As a result of extensive consultations held within
MPICC, the dual-cutoff approach of the AF
methodology – the one used in the Global MPI Report
– was considered suitable for the national MPI. The
national MPI model retains the ten indicators of the
global MPI model, staying closely aligned to the global
methodology. It also adds two indicators, viz., Maternal
Health and Bank Accounts in line with national
priorities.
Like the global MPI, India’s national MPI has three
equally weighted dimensions – Health, Education, and
Standard of living – which are represented by 12
indicators. These are depicted by the following graphic:

xiii
Indicators and their weights
Health
Education
Standard of
Living
1/3
1/3
1/3
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling
School Attendance
Cooking Fuel
Sanitation
Drinking Water
Housing
Electricity
Assets
Bank Account1/6
1/6
1/6
1/21
1/21
1/21
1/21
1/21
1/21
1/21
1/12
1/12
The indices of the national MPI comprise:
i) Headcount ratio (H): How many are poor?
Proportion of multidimensionally poor in the
population, which is arrived at by dividing number of
multidimensionally poor persons by total population.
ii) Intensity of poverty (A): How poor are the poor?
Average proportion of deprivations which is
experienced by multidimensionally poor individuals.
To compute intensity, the weighted deprivation scores
of all poor people are summed and then divided by the
total number of poor people.
MPI value is arrived at by multiplying the headcount
ratio (H) and the intensity of poverty (A), reflecting
both the share of people in poverty and the degree to
which they are deprived.
MPI = H x A
According to the AF methodology, an individual is considered MPI poor if their deprivation score equals or exceeds the poverty cutoff of 33.33%.
The national Multidimensional Poverty Index plays a
pivotal role in assessing advancements towards target
1.2 of the Sustainable Development Goals (SDGs)
which aims at reducing “at least by half the proportion
of men, women and children of all ages living in poverty
in all its dimensions”. NITI Aayog published the
national MPI Baseline Report in November 2021, with
estimates computed using the data from the 4th round
of the National Family Health Survey (NFHS-4)
conducted in 2015-16.
Sub-indices of the National MPI
EXECUTIVE SUMMARY MPI: PROGRESS REVIEW 2023

The National Multidimensional Poverty Index: A Progress Review 2023 presents the second edition of the national MPI
and is a follow-up to the Baseline Report published in November 2021. It provides multidimensional poverty estimates for
India’s 36 States & Union Territories, along with 707 administrative districts across 12 indicators of the national MPI. These
estimates have been computed using data from the 5th round of the NFHS (NFHS-5) conducted in 2019-21, employing the
same methodology as the baseline report. This edition also presents the changes in multidimensional poverty between the
survey periods of NFHS-4 (2015-16) and NFHS-5 (2019-21).
Key Results – Steep Decline in Poverty
India has achieved a remarkable reduction in its MPI value and Headcount Ratio between 2015-16 and 2019-21, indicating
success of the country’s commitment and action to address the multidimensional nature of poverty through its multisectoral
approach.
Introduction to the Second Edition
Highlights: MPI Progress Report 2023
135 million
people escaped
multidimensional
poverty between 2015-16 and 2019-21
indicators have
shown improvement
suggesting that impact of Government
interventions is increasingly visible on ground
All 12
The Intensity of poverty,
which measures the
average deprivation among
the people living in
multidimensional poverty
improved from about
Improvement in nutrition,
years of schooling,
sanitation, and cooking
fuel played a significant role
in reducing the MPI value
India on track to achieve
(reducing multi-dimensional
poverty by at least half)
much ahead of 2030
SDG
Target 1.2
Fastest decline in percentage
of multidimensional poor in
rural areas from
in urban areas
Steep decline in
24.85%
2015-16
14.96%
2019-21
2015-16
2019-21
2015-16
2015-16
2019-21
2019-21
UP, Bihar, MP, Odisha
and Rajasthan
recorded steepest
decline in number of
MPI poor
47.14%
44.39%
32.59%
19.28%
MPI
Value
8.65%
5.27%
(13.5 crore)Poverty
Headcount
Ratio
Reduction
in the incidence
of poverty
xiv
EXECUTIVE SUMMARY MPI: PROGRESS REVIEW 2023

Snapshot of Multidimensional Poverty in India
Year Headcount Ratio
(H)
Intensity of Poverty
(A)
MPI
(H x A)
2019-21 14.96% 44.39% 0.066
2015-16 24.85% 47.14% 0.117
The MPI estimates highlight a near-halving of India’s national MPI value and decline in the proportion of population in
multidimensional poverty from 24.85% to 14.96% between 2015-16 and 2019-21. This reduction of 9.89 percentage points
in multidimensional poverty indicates that, at the level of projected population in 2021, about 135.5 million persons have
escaped poverty between 2015-16 and 2019-21. It is a major contribution towards achieving SDG target 1.2 that aims to
reduce “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”. This indicates that India is well on course to achieve the SDG target 1.2 much ahead of
2030. At the same time, the Intensity of Poverty, which measures the average deprivation among the people living in
multidimensional poverty also reduced from 47.14% to 44.39%.
Disparities across Rural and Urban Areas
While disparities in multidimensional poverty still exist between rural and urban areas, with the proportion of
multidimensional poor in 2019-21 being 19.28% in rural areas compared to 5.27% in urban areas, the reduction in the MPI
value has been pro-poor in absolute terms.
The estimates indicate that rural areas saw a faster reduction in their MPI value, compared to urban areas. The incidence
of poverty fell from 32.59% to 19.28% in rural areas compared to a decline from 8.65% to 5.27% in urban areas between
2015-16 and 2019-21.
Rural
Headcount
Ratio
(H)
Intensity
of Poverty
(A)
19.28% 44.55%
Urban
Headcount
Ratio
(H)
Intensity
of Poverty
(A)
MPI
0.023 5.27% 43.10%
MPI
0.086
0.154 32.59% 47.38% 0.039 8.65% 45.27%
Year
xv
EXECUTIVE SUMMARYMPI: PROGRESS REVIEW 2023
2019-21
2015-16

xvi
The colour represents the MPI score of a state. 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 shows the range of MPI
scores in India, based on the values for 2015-16. Both the comparative maps use the same legend to represent the change in MPI scores
between 2015-16 to 2019-21.
Up to 0.054 0.055 to 0.107 0.108 to 0.159 0.160 to 0.211 0.212 and above
MPI based on NFHS-4 (2015-16)
Comparative Performance of States/UTs in the Multidimensional Poverty Index Score
The MPI estimates show that States/UTs have displayed notable improvements in their MPI score from 2015-16
to 2019-21.
EXECUTIVE SUMMARY MPI: PROGRESS REVIEW 2023

MPI based on NFHS-5 (2019-21)
xvii
The colour represents the MPI score of a state. 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 shows the range of MPI
scores in India, based on the values for 2015-16. Both the comparative maps use the same legend to represent the change in MPI scores
between 2015-16 to 2019-21.
Up to 0.054 0.055 to 0.107 0.108 to 0.159 0.160 to 0.211 0.212 and above
Fastest Absolute Reduction in MPI (State-wise)
Bihar, the state with the highest MPI value in NFHS-4 (2015-16), saw the fastest reduction in MPI value in absolute terms
with the proportion of multidimensional poor reducing from 51.89% to 33.76% in 2019-21. The next fastest reduction in the
MPI value was seen in Madhya Pradesh and Uttar Pradesh. The proportion of multidimensional poor in Madhya Pradesh
and Uttar Pradesh in NFHS-5 (2019-21) are 20.63% and 22.93% respectively. In terms of number of MPI poor, Uttar
Pradesh topped the list with 3.43 crore people escaping multidimensional poverty in the last five years, followed by Bihar
(2.25 crore) and Madhya Pradesh (1.36 crore).
EXECUTIVE SUMMARYMPI: PROGRESS REVIEW 2023

India : Headcount Ratio
Percentage of the total population who are multidimensionally poor in each State and UT
NFHS-5 (2019-21) NFHS-4 (2015-16)
States Union Territories
50.0% .0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%
Bihar
Jharkhand
Meghalaya
Uttar Pradesh
Madhya Pradesh
Assam
Chhattisgarh
Odisha
Nagaland
Rajasthan
Arunachal Pradesh
Tripura
West Bengal
Gujarat
Uttarakhand
Manipur
Maharashtra
Karnataka
Haryana
Andhra Pradesh
Telangana
Mizoram
Himachal Pradesh
Punjab
Sikkim
Tamil Nadu
Goa
Kerala
Dadra & Nagar Haveli & Daman & Diu
Jammu & Kashmir
Ladakh
Chandigarh
Delhi
Andaman & Nicobar Islands
Lakshadweep
Puducherry
33.76%
27.79%
22.93%
37.68%
20.63%
36.57%
19.35%
32.65%
16.37%
29.90%
15.68%
29.34%
15.43%
25.16%
15.31%
28.86%
13.76%
24.23%
13.11%
16.62%
11.89%
21.29%
11.66%
18.47%
9.67%
17.67%
8.10%
16.96%
7.81%
14.80%
7.58%
12.77%
7.07%
11.88%
6.06%
11.77%
5.88%
13.18%
5.30%
9.78%
4.93%
7.59%
4.75%
5.57%
2.60%
3.82%
2.20%
4.76%
0.84%
3.76%
0.70%
0.55%
9.21%
19.58%
4.80%
12.56%
3.53%
12.70%
3.52%
5.97%
3.43%
4.44%
2.30%
4.29%
1.11%
1.82%
0.85%
1.71%
32.54%
28.81%
42.10%
51.89%
% of population who are multidimensionally poor
Performance of States/UTs in Headcount Ratio
It is crucial to recognize the efforts of the States and UTs in reducing the proportion of multidimensional poor people in
the country. The progress of each State and UT between the two periods is indicated below.
xviii
EXECUTIVE SUMMARY MPI: PROGRESS REVIEW 2023

India : Changes over time for Headcount Ratio
State/ UT wise percentage point change in the headcount ratio between 2015-16 and 2019-21
States Union Territories
Bihar
Madhya Pradesh
Uttar Pradesh
Odisha
Rajasthan
Chhattisgarh
Assam
Jharkhand
Arunachal Pradesh
Nagaland
West Bengal
Manipur
Uttarakhand
Telangana
Maharashtra
Gujarat
Andhra Pradesh
Karnataka
Haryana
Meghalaya
Mizoram
Tripura
Goa
Himachal Pradesh
Tamil Nadu
Sikkim
Punjab
Kerala
Dadra & Nagar Haveli & Daman & Diu
Ladakh
Jammu & Kashmir
Chandigarh
Andaman & Nicobar Islands
Delhi
Puducherry
Lakshadweep
% point change in proportion of multidimensionally poor population
Changes over Time for Headcount Ratio
The estimates indicate an overall improvement in the proportion of multidimensional poor in States and UTs between the
time period 2015-16 to 2019-21.
xix
EXECUTIVE SUMMARYMPI: PROGRESS REVIEW 2023
-18.13
-15.94
-14.75
-13.65
-13.56
-13.53
-13.30
-13.29
-10.48
-9.73
-9.41
-8.86
-8.00
-7.30
-6.99
-6.81
-5.71
-5.20
-4.81
-4.75
-4.48
-3.50
-2.92
-2.65
-2.56
-1.21
-0.82
-0.15
-10.38
-9.17
-7.76
-2.46
-1.99
-1.02
-0.87
-0.71
-18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0

xx
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 shows the range of MPI
scores in India, based on values for 2015-16. Both the comparative maps use the same legend to represent the change in MPI scores
between 2015-16 to 2019-21. Regions where data is not available is shown in grey. Only 575 districts are comparable between the two
time periods of the two NFHS (2015-16 and 2019-21). Of these, 436 districts are statistically significant at 95% level of confidence.
MPI based on NFHS-4 (2015-16)
Comparative Performance of Districts in the Multidimensional Poverty Index Score
Up to 0.0450.046 to 0.0900.091 to 0.1360.137 to 0.1820.183 to 0.228 0.366 and above0.320 to 0.3650.274 to 0.3190.229 to 0.273
An important characteristic of the MPI is its ability to provide estimates at the district level. The disaggregated estimates
show that the most rapid reduction in the proportion of multidimensionally poor individuals occurred in districts located
within the states of Madhya Pradesh, Gujarat, Uttar Pradesh, and Rajasthan.
EXECUTIVE SUMMARY MPI: PROGRESS REVIEW 2023

xxi
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 shows the range of MPI
scores in India, based on values for 2015-16. Both the comparative maps use the same legend to represent the change in MPI scores
between 2015-16 to 2019-21. Regions where data is not available is shown in grey. Only 575 districts are comparable between the two
time periods of the two NFHS (2015-16 and 2019-21). Of these, 436 districts are statistically significant at 95% level of confidence.
MPI based on NFHS-5 (2019-21)
Up to 0.0450.046 to 0.0900.091 to 0.1360.137 to 0.1820.183 to 0.228 0.366 and above0.320 to 0.3650.274 to 0.3190.229 to 0.273
EXECUTIVE SUMMARYMPI: PROGRESS REVIEW 2023

xxii
India’s National MPI Report underlines the Government’s
commitment to understanding, measuring, and
addressing the many dimensions of poverty and
leveraging this understanding as a key tool in
policymaking. The baseline report of the national MPI has
been pivotal in raising awareness among state
governments, academia, civil society, and citizens about
the significance of using multidimensional poverty
measures as both a potent policy instrument as well as a
mechanism to measure progress. Consequent to the
release of the baseline report of National MPI, several
MPICC meetings were convened for preparation of
Reform Action Plans. Taking into account their priorities
and development challenges, various
Ministries/Departments have prepared action plans. More
than 50 reform actions have been identified in 16 reform
areas such as nutrition, financial inclusion, education,
rural development, and housing among others. The
Ministries in collaboration with States have started
implementing these reforms.
India’s stellar progress on the national MPI between
2015-16 and 2019-21 reflects the Government’s
commitment to improving the quality of people’s lives –
through targeted policies, schemes, and developmental
Conclusion
Indicator-wise Comparison of Deprivations
The following graph illustrates the percentage of India’s population deprived in an indicator. All the 12 indicators across
the three dimensions – Health, Education and Standard of living – saw statistically significant reduction across the two
time periods. Deprivations in sanitation (reduction by 21.8 % points) and cooking fuel (reduction by 14.6 % points) fell the
most during the period from 2015-16 to 2019-21. Overall, progress in nutrition, years of schooling, sanitation, and
cooking fuel has been the significant contributor to the decline in MPI value though there is further scope to make
improvements.
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
% of population deprived
31.52%
37.60%
2.06%
2.69%
19.17%
22.58%
11.40%
13.86%
5.27%
6.40%
43.90%
58.47%
30.13%
51.88%
7.32%
10.92%
3.27%
12.16%
41.37%
45.65%
10.16%
13.97%
3.69%
9.66%
programmes rolled out at both the national and
sub-national levels. The Government’s focus on
investments in critical areas of education, nutrition, water,
sanitation, cooking fuel, electricity, and housing has
played a pivotal role in driving these positive outcomes.
Key Government schemes such as Swachch Bharat
Mission (SBM), Jal Jeevan Mission (JJM), Poshan
Abhiyan, Samagra Shiksha, Pradhan Mantri Sahaj Bijli
Har Ghar Yojana (Saubhagya), Pradhan Mantri Ujjwala
Yojana (PMUY), Pradhan Mantri Jan Dhan Yojana
(PMJDY), Pradhan Mantri Awas Yojana (PMAY) and
many more have contributed significantly in driving the
tremendous progress presented in this report.
The findings from the second edition of the National MPI
will serve as a valuable resource for States and Union
Territories to identify and amplify actions that have
triggered progress since the findings of the Baseline
Report, right upto the district level. It will also enable them
to track the progress of the vulnerable hotspots and
pinpoint areas that require further targeted policy
interventions and programmatic action. NITI Aayog, along
with other line Ministries, is committed to providing
continuous support to the States in formulating and
implementing effective reform action plans.
EXECUTIVE SUMMARY MPI: PROGRESS REVIEW 2023
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension

Message from the Vice Chairperson, NITI Aayog II
Message from the CEO, NITI Aayog IV
Message from the Resident Representative,
United Nations Development Programme, India
VI
Message from the Director,
Oxford Poverty and Human Development Initiative
VIII
Foreword by Senior Adviser, NITI Aayog X
Executive Summary XII
contents
2–4
PAGE 1Context & Introduction2.Methodology 8–21
3.Way Forward 22–23
I
PAGE 7Methodology & Way Forward
II
1.Introduction

5. State/UT Results
States
Andhra Pradesh 50–57
Arunachal Pr adesh 58–65
Assam 66–73
Bihar 74–81
Chhattisgarh 82–89
Goa 90–95
Gujar at 96–103
Haryana 104–111
Himachal Pradesh 112–119
Jharkhand 120–127
Karnataka 128–135
Kerala 136–143
Madh ya Pradesh 144–151
Maharashtra 152–159
Manipur 160–167
Meghalaya 168–175
Mizoram 176–183
Nagaland 184–191
Odisha 192–199
Punjab 200–207
Rajas than 208–215
Sikkim 216–221
Tamil Nadu 222–229
Telangana 230–235
Tripura 236–241
Uttar Pr adesh 242–249
Uttar akhand 250–257
West Bengal 258–265
26–49
III
4. India
PAGE 25National & State/UT Results
6.Technical Notes 318–321
7.References 322
8.Index of Tables 323
9.Data Tables 324-380
PAGE 317
IV
Technical Notes &
Data Tables
Union Territories
Andaman & Nic obar Islands 266–271
Chandigarh 272–277
Dadra & Nagar Haveli & Daman & Diu 278–283
284–289Delhi
290–297Jammu & K ashmir
Ladakh 298–303
Lakshadweep 304–309
Puducherry 310–315

SECTION
I
Context &
Introduction

2
SECTION 1
INTRODUCTION
India’s National Multidimensional Poverty Index
The 2030 Agenda for Sustainable Development and
the 17 Sustainable Development Goals (SDGs)
address the economic, environmental, and social
aspects of societal well-being and are focused on the
core principle of “leaving no one behind.” When
individuals face deprivations or disadvantages due to
limited choices and opportunities, they tend to be left
behind, unable to benefit much from economic growth,
innovation, or globalization. Therefore, identifying and
empowering such vulnerable sections of the population
becomes essential for effective poverty reduction.
SDG 1 aims to eradicate poverty in all forms and
dimensions – using measures that include and go
beyond income. SDG target 1.2 aims to reduce by
2030 “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”.
In this context, a national Multidimensional Poverty
Index (MPI) for India enables estimation of
multidimensional poverty at the national, state, and
district levels. The district-wise estimation of the
national MPI can be used for reaching out to the
furthest behind first, through targeted interventions.
1.1 History of poverty measurement
India's endeavor to measure poverty has a
long-standing history dating to the pre-independence
era. In 1901, Dadabhai Naoroji's book titled 'Poverty
and Un-British Rule in India' marked the earliest
attempts to estimate poverty based on the cost of a
subsistence diet. Subsequently, the National Planning
Committee in 1938, and the authors of the Bombay
Plan in 1944, proposed poverty estimations based on
the minimum standard of living. Poverty estimation
continued to have significant importance
post-independence, and various expert groups worked
on this issue. Early efforts included the Working Group
in 1962, Dandekar and Rath in 1971, and the Task
Force on "Projections of Minimum Needs and Effective
Consumption Demand" led by Dr. Y. K. Alagh in 1979.
Subsequently, expert groups headed by Lakdawala
(1993), Tendulkar (2009), and Rangarajan (2014)
continued this exercise of estimating monetary poverty
based on consumption and expenditure surveys.
Over time, it has been recognized that poverty has
additional dimensions that affect individuals'
experiences and quality of life. Qualitative aspects of
life such as access to basic services like water and
sanitation that may not be directly related to household
income, constitute an important part of poverty
measurement. This realization has led to a growing
consensus that non-monetary measures must
complement monetary measures, and that income is only
one aspect of well-being and not its sole determinant
(Chakravarty, 2009). The estimation and understanding
of poverty, therefore, requires a holistic approach that
considers the many dimensions of poverty and the
complex ways in which they interact.
1.2 Conceptual framework of
multidimensional poverty
The Multidimensional Poverty Index (MPI) has been
used by the United Nations Development Programme
(UNDP) in its flagship Human Development Report since
2010 and is the most widely used non-monetary poverty
index in the world (Godinot & Walker, 2020). It captures
overlapping deprivations in health, education and living
standards (UNDP, 2010). The MPI complements
monetary poverty measures by capturing additional
information – including broader qualitative aspects of life,
like child mortality, housing conditions, and other basic
services such as water and sanitation (Greve, 2020).
Simple headcount ratios or poverty rates do not
provide any insights on the depth of poverty. It is
possible that while the number of poor individuals as
captured by the headcount ratio reduce, the poorest
may, in fact, get even poorer. Alternatively, gains
among the poor may be completely missed unless they
cross the ‘poverty line’ or exit poverty. To address this,
the Multidimensional Poverty Index, based on the
Alkire-Foster methodology, presents not just the extent of
poverty (the headcount ratio), but also the depth of poverty -
captured by the ‘MPI value’ or the adjusted headcount ratio.
The MPI value is arrived at by multiplying the headcount
ratio with the average deprivation score among the MPI
poor (Alkire & Foster, 2011).
The development and understanding of the
multidimensional poverty measure is important for
policy design and formulation. Not only does it provide
insights into the distribution of poverty within a country,
it also delineates the contribution of each indicator to
multidimensional poverty. This can be done at the
national, state, and district levels, as well as for
disaggregated population groups – enabling a more
focused policy response.
1.3 Global Indices for Reforms and Growth
(GIRG) mandate
In February 2020, the Cabinet Secretariat,
Government of India, identified 29 global indices under
the GIRG mandate to be monitored, analyzed and
evaluated with the aim of improving India's position in
global rankings. This mandate leverages the monitoring
mechanisms of important social, economic, and other
internationally recognized indices to drive systematic
reforms in government policies, enabling improvements
in people’s living standards, and driving inclusive
development. The results of this targeted approach will
also correspondingly reflect in the improvement of India’s
performance in these indices globally.
Under the GIRG mandate, NITI Aayog has been
identified as the nodal agency for the Multidimensional
Poverty Index.
1.4 The Process: MPI Coordination Committee
(MPICC)
Recognizing the value of the GIRG initiative in
leveraging global indices as tools for systemic reforms,
NITI Aayog has been coordinating with all relevant
Union Ministries and departments mapped to the
individual components of the MPI, to develop
comprehensive reform action plans. These pertain to
areas such as nutrition, electricity, rural and urban
development, among others.
As the nodal agency for MPI, NITI Aayog is also
responsible for constructing an indigenized index for
monitoring the performance of States and Union
Territories. Consequently, an inter-ministerial MPI
Coordination Committee (MPICC) was constituted
under NITI Aayog to ensure horizontal and vertical
policy coherence.
Engagements with - i) the technical partners — UNDP
and the Oxford Poverty and Human Development
Initiative (OPHI) and ii) others such as the survey
implementors of the National Family Health Survey
(NFHS) — International Institute for Population Sciences
(IIPS) of Ministry of Health and Family Welfare, has been
critical in developing the national MPI and ensuring its
technical rigour and robustness.
The MPICC engaged in extensive discussions to adapt
the global MPI to the Indian context. Members from
each Ministry of the MPICC reflected on their
experiences in public service delivery in a
demographically and geographically diverse country
such as India. Their rich experience in identifying past
and present challenges and anticipating future
constraints in their respective sectors, informed the
discussion on indicator selection and identification of
areas for reform. This was followed by an assessment
of the technical feasibility of the indicators in the NFHS
and the selection of respective weights. The
deliberations brought forth varied perspectives on
policies and the interventions needed to enhance
progress.
Following the process outlined above, the global MPI
was adapted to the Indian context and the national MPI
was constituted with 2 additional indicators. These are
outlined in detail in later sections.
The national MPI is a key resource in the arsenal of
policy makers, providing a powerful monitoring and
accountability tool for data-driven decision making and
targeted policy interventions. It can aid in integrated
and multi-sectoral policy making at national and
subnational levels (states and districts), enabling
progress on multiple deprivations at the same time.

3
The 2030 Agenda for Sustainable Development and
the 17 Sustainable Development Goals (SDGs)
address the economic, environmental, and social
aspects of societal well-being and are focused on the
core principle of “leaving no one behind.” When
individuals face deprivations or disadvantages due to
limited choices and opportunities, they tend to be left
behind, unable to benefit much from economic growth,
innovation, or globalization. Therefore, identifying and
empowering such vulnerable sections of the population
becomes essential for effective poverty reduction.
SDG 1 aims to eradicate poverty in all forms and
dimensions – using measures that include and go
beyond income. SDG target 1.2 aims to reduce by
2030 “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”.
In this context, a national Multidimensional Poverty
Index (MPI) for India enables estimation of
multidimensional poverty at the national, state, and
district levels. The district-wise estimation of the
national MPI can be used for reaching out to the
furthest behind first, through targeted interventions.
1.1 History of poverty measurement
India's endeavor to measure poverty has a
long-standing history dating to the pre-independence
era. In 1901, Dadabhai Naoroji's book titled 'Poverty
and Un-British Rule in India' marked the earliest
attempts to estimate poverty based on the cost of a
subsistence diet. Subsequently, the National Planning
Committee in 1938, and the authors of the Bombay
Plan in 1944, proposed poverty estimations based on
the minimum standard of living. Poverty estimation
continued to have significant importance
post-independence, and various expert groups worked
on this issue. Early efforts included the Working Group
in 1962, Dandekar and Rath in 1971, and the Task
Force on "Projections of Minimum Needs and Effective
Consumption Demand" led by Dr. Y. K. Alagh in 1979.
Subsequently, expert groups headed by Lakdawala
(1993), Tendulkar (2009), and Rangarajan (2014)
continued this exercise of estimating monetary poverty
based on consumption and expenditure surveys.
Over time, it has been recognized that poverty has
additional dimensions that affect individuals'
experiences and quality of life. Qualitative aspects of
life such as access to basic services like water and
sanitation that may not be directly related to household
income, constitute an important part of poverty
measurement. This realization has led to a growing
consensus that non-monetary measures must
complement monetary measures, and that income is only
one aspect of well-being and not its sole determinant
(Chakravarty, 2009). The estimation and understanding
of poverty, therefore, requires a holistic approach that
considers the many dimensions of poverty and the
complex ways in which they interact.
1.2 Conceptual framework of
multidimensional poverty
The Multidimensional Poverty Index (MPI) has been
used by the United Nations Development Programme
(UNDP) in its flagship Human Development Report since
2010 and is the most widely used non-monetary poverty
index in the world (Godinot & Walker, 2020). It captures
overlapping deprivations in health, education and living
standards (UNDP, 2010). The MPI complements
monetary poverty measures by capturing additional
information – including broader qualitative aspects of life,
like child mortality, housing conditions, and other basic
services such as water and sanitation (Greve, 2020).
Simple headcount ratios or poverty rates do not
provide any insights on the depth of poverty. It is
possible that while the number of poor individuals as
captured by the headcount ratio reduce, the poorest
may, in fact, get even poorer. Alternatively, gains
among the poor may be completely missed unless they
cross the ‘poverty line’ or exit poverty. To address this,
the Multidimensional Poverty Index, based on the
Alkire-Foster methodology, presents not just the extent of
poverty (the headcount ratio), but also the depth of poverty -
captured by the ‘MPI value’ or the adjusted headcount ratio.
The MPI value is arrived at by multiplying the headcount
ratio with the average deprivation score among the MPI
poor (Alkire & Foster, 2011).
The development and understanding of the
multidimensional poverty measure is important for
policy design and formulation. Not only does it provide
insights into the distribution of poverty within a country,
it also delineates the contribution of each indicator to
multidimensional poverty. This can be done at the
national, state, and district levels, as well as for
disaggregated population groups – enabling a more
focused policy response.
MPI Coordination Committee
Inter-Minis terial C oordina tion C ommitt ee for the MPI
Member Ministries
NITI Aay og
Ministry of Statistics and Pr ogramme Implemen tation
1
2
Ministry of W omen and Child De velopmen t3
Ministry of Petr oleum and Natur al Gas4
Ministry of Po wer5
6
Departmen t of Health and Family W elfare
Departmen t of Rural Developmen t
7
8
Departmen t of Fo od and Public Distribution9
Departmen t of Scho ol Education and Liter acy10
Departmen t of Drinking W ater and Sanitation11
Departmen t of Financial Services12
Technical Partners
United Nations De velopmen t Programme
Oxford Poverty and Human De velopmen t Initiativ e
1
2
Ministry of Housing and Urban Affairs
1.3 Global Indices for Reforms and Growth
(GIRG) mandate
In February 2020, the Cabinet Secretariat,
Government of India, identified 29 global indices under
the GIRG mandate to be monitored, analyzed and
evaluated with the aim of improving India's position in
global rankings. This mandate leverages the monitoring
mechanisms of important social, economic, and other
internationally recognized indices to drive systematic
reforms in government policies, enabling improvements
in people’s living standards, and driving inclusive
development. The results of this targeted approach will
also correspondingly reflect in the improvement of India’s
performance in these indices globally.
Under the GIRG mandate, NITI Aayog has been
identified as the nodal agency for the Multidimensional
Poverty Index.
1.4 The Process: MPI Coordination Committee
(MPICC)
Recognizing the value of the GIRG initiative in
leveraging global indices as tools for systemic reforms,
NITI Aayog has been coordinating with all relevant
Union Ministries and departments mapped to the
individual components of the MPI, to develop
comprehensive reform action plans. These pertain to
areas such as nutrition, electricity, rural and urban
development, among others.
As the nodal agency for MPI, NITI Aayog is also
responsible for constructing an indigenized index for
monitoring the performance of States and Union
Territories. Consequently, an inter-ministerial MPI
Coordination Committee (MPICC) was constituted
under NITI Aayog to ensure horizontal and vertical
policy coherence.
Engagements with - i) the technical partners — UNDP
and the Oxford Poverty and Human Development
Initiative (OPHI) and ii) others such as the survey
implementors of the National Family Health Survey
(NFHS) — International Institute for Population Sciences
(IIPS) of Ministry of Health and Family Welfare, has been
critical in developing the national MPI and ensuring its
technical rigour and robustness.
The MPICC engaged in extensive discussions to adapt
the global MPI to the Indian context. Members from
each Ministry of the MPICC reflected on their
experiences in public service delivery in a
demographically and geographically diverse country
such as India. Their rich experience in identifying past
and present challenges and anticipating future
constraints in their respective sectors, informed the
discussion on indicator selection and identification of
areas for reform. This was followed by an assessment
of the technical feasibility of the indicators in the NFHS
and the selection of respective weights. The
deliberations brought forth varied perspectives on
policies and the interventions needed to enhance
progress.
Following the process outlined above, the global MPI
was adapted to the Indian context and the national MPI
was constituted with 2 additional indicators. These are
outlined in detail in later sections.
The national MPI is a key resource in the arsenal of
policy makers, providing a powerful monitoring and
accountability tool for data-driven decision making and
targeted policy interventions. It can aid in integrated
and multi-sectoral policy making at national and
subnational levels (states and districts), enabling
progress on multiple deprivations at the same time.
INTRODUCTIONMPI: PROGRESS REVIEW 2023

4
INTRODUCTION MPI: PROGRESS REVIEW 2023
1.5 National MPI as a measure
India’s national MPI is a contribution towards
measuring progress on target 1.2 of the SDGs which
aims at reducing “at least by half the proportion of men,
women and children of all ages living in poverty in all its
dimensions.” Across three dimensions of health,
education, and standard of living, India’s national MPI
includes indicators on nutrition, child and adolescent
mortality, maternal health, years of schooling, school
attendance, cooking fuel, sanitation, drinking water,
electricity, housing, bank accounts and assets.
The National Multidimensional Poverty Index: Baseline
Report, was prepared in consultation with 12 line
Ministries, State governments, Union Territories (UTs)
and technical partners – OPHI and UNDP and
published in November 2021. The report provided
poverty estimates for India’s 36 States & Union
Territories as well as the 640 districts defined in the
2011 census. These estimates were computed using
data from the 4th round of the NFHS conducted in
2015-16.
This report presents the second edition of the National
MPI and provides multidimensional poverty estimates
for the 36 States & Union Territories, along with 707
administrative districts across 12 indicators of MPI.
These estimates were computed using data from the
5th round of the NFHS conducted in 2019-21,
employing the same methodology as the baseline
report.

The report also presents the changes in
multidimensional poverty between the two survey
periods: 2015-16 (NFHS-4) and 2019-21 (NFHS-5). It
is important to note that the poverty estimates
presented in this report may not fully assess the effects
of the COVID-19 pandemic on poverty, since more
than 70% of the data (NFHS-5) was collected before
the pandemic. At the same time, this report does not
capture the economic and social progress the country
has made in the last two years.
1.6 National MPI as a policy tool
The national MPI as a measure of multiple dimensions
of poverty complements monetary poverty statistics
and enables a close monitoring of individual indicators
and dimensions which overlap with several SDGs. It
allows for disaggregation at the levels of States and
districts and enables integrated, cross-sectoral policy
actions by capturing simultaneous deprivations.
Designing effective strategies to rapidly reduce poverty is
a challenging – yet possible – process. Over time,
multiple policies and programmes have defined India’s
deliberate and determined progress on poverty reduction.
The Economic Survey 2022-23 notes the role played
by government schemes including the 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), Pradhan Mantri Jan Dhan
Yojana (PMJDY), POSHAN Abhiyaan, Samagra
Shiksha among others in enhancing overall quality of
life of people in India.
The Hon’ble Prime Minister has underlined that India’s
development lies in the development of its states.
India’s federal system of governance inextricably links
the State and Union governments as partners and
pivotal stakeholders in the country’s social, and
economic development. The States of India reflect
significant disparities and socio-economic diversities.
For a policy tool to fully realize its potential and for
successful implementation of reform actions, it is
crucial to devise appropriate strategies at the State and
district levels.
NITI Aayog, at the time of computing the data for the
baseline report, organized several thematic
consultations in partnership with ministries and
subject
matter experts, at subnational levels for governments to
become familiar with the national MPI. These
deliberations were focused on State-specific
experiences in the domain of public service delivery
and challenges faced across various sectors. NITI
Aayog as the nodal agency has continued to provide
the necessary encouragement and support to forge
collaborative momentum. In the pursuit of 'Viksit
Bharat'— 'Empowering Citizens and Reaching the Last
Mile' by 2047, the focus on holistic development has
been embraced.
This latest edition of India’s national MPI presents
India’s remarkable progress in reducing
multidimensional poverty between NFHS-4 and NFHS-5
(survey period from 2015-16 to 2019-21) and indicates
the interventions required in this “Decade of Action”. It
will enable State and district administrations to not only
identify and replicate what has worked, but also identify
areas that need improvement and open the space for
peer learning.

SECTION
II
Methodology
& Way Forward

SECTION 2
METHODOLOGY
Computing India’s National MPI
2.1 The Alkire-Foster Methodology
At the core of the MPI is the Alkire-Foster (AF)
methodology. The AF methodology is a globally
accepted general framework for measuring
multidimensional poverty that identifies 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 whether each person is
“deprived” in that indicator. A person’s deprivations
across all indicators are then weighted and aggregated
to arrive at a deprivation score for each individual. The
second order cut-off is then applied to the deprivation
score to identify the individuals who are
multidimensionally poor. The AF methodology is an
extension of the widely accepted
Foster-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 poverty estimation.
Poised within a family of axiomatic measures, the AF
methodology achieves multiple technical milestones
associated with poverty measures including
dimensional monotonicity, subgroup decomposability,
dimensional breakdown, scale and replication
invariance, poverty and deprivation focus, and
symmetry. This ability of the AF methodology to
provide an idea of not only the amount of poverty, 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
multidimensional 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
towards 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
customization that is essential for the construction of a
multidimensional poverty measure suited to the
national context.
2.2 Steps in computing the MPI
The process of computing the MPI can be divided into
two broad categories, 1) Identification and
2) Aggregation. Both are outlined below.
2.2.1 Identification
i Determine 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
dimension of education.
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 considered
not deprived in an indicator. For example, the
individual has completed at least six years of
schooling.
iii Apply the cut-off and determine whether the
individual 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 indicators should
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 multidimensionally poor. India’s national MPI follows the poverty cutoff of 33.33 % used in the global MPI measure.
2.2.2 Aggregation
i Determine the proportion of individuals identified 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
indicators 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 individuals. This is known as the intensity of
poverty (A) in the MPI or the breadth of poverty, and
it broadly explains ‘how poor are the poor’.
iii Compute the MPI score (M
0
) as the product of the
two partial indices, headcount ratio and intensity.
2.3 Indicators in India’s National MPI
The national MPI model retains the ten original
indicators of the global MPI model, to be closely
aligned to the global methodology and rankings and
has added two indicators, viz., Maternal Health and
Bank Account, based on national priorities and
discussions with the MPICC. India’s MPI has three
equally weighted dimensions – health, education, and
standard of living – which are represented by 12
indicators as detailed in Table 1.
8

2.1 The Alkire-Foster Methodology
At the core of the MPI is the Alkire-Foster (AF)
methodology. The AF methodology is a globally
accepted general framework for measuring
multidimensional poverty that identifies 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 whether each person is
“deprived” in that indicator. A person’s deprivations
across all indicators are then weighted and aggregated
to arrive at a deprivation score for each individual. The
second order cut-off is then applied to the deprivation
score to identify the individuals who are
multidimensionally poor. The AF methodology is an
extension of the widely accepted
Foster-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 poverty estimation.
Poised within a family of axiomatic measures, the AF
methodology achieves multiple technical milestones
associated with poverty measures including
dimensional monotonicity, subgroup decomposability,
dimensional breakdown, scale and replication
invariance, poverty and deprivation focus, and
symmetry. This ability of the AF methodology to
provide an idea of not only the amount of poverty, 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
multidimensional 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
towards 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
customization that is essential for the construction of a
multidimensional poverty measure suited to the
national context.
2.2 Steps in computing the MPI
The process of computing the MPI can be divided into
two broad categories, 1) Identification and
2) Aggregation. Both are outlined below.
2.2.1 Identification
i Determine 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
dimension of education.
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 considered
not deprived in an indicator. For example, the
individual has completed at least six years of
schooling.
iii Apply the cut-off and determine whether the
individual 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 indicators should
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 multidimensionally
poor. India’s national MPI follows the poverty cutoff
of 33.33 % used in the global MPI measure.
2.2.2 Aggregation
i Determine the proportion of individuals identified 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
indicators 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 individuals. This is known as the intensity of
poverty (A) in the MPI or the breadth of poverty, and
it broadly explains ‘how poor are the poor’.
iii Compute the MPI score (M
0
) as the product of the
two partial indices, headcount ratio and intensity.
2.3 Indicators in India’s National MPI
The national MPI model retains the ten original
indicators of the global MPI model, to be closely
aligned to the global methodology and rankings and
has added two indicators, viz., Maternal Health and
Bank Account, based on national priorities and
discussions with the MPICC. India’s MPI has three
equally weighted dimensions – health, education, and
standard of living – which are represented by 12
indicators as detailed in Table 1.
Dimension
Table 1: Indicators in India’s National MPI
A Household is Considered Deprived If Weight (W)
Nutrition
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. 1/6
Child- Adolescent
Mortality
Maternal H ealth
1/12
1/12
Years of Schooling
A child/adolescent under 18 years of age has died in the family in the five-year
period preceding the survey.
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.
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.
1/6
School Attendance 1/6
Health
(1/3)
Education
(1/3)
Standar d of
Living (1/3)
Cooking Fuel 1/21
Sanitation 1/21
Drinking Water 1/21
Electricity 1/21
Housing 1/21
Assets 1/21
Bank Account
1/21
Indicator
METHODOLOGYMPI: PROGRESS REVIEW 2023
9

2.3.1 Dimension: Health
The Health dimension comprises indicators
representing nutrition, child mortality and maternal
health. The indicators for Nutrition and Child Mortality
echo the definitions and cut-offs followed by their
counter parts in the global MPI. The indicator for
Maternal Health is unique to India’s national MPI. A
point to note is that in the national MPI, the Child
Mortality indicator has been renamed as Child &
Adolescent Mortality. According to the parlance of the
Indian statistical system, the use of the term “Child
Mortality” is usually associated with mortality of
children below 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 precedence 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
remaining dimension weight is split across Child &
Adolescent Mortality and Maternal Health with each
indicator having a weight of 1/12. The sharing of
weights between the Child & Adolescent Mortality and
Maternal Health 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 monitoring 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 retain its original share from
the global MPI in India’s national MPI, enabling
uniformity in reporting across both.
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
or the age-specific BMI
cutoff for individuals aged 15-19 years, when
information is available. Children under 5 years of age
are considered malnourished if their z-score of
height-for-age (stunting) or weight-for-age
(underweight) 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 because of two primary reasons: 1) the unit of analysis is the household and 2) the indicator for nutrition operates 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 household.
Contributing to nearly one-third of the multidimensional
poverty in India, nutrition is arguably one of the most
important indicators in India’s national MPI.
Malnutrition has significant consequences for early
childhood development as well as on 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.
The Child & Adolescent Mortality indicator is based on
the 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-adolescent mortality, then the household is
reported to be not deprived. A household with no
children would also be treated as not deprived.
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, malnutrition,
iron-deficiency (anemia), or an unsafe environment are
all contributors to child and adolescent mortality (WHO,
2017). The death of a child or adolescent may therefore
indicate the deprivations experienced by a household in
one or more of these factors. Furthermore, 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
individuals, and by extension, the deprivation status of
the individuals in that household. These externalities
can manifest in a number of different ways over time.
The indicator for Child & Adolescent Mortality carries a
weight of 1/12 and its definition remains aligned with
the global MPI.
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.
2.3.1 i Nutrition
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.
2.3.1 ii Child & Adolescent Mortality
METHODOLOGY MPI: PROGRESS REVIEW 2023
10

Introduced as an indicator to India’s national MPI, the
indicator for Maternal Health is a union of two distinct
components – antenatal care and assisted delivery.
The indicator captures if a woman in the household 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 personnel
during the most recent childbirth. Not fulfilling any one
of the two criteria would cause the household to be
considered as deprived. If the household has not had
any births in the 5 years preceding the survey, it would
be considered non-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-born
children 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 is instrumental in the early
identification of complications in pregnancy, monitoring
of feotal growth and the management of complications
through the referral of mothers to the appropriate facility
for further treatment.
The causes of nearly 80% 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
infections. Thus, ANC cannot be looked at in isolation
as prevention of intrapartum deaths requires quality
care provided during childbirth.
India’s national MPI adopts 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
assistance by skilled personnel during childbirth.
The maternal health indicator in the national MPI aims
to enforce strict compliance to the SDG targets of
reducing maternal mortality and ending preventable
deaths of new-born children in the country.

2.3.2 Dimension: Education
The Education dimension is represented by indicators
pertaining to school attendance and years of
schooling, with each indicator – weighted at 1/6 –
carrying 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.
The indicator 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 (in terms of increase in
economic opportunities such as the ability to enter high
paying employment or in terms of improvement in
social standing) 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 schooling
is considered to be non-deprived, even though they
themselves may not have attended school. The
indicator carries a weight of 1/6.
The indicator School Attendance is the logical
precursor 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 unfold
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 household that
acts as an impediment to the education of the child.
Furthermore, because the child is not attending school,
the household members will be deprived of the positive
externalities that arise from having a formally educated
member in the household.
An individual living in a household where there is at
least one child not attending school is treated as
deprived in this indicator, even though they themselves
may have completed schooling. The indicator has a
weight of 1/6.
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 and skilled medical personnel during the most
recent childbirth.
2.3.1 iii Maternal Health
A household is deprived if not even one member of the household aged 10 years or older has completed six years of schooling.
2.3.2 i Years of Schooling
A household is deprived if any school-aged child is not attending school up to the age at which he/she will complete class 8.
2.3.2 ii School Attendance
METHODOLOGYMPI: PROGRESS REVIEW 2023
11

Safe or improved sources of drinking water include
piped water, public taps, standpipes, tube wells,
2.3.3 Dimension: Standard of Living
Lastly, the dimension Standard of Living comprises
indicators representing access of the household to
electricity, clean cooking fuel, improved sources of safe
drinking water, improved sanitation, pucca housing
(proper flooring, roof and walls), bank account, and
household assets. All indicators with the exception of
the indicator for bank accounts – which is unique to
India’s national MPI – align with global MPI definitions
and cut-offs. The dimension weight of 1/3 is split evenly
across all indicators therefore giving each a weight of
1/21.
Improved or safe sources of cooking fuel include
electricity, LPG/natural gas, or 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 an 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.
Improved sanitation includes any toilet of the following
types: flush/pour flush toilets to piped sewer systems,
septic tanks, pit latrines, or an unknown destination;
ventilated 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 considered
not deprived in this indicator.
The indicator for bank accounts is an additional indicator in India’s national MPI. The ownership of a bank account or post office account is the key to the financial inclusion of the 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, increasing access to higher 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 lines of credit and also act as long-term savings instruments - either through self-deposits or through institutionalized savings schemes.
Access to electricity has a multiplier effect on any household and deprivation in this basic and essential service is ground for treating any household as deprived.
Mud, clay, earth, sand and dung are considered natural
materials, and low-quality materials such as thatch are
considered rudimentary.
In the case of the indicator for assets, the criteria for
the car or truck acts as an exclusion criteria. Therefore,
even if a household does not have a radio, TV,
telephone, computer, animal cart, bicycle, motorbike,
or refrigerator, but has either a car or a truck, then the
household will be treated as not deprived.
A household is deprived if the primary source of
cooking fuel is dung, agricultural crops, shrubs, wood,
charcoal or coal.
2.3.3 i Cooking Fuel
A household is deprived if it does not have access to an improved source of safe drinking water or safe drinking water is more than a 30-minute walk from home (as a round trip).
2.3.3 iii Drinking Water
The household has unimproved or no sanitation facility or it is improved but shared with other households.
2.3.3 ii Sanitation
A household is deprived if it has inadequate housing: the floor is made of natural materials, or the roof or walls are made of rudimentary materials.
2.3.3 v Housing
No household member has a bank account or a post office account.
2.3.3 vii Bank Account
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.
2.3.3 vi Ownership of Assets
A household is deprived if it has no electricity.
2.3.3 iv Electricity
boreholes, protected dug wells and springs, rainwater,
tanker truck, cart with small tank, bottled water, and
community reverse osmosis (RO) plants. Even if a
household has access to an improved water source, it
will be considered deprived in this indicator if the source
is more than a 30-minute roundtrip walk from home.
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 the significant correlation between areas with lower banking access and higher or relatively severe incidences of poverty (Iqbal, Roy, & Alam, 2020). Other studies which have probed demographic datasets have concluded that financial inclusion plays an important 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 addition of an indicator
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 efforts to increase
banking inclusion in India are sustained.
2.4 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
multidimensionally poor for a given cutoff. Aggregation
involves the estimation of two partial indices – headcount
ratio and intensity – the product of which provides us with
the MPI. Each of the aforementioned concepts have
been detailed in the following paragraphs.
METHODOLOGY MPI: PROGRESS REVIEW 2023
12

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 the significant correlation between areas with
lower banking access and higher or relatively severe
incidences of poverty (Iqbal, Roy, & Alam, 2020). Other
studies which have probed demographic datasets
have concluded that financial inclusion plays an
important 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 addition of an indicator
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 efforts to increase
banking inclusion in India are sustained.
2.4 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
multidimensionally poor for a given cutoff. Aggregation
involves the estimation of two partial indices – headcount
ratio and intensity – the product of which provides us with
the MPI. Each of the aforementioned concepts have
been detailed in the following paragraphs.
known as the second-order cutoff) is used to finally determine who is multidimensionally poor. Both concepts have been detailed upon in the following sections.
2.4.1 i Deprivation Score
Each individual (and in extension everyone in the same
household), is first marked as deprived (denoted 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 schooling,
they do 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 schooling).
Therefore, A is considered deprived in the indicator 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 A and B
have been assigned a score for all indicators.
2.4.1 Identifying the Poor
Based on the AF methodology, identification of the
poor is dependent on a set of within-indicator
deprivation cutoff as well as an across-indicators
deprivation cutoff (hence the term dual-cutoff
approach). The cutoff within indicators (also known as
the first-order cutoff) is used to determine the
deprivation score while the across-indicator cutoff (also
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
deprived in the indicator for years of schooling. The
weighted status of the indicator for A would then be 1
(the number assigned to them denoting that they are
deprived) multiplied by 1/6 (which is the weight
assigned to the indicator for years of schooling. Thus,
A’s weighted status for indicator on years of schooling
would be 1/6 or 0.167. Following this, the weighted
status for individual B would be 0. This is repeated for all
If the achievement of an individual i in indicator j is
denoted by x
ij
, the first order cut-off for indicator j is
denoted by z
j
, and the status of the individual is
denoted as g
ij
0
, then.
g
ij
0
=1 if x
ij
< z
j
and g
ij
0
= 0 otherwise for all i =1, 2
...
n
and j = 1, 2
...
d
Depriv ation Status
Example: Finding g
0
for Individual A
Has 6 y ears of scho oling
Does not hav e 6 years of scho oling
Indicator Depriv ed? Status
Individual
A
1Yes
0No
(g
0
)
Steps in Computing the MPI
Identification1
Calculate the Intensity of Po verty
(A):
On av erage, ho w poor ar e the p oor?
Calculate the Headcount Ratio
(H):
How man y are poor?
Compute the MPI by taking the product of H and A (MPI=HxA)
Aggregation2
Build a deprivation profile by applying cutoffs within an indicator
Identify who is multidimensionally poor by applying a cut-off across
all indicators
METHODOLOGYMPI: PROGRESS REVIEW 2023
13

2.4.1 ii Poverty Cut-off
The second-order cutoff (k), defined in the AF
methodology as the poverty cut-off marks the minimum
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 (33%)
and individual A has a deprivation score of 0.54, then A
is considered multidimensionally poor. Likewise, if
individual B has a deprivation score of 0.28, they will
not be considered multidimensionally poor even
though they have a non-zero deprivation score.
the indicators, following which the weighted scores are
added, giving us the deprivation scores for A and B.
India for its national MPI has adopted the second-order
cutoff of 0.33 which is also the standard cutoff used
globally. Thus, for an individual to be considered as
multidimensionally poor, they should be deprived of at
least 1/3rd of weighted indicators.
It is at this juncture that potential of the AF methodology is
realized. The union method of multidimensional poverty
identification considers an individual to be poor if they are
deprived in even one indicator – leading to overestimation
– while the intersection method only considers an
individual as poor if they are deprived in all indicators –
leading to underestimation. Neither of these therefore
provide sufficient insights to a policy maker. The
AF-methodology, with its dual cutoff approach thus
provides a realistic middle ground for poverty estimation.
2.4.1 iii Censoring
Following the computation of the deprivation scores for
all individuals, a score less than the second order
cut-off is replaced with 0. This process is known as
censoring in multidimensional poverty estimations.

Following our example, the deprivation score of
individual A (0.52) will remain unaltered while the score
of individual B (0.20) will be replaced with 0.
Example: Calculating the Depriv ation Scor e for Individual A
Indicator WeightsDepriv ed? Status (g
0
) Score (w g
0
)
1/6Yes 1 0.17X =Nutrition
Child & Adolescen t Mortality 1/12No 0 0X =
Maternal Health 1/12Yes 1 0.08X =
Years of Scho oling 1/6Yes 1 0.17X =
School Attendance 1/6No 0 0X =
Cooking Fuel 1/21Yes 1 0.05X =
Sanitation 1/21No 0 0X =
Electricity 1/21No 0 0X =
Drinking W ater 1/21No 0 0X =
Housing 1/21Yes 1 0.05X =
X =Assets 1/21No 0 0
X =1/21No 0 0Bank Accoun t
Depriv ation Scor e (c
i
)0.52=
The counting vector for individual i up to the j
th

indicator (denoted by c
i
), also known as deprivation
score, is their status in each indicator (g
ij
0
) multiplied
by the weight (w
j
) assigned to that indicator.
The deprivation score (or weighted deprivation) of
individual i can thus be denoted as:
c
i
= w
1
g
i1
0
+ w
2
g
i2
0
+ … + w
j
g
ij
0
or c
i
=

w
j
g
0
i
Because the weight structure follows the AF
methodology, the sum of the relative weights of all
the indicators equals to 1. Therefore:

w
j
= 1
⅀
d
Counting Vector and Depriv ation Score
j=1
j
⅀
d
j=1
The identification function for multidimensional poverty denoted by p. The function p is dependent on the deprivation status of an individual (x
i
) given
the cutoffs within an indicator (z) as well as on the cutoffs across indicators (k) and is therefore represented by
р
k
(x
i
; z) = 1 if c
i
≥k and р
k
(x
i
; z) = 0 otherwise
Therefore, the function p considers an individual i as multidimensionally poor when their deprivation score (c
i
) is greater than or equal to the
second-order cutoff (k).
Applying the Poverty Cut-off
Depriv ation
Score (c)
Higher than
0.33? (c
Is MPI Poor?
Score
ρ
Individual A
No No 0Individual B 0.20
0.52 Yes Yes 1
METHODOLOGY MPI: PROGRESS REVIEW 2023
14

2.4.2 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 of how many are poor?
2.4.2 i Uncensored (Raw) Headcount Ratios
While the headcount ratio (H) provides the proportion
of multidimensionally poor individuals in the
population, the uncensored headcount ratio (denoted
by h
j
) provides the proportion of individuals who are
deprived in an indicator j irrespective of whether they
are multidimensionally poor or not.
Censored scores are denoted as c
i
(k) to differentiate
them from deprivation scores (c
i
). After censoring,
if c
i
<k, then c
i
(k)=0 and if c
i
≥k then c
i
(k)=c
i
To put it in the simplest sense, if c
i
(k) > 0, it is the
deprivation score of a multidimensionally poor
person; if c
i
(k) = 0, then that person is non-poor.
Censored Deprivation Score
Depriv ation
Score (c)
Higher than
0.33? (c
Is MPI Po or?
Censored
Deprivation
Score
(c
i
(k))
Individual A
No No 0Individual B 0.20
0.52 Yes Yes 0.48
Example: Censoring in MPI
H =
where q is the total number of multidimensionally poor individuals identified in the previous steps (i.e., the total number of individuals for whom р
k
(x
i.
; z) = 1) and
n is the total population. In this report, the headcount ratio has been reported as a percentage (H×100).
q
n
Headcount Ratio
The uncensored headcount ratio may be presented as
h
j
= g
i j
0
where denotes the sum of the deprivation status
up to the i
th
individual for the indicator j and n is the
total population. In this report, the uncensored headcount ratios have been reported as percentages (h
j
×100).
1
n
⅀
n
i = 1
Uncensored Headcount Ratio
g
i j
0
⅀
n
i = 1
The uncensored headcount ratios of the indicators in
India’s national MPI have been provided in Figure 1.
Each bar represents the percentage of India’s
population who are deprived in that indicator.
The censored headcount ratio may be presented as
where n is the number of individuals in the
population, and
g
i j
0
(k) is the censored deprivation
score of individual
i in indicator j using a
second-order cutoff
(k) of 33.33 percent. In this
report, the censored headcount ratios have been
reported as percentages (h
j
(k)×100).
Censored Headcount Ratio
1
n
⅀
n
i = 1
h
j
(k) = g
i j
0
(k)
2.4.2 ii Censored Headcount Ratio
The censored headcount ratio (denoted by h
j
(k))
provides the proportion of the population who fulfill two
criteria: they are 1) multidimensionally poor individuals
and 2) are deprived in an indicator j.
The censored headcount ratios of the indicators in
India’s national MPI have been provided in Figure 2.
Each bar represents the percentage of individuals who
are multidimensionally poor and are deprived in that
indicator.
METHODOLOGYMPI: PROGRESS REVIEW 2023
15

Percentage of the total p opulation of India who ar e depriv ed in each indicator
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
31.52%
37.60%
2.06%
2.69%
19.17%
22.58%
11.40%
13.86%
5.27%
6.40%
43.90%
58.47%
30.13%
51.88%
7.32%
10.92%
3.27%
12.16%
41.37%
45.65%
10.16%
13.97%
3.69%
9.66%
Nutrition Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
SanitationDrinking
Water
ElectricityHousing Assets Bank
Account
Health Education Standard of Living
Figure 1. India: Uncensor ed Headcount Ratio
Health Education Standard of Living
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
11.90%
19.79%
1.18%
1.87%
9.35%
14.64%
6.63%
10.67%
3.63%
5.22%
12.30%
23.03%
9.25%
21.20%
2.23%
5.05%
1.84%
8.28%
12.07%
20.48%
4.72%
8.84%
1.09%
5.36%
Nutrition Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
SanitationDrinking
Water
ElectricityHousing Assets Bank
Account
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Percentage of total p opulation of India who ar e multidimensionally poor and depriv ed in each indicator
Figure 2. India: Censor ed Headcoun t Ratio
METHODOLOGY MPI: PROGRESS REVIEW 2023
16

Headcoun t Ratio
The Headcoun t Ratio is computed by di-
viding the total numb er of multidimen-
sional po or (q) by the total p opulation ()
H=
q
7+5
7+5+4
12
16
0.75= ==
In this illustr ation, 75% of individuals are
multidimensionally po or
Intensity of Po verty
The Intensity of multidimensional poverty
is computed by summing the weighted
deprivation scores of all the MPI poor di-
vided b y the total numb er of MPI poor
On an average, an MPI poor individual is
deprived in 59% of w eighted indicators
7+5
0.74x7+0.52x5
= 0.648=c
q
⅀A=
1
q
Multidimensional Po verty Inde x
The MPI score is the product of the head-
count ratio and intensity . It is known as the
adjusted headcoun t ratio
MPI=
=0.75 x 0.648 = 0.486
Example: Calculating the Headcount Ratio, In tensity and MPI f or 3 Households
Indicator
Nutrition
Child & Adolescen t Mortality
Maternal Health
Years of Scho oling
School Attendance
Cooking Fuel
Sanitation
Electricity
Drinking W ater
Housing
Assets
Bank Accoun t
Depriv ation Scor e (c) =
Censor ed Depriv ation Scor e (c =
Memb ers of HH1 and HH2 ar e
multidimensionally po or
HH2
5 memb ers
Status (g
0
)
1
0
1
1
0
1
0
0
0
1
0
0
HH1
7 memb ers
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
X1/21
Weights
HH2
5 memb ers
0.17
0
0.08
0.17
0
0.05
0
0
0
0.05
0
0
Score (w g
0
)
HH3
4 memb ers
0
0.08
0.08
0
0
0.05
0
0
0
0
0
0
Score (w g
0
)
HH3
4 memb ers
Status (g
0
)
0
1
1
0
0
1
0
0
0
0
0
0
0.74
0.74
0.52
0.52
0.21
0
HH1
7 memb ers
0.17
0
0.08
0.17
0.17
0.05
0.05
0
0
0.05
0
0
Score (w g
0
)
X =
2.4.3 Intensity of Poverty
The intensity of poverty (denoted by A) is the average
proportion of deprivations which is experienced by
multidimensionally poor individuals. Simply put, it is the
average deprivation score of all multidimensionally
poor individuals. A is the second partial index used in
the construction of the MPI and answers the question
‘how poor are the poor?’.
2.4.4 The MPI
The Multidimensional Poverty Index reflects both the
incidence and the intensity of multidimensional
poverty. The index (denoted by M
0
) is the product of the
two partial indices - the headcount ratio (H) and
intensity (A) of multidimensional poverty.
Intensity of poverty is represented as
where c
i
(k) is the censored deprivation score (i.e.
deprivation score of multidimensionally poor
individuals) up to the i
th
individual and q is the
number of multidimensionally poor individuals.
A = c
i
(k)
1
q
⅀
q
i = 1
Intensity
The MPI is represented as
M
0
= H × A

The MPI therefore is the share of weighted
deprivations faced by multidimensionally poor
individuals divided by the total population. Hence
the MPI is known as the adjusted headcount ratio.
or H×A = x c
i
(k) = c
i
(k) = w
j
g
ij
0
(k)
q
n
1
q
⅀
q
i = 1
1
n
⅀
n
i = 1
1
n
⅀
n
i = 1
⅀
d
j = 1
Multidimensional Poverty Index
METHODOLOGYMPI: PROGRESS REVIEW 2023
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METHODOLOGY MPI: PROGRESS REVIEW 2023
18
2.5 Deconstruction of Estimates and
Indicators
One of the defining characteristics of the AF
methodology is sub-group decomposability, i.e.,
breaking down sub-groups such as geographical region
and population groups. The AF methodology also allows
for break down by indicators which can allow for the
determination of the contribution of each indicator to the
MPI. This contribution can be determined 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 administrative level in India, from the
Union Government, State Government and even the
district administration.
2.5.1 Estimates by geographical level and
population 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 State i,
we will take all households in State i and compute the
MPI like we would do for the total population, i.e., we will
carry out the entire process of assigning deprivation
scores, applying the second-order cutoff, determining
who is multidimensionally poor and compute the
aggregate estimates for only the population in State i.
Thus, the headcount ratio for the State would become:
Where q
i
is the number of multidimensionally poor
individuals in State i and n
i
is the population for State i.
This process is repeated for each State and similarly
for each district.
q
i
n
i
H
i
=
Estimates for a region: Example, Headcount ratio
2.4.4 i Why is the adjustment important?
An understandable question at this point would be, why
is the adjustment (using the intensity of poverty)
required when the headcount ratio already identifies
who is multidimensionally poor?
Traditionally poverty measures (such as poverty lines)
would utilize a single threshold to determine if an
individual was poor or not. However, this would only
convey the information regarding the 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 poverty 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 measures
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 meant that these measures violated the
axiom of dimensional monotonicity in poverty
measurement i.e., if the number of deprivations faced
by poor individuals decrease, 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 absolute
number of people in poverty decrease / increase) or if the
deprivations faced by multidimensionally poor
individuals decrease / increase (which may happen
without changing the headcount ratio). Therefore, the
MPI adheres to the axiom of dimensional monotonicity.

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19
Analogous to the process of decomposition by
geographical and population sub-groups, the
contribution of each region (e.g. how much a district
contributes 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.
2.5.3 Why is looking at contributions important?
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, what share of individuals
in the total population are deprived in an indicator (for
uncensored) and what share of individuals are both
multidimensionally poor and deprived in an indicator
(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
across multiple indicators form one determinant factor
of the MPI, the weights assigned to those indicators
also play an important role. In order to understand this
with more clarity, we can look at Table 2 which portrays
the uncensored headcount, censored headcount, and
contribution for each indicator in India’s national MPI.
To arrive at an objective assessment of poverty it is
therefore important to consider all three factors:
Similarly, if we would like to look even further and
determine the estimates for the rural areas within State i,
then we would carry out the identification and
aggregation process for only the population living in the
rural area within State i.
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 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.
2.5.2 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 also look at the
factors (i.e., indicators) which are contributing to
multidimensional poverty for that sub-group.
The contribution of indicators is determined by dividing
the weighted censored headcount ratio for each
indicator by the MPI. This is multiplied by 100 to arrive
at the percentage contribution.
Let us assume that the MPI for State i is MPI
i
and the
MPI for the urban and rural areas within State i is
MPI
ui
and MPI
ri
, therefore,
Where n
i
denotes the total population in State i, n
ui
is
the population living in the urban areas of State i, and
n
ri
is the population living in the rural areas of State i
assuming that n
i
= n
ui
+ n
ri
.
Taking this example forward, if we want to arrive at the
MPI for India from the MPI of the 36 States and Union
Territories in India’s national MPI, then:
Where, MPI
c
and n are India’s MPI and population
respectively, MPI
i
and n
i
are the MPI and population
for the i
th
State with i taking a value up to 36 -
equivalent to the number of States and Union
Territories in the country as of 2021.
n
ui
n
i
n
ri
n
i
MPI
i
= MPI
ui
+MPI
ri
n
1
n
n
2
n
n
i
nMPI
c
=MPI
1
+ MPI
i
+ ...+MPI
2
1
nor MPI
c
= n
i
MPI
i
⅀
i
Estimates for a region: Example, Headcount ratio
The process for determining the contribution of an indicator is a derivative of the fact that the sum of weighted censored headcount ratios for all indicators provides us with the MPI. As shown earlier, the censored headcount ratio is represented as h
j
(k)
where j is the indicator and k is the second-order cutoff at which the censoring was done. Therefore,
Where, MPI
c
is India’s MPI, w
j
is the weight of the j
th

indicator with j taking a value up to 12 - equivalent to
the number of indicators in India’s national MPI. Thus,
the contribution of each indicator j is,
MPI
c
=w
1
h
1
(k) + w
2
h
2
(k) + ... + w
j
h
j
(k)
w
j
h
j
(k)
MPI
c
Contribution
j
= x 100
or MPI
c
=w
j
h
j
(k)⅀
12
j = 1
Determining the Contribution of an Indicator

METHODOLOGY MPI: PROGRESS REVIEW 2023
20
From the point of view of a policy maker, the
uncensored headcount outlines the broader priorities
for intervention required for the benefit of the entire
population, the censored headcount outlines the
immediate priorities required to benefit the
multidimensionally poor population and the
contribution outlines which interventions would lead to
the maximum reduction of the overall MPI of the
population.
1) The uncensored headcount gives us the absolute
number of individuals who are deprived in an indicator; it gives us the status of deprivations among the entire population.
2) The censored headcount gives us the proportion of
individuals who are multidimensionally poor and deprived in an indicator; it gives us the composition of deprivations among the multidimensionally poor.
3) The contribution of an indicator gives us the
percentage contribution of an indicator to the overall MPI considering the weights attached to each indicator.
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. To create household deprivation profiles, it is presently neither possible nor feasible to collate data on a single household from several different surveys i.e., health indicators from the different rounds of National Sample Surveys, education indicators from the National Achievement Surveys etc.
2.6 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 indicators in which an individual is deprived at the same time. Thus, the prerequisite for the construction
IndicatorDimension
Uncensor ed
Headcount
Censor ed
Headcount (CH)
Weight (W) Contribution =
(CH x W) ÷ M0
Health
Nutrition 31.52% 11.90% 1/6 29.86%
Child- Adolescent M ortality 2.06% 1.18% 1/12 1.48%
Maternal H ealth 19.17% 9.35% 1/12 11.73%
Education
Years o f Schooling
5.27% 3.63% 1/6 9.10%School Attendanc e
11.40% 6.63% 1/6 16.65%
Standar d of
Living
Electricity 3.27% 1.84% 1/21 1.32%
Drinking Water 7.32% 2.23% 1/21 1.60%
Sanit ation 30.13% 9.25% 1/21 6.63%
Housing 41.37% 12.07% 1/21 8.65%
Cooking F uel 43.90% 12.30% 1/21 8.82%
Assets 10.16% 4.72% 1/21 3.39%
Bank Accoun t 3.69% 1.09% 1/21 0.78%
MPI (M
0
) = Sum of (CH × W) = 0 .066
Table 2: Contribution of indicators to India’s MPI score – NFHS-5 (2019-21)

METHODOLOGYMPI: PROGRESS REVIEW 2023
21
NFHS-5 (2019-21). There are certain indicators within
NFHS-5 that have undergone improvements in their
definitions. These are:
1) Sanitation: households with toilet flush to unknown
destination will also be considered as having access
to improved sanitation facility.
2) Drinking Water: households with access to drinking
water through tanker truck, cart with small tank or
bottled water will also be considered as having
access to improved drinking water source.
For all remaining indicators included in the national MPI
estimation, the definitions remain same across both
NFHS-4 and NFHS-5. Following these improvements
made by the IIPS in the NFHS-5, the national MPI
baseline estimates based on NFHS-4 have also been
recomputed in accordance with the updated definitions
of the indicators given above.
2.9.2 Comparability across states and districts
The NFHS-4 and NFHS-5 provide representative data
for all 28 States and 8 Union Territories. The estimates
for the newly established Union Territories of Jammu
and Kashmir, Ladakh, and Dadra and Nagar Haveli and
Daman and Diu and their respective districts have been
provided in accordance with their present administrative
status.

The NFHS-4 provides data for 640 administrative
districts as per the 2011 Census of India and the
NFHS-5 provides data for 707 administrative districts
as on 2017. Since certain districts that were part of the
2011 Census of India were subsequently divided into
multiple smaller administrative districts as of 2017, only
575 districts remain comparable between the two time
periods covered by the NFHS (2015-16 and 2019-21).
Therefore, estimates for changes in the national MPI
and its component indicators over time have been
provided for 575 districts that remain comparable
across two time periods. However, the point estimates
for the national MPI, i.e. the estimates at a fixed period
in time have been provided for all 707 districts covered
under NFHS-5 and all 640 districts covered under the
NFHS-4.

All point estimates and estimates for changes over time
have been provided for all States and Union Territories
and the country. All changes over time trends presented
in the report are the absolute percentage point changes
(simple difference) between two time periods of the
NFHS (2015-16 and 2019-21).2.7 The National Family Health Survey
The global MPI is constructed using Demographic and
Health Surveys (DHS) in countries where it is available.
This is because the DHS follows a standardized survey
methodology and guidelines for collection of data for
indicators that allows for cross-country comparisons of
the indicators of the MPI. The DHS also allows multiple
levels of disaggregation either geographically or by
population sub-groups. 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 Ministry of
Health and Family Welfare (MoHFW), Government of
India.
The latest iteration of national MPI is based on the 5th
round of the NFHS (NFHS-5) conducted through
2019-21 and is comparable with the baseline statistics
of the national MPI computed using the data from the
4th round of the NFHS (NFHS-4) conducted through
2015-16. The data for both the surveys are
representative at national, state and district levels. The
NFHS-4 provided representative data for urban and
rural areas up to the district level, while the NFHS-5
provides representative data for urban and rural areas
up to the level of States and Union Territories.
2.8 The Unit of Identification and Analysis
The unit of identification, i.e., the entity that is identified
as poor or non-poor for India’s national MPI is the
household. The information for all members in a
household is considered all-together. Therefore, all
members in a household are assigned the same
deprivation scores. This also acknowledges the
intra-household positive or negative externalities in
factors such as nutrition, maternal health, and
education. The unit of analysis i.e., the unit for the
analysis and reporting of the results is the individual.
Therefore, the headcount ratio provides the percentage
of individuals who are poor rather than the percentage
of households who are poor. This approach treats every
individual as equal in terms of reporting and differential
treatment of the deprivations faced by individuals within
the same household.
2.9 Calculating Changes Over Time
2.9.1 Harmonisation of the indicators
This version of the national MPI compares the
estimated data based on the same survey (NFHS)
across two time periods NFHS-4 (2015-16) and

India’s National MPI Report underlines the government’s
commitment to understanding, measuring, and
addressing the many dimensions of poverty and
vulnerability; and leveraging this understanding as a key
tool in policymaking. The baseline report of the national
MPI has been pivotal in raising awareness among State
governments, academia, civil society, and citizens about
the significance of using and addressing
multidimensional poverty measures as both a potent
policy instrument as well to measure progress. The
Baseline National MPI estimates have helped the
Central and State Governments to gain insights into the
gaps that must be bridged to meet India’s commitment to
the 2030 Agenda and implement impactful interventions
in this “Decade of Action”. Much of this focused action
has borne fruit and is visible in the tremendous progress
reflected in the findings of this report.
NITI Aayog will continue to play its role in paving the way
forward and providing support to State governments in
their actions, in line with their priorities.
Reform Action Plan for the States/UTs
Following the release of the National MPI: Baseline
Report, NITI Aayog as the country's premier policy think
tank, actively supported States and Union Territories in
formulating reform action plans based on the findings of
baseline estimates. These plans were a direct response
to the gaps visible in the Baseline report and included
targeted action to address these gaps and alleviate
deprivations.
The Baseline report was based on data from the
NFHS-4 (2015-16), which preceded the full roll out of the
flagship schemes related to housing, drinking water,
sanitation, electricity, cooking fuel, financial inclusion, and
other important initiatives targeting improvements in school
attendance, nutrition, and maternal and child health.
This report finds that between the years 2015-16 and
2019-21, the proportion of the multidimensionally poor
population in India decreased from 24.85% to 14.96%. It
is estimated that nearly 135 million have escaped
multidimensional poverty in this period.
India’s remarkable progress on the national MPI
between 2015-16 and 2019-21 is testimony to the
government’s strong commitment to improving the
quality of people’s lives – through targeted policies,
schemes, and development programs rolled out at both
the national and sub-national levels. The government’s
strategic focus on achieving universal coverage in
critical areas of education, nutrition, water, sanitation,
employment, and housing has played a pivotal role in
driving these positive outcomes. The efforts of the State
governments towards enhancing access to basic
services have also been instrumental.
The findings from the second edition of the national MPI
will serve as a valuable resource for States and Union
Territories to identify and amplify actions that have
triggered progress since the findings of the Baseline
report. It will help to ascertain the progress of
vulnerable hotspots and pinpoint areas that require
further targeted policy interventions and programmatic
action. NITI Aayog is committed to providing continuous
support to the States in formulating and implementing
effective reform action plans. Effective targeting,
regular monitoring of progress, and course correcting
will be essential components of the centre-state
partnership to ensure continued success in tackling
multidimensional poverty.
State Support Mission
The State Support Mission (SSM) is an overarching
umbrella initiative of NITI Aayog to reinvigorate its
ongoing engagement with States and Union Territories
in a more structured and institutionalized manner.
Under this mission, NITI Aayog supports the States/UTs
in capacity building and setting up State Institutions for
Transformation (SIT). These SITs are expected to steer
the development strategies required in the States/UTs
to achieve their stated goals. Additionally, NITI Aayog
would continue to provide holistic support to
States/UTs, including support for developing the States’
economic vision, establishing robust monitoring and
evaluation systems, and promoting an innovation
ecosystem. NITI Aayog has already reached out to all
the States to advocate the merit of having SITs in their
WAY FORWARD
respective States. A few States have announced the establishment of SITs, which include Karnataka (State Institute for Transformation of Karnataka), Maharashtra (MITRA – Maharashtra Institute for Transformation), Uttar Pradesh (STC – State Transformation Commission), and Uttarakhand (SETU – State Institute of Empowering and Transforming Uttarakhand). Further, requests have been received from other States and Union Territories, such as Rajasthan, Puducherry, Chhattisgarh, Chandigarh, and Nagaland, seeking knowledge and technical support from NITI Aayog to prepare State Vision documents and development strategies. In due course, SSM would facilitate further strengthening of SDG localization efforts in the States which in turn would aide further reduction in multidimensional poverty.
Progress Dashboard
While the periodic NFHS surveys will measure outcomes and aid in revising MPI estimates, it is crucial to strengthen implementation efforts to drive improved outcomes.
To effectively monitor the progress of implementation, a
dashboard has been developed by the Development
Monitoring and Evaluation Office (DMEO), an attached
office under NITI Aayog, to leverage the monitoring of
select Global Indices including MPI.
The dashboard enables concerned Ministries /
Departments/States to track and monitor India’s
progress on the (i) global level, (ii) national and state
level, and (iii) identified reform areas and reform
actions. This dashboard can track the progress of
State-led reforms aimed at improving outcomes for a
reduction in multidimensional poverty. The data from
this edition of the national MPI will also be made
available to States on the dashboard to enable
real-time tracking of multidimensional poverty across
various indicators.
Technical Support to States
NITI Aayog continues to encourage States to pursue
analysis at multiple levels. This can be achieved by
designing and conducting household surveys to
estimate MPI at the block or district levels with higher
frequency. Such surveys provide insights into
block-level estimates, which are not possible with
NFHS due to its sample design and size. A good
example of this is the initiative taken by the
Government of Andhra Pradesh, which conducted a
household survey in 2016 exclusively to estimate MPI
at the State and district levels. More such estimation
exercises may be undertaken by States for better and
more disaggregated data, which will help improve the
action plans. It is also important to explore how climate
and gender vulnerabilities interact with
multidimensional poverty.
The utility, relevance, and acceptance of the national
MPI as a powerful policy tool for fast-tracking
development and ensuring inclusivity at national and
local levels will ultimately shape the discourse on
developmental policy in the country and the global
arena in days to come.
WAY FORWARD MPI: PROGRESS REVIEW 2023
22

India’s National MPI Report underlines the government’s
commitment to understanding, measuring, and
addressing the many dimensions of poverty and
vulnerability; and leveraging this understanding as a key
tool in policymaking. The baseline report of the national
MPI has been pivotal in raising awareness among State
governments, academia, civil society, and citizens about
the significance of using and addressing
multidimensional poverty measures as both a potent
policy instrument as well to measure progress. The
Baseline National MPI estimates have helped the
Central and State Governments to gain insights into the
gaps that must be bridged to meet India’s commitment to
the 2030 Agenda and implement impactful interventions
in this “Decade of Action”. Much of this focused action
has borne fruit and is visible in the tremendous progress
reflected in the findings of this report.
NITI Aayog will continue to play its role in paving the way
forward and providing support to State governments in
their actions, in line with their priorities.
Reform Action Plan for the States/UTs
Following the release of the National MPI: Baseline
Report, NITI Aayog as the country's premier policy think
tank, actively supported States and Union Territories in
formulating reform action plans based on the findings of
baseline estimates. These plans were a direct response
to the gaps visible in the Baseline report and included
targeted action to address these gaps and alleviate
deprivations.
The Baseline report was based on data from the
NFHS-4 (2015-16), which preceded the full roll out of the
flagship schemes related to housing, drinking water,
sanitation, electricity, cooking fuel, financial inclusion, and
other important initiatives targeting improvements in school
attendance, nutrition, and maternal and child health.
This report finds that between the years 2015-16 and
2019-21, the proportion of the multidimensionally poor
population in India decreased from 24.85% to 14.96%. It
is estimated that nearly 135 million have escaped
multidimensional poverty in this period.
India’s remarkable progress on the national MPI
between 2015-16 and 2019-21 is testimony to the
government’s strong commitment to improving the
quality of people’s lives – through targeted policies,
schemes, and development programs rolled out at both
the national and sub-national levels. The government’s
strategic focus on achieving universal coverage in
critical areas of education, nutrition, water, sanitation,
employment, and housing has played a pivotal role in
driving these positive outcomes. The efforts of the State
governments towards enhancing access to basic
services have also been instrumental.
The findings from the second edition of the national MPI
will serve as a valuable resource for States and Union
Territories to identify and amplify actions that have
triggered progress since the findings of the Baseline
report. It will help to ascertain the progress of
vulnerable hotspots and pinpoint areas that require
further targeted policy interventions and programmatic
action. NITI Aayog is committed to providing continuous
support to the States in formulating and implementing
effective reform action plans. Effective targeting,
regular monitoring of progress, and course correcting
will be essential components of the centre-state
partnership to ensure continued success in tackling
multidimensional poverty.
State Support Mission
The State Support Mission (SSM) is an overarching
umbrella initiative of NITI Aayog to reinvigorate its
ongoing engagement with States and Union Territories
in a more structured and institutionalized manner.
Under this mission, NITI Aayog supports the States/UTs
in capacity building and setting up State Institutions for
Transformation (SIT). These SITs are expected to steer
the development strategies required in the States/UTs
to achieve their stated goals. Additionally, NITI Aayog
would continue to provide holistic support to
States/UTs, including support for developing the States’
economic vision, establishing robust monitoring and
evaluation systems, and promoting an innovation
ecosystem. NITI Aayog has already reached out to all
the States to advocate the merit of having SITs in their
respective States. A few States have announced the
establishment of SITs, which include Karnataka (State
Institute for Transformation of Karnataka), Maharashtra
(MITRA – Maharashtra Institute for Transformation),
Uttar Pradesh (STC – State Transformation
Commission), and Uttarakhand (SETU – State Institute
of Empowering and Transforming Uttarakhand).
Further, requests have been received from other States
and Union Territories, such as Rajasthan, Puducherry,
Chhattisgarh, Chandigarh, and Nagaland, seeking
knowledge and technical support from NITI Aayog to
prepare State Vision documents and development
strategies. In due course, SSM would facilitate further
strengthening of SDG localization efforts in the States
which in turn would aide further reduction in
multidimensional poverty.
Progress Dashboard
While the periodic NFHS surveys will measure
outcomes and aid in revising MPI estimates, it is crucial
to strengthen implementation efforts to drive improved
outcomes.
To effectively monitor the progress of implementation, a
dashboard has been developed by the Development
Monitoring and Evaluation Office (DMEO), an attached
office under NITI Aayog, to leverage the monitoring of
select Global Indices including MPI.
The dashboard enables concerned Ministries /
Departments/States to track and monitor India’s
progress on the (i) global level, (ii) national and state
level, and (iii) identified reform areas and reform
actions. This dashboard can track the progress of
State-led reforms aimed at improving outcomes for a
reduction in multidimensional poverty. The data from
this edition of the national MPI will also be made
available to States on the dashboard to enable
real-time tracking of multidimensional poverty across
various indicators.
Technical Support to States
NITI Aayog continues to encourage States to pursue
analysis at multiple levels. This can be achieved by
designing and conducting household surveys to
estimate MPI at the block or district levels with higher
frequency. Such surveys provide insights into
block-level estimates, which are not possible with
NFHS due to its sample design and size. A good
example of this is the initiative taken by the
Government of Andhra Pradesh, which conducted a
household survey in 2016 exclusively to estimate MPI
at the State and district levels. More such estimation
exercises may be undertaken by States for better and
more disaggregated data, which will help improve the
action plans. It is also important to explore how climate
and gender vulnerabilities interact with
multidimensional poverty.
The utility, relevance, and acceptance of the national
MPI as a powerful policy tool for fast-tracking
development and ensuring inclusivity at national and
local levels will ultimately shape the discourse on
developmental policy in the country and the global
arena in days to come.
WAY FORWARDMPI: PROGRESS REVIEW 2023
23

SECTION
III
National &
State/UT Results

MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in India
INDIA
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
Overview
India's Headcount Ratio, Intensity and MPI
India: Indicator Contribution to the MPI
Percentage contribution of each indicator to India's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
14.96%2019-21 0.06644.39%
24.85%2015-16 0.11747.14%
Rural
Headcount Ratio Intensity MPI
0.08619.28% 44.55%
Urban
Headcount Ratio Intensity MPI
0.0235.27% 43.10%
0.15432.59% 47.38% 0.0398.65% 45.27%
Multidimensional Poverty in India's Rural and Urban Areas
2019-21
2015-16
Year
Nutrition: 29.86%
Child & Adolescent Mortality: 1.48%
Maternal Health: 11.73%
Years of Schooling: 16.65%
School Attendance: 9.10%
Cooking Fuel: 8.82%
Sanitation: 6.63%
Drinking Water: 1.60%
Electricity: 1.32%
Housing: 8.65%
Assets: 3.39%
Bank Account: 0.78%
Nutrition: 28.15%
Child & Adolescent Mortality: 1.33%
Maternal Health: 10.41%
Years of Schooling: 15.18%
School Attendance: 7.42%
Cooking Fuel: 9.36%
Sanitation: 8.62%
Drinking Water: 2.05%
Electricity: 3.37%
Housing: 8.33%
Assets: 3.59%
Bank Account: 2.18%
26

Percentage of the total population of India who are deprived in each indicator
India: Uncensored Headcount Ratio
Percentage of total population of India who are multidimensionally poor and deprived in each indicator
India: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
31.52%
37.60%
2.06%
2.69%
19.17%
22.58%
11.40%
13.86%
5.27%
6.40%
43.90%
58.47%
30.13%
51.88%
7.32%
10.92%
3.27%
12.16%
41.37%
45.65%
10.16%
13.97%
3.69%
9.66%
11.90%
19.79%
1.18%
1.87%
9.35%
14.64%
6.63%
10.67%
3.63%
5.22%
12.30%
23.03%
9.25%
21.20%
2.23%
5.05%
1.84%
8.28%
12.07%
20.48%
4.72%
8.84%
1.09%
5.36%
INDIAMPI: PROGRESS REVIEW 2023
27

India: States and Union Territories
Multidimensional Poverty Index Score (State/UT-wise): NFHS-5(2019-21)
The colour represents the MPI score of a State/ UT. The legend provides the range of MPI scores for 2019-21.
Up to 0.033 0.034 to 0.064 0.065 to 0.096 0.097 to 0.127 0.128 and above
INDIA MPI: PROGRESS REVIEW 2023
28

India: Districts
Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores for 2019-21. Regions for which data is not
available is shown in grey.
Up to 0.0310.032 to 0.0620.063 to 0.0940.095 to 0.1260.127 to 0.158 0.190 to 0.2210.159 to 0.189 0.222 to 0.253 0.254 and above
INDIAMPI: PROGRESS REVIEW 2023
29

India: States and Union Territories
Comparative view of the Multidimensional Poverty Index Score (State/UT-wise): NFHS-4(2015-16)
The colour represents the MPI score of a State/ UT. The legend provides the range of MPI scores based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.054 0.055 to 0.107 0.108 to 0.159 0.160 to 0.211 0.212 and above
INDIA MPI: PROGRESS REVIEW 2023
30

India: States and Union Territories
Comparative view of the Multidimensional Poverty Index Score (State/UT-wise): NFHS-5(2019-21)
The colour represents the MPI score of a State/ UT. The legend provides the range of MPI scores based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.054 0.055 to 0.107 0.108 to 0.159 0.160 to 0.211 0.212 and above
INDIAMPI: PROGRESS REVIEW 2023
31

India: Districts
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4 (2015-16)
The colour represents the MPI score of a district. The legend provides the range of MPI scores based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21. Only 575 districts are
comparable between the two time periods of the two NFHS (2015-16 and 2019-21). Of these, 436 districts are statistically significant at
95% level of confidence.
Up to 0.0450.046 to 0.0900.091 to 0.1360.137 to 0.1820.183 to 0.228 0.274 to 0.3190.229 to 0.273 0.320 to 0.365 0.366 and above © 2023 Mapbox © OpenStreetMap
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 shows the
highest and lowest District MPI scores in India as on 2015-16. Both maps use the same legend to represent the change in MPI scores
between 2015-16 and 2019-21. Regions with no data are shown in grey.
Only 575 districts are comparable between the two time periods of the two NFHS (2015-16 and 2019-21). Of these 436 districts are
statistically significant at 95% level of confidence.
INDIA MPI: PROGRESS REVIEW 2023
32

India: Districts
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21. Only 575 districts are
comparable between the two time periods of the two NFHS (2015-16 and 2019-21). Of these, 436 districts are statistically significant at
95% level of confidence.
Up to 0.0450.046 to 0.0900.091 to 0.1360.137 to 0.1820.183 to 0.228 0.274 to 0.3190.229 to 0.273 0.320 to 0.365 0.366 and above
INDIAMPI: PROGRESS REVIEW 2023
33

INDIA MPI: PROGRESS REVIEW 2023
34
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
50.0% .0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%
Bihar
Jharkhand
Meghalaya
Uttar Pradesh
Madhya Pradesh
Assam
Chhattisgarh
Odisha
Nagaland
Rajasthan
Arunachal Pradesh
Tripura
West Bengal
Gujarat
Uttarakhand
Manipur
Maharashtra
Karnataka
Haryana
Andhra Pradesh
Telangana
Mizoram
Himachal Pradesh
Punjab
Sikkim
Tamil Nadu
Goa
Kerala
Dadra & Nagar Haveli & Daman & Diu
Jammu & Kashmir
Ladakh
Chandigarh
Delhi
Andaman & Nicobar Islands
Lakshadweep
Puducherry
33.76%
27.79%
22.93%
37.68%
20.63%
36.57%
19.35%
32.65%
16.37%
29.90%
15.68%
29.34%
15.43%
25.16%
15.31%
28.86%
13.76%
24.23%
13.11%
16.62%
11.89%
21.29%
11.66%
18.47%
9.67%
17.67%
8.10%
16.96%
7.81%
14.80%
7.58%
12.77%
7.07%
11.88%
6.06%
11.77%
5.88%
13.18%
5.30%
9.78%
4.93%
7.59%
4.75%
5.57%
2.60%
3.82%
2.20%
4.76%
0.84%
3.76%
0.70%
0.55%
9.21%
19.58%
4.80%
12.56%
3.53%
12.70%
3.52%
5.97%
3.43%
4.44%
2.30%
4.29%
1.11%
1.82%
0.85%
1.71%
32.54%
28.81%
42.10%
51.89%
States Union Territories
India : Headcount Ratio
Percentage of the total population who are multidimensionally poor in each State and UT

INDIAMPI: PROGRESS REVIEW 2023
35
States Union Territories
-18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
Bihar
Madhya Pradesh
Uttar Pradesh
Odisha
Rajasthan
Chhattisgarh
Assam
Jharkhand
Arunachal Pradesh
Nagaland
West Bengal
Manipur
Uttarakhand
Telangana
Maharashtra
Gujarat
Andhra Pradesh
Karnataka
Haryana
Meghalaya
Mizoram
Tripura
Goa
Himachal Pradesh
Tamil Nadu
Sikkim
Punjab
Kerala
Dadra & Nagar Haveli & Daman & Diu
Ladakh
Jammu & Kashmir
Chandigarh
Andaman & Nicobar Islands
Delhi
Puducherry
Lakshadweep
% point change in proportion of multidimensionally poor population
India : Changes over time for Headcount Ratio
State/ UT wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-18.13
-15.94
-14.75
-13.65
-13.56
-13.53
-13.30
-13.29
-10.48
-9.73
-9.41
-8.86
-8.00
-7.30
-6.99
-6.81
-5.71
-5.20
-4.81
-4.75
-4.48
-3.50
-2.92
-2.65
-2.56
-1.21
-0.82
-0.15
-10.38
-9.17
-7.76
-2.46
-1.99
-1.02
-0.87
-0.71

INDIA MPI: PROGRESS REVIEW 2023
36
Headcount
Ratio
Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Headcount
Ratio
Intensity MPI
0.097
0.078
0.179
0.075
0.057
0.019
0.016
0.137
0.024
0.136
0.116
0.046
0.156
0.076
0.065
0.173
0.003
0.055
0.202
0.030
0.053
0.083
0.015
0.133
0.265
0.156
0.115
0.051
45.50%
44.35%
47.60%
45.03%
43.29%
39.97%
41.20%
47.34%
43.74%
46.42%
46.29%
47.42%
48.08%
44.61%
43.76%
47.25%
38.99%
42.76%
47.92%
39.44%
44.40%
44.97%
40.13%
44.64%
51.01%
47.88%
47.25%
43.28%
21.29%
17.67%
37.68%
16.62%
13.18%
4.76%
3.82%
28.86%
5.57%
29.34%
25.16%
9.78%
32.54%
16.96%
14.80%
36.57%
0.70%
12.77%
42.10%
7.59%
11.88%
18.47%
3.76%
29.90%
51.89%
32.65%
24.23%
1Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Goa
Gujarat
Haryana
Himachal Pradesh
Jharkhand
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Telangana
Tripura
Uttar Pradesh
Uttarakhand
West Bengal
Andaman & Nicobar Islands
Chandigarh
Dadra & Nagar Haveli & Daman & Diu
Delhi
Jammu & Kashmir
Ladakh
Lakshadweep
Puducherry
1.77%
0.050
0.041
0.103
0.056
0.024
0.009
0.011
0.065
0.020
0.070
0.066
0.024
0.133
0.034
0.033
0.090
0.002
0.031
0.131
0.020
0.031
0.050
0.003
0.070
0.160
0.086
0.059
0.025
42.35%
41.99%
44.83%
42.68%
40.85%
38.70%
41.02%
42.70%
41.22%
44.50%
42.61%
45.62%
48.01%
41.91%
41.77%
43.70%
36.92%
41.21%
45.59%
40.22%
43.34%
43.25%
38.69%
42.61%
47.40%
44.41
%
43.04%
41.12%
11.89%
9.67%
22.93%
13.11%
5.88%
2.20%
2.60%
15.31%
4.75%
15.68%
15.43%
5.30%
27.79%
8.10%
7.81%
20.63%
0.55%
7.58%
28.81%
4.93%
7.07%
11.66%
0.84%
16.37%
33.76%
19.35%
13.76%
6.06%
0.007
0.007
0.051
0.055
0.020
0.087
0.026
0.017
38.55%
35.80%
40.37%
44.17%
43.92%
44.23%
43.39%
40.50%
1.71%
1.82%
12.70%
12.56%
4.44%
19.58%
5.97%
4.29%
0.003
0.004
0.015
0.020
0.014
0.039
0.017
0.009
38.03%
36.47%
41.20%
42.11%
41.99%
42.15%
47.41%
40.62%
0.85%
1.11%
3.53%
4.80%
3.43%
9.21%
3.52%
2.30%
States Union Territories
Overview of States and UTs
Headcount Ratio, Intensity and MPI

INDIAMPI: PROGRESS REVIEW 2023
37
Region
2019-212015-16
Population
share
Headcount
Ratio
State Union Territories
Andhra Pradesh 3.86% 11.77% 6.06% 30,19,718
Arunachal Pradesh 0.11% 24.23% 13.76% 1,61,358
Assam 2.57% 32.65% 19.35% 46,87,541
Bihar 9.06% 51.89% 33.76% 2,25,11,679
Chhattisgarh 2.17% 29.90% 16.37% 40,18,328
Goa 0.11% 3.76% 0.84% 45,564
Gujarat 5.13% 18.47% 11.66% 47,84,122
Haryana 2.17% 11.88% 7.07% 14,29,341
Himachal Pradesh 0.54% 7.59% 4.93% 1,96,579
Jharkhand 2.83% 42.10% 28.81% 51,52,626
Karnataka 4.90% 12.77% 7.58% 34,87,223
Kerala 2.60% 0.70% 0.55% 53,239
Madhya Pradesh 6.21% 36.57% 20.63% 1,35,69,242
Maharashtra 9.12% 14.80% 7.81% 87,37,064
Manipur 0.23% 16.96% 8.10% 2,81,803
Meghalaya 0.24% 32.54% 27.79% 1,56,738
Mizoram 0.09% 9.78% 5.30% 54,665
Nagaland 0.16% 25.16% 15.43% 2,14,354
Odisha 3.35% 29.34% 15.68% 62,62,852
Punjab 2.22% 5.57% 4.75% 2,50,586
Rajasthan 5.82% 28.86% 15.31% 1,08,16,230
Sikkim 0.05% 3.82% 2.60% 8,236
Tamil Nadu 5.59% 4.76% 2.20% 19,58,454
Telangana 2.76% 13.18% 5.88% 27,61,201
Tripura 0.30% 16.62% 13.11% 1,43,237
Uttar Pradesh 16.95% 37.68% 22.93% 3,42,72,484
Uttarakhand 0.84% 17.67% 9.67% 9,17,299
West Bengal 7.18% 21.29% 11.89% 92,58,462
Andaman & Nicobar Islands 0.03% 4.29% 2.30% 7,999
Chandigarh 0.09% 5.97% 3.52% 29,845
Dadra & Nagar Haveli and Daman & Diu 0.08% 19.58% 9.21% 1,17,484
Delhi 1.52% 4.44% 3.43% 2,11,163
Jammu & Kashmir 0.98% 12.56% 4.80% 10,44,860
Ladakh 0.02% 12.70% 3.53% 27,315
Lakshadweep 0.00% 1.82% 1.11% 484
Puducherry 0.12% 1.71% 0.85% 13,804
India 100% 24.85% 14.96% 13,54,61,035
Number of people who
escaped
multidimensional
poverty
The estimates are based on the India and State/ UT population projections for the year 2021 by MoHFW

INDIA MPI: PROGRESS REVIEW 2023
38
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%
51.87%
42.20%
48.02%
40.32%
41.37%
38.09%
44.47%
36.43%
43.02%
35.12%
37.05%
34.72%
45.49%
34.63%
42.62%
34.09%
36.10%
32.29%
39.67%
31.83%
37.27%
30.77%
33.56%
29.97%
31.09%
28.35%
33.62%
27.28%
32.34%
26.19%
28.02%
26.13%
32.85%
23.68%
27.18%
22.98%
26.38%
22.94%
22.11%
20.80%
24.49%
20.61%
24.65%
20.23%
24.77%
19.17%
23.57%
17.87%
21.05%
17.10%
15.29%
16.44%
21.38%
15.63%
13.32%
10.36%
36.71%
37.81%
31.47%
24.63%
23.11%
21.57%
23.41%
20.38%
25.88%
15.52%
22.05%
15.09%
26.72%
14.40%
21.87%
13.88%
Bihar
Jharkhand
Gujarat
Uttar Pradesh
Chhattisgarh
Meghalaya
Madhya Pradesh
Rajasthan
Maharashtra
Assam
Odisha
Karnataka
Telangana
West Bengal
Haryana
Tripura
Uttarakhand
Himachal Pradesh
Andhra Pradesh
Punjab
Nagaland
Goa
Tamil Nadu
Manipur
Arunachal Pradesh
Kerala
Mizoram
Sikkim
Dadra & Nagar Haveli
& Daman & Diu
Chandigarh
Delhi
Jammu & Kashmir
Andaman & Nicobar
Islands
Ladakh
Puducherry
Union TerritoriesStates
Lakshadweep
NFHS-5 (2019-21) NFHS-4 (2015-16)
% of population deprived in the indicator
Uncensored Headcount : Nutrition
State/UT-wise percentage of population deprived

INDIAMPI: PROGRESS REVIEW 2023
39
Uncensored Headcount : Child & Adolescent Mortality
State/UT-wise percentage of population deprived
0.0%0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5%
4.58%
4.14%
4.97%
3.54%
3.10%
2.99%
3.32%
2.57%
3.32%
2.33%
3.60%
2.32%
2.95%
2.14%
2.58%
1.89%
2.17%
1.85%
2.21%
1.81%
2.90%
1.77%
1.80%
1.66%
2.23%
1.57%
1.28%
1.55%
2.06%
1.42%
1.39%
1.32%
1.34%
1.29%
1.82%
1.27%
1.38%
1.15%
1.42%
1.11%
1.97%
1.10%
1.66%
1.07%
1.50%
1.06%
2.30%
0.93%
1.15%
0.84%
0.57%
0.39%
1.00%
0.26%
0.19%
0.20%
1.63%
1.73%
1.91%
1.38%
1.16%
1.17%
0.83%
0.91%
2.11%
0.91%
1.85%
0.73%
1.96%
0.32%
0.66%
0.23%
Bihar
Uttar Pradesh
Meghalaya
Jharkhand
Chhattisgarh
Madhya Pradesh
Rajasthan
Uttarakhand
Haryana
Gujarat
Assam
Manipur
Odisha
Tripura
Nagaland
Punjab
Karnataka
Andhra Pradesh
Telangana
Maharashtra
Arunachal Pradesh
Himachal Pradesh
West Bengal
Mizoram
Tamil Nadu
Goa
Sikkim
Kerala
Dadra & Nagar Haveli
& Daman & Diu
Chandigarh
States Union Territories
Andaman & Nicobar
Islands
Ladakh
Jammu & Kashmir
Lakshadweep
Puducherry
Delhi
NFHS-5 (2019-21) NFHS-4 (2015-16)
% of population deprived in the indicator

Uncensored Headcount : Maternal Health
State/UT-wise percentage of population deprived
NFHS-5 (2019-21) NFHS-4 (2015-16)
% of population deprived in the indicator
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
45.61%
37.21%
31.70%
31.39%
35.44%
30.03%
33.07%
29.75%
28.34%
22.21%
33.05%
22.15%
29.38%
21.40%
25.44%
21.40%
26.33%
21.17%
28.54%
20.42%
24.70%
20.21%
23.86%
16.83%
13.49%
16.07%
15.95%
15.32%
19.49%
14.83%
12.70%
14.24%
10.87%
13.17%
14.77%
12.72%
12.36%
12.58%
17.42%
12.51%
17.66%
12.26%
14.38%
11.43%
16.11%
11.32%
9.66%
10.77%
5.42%
6.72%
6.70%
3.31%
1.73%
3.30%
7.14%
1.88%
15.20%
10.07%
13.80%
8.06%
12.73%
7.58%
11.05%
7.32%
11.62%
7.07%
5.11%
4.02%
4.13%
3.32%
6.50%
2.11%
Bihar
Meghalaya
Uttar Pradesh
Jharkhand
Arunachal Pradesh
Nagaland
Madhya Pradesh
Assam
Rajasthan
Uttarakhand
Chhattisgarh
Haryana
Tripura
Maharashtra
Odisha
Punjab
Telangana
Gujarat
Karnataka
Himachal Pradesh
Manipur
West Bengal
Mizoram
Andhra Pradesh
Sikkim
Tamil Nadu
Kerala
Goa
Delhi
Dadra & Nagar Haveli
& Daman & Diu
Chandigarh
Ladakh
Andaman & Nicobar
Islands
Puducherry
Lakshadweep
States Union Territories
Jammu & Kashmir
INDIA MPI: PROGRESS REVIEW 2023
40

NFHS-5 (2019-21) NFHS-4 (2015-16)
% of population deprived in the indicator
Uncensored Headcount : Years of Schooling
State/UT-wise percentage of population deprived
0.0%2.0% 4.0%6.0% 8.0%10.0%12.0%14.0%16.0%18.0%20.0%22.0%24.0%26.0%28.0%
26.26%
22.29%
19.71%
16.70%
18.30%
16.17%
16.90%
15.81%
15.83%
14.56%
17.75%
14.22%
16.64%
13.44%
17.49%
13.18%
15.84%
12.87%
16.18%
12.35%
16.07%
12.14%
13.47%
10.57%
13.61%
10.49%
10.79%
10.47%
17.09%
10.06%
8.20%
8.59%
6.61%
8.53%
9.82%
7.94%
9.76%
7.89%
8.69%
7.15%
7.92%
6.79%
7.28%
6.72%
6.54%
5.91%
7.09%
5.51%
3.78%
4.63%
5.35%
4.59%
4.70%
2.53%
1.78%
2.49%
7.53%
8.16%
4.87%
5.93%
5.83%
4.54%
5.93%
4.37%
6.83%
4.25%
7.04%
4.08%
3.29%
3.41%
0.95%
1.84%
Bihar
Meghalaya
Jharkhand
Andhra Pradesh
Telangana
Arunachal Pradesh
Odisha
Uttar Pradesh
West Bengal
Assam
Madhya Pradesh
Chhattisgarh
Nagaland
Tripura
Rajasthan
Sikkim
Tamil Nadu
Gujarat
Uttarakhand
Karnataka
Mizoram
Punjab
Maharashtra
Haryana
Himachal Pradesh
Manipur
Goa
Kerala
Dadra & Nagar Haveli
& Daman & Diu
Andaman & Nicobar
Islands
Chandigarh
Delhi
Jammu & Kashmir
Ladakh
Puducherry
Lakshadweep
States Union Territories
INDIAMPI: PROGRESS REVIEW 2023
41

NFHS-5 (2019-21) NFHS-4 (2015-16)
% of population deprived in the indicator
0.0%1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0%10.0%11.0%12.0%13.0%
11.91%
10.91%
12.53%
10.61%
8.19%
8.45%
6.15%
7.41%
8.38%
6.76%
5.38%
5.50%
8.15%
5.19%
6.68%
5.06%
4.37%
4.65%
4.81%
4.45%
3.82%
4.31%
6.54%
4.31%
8.48%
4.25%
4.95%
3.92%
2.59%
2.77%
3.53%
2.50%
3.75%
2.50%
2.19%
2.50%
4.20%
2.35%
2.36%
2.33%
3.82%
2.12%
2.10%
1.35%
2.34%
1.35%
1.03%
1.30%
1.42%
1.15%
0.89%
0.91%
0.96%
0.70%
0.54%
0.25%
1.76%
3.97%
6.71%
3.29%
3.74%
2.94%
2.24%
2.86%
2.63%
2.77%
1.21%
1.67%
1.43%
0.84%
0.92%
0.63%
Uttar Pradesh
Bihar
Jharkhand
Meghalaya
Madhya Pradesh
Chhattisgarh
Arunachal Pradesh
Gujarat
Uttarakhand
Nagaland
Haryana
Assam
Rajasthan
Odisha
Punjab
Karnataka
Mizoram
Tripura
Maharashtra
Manipur
West Bengal
Telangana
Andhra Pradesh
Tamil Nadu
Sikkim
Himachal Pradesh
Goa
Kerala
Chandigarh
Dadra & Nagar Haveli
& Daman & Diu
Jammu & Kashmir
Ladakh
Delhi
Puducherry
Lakshadweep
Andaman & Nicobar
Islands
States Union Territories
Uncensored Headcount : School Attendance
State/UT-wise percentage of population deprived
INDIA MPI: PROGRESS REVIEW 2023
42

NFHS-5 (2019-21) NFHS-4 (2015-16)
% of population deprived in the indicator
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
82.14%
69.12%
77.08%
67.63%
78.04%
66.85%
80.94%
65.94%
82.92%
63.30%
73.01%
61.25%
71.24%
60.88%
69.94%
60.56%
77.12%
59.33%
69.28%
56.48%
65.84%
54.75%
68.85%
52.92%
67.90%
52.74%
57.78%
48.05%
52.06%
44.13%
51.24%
43.93%
48.79%
34.74%
58.92%
28.75%
43.89%
28.12%
36.40%
25.33%
42.20%
24.50%
45.54%
21.47%
39.49%
20.07%
32.17%
17.06%
37.90%
16.09%
24.06%
15.10%
31.67%
7.93%
14.91%
2.57%
58.15%
35.31%
45.38%
32.23%
34.80%
24.52%
33.68%
22.54%
24.53%
15.73%
4.85%
5.23%
13.51%
4.73%
2.21%
0.91%
Jharkhand
Meghalaya
Chhattisgarh
Odisha
Bihar
West Bengal
Madhya Pradesh
Rajasthan
Assam
Nagaland
Tripura
Uttar Pradesh
Himachal Pradesh
Arunachal Pradesh
Uttarakhand
Haryana
Gujarat
Manipur
Kerala
Punjab
Sikkim
Karnataka
Maharashtra
Mizoram
Andhra Pradesh
Tamil Nadu
Telangana
Goa
Lakshadweep
Jammu & Kashmir
Ladakh
Dadra & Nagar Haveli
& Daman & Diu
Andaman & Nicobar
Islands
Chandigarh
Puducherry
Delhi
States Union Territories
Uncensored Headcount : Cooking Fuel
State/UT-wise percentage of population deprived
INDIAMPI: PROGRESS REVIEW 2023
43

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
73.49%
50.78%
75.32%
43.36%
70.32%
39.85%
65.15%
35.51%
47.54%
35.23%
47.81%
31.91%
63.65%
31.61%
51.19%
31.58%
53.90%
29.03%
47.94%
28.33%
47.55%
27.95%
36.36%
26.56%
37.09%
26.05%
42.67%
25.65%
49.01%
24.41%
65.37%
23.16%
46.38%
22.84%
33.93%
21.70%
27.63%
18.27%
38.55%
17.13%
38.56%
17.10%
19.19%
15.11%
17.28%
13.69%
10.36%
12.71%
21.38%
12.26%
23.18%
12.24%
15.81%
4.66%
1.83%
1.27%
82.56%
57.40%
56.32%
34.59%
46.23%
24.30%
26.41%
19.21%
19.04%
17.82%
35.06%
15.25%
24.37%
12.12%
0.44%
0.20%
Bihar
Jharkhand
Odisha
Madhya Pradesh
Manipur
West Bengal
Uttar Pradesh
Assam
Rajasthan
Maharashtra
Tamil Nadu
Tripura
Gujarat
Karnataka
Telangana
Chhattisgarh
Andhra Pradesh
Uttarakhand
Himachal Pradesh
Arunachal Pradesh
Meghalaya
Haryana
Punjab
Sikkim
Goa
Nagaland
Mizoram
Kerala
Ladakh
Dadra & Nagar Haveli
& Daman & Diu
Jammu & Kashmir
Delhi
Chandigarh
Puducherry
Andaman & Nicobar
Islands
Lakshadweep
States Union Territories
Uncensored Headcount : Sanitation
State/UT-wise percentage of population deprived
% of Population Deprived in the Indicator
NFHS-5 (2019-21) NFHS-4 (2015-16)
INDIA MPI: PROGRESS REVIEW 2023
44

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
38.50%
26.77%
31.77%
23.10%
29.25%
21.73%
30.32%
18.61%
17.43%
14.91%
16.18%
13.87%
20.61%
13.55%
19.26%
10.47%
19.18%
10.24%
12.61%
9.53%
12.33%
9.14%
18.14%
8.37%
2.24%
7.84%
9.44%
7.06%
6.63%
6.71%
8.65%
6.63%
14.81%
6.62%
6.05%
5.71%
5.56%
5.40%
7.71%
5.31%
7.72%
5.14%
9.46%
4.97%
7.79%
4.82%
10.80%
3.36%
3.66%
2.06%
1.54%
1.84%
2.12%
1.64%
3.34%
1.52%
22.41%
15.41%
13.77%
10.37%
9.17%
7.27%
9.69%
5.74%
5.65%
5.04%
1.85%
3.23%
2.03%
2.20%
4.45%
1.92%
Manipur
Meghalaya
Madhya Pradesh
Jharkhand
Assam
Tripura
Odisha
Nagaland
Rajasthan
Maharashtra
Andhra Pradesh
Chhattisgarh
Sikkim
Karnataka
Haryana
Uttarakhand
Arunachal Pradesh
Tamil Nadu
Kerala
Gujarat
Himachal Pradesh
West bengal
Mizoram
Telangana
Uttar Pradesh
Punjab
Bihar
Goa
Ladakh
Jammu & Kashmir
Lakshadweep
Dadra & Nagar Haveli
& Daman & Diu
Andaman & Nicobar
Islands
Chandigarh
Puducherry
Delhi
States Union Territories
Uncensored Headcount : Drinking Water
State/UT-wise percentage of population deprived
% of Population Deprived in the Indicator
NFHS-5 (2019-21) NFHS-4 (2015-16)
INDIAMPI: PROGRESS REVIEW 2023
45

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
27.43%
9.16%
8.18%
8.24%
21.77%
7.44%
18.80%
5.67%
11.83%
5.25%
39.86%
3.67%
13.36%
3.04%
5.75%
2.50%
3.75%
2.44%
6.59%
2.29%
7.31%
1.94%
4.08%
1.92%
8.73%
1.86%
7.18%
1.75%
8.95%
1.57%
3.25%
1.46%
3.64%
1.19%
1.71%
0.89%
0.65%
0.77%
0.97%
0.67%
0.77%
0.56%
0.49%
0.54%
1.23%
0.44%
0.74%
0.41%
1.06%
0.40%
2.17%
0.39%
0.39%
0.34%
0.18%
0.00%
2.72%
2.47%
2.80%
0.76%
1.38%
0.50%
1.73%
0.35%
0.05%
0.22%
0.28%
0.14%
0.24%
0.13%
0.48%
0.04%
Uttar Pradesh
Meghalaya
Assam
Jharkhand
Arunachal Pradesh
Bihar
Odisha
West Bengal
Gujarat
Maharashtra
Manipur
Mizoram
Rajasthan
Tripura
Madhya Pradesh
Nagaland
Chhattisgarh
Karnataka
Sikkim
Tamil Nadu
Andhra Pradesh
Himachal Pradesh
Telangana
Kerala
Haryana
Uttarakhand
Punjab
Goa
Andaman & Nicobar
Islands
Jammu & Kashmir
Ladakh
Dadra & Nagar Haveli
& Daman & Diu
Lakshadweep
Delhi
Puducherry
Chandigarh
States Union Territories
Uncensored Headcount : Electricity
State/UT-wise percentage of population deprived
% of Population Deprived in the Indicator
NFHS-5 (2019-21) NFHS-4 (2015-16)
INDIA MPI: PROGRESS REVIEW 2023
46

Uncensored Headcount : Housing
State/UT-wise percentage of population deprived
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%
81.49%
75.50%
76.14%
74.34%
75.89%
69.37%
74.66%
66.83%
73.73%
65.37%
70.97%
64.60%
67.52%
60.09%
61.78%
56.93%
63.31%
55.06%
64.38%
54.65%
50.40%
53.40%
54.25%
47.17%
35.55%
45.73%
55.80%
40.70%
37.30%
36.20%
24.18%
30.70%
35.58%
24.19%
26.71%
24.15%
27.90%
24.02%
24.26%
23.95%
29.30%
23.73%
24.24%
23.30%
19.30%
21.96%
25.54%
20.49%
10.76%
16.67%
17.55%
14.67%
20.17%
11.37%
16.16%
9.50%
88.20%
56.98%
40.15%
31.61%
33.61%
30.10%
28.65%
25.36%
1.54%
11.32%
17.59%
11.31%
10.80%
6.25%
6.40%
4.33%
Manipur
Arunachal Pradesh
Assam
Tripura
Bihar
Nagaland
Uttar Pradesh
Jharkhand
Chhattisgarh
Madhya Pradesh
Meghalaya
West Bengal
Rajasthan
Odisha
Karnataka
Mizoram
Uttarakhand
Sikkim
Maharashtra
Haryana
Himachal Pradesh
Gujarat
Punjab
Telangana
Kerala
Andhra Pradesh
Tamil Nadu
Goa
Ladakh
Dadra & Nagar Haveli
& Daman & Diu
Andaman & Nicobar
Islands
Jammu & Kashmir
Lakshadweep
Puducherry
Delhi
Chandigarh
States Union Territories
% of Population Deprived in the Indicator
NFHS-5 (2019-21) NFHS-4 (2015-16)
INDIAMPI: PROGRESS REVIEW 2023
47

INDIA MPI: PROGRESS REVIEW 2023
48
Uncensored Headcount : Assets
State/UT-wise percentage of population deprived
29.88%
20.25%
24.32%
37.07%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
Meghalaya
Nagaland
Bihar
Madhya Pradesh
Jharkhand
Assam
Tripura
Sikkim
Arunachal Pradesh
Manipur
Mizoram
Odisha
Gujarat
Rajasthan
Chhattisgarh
Maharashtra
Uttarakhand
Telangana
West Bengal
Andhra Pradesh
Uttar Pradesh
Karnataka
Himachal Pradesh
Haryana
Tamil Nadu
Kerala
Goa
Punjab
Dadra & Nagar Haveli
& Daman & Diu
States Union Territories
Jammu & Kashmir
Andaman & Nicobar Islands
Delhi
Ladakh
Puducherry
Lakshdweep
Chandigarh
29.53%
33.90%
16.05%
19.31%
15.48%
15.02%
19.94%
14.83%
14.42%
9.52%
14.31%
23.35%
13.92%
12.63%
13.94%
19.22%
11.37%
10.77%
20.50%
10.51%
10.04%
13.97%
9.10%
8.51%
8.13%
14.10%
10.96%
12.44%
10.05%
7.31%
6.75%
7.80%
8.11%
12.79%
13.84%
13.59%
12.35%
12.30%
18.76%
21.37%
14.92%
8.03%
16.79%
18.68%
16.24%
7.90%
7.10%
4.42%
5.54%
3.32%
9.10%
2.10%
1.65%
1.70%
1.02%
0.59%
2.71%
1.72%
1.60%
2.97%
1.77%
2.94%
3.05%
3.38%
3.89%
4.65%
5.21%
7.52%
% of Population Deprived in the Indicator
NFHS-5 (2019-21) NFHS-4 (2015-16)

INDIAMPI: PROGRESS REVIEW 2023
49
Uncensored Headcount : Bank Account
State/UT-wise percentage of population deprived
2.0%4.0%6.0%8.0%10.0%12.0%14.0%16.0%18.0%20.0%22.0%24.0%26.0%28.0%30.0%
19.91%
9.01%
15.40%
7.38%
28.67%
7.04%
8.38%
5.99%
8.83%
4.97%
10.35%
4.96%
5.74%
4.55%
13.82%
4.44%
9.42%
4.40%
21.53%
4.04%
8.97%
3.97%
26.00%
3.90%
3.71%
3.88%
11.15%
3.84%
15.38%
3.65%
4.73%
3.56%
8.17%
3.56%
5.81%
3.30%
4.32%
3.22%
3.63%
3.02%
4.87%
2.96%
6.89%
2.89%
7.46%
2.74%
4.02%
2.71%
6.35%
2.56%
10.94%
2.53%
4.03%
2.18%
2.69%
2.11%
11.40%
6.65%
8.36%
5.78%
1.84%
3.88%
5.62%
3.10%
3.98%
2.93%
1.57%
2.57%
5.35%
2.11%
3.97%
1.75%
Meghalaya
Arunachal Pradesh
Nagaland
Sikkim
Karnataka
Maharashtra
Chhattisgarh
West Bengal
Gujarat
Manipur
Jharkhand
BIhar
Punjab
Madhya Pradesh
Assam
Andhra Pradesh
Haryana
Mizoram
Kerala
Tripura
Uttar Pradesh
Uttarakhand
Telangana
Goa
Tamil Nadu
Odisha
Rajasthan
Himachal Pradesh
Dadra & Nagar Haveli
& Daman & Diu
Delhi
Ladakh
Lakshadweep
Jammu & Kashmir
Andaman & Nicobar
Islands
Puducherry
Chandigarh
States Union Territories
0.0%
% of Population Deprived in the Indicator
NFHS-5 (2019-21) NFHS-4 (2015-16)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Nutrition: 32.60%
Child & Adolescent Mortality: 1.47%
Maternal Health: 9.44%
Years of Schooling: 25.78%
School Attendance: 4.29%
Cooking Fuel: 6.33%
Sanitation: 7.13%
Drinking Water: 2.89%
Electricity: 0.46%
Housing: 5.07%
Assets: 3.75%
Bank Account: 0.77%
Nutrition: 29.15%
Child & Adolescent Mortality: 1.40%
Maternal Health: 7.39%
Years of Schooling: 24.60%
School Attendance: 4.71%
Cooking Fuel: 8.84%
Sanitation: 9.48%
Drinking Water: 2.85%
Electricity: 0.56%
Housing: 5.10%
Assets: 4.35%
Bank Account: 1.56%
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Andhra Pradesh
ANDHRA PRADESH
Overview
Andhra Pradesh's Headcount Ratio, Intensity and MPI
Andhra Pradesh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Andhra Pradesh's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
6.06%2019-21 0.02541.12%
11.77%2015-16 0.05143.28%
Rural
Headcount Ratio Intensity MPI
0.0327.71% 41.41%
Urban
Headcount Ratio Intensity MPI
0.0092.20% 38.77%
0.06414.72% 43.32% 0.0204.63% 42.97%
Multidimensional Poverty in Andhra Pradesh's Rural and Urban Areas
2019-21
2015-16
Year
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
50

Percentage of total population who are deprived in each indicator
Andhra Pradesh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Andhra Pradesh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
22.94%
26.38%
1.27%
1.82%
10.77%
9.66%
15.81%
16.90%
1.35%
2.34%
16.09%
37.90%
22.84%
46.38%
9.14%
12.33%
0.56%
0.77%
14.67%
17.55%
8.11%
10.96%
3.56%
4.73%
4.87%
8.91%
0.44%
0.86%
2.82%
4.52%
3.85%
7.52%
0.64%
1.44%
3.31%
9.45%
3.73%
10.14%
1.51%
3.05%
0.24%
0.60%
2.65%
5.45%
1.96%
4.66%
0.40%
1.66%
ANDHRA PRADESHMPI: PROGRESS REVIEW 2023
51

Andhra Pradesh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Andhra Pradesh for 2019-21.
Up to 0.016 0.017 to 0.0220.023 to 0.0280.029 to 0.0340.035 to 0.0410.042 to 0.047 0.048 and above
ANDHRA PRADESH MPI: PROGRESS REVIEW 2023
52

Andhra Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Andhra Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Andhra Pradesh, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.0370.038 to 0.0460.047 to 0.0550.056 to 0.0630.064 to 0.0720.073 to 0.081 0.082 and above
53
ANDHRA PRADESHMPI: PROGRESS REVIEW 2023

Andhra Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
19.64%
12.84%
19.00%
8.66%
15.10%
7.6%
12.47%
6.74%
13.84%
6.28%
8.51%
6.13%
9.64%
5.66%
11.27%
5.41%
14.01%
5.20%
8.69%
4.38%
7.26%
4.36%
9.14%
3.34%
9.11%
2.42%
Kurnool
Vizianagaram
Visakhapatanam
Anantapur
Prakasam
East Godavari
Chittoor
SPSR Nellore
Srikakulam
Krishna
Guntur
Y.S.R. (Kadapa)
West Godavari
District
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0% 22.0%
ANDHRA PRADESH MPI: PROGRESS REVIEW 2023
54

Andhra Pradesh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
-11.0 -10.0 -9.0 -8.0 -7.0 -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0
% point change in proportion of multidimensionally poor population
Vizianagaram
Srikakulam
Prakasam
Visakhapatanam
Kurnool
West Godavari
SPSR Nellore
Y.S.R. (Kadapa)
Anantapur
Krishna
Chittoor
Guntur
East Godavari -2.38
-2.91
-3.99
-4.31
-5.73
-5.80
-5.86
-6.70
-6.80
-7.51
-7.56
-8.81
-10.34
55
ANDHRA PRADESHMPI: PROGRESS REVIEW 2023

Andhra Pradesh: Overview of Districts
Headcount Ratio, Intensity and MPI
0.038
0.036
0.081
0.071
0.058
0.049
0.063
0.090
0.036
0.030
0.035
0.041
0.052
41.83%
39.79%
42.42%
46.99%
41.56%
43.79%
45.44%
45.87%
41.56%
41.12%
41.34%
42.65%
42.00%
9.14%
9.11%
19.00%
15.10%
14.01%
11.27%
13.84%
19.64%
8.69%
7.26%
8.51%
9.64%
12.47%
0.013
0.010
0.035
0.031
0.022
0.023
0.027
0.054
0.017
0.016
0.027
0.022
0.027
38.51%
42.56%
40.20%
40.81%
41.83%
43.06%
43.60%
42.32%
38.22%
37.58%
43.65%
39.20%
40.56%
3.34%
2.42%
8.66%
7.60%
5.20%
5.41%
6.28%
12.84%
4.38%
4.36%
6.13%
5.66%
6.74%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Anantapur
Chittoor
East Godavari
Guntur
Krishna
Kurnool
Prakasam
SPSR Nellore
Srikakulam
Visakhapatanam
Vizianagaram
West Godavari
Y.S.R. (Kadapa)
District
ANDHRA PRADESH MPI: PROGRESS REVIEW 2023
56

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
58
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Arunachal Pradesh
ARUNACHAL PRADESH
Overview
Arunachal Pradesh's Headcount Ratio, Intensity and MPI
Arunachal Pradesh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Arunachal Pradesh's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
13.76%2019-21 0.05943.04%
24.23%2015-16 0.11547.25%
Rural
Headcount Ratio Intensity MPI
0.06515.14% 43.15%
Urban
Headcount Ratio Intensity MPI
0.0255.90% 41.53%
0.13929.20% 47.59% 0.0358.08% 43.24%
Multidimensional Poverty in Arunachal Pradesh's Rural and Urban Areas
Nutrition: 24.05%
Child & Adolescent Mortality: 0.74%
Maternal Health: 13.33%
Years of Schooling: 20.42%
School Attendance: 8.96%
Cooking Fuel: 8.80%
Sanitation: 3.73%
Drinking Water: 1.80%
Electricity: 1.51%
Housing: 10.54%
Assets: 4.25%
Bank Account: 1.88%
Nutrition: 20.09%
Child & Adolescent Mortality: 0.87%
Maternal Health: 10.81%
Years of Schooling: 19.58%
School Attendance: 8.59%
Cooking Fuel: 8.84%
Sanitation: 6.86%
Drinking Water: 2.55%
Electricity: 2.98%
Housing: 9.67%
Assets: 5.34%
Bank Account: 3.83%
2019-21
2015-16
Year

Percentage of total population who are deprived in each indicator
Arunachal Pradesh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Arunachal Pradesh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
59
ARUNACHAL PRADESHMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
17.10%
21.05%
1.10%
1.97%
22.21%
28.34%
14.22%
17.75%
5.19%
8.15%
48.05%
57.78%
17.13%
38.55%
6.62%
14.81%
5.25%
11.83%
74.34%
76.14%
14.31%
23.35%
7.38%
15.40%
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
8.54%
13.80%
0.52%
1.19%
9.47%
14.86%
7.25%
13.45%
3.18%
5.90%
10.94%
21.26%
4.64%
16.49%
2.23%
6.14%
1.87%
7.15%
13.10%
23.25%
5.28%
12.83%
2.34%
9.22%

ARUNACHAL PRADESH MPI: PROGRESS REVIEW 2023
60
Arunachal Pradesh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Arunachal Pradesh for 2019-21.
Up to 0.030 0.031 to 0.0410.042 to 0.0530.054 to 0.0650.066 to 0.0770.078 to 0.088 0.089 and above

Arunachal Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Arunachal Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
61
ARUNACHAL PRADESHMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Arunachal Pradesh, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.0610.062 to 0.0870.088 to 0.1120.113 to 0.1380.139 to 0.1630.164 to 0.189 0.190 and above

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%
22.11%
44.03%
.2205%
20.97%
29.78%
19.71%
18.91%
28.30%
14.78%
26.47%
14.39%
14.49%
13.85%
12.85%
13.76%
22.86%
13.09%
31.97%
12.39%
39.55%
11.14%
16.95%
10.11%
9.15%
23.56%
9.08%
15.88%
8.74%
31.25%
8.46%
8.84%
8.11%
15.80%
6.39%
22.44%
4.74%
West Kameng
Lower Subansiri
East Siang
Tawang
Upper Siang
Siang
Dibang Valley
Kurung Kumey
Lohit
Anjaw
Papum Pare
West Siang
Tirap
Changlang
Kra Daadi
Upper Subansiri
Longding
East Kameng
Namsai
Lower Dibang
Valley
District
% of population who are multidimensionally poor
Arunachal Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
ARUNACHAL PRADESH MPI: PROGRESS REVIEW 2023
62

Arunachal Pradesh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
% point change in proportion of multidimensionally poor population
Tawang
East Kameng
West Kameng
Lower Dibang Valley
Changlang
Upper Subansiri
Anjaw
Lower Subansiri
Upper Siang
Dibang Valley
Papum Pare
0.91
-6.83
-7.14
-9.41
-9.78
-10.07
-12.07
-14.48
-17.70
-21.98
-22.79
-24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0
63
ARUNACHAL PRADESHMPI: PROGRESS REVIEW 2023

Arunachal Pradesh: Overview of Districts
Headcount Ratio, Intensity and MPI
0.065
0.098
0.134
0.066
0.136
0.143
0.059
0.069
0.108
0.164
0.188
0.037
0.215
0.070
0.127
0.098
45.15%
43.65%
45.02%
41.39%
48.12%
45.60%
45.91%
43.96%
45.96%
51.28%
47.44%
41.61%
48.87%
41.31%
48.08%
42.92%
14.49%
22.44%
29.78%
15.88%
28.30%
31.25%
12.85%
15.80%
23.56%
31.97%
39.55%
8.84%
44.03%
16.95%
26.47%
22.86%
0.058
0.019
0.083
0.035
0.067
0.035
0.036
0.060
0.101
0.025
0.037
0.092
0.053
0.045
0.083
0.033
0.095
0.043
0.063
0.057
41.89%
39.77%
42.03%
39.75%
45.08%
41.95%
38.97%
43.55%
45.76%
39.83%
41.21%
43.73%
42.69%
40.52%
44.13%
40.69%
43.20%
42.72%
43.49%
43.24%
13.85%
4.74%
19.71%
8.74%
14.78%
8.46%
9.15%
13.76%
22.11%
6.39%
9.08%
20.97%
12.39%
11.14%
18.91%
8.11%
22.05%
10.11%
14.39%
13.09%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Anjaw
Changlang
Dibang Valley
East Kameng
East Siang
Kra Daadi
Kurung Kumey
Lohit
Longding
Lower Dibang Valley
Lower Subansiri
Namsai
Papum Pare
Siang
Tawang
Tirap
Upper Siang
Upper Subansiri
West Kameng
West Siang
ARUNACHAL PRADESH MPI: PROGRESS REVIEW 2023
64
District
–––
–––
–––
–––

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Assam
ASSAM
Overview
Assam's Headcount Ratio, Intensity and MPI
Assam: Indicator Contribution to the MPI
Percentage contribution of each indicator to Assam's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2019-21
2015-16
Rural
Headcount Ratio Intensity MPI
Urban
Headcount Ratio Intensity MPI
Multidimensional Poverty in Assam's Rural and Urban Areas
66
2019-21
2015-16
Year
19.35% 0.08644.41%
32.65% 0.15647.88%
0.09521.41% 44.50% 0.0296.88% 42.61%
0.17436.14% 48.06% 0.0439.94% 43.57%
Nutrition: 29.47%
Child & Adolescent Mortality: 1.04%
Maternal Health: 11.49%
Years of Schooling: 16.50%
School Attendance: 6.03%
Cooking Fuel: 9.26%
Sanitation: 5.90%
Drinking Water: 2.84%
Electricity: 2.29%
Housing: 10.10%
Assets: 4.14%
Bank Account: 0.95%
Nutrition: 27.13%
Child & Adolescent Mortality: 1.16%
Maternal Health: 9.48%
Years of Schooling: 15.19%
School Attendance: 5.99%
Cooking Fuel: 9.64%
Sanitation: 7.44%
Drinking Water: 2.50%
Electricity: 4.48%
Housing: 9.56%
Assets: 4.23%
Bank Account: 3.20%

Percentage of total population who are deprived in each indicator
Assam: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Assam: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
67
ASSAMMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
31.83%
39.67%
1.77%
2.90%
21.40%
25.44%
12.35%
16.18%
4.31%
6.54%
59.33%
77.12%
31.58%
51.19%
14.91%
17.43%
7.44%
21.77%
69.37%
75.89%
15.02%
19.94%
3.65%
15.38%15.19%
25.45%
1.07%
2.18%
11.84%
17.77%
8.50%
14.25%
3.11%
5.62%
16.71%
31.63%
10.64%
24.42%
5.13%
8.21%
4.13%
14.70%
18.22%
31.38%
7.47%
13.90%
1.71%
10.49%

ASSAM MPI: PROGRESS REVIEW 2023
68
Assam
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Assam for 2019-21.
Up to 0.043 0.044 to 0.0630.064 to 0.0830.084 to 0.1030.104 to 0.1230.124 to 0.143 0.144 and above

Assam
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Assam
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
69
ASSAMMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Assam, based on values for 2015-16. Both
the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.080 0.081 to 0.1100.111 to 0.1400.141 to 0.1690.170 to 0.1990.200 to 0.229 0.230 and above

Assam: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%
51.07%
36.22%
46.02%
32.93%
42.29%
30.58%
28.24%
51.06%
26.02%
24.09%
38.22%
23.65%
36.75%
22.46%
22.09%
30.51%
20.84%
20.10%
25.32%
19.94%
29.46%
19.16%
39.41%
19.12%
32.14%
18.92%
40.15%
18.34%
36.70%
17.66%
33.77%
17.39%
36.20%
16.79%
37.59%
16.20%
23.59%
15.60%
20.60%
14.60%
14.41%
14.13%
24.23%
14.06%
27.71%
13.73%
31.07%
13.62%
26.22%
12.71%
28.97%
12.26%
20.24%
11.49%
16.94%
11.24%
25.55%
10.28%
10.93%
5.63%
% of population who are multidimensionally poor
District
Hailakandi
Karimganj
Cachar
South Salmara Mancachar
Dhubri
West Karbi Anglong
Darrang
Marigaon
Biswanath
Nagaon
Charaideo
Sonitpur
Udalguri
Barpeta
Kokrajhar
Goalpara
Tinsukia
Bongaigaon
Chirang
Karbi Anglong
Baksa
Golaghat
Majuli
Hojai
Lakhimpur
Dhemaji
Dima Hasao
Kamrup
Dibrugarh
Jorhat
Nalbari
Kamrup Metro
Sivasagar
NFHS-5 (2019-21) NFHS-4 (2015-16)
ASSAM MPI: PROGRESS REVIEW 2023
70

Assam: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
-5.30
-5.71
-6.00
-7.98
-10.17
-10.30
-11.71
-13.09
-13.22
-13.51
-13.98
-14.29
-14.57
-14.85
-16.37
-16.71
-17.45
-19.04
-19.40
-20.30
-21.80
% point change in proportion of multidimensionally poor population
-24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
Goalpara
Barpeta
Chirang
Tinsukia
Dima Hasao
Dibrugarh
Bongaigaon
Hailakandi
Darrang
Marigaon
Dhemaji
Kamrup
Kokrajhar
Karimganj
Cachar
Udalguri
Lakhimpur
Baksa
Golaghat
Nalbari
Kamrup Metro
71
ASSAMMPI: PROGRESS REVIEW 2023

Assam: Overview of Districts
Headcount Ratio, Intensity and MPI
0.132
0.191
0.118
0.125
0.076
0.144
0.175
0.113
0.148
0.223
0.181
0.051
0.118
0.088
0.251
0.094
0.203
0.155
0.136
0.260
0.125
0.189
0.165
0.209
0.155
0.183
0.102
44.74%
52.07%
46.55%
48.95%
44.69%
47.10%
47.71%
46.80%
46.11%
48.45%
48.10%
46.95%
45.03%
43.62%
49.21%
45.64%
50.62%
49.97%
47.05%
50.85%
45.03%
49.49%
45.71%
49.49%
45.80%
46.50%
43.42%
29.46%
36.70%
25.32%
25.55%
16.94%
30.51%
36.75%
24.23%
32.14%
46.02%
37.59%
10.93%
26.22%
20.24%
51.07%
20.60%
40.15%
31.07%
28.97%
51.06%
27.71%
38.22%
36.20%
42.29%
33.77%
39.41%
23.59%
0.107
0.082
0.081
0.131
0.090
0.044
0.049
0.093
0.100
0.056
0.061
0.083
0.153
0.068
0.024
0.053
0.051
0.059
0.164
0.065
0.081
0.061
0.056
0.116
0.056
0.100
0.071
0.095
0.140
0.075
0.102
0.083
0.064
44.40%
42.99%
46.04%
46.45%
44.95%
42.94%
43.47%
44.61%
44.63%
39.18%
43.10%
43.99%
46.54%
42.24%
43.08%
41.89%
44.49%
41.83%
45.30%
44.22%
44.23%
44.47%
45.51%
44.48%
40.72%
42.20%
42.48%
47.03%
45.63%
42.99%
46.03%
43.38%
41.10%
24.09%
19.16%
17.66%
28.24%
19.94%
10.28%
11.24%
20.84%
22.46%
14.41%
14.06%
18.92%
32.93%
16.20%
5.63%
12.71%
11.49%
14.13%
36.22%
14.60%
18.34%
13.62%
12.26%
26.02%
13.73%
23.65%
16.79%
20.10%
30.58%
17.39%
22.09%
19.12%
15.60%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Baksa
Barpeta
Biswanath
Bongaigaon
Cachar
Charaideo
Chirang
Darrang
Dhemaji
Dhubri
Dibrugarh
Dima Hasao
Goalpara
Golaghat
Hailakandi
Hojai
Jorhat
Kamrup
Kamrup Metro
Karbi Anglong
Karimganj
Kokrajhar
Lakhimpur
Majuli
Marigaon
Nagaon
Nalbari
Sivasagar
Sonitpur
South Salmara Mancachar
Tinsukia
Udalguri
West Karbi Anglong
District
–––
–––
–––
–––
–––
–––
ASSAM MPI: PROGRESS REVIEW 2023
72

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Bihar
BIHAR
Overview
Bihar's Headcount Ratio, Intensity and MPI
Bihar: Indicator Contribution to the MPI
Percentage contribution of each indicator to Bihar's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2019-21
2015-16
Rural
Headcount Ratio Intensity MPI
Urban
Headcount Ratio Intensity MPI
Multidimensional Poverty in Bihar's Rural and Urban Areas
2019-21
2015-16
Year
33.76% 0.16047.40%
51.89% 0.26551.01%
0.17636.95% 47.52% 0.07716.67% 45.95%
0.28656.00% 51.14% 0.11723.85% 49.02%
Nutrition: 27.95%
Child & Adolescent Mortality: 1.55%
Maternal Health: 13.11%
Years of Schooling: 18.32%
School Attendance: 8.99%
Cooking Fuel: 8.49%
Sanitation: 7.37%
Drinking Water: 0.27%
Electricity: 0.76%
Housing: 8.77%
Assets: 3.81%
Bank Account: 0.60%
Nutrition: 26.19%
Maternal Health: 11.49%
Years of Schooling: 15.55%
School Attendance: 7.32%
Cooking Fuel: 9.03%
Sanitation: 8.37%
Drinking Water: 0.28%
Electricity: 5.18%
Housing: 8.47%
Assets: 3.36%
Bank Account: 3.53%
Child & Adolescent Mortality: 1.23%
74

Percentage of total population who are deprived in each indicator
Bihar: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Bihar: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
42.20%
51.87%
4.14%
4.58%
37.21%
45.61%
22.29%
26.26%
10.61%
12.53%
63.30%
82.92%
50.78%
73.49
%
1.64%
2.12%
3.67%
39.86%
65.37%
73.73%
20.25%
24.32%
3.90%
26.00%
26.84%
41.59%
2.99%
3.92%
25.16%
36.50%
17.59%
24.70%
8.63%
11.63%
28.52%
50.19%
24.78%
46.53%
0.91%
1.58%
2.57%
28.78%
29.47%
47.09%
12.81%
18.70%
2.01%
19.60%
75
BIHARMPI: PROGRESS REVIEW 2023

Bihar
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
BIHAR MPI: PROGRESS REVIEW 2023
76
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Bihar for 2019-21.
Up to 0.104 0.105 to 0.1310.132 to 0.1580.159 to 0.1840.185 to 0.2110.212 to 0.238 0.239 and above

Bihar
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Bihar
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Bihar, based on values for 2015-16. Both
the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.168 0.169 to 0.1990.200 to 0.2310.232 to 0.2620.263 to 0.2930.294 to 0.324 0.325 and above
BIHARMPI: PROGRESS REVIEW 2023
77

Bihar: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
BIHAR MPI: PROGRESS REVIEW 2023
78
% of population who are multidimensionally poor
District
50.0%.0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%60.0%65.0%70.0%
64.65%
52.07%
63.31%
50.70%
63.90%
50.64%
61.46%
49.00%
64.43%
45.78%
64.75%
45.55%
62.38%
44.21%
63.46%
42.82%
64.01%
41.94%
58.23%
41.38%
57.83%
38.22%
55.87%
37.36%
57.50%
36.67%
64.13%
36.39%
54.67%
35.43%
56.45%
35.07%
55.41%
33.91%
60.03%
33.80%
51.72%
32.13%
45.41%
31.77%
52.18%
31.72%
43.90%
31.46%
52.70%
31.17%
46.61%
30.02%
44.48%
29.95%
47.56%
29.22%
50.68%
28.28%
42.80%
27.74%
48.00%
27.61%
45.60%
27.40%
43.94%
26.80%
40.50%
26.35%
41.84%
26.00%
42.75%
23.23%
29.20%
23.09%
40.74%
21.93%
40.73%
21.63%
40.55%
17.41%
Araria
Purnia
Supaul
Saharsa
Madhepura
Kishanganj
Katihar
Sitamarhi
Jamui
Khagaria
Banka
Samastipur
Gaya
Darbhanga
Madhubani
Sheohar
Nawada
Jehanabad
Arwal
Lakhisarai
Sheikhpura
Nalanda
Kaimur (Bhabua)
Vaishali
Begusarai
Saran
Muzaffarpur
Bhagalpur
Aurangabad
Bhojpur
Buxar
Gopalganj
Patna
Rohtas
Munger
Siwan
Pashchim
Champaran
Purbi
Champaran
NFHS-5 (2019-21) NFHS-4 (2015-16)

Bihar: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-30.0 -28.0 -26.0 -24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0
-6.12
-12.43
-12.46
-12.58
-12.61
-13.27
-13.64
-14.15
-14.53
-15.06
-15.84
-16.59
-16.85
-17.14
-18.17
-18.21
-18.34
-18.51
-18.65
-18.81
-19.11
-19.20
-19.24
-19.52
-19.59
-19.61
-20.39
-20.47
-20.64
-20.83
-21.37
-21.49
-21.53
-22.07
-22.40
-23.14
-26.24
-27.74
District
Purbi Champaran
Sheohar
Siwan
Begusarai
Jamui
Sheikhpura
Madhubani
Darbhanga
Pashchim Champaran
Sitamarhi
Arwal
Muzaffarpur
Banka
Nawada
Gopalganj
Gaya
Kishanganj
Munger
Rohtas
Madhepura
Samastipur
Vaishali
Bhagalpur
Katihar
Aurangabad
Khagaria
Nalanda
Buxar
Saran
Kaimur (Bhabua)
Bhojpur
Jehanabad
Supaul
Purnia
Araria
Saharsa
Lakhisarai
Patna
% point change in proportion of multidimensionally poor population
0.0
BIHARMPI: PROGRESS REVIEW 2023
79

Bihar: Overview of Districts
Headcount Ratio, Intensity and MPI
0.132
0.191
0.118
0.125
0.076
0.144
0.175
0.113
0.148
0.223
0.181
0.051
0.118
0.088
0.251
0.094
0.203
0.155
0.136
0.260
0.125
0.189
0.165
0.209
0.155
0.183
0.102
44.74%
52.07%
46.55%
48.95%
44.69%
47.10%
47.71%
46.80%
46.11%
48.45%
48.10%
46.95%
45.03%
43.62%
49.21%
45.64%
50.62%
49.97%
47.05%
50.85%
45.03%
49.49%
45.71%
49.49%
45.80%
46.50%
43.42%
29.46%
36.70%
25.32%
25.55%
16.94%
30.51%
36.75%
24.23%
32.14%
46.02%
37.59%
10.93%
26.22%
20.24%
51.07%
20.60%
40.15%
31.07%
28.97%
51.06%
27.71%
38.22%
36.20%
42.29%
33.77%
39.41%
23.59%
0.107
0.082
0.081
0.131
0.090
0.044
0.049
0.093
0.100
0.056
0.061
0.083
0.153
0.068
0.024
0.053
0.051
0.059
0.164
0.065
0.081
0.061
0.056
0.116
0.056
0.100
0.071
0.095
0.140
0.075
0.102
0.083
0.064
44.40%
42.99%
46.04%
46.45%
44.95%
42.94%
43.47%
44.61%
44.63%
39.18%
43.10%
43.99%
46.54%
42.24%
43.08%
41.89%
44.49%
41.83%
45.30%
44.22%
44.23%
44.47%
45.51%
44.48%
40.72%
42.20%
42.48%
47.03%
45.63%
42.99%
46.03%
43.38%
41.10%
24.09%
19.16%
17.66%
28.24%
19.94%
10.28%
11.24%
20.84%
22.46%
14.41%
14.06%
18.92%
32.93%
16.20%
5.63%
12.71%
11.49%
14.13%
36.22%
14.60%
18.34%
13.62%
12.26%
26.02%
13.73%
23.65%
16.79%
20.10%
30.58%
17.39%
22.09%
19.12%
15.60%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Baksa
Barpeta
Biswanath
Bongaigaon
Cachar
Charaideo
Chirang
Darrang
Dhemaji
Dhubri
Dibrugarh
Dima Hasao
Goalpara
Golaghat
Hailakandi
Hojai
Jorhat
Kamrup
Kamrup Metropolitan
Karbi Anglong
Karimganj
Kokrajhar
Lakhimpur
Majuli
Morigaon
Nagaon
Nalbari
Sivasagar
Sonitpur
South Salmara Mancachar
Tinsukia
Udalguri
West Karbi Anglong
0.232
0.331
0.187
0.334
0.311
0.260
0.207
0.294
0.337
0.181
0.345
0.338
0.138
0.304
0.261
0.236
0.239
0.200
0.283
0.351
0.222
0.349
0.317
0.336
0.213
0.229
0.324
0.202
0.272
0.292
0.190
0.188
0.237
0.259
0.291
0.206
0.250
0.356
48.74%
51.83%
46.18%
52.67%
51.84%
49.41%
48.35%
52.58%
54.82%
44.38%
54.53%
52.78%
47.23%
52.79%
50.53%
50.62%
49.81%
49.05%
51.14%
54.42%
50.61%
53.87%
54.38%
53.82%
47.79%
50.42%
50.70%
47.21%
49.67%
51.77%
45.48%
46.54%
51.97%
51.16%
50.39%
46.91%
47.83%
55.12%
47.56%
63.90%
40.55%
63.46%
60.03%
52.70%
42.80%
55.87%
61.46%
40.74%
63.31%
64.13%
29.20%
57.50%
51.72%
46.61%
48.00%
40.73%
55.41%
64.43%
43.90%
64.75%
58.23%
62.38%
44.48%
45.41%
64.01%
42.75%
54.67%
56.45%
41.84%
40.50%
45.60%
50.68%
57.83%
43.94%
52.18%
64.65%
0.135
0.239
0.078
0.200
0.159
0.150
0.126
0.175
0.244
0.095
0.262
0.175
0.107
0.175
0.147
0.142
0.127
0.101
0.159
0.226
0.154
0.226
0.206
0.215
0.132
0.147
0.198
0.100
0.169
0.172
0.114
0.121
0.128
0.128
0.177
0.123
0.149
0.266
46.22%
47.25%
44.92%
46.81%
47.00%
48.09%
45.57%
46.79%
49.81%
43.37%
51.71%
47.99%
46.53%
47.79%
45.64%
47.40%
45.88%
46.56%
46.78%
49.38%
49.04%
49.52%
49.71%
48.61%
43.91%
46.17%
47.19%
43.25%
47.61%
49.01%
43.70%
45.76%
46.62%
45.16%
46.42%
45.72%
46.91%
51.04%
29.22%
50.64%
17.41%
42.82%
33.80%
31.17%
27.74%
37.36%
49.00%
21.93%
50.70%
36.39%
23.09%
36.67%
32.13%
30.02%
27.61%
21.63%
33.91%
45.78%
31.46%
45.55%
41.38%
44.21%
29.95%
31.77%
41.94%
23.23%
35.43%
35.07%
26.00%
26.35%
27.40%
28.28%
38.22%
26.80%
31.72%
52.07%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Araria
Arwal
Aurangabad
Banka
Begusarai
Bhagalpur
Bhojpur
Buxar
Darbhanga
Gaya
Gopalganj
Jamui
Jehanabad
Kaimur (Bhabua)
Katihar
Khagaria
Kishanganj
Lakhisarai
Madhepura
Madhubani
Munger
Muzaffarpur
Nalanda
Nawada
Pashchim Champaran
Patna
Purbi Champaran
Purnia
Rohtas
Saharsa
Samastipur
Saran
Sheikhpura
Sheohar
Sitamarhi
Siwan
Supaul
Vaishali
BIHAR MPI: PROGRESS REVIEW 2023
80
District

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Chhattisgarh
CHHATTISGARH
Overview
Chhattisgarh's Headcount Ratio, Intensity and MPI
Chhattisgarh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Chhattisgarh's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2019-21
2015-16
Rural
Headcount Ratio Intensity MPI
Urban
Headcount Ratio Intensity MPI
Multidimensional Poverty in Chhattisgarh's Rural and Urban Areas
2019-21
2015-16
Year
16.37% 0.07042.61%
29.90% 0.13344.64%
0.08419.71% 42.67% 0.0194.59% 41.69%
0.16035.73% 44.83% 0.04310.17% 42.34%
Nutrition: 31.54%
Child & Adolescent Mortality: 1.65%
Maternal Health: 11.69%
Years of Schooling: 13.61%
School Attendance: 8.62%
Cooking Fuel: 10.45%
Sanitation: 5.42%
Drinking Water: 2.23%
Electricity: 0.54%
Housing: 9.88%
Assets: 3.40%
Bank Account: 0.98%
Nutrition: 30.01%
Child & Adolescent Mortality: 1.40%
Maternal Health: 10.59%
Years of Schooling: 13.62%
School Attendance: 5.38%
Cooking Fuel: 10.39%
Sanitation: 9.50%
Drinking Water: 3.62%
Electricity: 0.99%
Housing: 9.55%
Assets: 3.72%
Bank Account: 1.21%
82

Percentage of total population who are deprived in each indicator
Chhattisgarh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Chhattisgarh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
83
CHHATTISGARHMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
35.12%
43.02%
2.33%
3.32%
20.21%
24.70%
10.57%
13.47%
5.50%
5.38%
66.85%
78.04%
23.16%
65.37%
8.37%
18.14%
1.19%
3.64%
55.06%
63.31%
10.51%
14.92%
4.55%
5.74%
13.20%
24.04%
1.38%
2.25%
9.79%
16.96%
5.69%
10.91%
3.61%
4.31%
15.31%
29.14%
7.93%
26.62%
3.26%
10.14%
0.79%
2.78%
14.47%
26.78%
4.98%
10.42%
1.43%
3.40%

Chhattisgarh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Chhattisgarh for 2019-21.
Up to 0.049 0.050 to 0.0840.085 to 0.1200.121 to 0.1550.156 to 0.1910.192 to 0.226 0.227 and above
CHHATTISGARH MPI: PROGRESS REVIEW 2023
84

Chhattisgarh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Chhattisgarh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Chhattisgarh, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.105 0.106 to 0.1350.136 to 0.1660.167 to 0.1960.197 to 0.2260.227 to 0.257 0.258 and above
85
CHHATTISGARHMPI: PROGRESS REVIEW 2023

Chhattisgarh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
District
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0%35.0%40.0%45.0%50.0% 55.0%60.0%
41.20%
49.72%
42.34%
51.52%
36.65%
46.95%
34.69%
32.45%
54.35%
29.53%
26.79%
45.85%
25.41%
47.37%
24.33%
22.41%
38.24%
20.03%
30.82%
19.08%
31.85%
18.11%
17.26%
17.14%
25.66%
16.74%
15.61%
15.31%
27.03%
13.90%
39.56%
13.78%
29.85%
12.50%
23.16%
11.68%
23.14%
10.77%
21.82%
8.73%
18.59%
5.81%
5.77%
19.98%
3.55%
Bijapur
Sukma
Narayanpur
Bastar
Balrampur
Dantewada
Kondagaon
Jashpur
Surguja
Surajpur
Korea
Raigarh
Korba
Bemetara
Gariyaband
Bilaspur
Baloda Bazar
Mungeli
Kanker
Kabirdham
Mahasamund
Janjgir-Champa
Rajnandgaon
Raipur
Dhamtari
Balod
Durg
NFHS-5 (2019-21) NFHS-4 (2015-16)
CHHATTISGARH MPI: PROGRESS REVIEW 2023
86

Chhattisgarh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
8.52
-11.48
-11.74
-12.37
-12.78
-13.12
-13.74
-14.87
-17.35
-18.21
-20.45
-25.79
-30.0 -25.0 -20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0
Kabirdham
Jashpur
Korea
Mahasamund
Narayanpur
Korba
Kanker
Dhamtari
Rajnandgaon
Raigarh
Janjgir-Champa
Bijapur
% point change in proportion of multidimensionally poor population
87
CHHATTISGARHMPI: PROGRESS REVIEW 2023

Chhattisgarh: Overview of Districts
Headcount Ratio, Intensity and MPI
0.117
0.221
0.093
0.095
0.134
0.255
0.125
0.172
0.146
0.184
0.211
0.096
0.084
0.076
0.288
0.111
0.183
0.226
43.47%
46.60%
40.37%
43.49%
43.48%
49.42%
42.03%
44.88%
45.86%
46.50%
46.09%
41.55%
41.93%
40.74%
53.00%
43.23%
44.52%
48.22%
27.03%
47.37%
23.14%
21.82%
30.82%
51.52%
29.85%
38.24%
31.85%
39.56%
45.85%
23.16%
19.98%
18.59%
54.35%
25.66%
41.20%
46.95%
0.055
0.107
0.095
0.205
0.043
0.036
0.080
0.183
0.066
0.051
0.085
0.077
0.117
0.057
0.107
0.046
0.072
0.014
0.023
0.135
0.068
0.262
0.070
0.159
0.143
0.066
0.022
39.85%
44.06%
42.42%
48.36%
39.68%
41.08%
41.86%
49.96%
42.79%
41.11%
42.45%
42.32%
43.72%
41.72%
41.94%
39.80%
41.99%
40.53%
40.17%
45.88%
40.66%
52.79%
40.61%
45.94%
43.92%
42.60%
38.81%
13.90%
24.33%
22.41%
42.34%
10.77%
8.73%
19.08%
36.65%
15.31%
12.50%
20.03%
18.11%
26.79%
13.78%
25.41%
11.68%
17.14%
3.55%
5.81%
29.53%
16.74%
49.72%
17.26%
34.69%
32.45%
15.61%
5.77%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Balod
Baloda Bazar
Balrampur
Bastar
Bemetara
Bijapur
Bilaspur
Dantewada
Dhamtari
Durg
Gariyaband
Janjgir-Champa
Jashpur
Kabirdham
Kondagaon
Korba
Korea
Mahasamund
Mungeli
Narayanpur
Raigarh
Raipur
Rajnandgaon
Sukma
Surajpur
Surguja
District
–––
–––
–––
–––
–––
–––
–––
–––
–––
CHHATTISGARH MPI: PROGRESS REVIEW 2023
88
Uttar Bastar Kanker
(Kanker)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education
Standard
of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Goa
GOA
Overview
Goa's Headcount Ratio, Intensity and MPI
Goa: Indicator Contribution to the MPI
Percentage contribution of each indicator to Goa's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
0.84%2019-21 0.00338.69%
3.76%2015-16 0.01540.13%
Rural
Headcount Ratio Intensity MPI
0.0071.90% 39.15%
Urban
Headcount Ratio Intensity MPI
0.0000.12% 33.94%
0.0174.44% 39.25% 0.0143.34% 40.84%
Multidimensional Poverty in Goa's Rural and Urban Areas
Nutrition: 38.20%
Child & Adolescent Mortality: 4.25%
Child & Adolescent Mortality: 1.09%
Maternal Health: 0.97%
Years of Schooling: 31.72%
School Attendance: 9.84%
Cooking Fuel: 3.07%
Sanitation: 5.30%
Housing: 3.55%
Assets: 2.83%
Bank Account: 0.27%
Nutrition: 32.67%
Maternal Health: 7.68%
Years of Schooling: 24.80%
School Attendance: 6.50%
Cooking Fuel: 6.50%
Sanitation: 8.87%
Drinking Water: 0.88%
Housing: 5.79%
Assets: 2.72%
Bank Account: 2.50%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Drinking Water: 0.00%
Electricity: 0.00%
Electricity: 0.00%
90

Percentage of total population who are deprived in each indicator
Goa: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Goa: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
20.23%
24.65%
0.39%
0.57%
1.88%
7.14%
2.53%
4.70%
0.70%
0.96%
2.57%
14.91%
12.26%
21.38%
1.52%
3.34%
0.00%
0.18%
9.50%
16.16%
1.77%
2.97%
2.71%
4.02%
0.75%
2.96%
0.17%
0.20%
0.04%
1.39%
0.62%
2.24%
0.19%
0.59%
0.21%
2.06%
0.36%
2.81%
0.00%
0.28%
0.00%
0.00%
0.24%
1.83%
0.19%
0.86%
0.02%
0.79%
91
GOAMPI: PROGRESS REVIEW 2023

Goa
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
GOA MPI: PROGRESS REVIEW 2023
92
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.054 0.055 and above
© 2023 Mapbox © OpenStreetMap

Goa
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Goa
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.139 0.140 and above
93
GOAMPI: PROGRESS REVIEW 2023

Goa: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
GOA MPI: PROGRESS REVIEW 2023
94
Goa: Overview of Districts
Headcount Ratio, Intensity and MPI
0.017
0.014
39.28%
40.92%
4.37%
3.33%
0.003
0.004
33.94%
42.37%
0.84%
0.84%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
North Goa
South Goa
Goa: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-3.8-3.6-3.4-3.2-3.0-2.8-2.6-2.4-2.2-2.0-1.8-1.6-1.4-1.2-1.0-0.8-0.6-0.4-0.20.0
-2.49
-3.53
District
% point change in proportion of multidimensionally poor population
South Goa
North Goa
District
0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5%
3.33%
0.84%
4.37%
0.84%
North Goa
South Goa
District
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Gujarat
GUJARAT
Overview
Gujarat's Headcount Ratio, Intensity and MPI
Gujarat: Indicator Contribution to the MPI
Percentage contribution of each indicator to Gujarat's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
11.66%2019-21 0.05043.25%
18.47%2015-16 0.08344.97%
Rural
Headcount Ratio Intensity MPI
0.07517.15% 43.47%
Urban
Headcount Ratio Intensity MPI
0.0163.81% 41.79%
0.12327.25% 45.11% 0.0296.49% 44.19%
Multidimensional Poverty in Gujarat's Rural and Urban Areas
Nutrition: 31.81%
Child & Adolescent Mortality: 1.58%
Maternal Health: 8.50%
Years of Schooling: 14.49%
School Attendance: 10.44%
Cooking Fuel: 9.19%
Sanitation: 7.66%
Drinking Water: 1.91%
Electricity: 1.53%
Housing: 6.97%
Assets: 4.74%
Bank Account: 1.18%
Nutrition: 30.74%
Child & Adolescent Mortality: 1.11%
Maternal Health: 8.74%
Years of Schooling: 13.36%
School Attendance: 9.59%
Cooking Fuel: 9.84%
Sanitation: 8.84%
Drinking Water: 2.46%
Electricity: 1.66%
Housing: 6.50%
Assets: 4.69%
Bank Account: 2.48%
2019-21
2015-16
Year
96

Percentage of total population who are deprived in each indicator
Gujarat: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Gujarat: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
38.09%
41.37%
1.81%
2.21%
12.72%
14.77%
7.94%
9.82%
5.06%
6.68%
34.74%
48.79%
26.05%
37.09%
5.31%
7.71%
2.44%
3.75%
23.30%
24.24%
11.37%
13.59%
4.40%
9.42%
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
9.63%
15.32%
0.95%
1.11%
5.14%
8.71%
4.38%
6.66%
3.16%
4.78%
9.74%
17.16%
8.12%
15.42%
2.03%
4.29%
1.62%
2.89%
7.39%
11.34%
5.02%
8.18%
1.25%
4.32%
97
GUJARATMPI: PROGRESS REVIEW 2023

Gujarat
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Gujarat for 2019-21.
Up to 0.037 0.038 to 0.0600.061 to 0.0830.084 to 0.1050.106 to 0.1280.129 to 0.1500.151 and above
© 2023 Mapbox © OpenStreetMap
GUJARAT MPI: PROGRESS REVIEW 2023
98

Gujarat
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Gujarat
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Gujarat, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
© 2023 Mapbox © OpenStreetMap
Up to 0.059 0.060 to 0.0950.096 to 0.1320.133 to 0.1680.169 to 0.2050.206 to 0.241 0.242 and above
99
GUJARATMPI: PROGRESS REVIEW 2023

Gujarat: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.0%5.0% 10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%60.0%
54.93%
38.27%
57.33%
26.61%
25.24%
31.24%
23.83%
37.11%
22.62%
24.85%
19.96%
19.19%
41.52%
18.11%
17.93%
25.50%
17.06%
16.72%
27.59%
16.03%
21.10%
12.93%
17.38%
12.28%
17.90%
12.12%
24.97%
11.45%
28.30%
10.52%
14.81%
10.47%
9.77%
9.41%
10.43%
9.11%
8.60%
19.95%
8.11%
11.95%
7.47%
13.05%
7.16%
10.08%
7.02%
21.24%
6.53%
16.57%
5.66%
5.85%
5.49%
9.22%
5.29%
9.75%
4.84%
8.45%
4.07%
8.57%
3.98%
District
Dohad
Dang
Chhotaudepur
Narmada
Sabar Kantha
Arvalli
Panch Mahals
Devbhumi Dwarka
Kheda
Mahisagar
Tapi
Patan
Bharuch
Bhavnagar
Surendranagar
Kachchh
Anand
Botad
Gir Somnath
Mahesana
Morbi
Valsad
Amreli
Jamnagar
Junagadh
Vadodara
Gandhinagar
Ahmedabad
Surat
Navsari
Porbandar
Rajkot
Banas Kantha
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
GUJARAT MPI: PROGRESS REVIEW 2023
100

Gujarat: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
-1.32
-3.92
-4.33
-4.38
-4.48
-4.92
-5.10
-7.41
-8.18
-10.91
-11.56
-11.84
-14.48
-16.66
-17.78
-30.72
-32.0 -30.0 -28.0 -26.0 -24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0
% point change in proportion of multidimensionally poor population
Dang
Kachchh
Dohad
Narmada
Valsad
Tapi
Gandhinagar
Patan
Banas Kantha
Bharuch
Navsari
Amreli
Porbandar
Anand
Surat
Mahesana
0.0
101
GUJARATMPI: PROGRESS REVIEW 2023

Gujarat: Overview of Districts
Headcount Ratio, Intensity and MPI
0.096
0.098
0.114
0.120
0.041
0.108
0.037
0.036
0.093
0.189
0.041
0.161
0.046
0.108
0.141
0.044
0.060
0.078
0.258
0.278
0.075
0.076
0.144
0.062
0.051
0.024
48.10%
46.08%
41.38%
48.15%
44.14%
43.46%
43.34%
42.43%
43.98%
45.50%
42.32%
43.31%
43.74%
42.50%
49.69%
43.64%
46.24%
47.19%
46.92%
48.54%
41.85%
43.81%
46.14%
41.91%
42.61%
40.47%
19.95%
21.24%
27.59%
24.97%
9.22%
24.85%
8.57%
8.45%
21.10%
41.52%
9.75%
37.11%
10.43%
25.50%
28.30%
10.08%
13.05%
16.57%
54.93%
57.33%
17.90%
17.38%
31.24%
14.81%
11.95%
5.85%
0.034
0.027
0.070
0.048
0.022
0.095
0.016
0.016
0.054
0.076
0.020
0.099
0.036
0.068
0.040
0.070
0.047
0.030
0.031
0.037
0.023
0.174
0.078
0.113
0.116
0.043
0.053
0.051
0.110
0.080
0.044
0.030
0.022
41.93%
41.77%
43.96%
42.34%
41.11%
47.36%
41.28%
39.10%
41.53%
42.18%
41.34%
43.83%
41.86%
40.97%
44.32%
40.86%
45.10%
42.84%
43.54%
39.71%
41.11%
45.39%
43.73%
42.40%
45.85%
43.90%
44.00%
41.55%
46.13%
41.63%
41.66%
39.86%
40.40%
8.11%
6.53%
16.03%
11.45%
5.29%
19.96%
3.98%
4.07%
12.93%
18.11%
4.84%
22.62%
8.60%
16.72%
9.11%
17.06%
10.52%
7.02%
7.16%
9.41%
5.66%
38.27%
17.93%
26.61%
25.24%
9.77%
12.12%
12.28%
23.83%
19.19%
10.47%
7.47%
5.49%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
Ahmedabad
Amreli
Anand
Arvalli
Banas Kantha
Bharuch
Bhavnagar
Botad
Chhotaudepur
Dang
Devbhumi Dwarka
Dohad
Gandhinagar
Gir Somnath
Jamnagar
Junagadh
Kachchh
Kheda
Mahesana
Mahisagar
Morbi
Narmada
Navsari
Panch Mahals
Patan
Porbandar
Rajkot
Sabar Kantha
Surat
Surendranagar
Tapi
Vadodara
Valsad
NFHS-4 (2015-16) NFHS-5 (2019-21)
–––
–––
–––
–––
–––
–––
–––
District
GUJARAT MPI: PROGRESS REVIEW 2023
102

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Haryana
HARYANA
Overview
Haryana's Headcount Ratio, Intensity and MPI
Haryana: Indicator Contribution to the MPI
Percentage contribution of each indicator to Haryana's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
7.07%2019-21 0.03143.34%
11.88%2015-16 0.05344.40%
Rural
Headcount Ratio Intensity MPI
0.0378.41% 43.42%
Urban
Headcount Ratio Intensity MPI
0.0184.26% 43.00%
0.06514.61% 44.29% 0.0347.52% 44.74%
Multidimensional Poverty in Haryana's Rural and Urban Areas
Nutrition: 31.14%
Child & Adolescent Mortality: 1.99%
Maternal Health: 12.60%
Years of Schooling: 15.67%
School Attendance: 12.99%
Cooking Fuel: 8.64%
Sanitation: 4.50%
Drinking Water: 2.25%
Electricity: 0.28%
Housing: 6.60%
Assets: 2.38%
Bank Account: 0.95%
Nutrition: 31.76%
Child & Adolescent Mortality: 1.87%
Maternal Health: 14.39%
Years of Schooling: 14.49%
School Attendance: 8.91%
Cooking Fuel: 9.00%
Sanitation: 5.39%
Drinking Water: 1.97%
Electricity: 0.67%
Housing: 6.64%
Assets: 2.29%
Bank Account: 2.60%
2019-21
2015-16
Year
104

Percentage of total population who are deprived in each indicator
Haryana: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Haryana: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
26.19%
32.34%
1.85%
2.17%
16.83%
23.86%
5.51%
7.09%
4.31%
3.82%
43.93%
51.24%
15.11%
19.19%
6.71%
6.63%
0.40%
1.06%
23.95%
24.26%
5.21%
4.65%
3.56%
8.17%
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
5.72%
10.05%
0.73%
1.19%
4.63%
9.11%
2.88%
4.59%
2.39%
2.82%
5.56%
9.97%
2.89%
5.98%
1.45%
2.19%
0.18%
0.74%
4.25%
7.35%
1.53%
2.54%
0.61%
2.88%
105
HARYANAMPI: PROGRESS REVIEW 2023

Haryana
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
HARYANA MPI: PROGRESS REVIEW 2023
106
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Haryana for 2019-21.
Up to 0.032 0.033 to 0.0590.060 to 0.0860.087 to 0.1130.114 to 0.1400.141 to 0.167 0.168 and above

Haryana
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Haryana
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
107
HARYANAMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Haryana, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.053 0.054 to 0.0990.100 to 0.1450.146 to 0.1900.191 to 0.2360.237 to 0.282 0.283 and above

Haryana: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
50.0%.0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%60.0%65.0%
62.50%
39.99%
26.98%
14.71%
11.02%
7.51%
9.16%
7.42%
10.70%
7.42%
8.12%
7.39%
14.52%
7.16%
12.78%
6.62%
9.71%
5.62%
6.29%
5.04%
4.47%
4.70%
13.72%
4.69%
6.42%
4.60%
6.35%
4.01%
1.99%
3.83%
7.83%
3.62%
6.40%
3.43%
10.39%
3.29%
5.82%
3.20%
11.08%
2.91%
2.85%
2.47%
1.42%
District
Nuh (Mewat)
Palwal
Fatehabad
Jind
Faridabad
Panipat
Sirsa
Bhiwani
Hisar
Mahendragarh
Yamunanagar
Rohtak
Kurukshetra
Sonipat
Ambala
Kaithal
Karnal
Gurugram
Jhajjar
Rewari
Panchkula
Charki Dadri
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
HARYANA MPI: PROGRESS REVIEW 2023
108

Haryana: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-24.0-22.0-20.0-18.0-16.0-14.0-12.0-10.0-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0
1.84
0.24
-0.73
-1.05
-1.25
-1.74
-1.82
-2.35
-2.61
-2.97
-3.29
-3.51
-4.09
-4.21
-7.10
-7.37
-8.17
-9.03
-12.27
-22.51
% point change in proportion of multidimensionally poor population
Nuh (Mewat)
Palwal
Rohtak
Rewari
Sirsa
Gurugram
Kaithal
Hisar
Fatehabad
Faridabad
Karnal
Jhajjar
Sonipat
Kurukshetra
Jind
Mahendragarh
Panchkula
Panipat
Yamunanagar
Ambala
District
109
HARYANAMPI: PROGRESS REVIEW 2023

Haryana: Overview of Districts
Headcount Ratio, Intensity and MPI
0.019
0.025
0.060
0.058
0.044
0.035
0.010
0.126
0.329
0.024
0.027
0.027
0.033
0.036
0.023
0.039
0.044
0.046
0.047
0.051
0.008
43.11%
39.87%
40.98%
41.93%
39.41%
43.43%
40.83%
46.59%
52.64%
38.15%
42.22%
42.92%
41.65%
39.53%
39.51%
39.98%
42.07%
41.37%
44.28%
39.54%
39.52%
4.47%
6.35%
14.52%
13.72%
11.08%
8.12%
2.47%
26.98%
62.50%
6.29%
6.42%
6.40%
7.83%
9.16%
5.82%
9.71%
10.39%
11.02%
10.70%
12.78%
1.99%
0.019
0.016
0.029
0.020
0.012
0.030
0.006
0.068
0.195
0.020
0.018
0.014
0.015
0.029
0.013
0.023
0.013
0.031
0.032
0.012
0.026
0.016
40.91%
40.85%
40.76%
42.56%
40.04%
41.12%
40.51%
46.05%
48.78%
39.35%
39.81%
39.89%
42.41%
38.70%
42.07%
40.15%
40.19%
41.25%
42.91%
41.54%
39.78%
42.86%
4.70%
4.01%
7.16%
4.69%
2.91%
7.39%
1.42%
14.71%
39.99%
5.04%
4.60%
3.43%
3.62%
7.42%
3.20%
5.62%
3.29%
7.51%
7.42%
2.85%
6.62%
3.83%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Ambala
Bhiwani
Charki Dadri
Faridabad
Fatehabad
Gurugram
Hisar
Jhajjar
Jind
Kaithal
Karnal
Kurukshetra
Mahendragarh
Nuh (Mewat)
Palwal
Panchkula
Panipat
Rewari
Rohtak
Sirsa
Sonipat
Yamunanagar
District
–––
HARYANA MPI: PROGRESS REVIEW 2023
110

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Standard of LivingEducation
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Himachal Pradesh
HIMACHAL PRADESH
Overview
Himachal Pradesh's Headcount Ratio, Intensity and MPI
Himachal Pradesh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Himachal Pradesh's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
4.93%2019-21 0.02040.22%
7.59%2015-16 0.03039.44%
Rural
Headcount Ratio Intensity MPI
0.0215.23% 39.46%
Urban
Headcount Ratio Intensity MPI
0.0152.96% 49.27%
0.0328.21% 39.29% 0.0071.46% 47.61%
Multidimensional Poverty in Himachal Pradesh's Rural and Urban Areas
Nutrition: 34.97%
Child & Adolescent Mortality: 2.01%
Maternal Health: 14.78%
Years of Schooling: 12.17%
School Attendance: 3.94%
Cooking Fuel: 10.34%
Sanitation: 6.33%
Drinking Water: 2.29%
Electricity: 0.58%
Housing: 7.63%
Assets: 3.88%
Bank Account: 1.09%
Nutrition: 37.63%
Child & Adolescent Mortality: 1.63%
Maternal Health: 15.92%
Years of Schooling: 8.20%
School Attendance: 2.39%
Cooking Fuel: 11.30%
Sanitation: 7.61%
Drinking Water: 2.15%
Electricity: 0.38%
Housing: 8.30%
Assets: 3.44%
Bank Account: 1.04%
2019-21
2015-16
Year
112

Percentage of total population who are deprived in each indicator
Himachal Pradesh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Himachal Pradesh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
22.98%
27.18%
1.07%
1.66%
12.51%
17.42%
4.63%
3.78%
0.91%
0.89%
52.74%
67.90%
18.27%
27.63%
5.14%
7.72%
0.54%
0.49%
23.73%
29.30%
6.75%
7.52%
2.11%
2.69%
4.16%
6.75%
0.48%
0.59%
3.52%
5.71%
1.45%
1.47%
0.47%
0.43%
4.31%
7.10%
2.64%
4.78%
0.95%
1.35%
0.24%
0.24%
3.18%
5.22%
1.62%
2.16%
0.45%
0.65%
113
HIMACHAL PRADESHMPI: PROGRESS REVIEW 2023

Himachal Pradesh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Himachal Pradesh for 2019-21.
Up to 0.015 0.016 to 0.0190.020 to 0.0230.024 to 0.0270.028 to 0.0310.032 to 0.035 0.036 and above
© 2023 Mapbox © OpenStreetMap
HIMACHAL PRADESH MPI: PROGRESS REVIEW 2023
114

Himachal Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Himachal Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
115
HIMACHAL PRADESHMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Himachal Pradesh, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.0200.021 to 0.0240.025 to 0.0280.029 to 0.0330.034 to 0.0370.038 to 0.042 0.043 and above
© 2023 Mapbox © OpenStreetMap

Himachal Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.0%1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%11.0%12.0%
11.27%
9.99%
7.72%
7.71%
8.97%
7.18%
10.88%
5.84%
7.54%
5.15%
4.43%
4.99%
8.35%
4.47%
5.00%
4.47%
5.88%
4.25%
9.10%
4.02%
5.10%
3.95%
7.46%
3.09%
District
Chamba
Lahul and Spiti
Kullu
Sirmaur
Bilaspur
Hamirpur
Mandi
Una
Kangra
Solan
Kinnaur
Shimla
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
HIMACHAL PRADESH MPI: PROGRESS REVIEW 2023
116

Himachal Pradesh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Solan
Sirmaur
Shimla
Mandi
Bilaspur
Kullu
Kangra
Chamba
Kinnaur
Una
Lahul and Spiti
Hamirpur
-5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0
0.56
0.00
-0.54
-1.15
-1.28
-1.64
-1.79
-2.39
-3.87
-4.38
-5.04
-5.08
% point change in proportion of multidimensionally poor population
117
HIMACHAL PRADESHMPI: PROGRESS REVIEW 2023

Himachal Pradesh: Overview of Districts
Headcount Ratio, Intensity and MPI
0.019
0.037
0.047
0.030
0.033
0.030
0.035
0.020
0.022
0.016
0.046
0.028
38.90%
40.38%
43.14%
40.07%
39.09%
38.38%
38.98%
38.60%
37.40%
36.43%
41.25%
36.62%
5.00%
9.10%
10.88%
7.46%
8.35%
7.72%
8.97%
5.10%
5.88%
4.43%
11.27%
7.54%
0.023
0.016
0.024
0.013
0.017
0.029
0.028
0.016
0.017
0.020
0.040
0.020
50.50%
38.94%
41.14%
40.58%
37.44%
37.82%
39.23%
39.91%
39.62%
40.43%
39.90%
39.63%
4.47%
4.02%
5.84%
3.09%
4.47%
7.71%
7.18%
3.95%
4.25%
4.99%
9.99%
5.15%Bilaspur
Chamba
Hamirpur
Kangra
Kinnaur
Kullu
Lahul and Spiti
Mandi
Shimla
Sirmaur
Solan
Una
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
District
HIMACHAL PRADESH MPI: PROGRESS REVIEW 2023
118

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Jharkhand
JHARKHAND
Overview
Jharkhand's Headcount Ratio, Intensity and MPI
Jharkhand: Indicator Contribution to the MPI
Percentage contribution of each indicator to Jharkhand's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
28.81%2019-21 0.13145.59%
42.10%2015-16 0.20247.92%
Rural
Headcount Ratio Intensity MPI
0.16034.93% 45.76%
Urban
Headcount Ratio Intensity MPI
0.0388.67% 43.24%
0.24650.92% 48.26% 0.06715.04% 44.32%
Multidimensional Poverty in Jharkhand's Rural and Urban Areas
Nutrition: 29.48%
Child & Adolescent Mortality: 1.11%
Child & Adolescent Mortality: 1.13%
Maternal Health: 12.23%
Years of Schooling: 15.19%
School Attendance: 8.46%
Cooking Fuel: 9.70%
Sanitation: 6.76%
Drinking Water: 3.04%
Electricity: 1.36%
Housing: 8.66%
Assets: 3.38%
Bank Account: 0.64%
Nutrition: 28.41%
Maternal Health: 10.94%
Years of Schooling: 13.58%
School Attendance: 5.93%
Cooking Fuel: 9.73%
Sanitation: 9.29%
Drinking Water: 4.09%
Electricity: 3.21%
Housing: 8.48%
Assets: 3.66%
Bank Account: 1.56%
2019-21
2015-16
Year
Education
120

Percentage of total population who are deprived in each indicator
Jharkhand: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Jharkhand: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
40.32%
48.02%
2.57%
3.32%
29.75%
33.07%
16.17%
18.30%
8.45%
8.19%
69.12%
82.14%
43.36%
75.32%
18.61%
30.32%
5.67%
18.80%
56.93%
61.78%
15.48%
21.37%
3.97%
8.97%
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
23.22%
34.39%
1.75%
2.74%
19.28%
26.47%
11.97%
16.44%
6.66%
7.17%
26.76%
41.20%
18.63%
39.33%
8.38%
17.32%
3.74%
13.58%
23.88%
35.92%
9.31%
15.52%
1.76%
6.61%
121
JHARKHANDMPI: PROGRESS REVIEW 2023

Jharkhand
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
JHARKHAND MPI: PROGRESS REVIEW 2023
122
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Jharkhand for 2019-21.
Up to 0.090 0.091 to 0.1150.116 to 0.1410.142 to 0.1670.168 to 0.1920.193 to 0.218 0.219 and above

Jharkhand
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Jharkhand
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Jharkhand, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.138 0.139 to 0.1680.169 to 0.1970.198 to 0.2260.227 to 0.2560.257 to 0.285 0.286 and above
123
JHARKHANDMPI: PROGRESS REVIEW 2023

Jharkhand: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
0.0%5.0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%60.0%65.0%
60.66%
49.87%
55.72%
48.45%
57.60%
47.81%
52.91%
41.68%
52.93%
37.98%
47.40%
37.04%
60.74%
37.00%
51.81%
36.36%
53.21%
35.84%
48.65%
33.38%
45.46%
32.34%
49.98%
31.41%
46.59%
30.76%
47.53%
30.29%
50.62%
28.97%
32.68%
28.21%
35.75%
26.10%
41.79%
23.16%
45.33%
22.71%
29.57%
18.06%
28.57%
17.09%
27.60%
15.80%
29.33%
15.28%
23.99%
15.10%
Pakur
Sahebganj
West Singhbhum
Latehar
Dumka
Deoghar
Chatra
Godda
Garhwa
Khunti
Palamu
Simdega
Gumla
Giridih
Jamtara
Koderma
Hazaribagh
Saraikela-
Kharsawan
Lohardaga
Ramgarh
Dhanbad
Ranchi
Bokaro
East Singhbhum
District
NFHS-5 (2019-21) NFHS-4 (2015-16)
JHARKHAND MPI: PROGRESS REVIEW 2023
124

Jharkhand: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
Chatra
Lohardaga
Jamtara
Simdega
Garhwa
Giridih
Gumla
Godda
Khunti
Dumka
Bokaro
Palamu
Ranchi
Ramgarh
Dhanbad
Latehar
Pakur
Deoghar
West Singhbhum
Hazaribagh
East Singhbhum
Sahebganj
Koderma
-26.0 -24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-4.46
-7.27
-8.89
-9.66
-9.79
-10.36
-10.79
-11.23
-11.48
-11.51
-11.80
-13.12
-14.05
-14.95
-15.27
-15.45
-15.83
-17.23
-17.37
-18.57
-18.63
-21.65
-22.62
-23.74
% point change in proportion of multidimensionally poor population
District
Saraikela
-Kharsawan
125
JHARKHANDMPI: PROGRESS REVIEW 2023

Jharkhand: Overview of Districts
Headcount Ratio, Intensity and MPI
0.236
0.194
0.293
0.121
0.131
0.110
0.310
0.232
0.315
0.213
0.267
0.146
0.230
0.241
0.156
0.220
0.254
0.228
0.257
0.256
0.125
0.221
0.306
0.130
47.30%
46.33%
52.57%
43.72%
44.26%
45.92%
53.90%
51.05%
51.95%
47.04%
50.52%
44.69%
47.27%
47.54%
43.70%
47.12%
49.00%
47.97%
48.33%
48.33%
43.84%
46.71%
50.40%
44.19%
49.98%
41.79%
55.72%
27.60%
29.57%
23.99%
57.60%
45.46%
60.66%
45.33%
52.91%
32.68%
48.65%
50.62%
35.75%
46.59%
51.81%
47.53%
53.21%
52.93%
28.57%
47.40%
60.74%
29.33%
0.139
0.103
0.243
0.065
0.077
0.065
0.241
0.144
0.244
0.099
0.189
0.121
0.152
0.129
0.110
0.137
0.168
0.133
0.160
0.178
0.074
0.177
0.177
0.067
44.22%
44.34%
50.20%
41.23%
42.46%
43.13%
50.42%
44.57%
49.02%
43.49%
45.40%
42.73%
45.41%
44.58%
42.16%
44.44%
46.30%
43.98%
44.55%
46.75%
43.20%
47.68%
47.78%
43.63%
31.41%
23.16%
48.45%
15.80%
18.06%
15.10%
47.81%
32.34%
49.87%
22.71%
41.68%
28.21%
33.38%
28.97%
26.10%
30.76%
36.36%
30.29%
35.84%
37.98%
17.09%
37.04%
37.00%
15.28%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Bokaro
Chatra
Deoghar
Dhanbad
Dumka
Garhwa
Giridih
Godda
Gumla
Hazaribagh
Jamtara
Khunti
Koderma
Latehar
Lohardaga
Pakur
Palamu
Ramgarh
Ranchi
Sahebganj
Saraikela-Kharsawan
Simdega
District
JHARKHAND MPI: PROGRESS REVIEW 2023
126
Pashchimi Singhbhum
(West Singhbhum)
Purbi Singhbhum
(East Singhbhum)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Karnataka
KARNATAKA
Overview
Karnataka's Headcount Ratio, Intensity and MPI
Karnataka: Indicator Contribution to the MPI
Percentage contribution of each indicator to Karnataka's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
7.58%2019-21 0.03141.21%
12.77%2015-16 0.05542.76%
Rural
Headcount Ratio Intensity MPI
0.04310.33% 41.36%
Urban
Headcount Ratio Intensity MPI
0.0133.22% 40.47%
0.07918.45% 42.87% 0.0214.92% 42.22%
Multidimensional Poverty in Karnataka's Rural and Urban Areas
Nutrition: 34.52%
Child & Adolescent Mortality: 1.56%
Child & Adolescent Mortality: 1.08%
Maternal Health: 11.75%
Years of Schooling: 15.11%
School Attendance: 7.55%
Cooking Fuel: 7.18%
Sanitation: 7.67%
Drinking Water: 1.79%
Electricity: 0.56%
Housing: 7.71%
Assets: 3.25%
Bank Account: 1.34%
Nutrition: 29.82%
Maternal Health: 8.06%
Years of Schooling: 16.44%
School Attendance: 7.11%
Cooking Fuel: 9.80%
Sanitation: 9.34%
Drinking Water: 2.34%
Electricity: 0.84%
Housing: 7.97%
Assets: 4.30%
Bank Account: 2.92%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
128

Percentage of total population who are deprived in each indicator
Karnataka: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Karnataka: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
129
KARNATAKAMPI: PROGRESS REVIEW 2023
29.97%
33.56%
1.29%
1.34%
12.58%
12.36%
7.15%
8.69%
2.50%
3.53%
21.47%
45.54%
25.65%
42.67%
7.06%
9.44%
0.89%
1.71%
36.20%
37.30%
7.31%
10.05%
4.97%
8.83%
6.47%
9.77%
0.59%
0.71%
4.40%
5.28%
2.83%
5.39%
1.41%
2.33%
4.71%
11.24%
5.03%
10.71%
1.18%
2.68%
0.37%
0.96%
5.06%
9.14%
2.13%
4.93%
0.88%
3.35%

Karnataka
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Karnataka for 2019-21.
Up to 0.019 0.020 to 0.0350.036 to 0.0510.052 to 0.0670.068 to 0.0830.084 to 0.099 0.100 and above
© 2023 Mapbox © OpenStreetMap
KARNATAKA MPI: PROGRESS REVIEW 2023
130

Karnataka
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Karnataka
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
131
KARNATAKAMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Karnataka, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.034 0.035 to 0.0610.062 to 0.0880.089 to 0.1150.116 to 0.1410.142 to 0.168 0.169 and above

Karnataka: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
KARNATAKA MPI: PROGRESS REVIEW 2023
132
District
Yadgir
Raichur
Kalaburagi
Koppal
Vijayapura
Gadag
Ballari
Haveri
Bidar
Bagalkote
Belagavi
Davangere
Chitradurga
Dharwad
Chamarajanagara
Tumakuru
Kodagu
Uttara Kannada
Udupi
Chikkamagaluru
Shivamogga
Chikkaballapura
Mandya
Hassan
Mysuru
Kolar
Dakshina Kannada
Bengaluru Urban
Bengaluru Rural
Ramanagara
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%
41.67%
25.38%
31.65%
20.19%
21.10%
18.63%
24.31%
18.04%
21.90%
16.30%
19.50%
15.32%
22.91%
12.22%
15.28%
11.38%
18.99%
11.25%
19.94%
10.85%
12.26%
9.41%
11.46%
5.95%
14.81%
5.84%
9.53%
5.71%
18.45%
5.15%
12.71%
4.69%
8.64%
4.67%
13.21%
4.59%
10.32%
4.13%
9.98%
3.74%
12.64%
3.39%
13.41%
3.39%
6.30%
2.47%
6.33%
2.43%
7.79%
2.30%
9.53%
1.78%
6.69%
1.71%
2.05%
1.47%
7.03%
0.99%
8.73%
0.88%
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)

-18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-0.58
-2.47
-2.85
-3.81
-3.83
-3.89
-3.90
-3.97
-4.18
-4.99
-5.49
-5.51
-5.60
-6.04
-6.18
-6.24
-6.28
-7.74
-7.76
-7.84
-8.02
-8.62
-8.97
-9.10
-9.25
-10.02
-10.69
-11.46
-13.30
-16.30
Karnataka: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Yadgir
Chamarajanagara
Raichur
Ballari
Chikkaballapura
Shivamogga
Bagalkote
Chitradurga
Uttara Kannada
Tumakuru
Ramanagara
Kolar
Bidar
Koppal
Chikkamagaluru
Udupi
Bengaluru Rural
Vijayapura
Davangere
Mysuru
Dakshina Kannada
Gadag
Kodagu
Hassan
Haveri
Mandya
Dharwad
Belagavi
Kalaburagi
Bengaluru Urban
% point change in proportion of multidimensionally poor population
133
KARNATAKAMPI: PROGRESS REVIEW 2023

Karnataka: Overview of Districts
Headcount Ratio, Intensity and MPI
0.196
0.056
0.043
0.052
0.052
0.033
0.144
0.032
0.027
0.103
0.038
0.038
0.063
0.026
0.094
0.084
0.038
0.049
0.027
0.061
0.042
0.056
0.078
0.093
0.079
0.106
0.049
0.028
0.008
0.087
46.99%
42.64%
41.24%
41.26%
40.95%
38.38%
45.54%
41.16%
43.52%
42.50%
40.26%
43.75%
41.12%
40.43%
44.44%
43.28%
40.27%
42.59%
40.32%
41.29%
41.81%
42.07%
42.02%
42.57%
41.64%
46.48%
39.94%
40.01%
41.03%
43.41%
41.67%
13.21%
10.32%
12.71%
12.64%
8.73%
31.65%
7.79%
6.30%
24.31%
9.53%
8.64%
15.28%
6.33%
21.10%
19.50%
9.53%
11.46%
6.69%
14.81%
9.98%
13.41%
18.45%
21.90%
18.99%
22.91%
12.26%
7.03%
2.05%
19.94%
0.116
0.018
0.015
0.017
0.013
0.004
0.090
0.008
0.009
0.078
0.007
0.018
0.045
0.009
0.075
0.062
0.022
0.023
0.006
0.023
0.015
0.014
0.021
0.068
0.045
0.052
0.037
0.004
0.007
0.044
45.59%
40.29%
36.51%
37.28%
38.41%
40.51%
44.61%
36.82%
35.55%
43.35%
39.11%
39.01%
39.19%
38.76%
40.44%
40.64%
39.07%
38.68%
36.72%
39.23%
39.80%
39.89%
40.83%
41.71%
40.00%
42.66%
39.60%
36.71%
45.68%
40.74%
25.38%
4.59%
4.13%
4.69%
3.39%
0.88%
20.19%
2.30%
2.47%
18.04%
1.78%
4.67%
11.38%
2.43%
18.63%
15.32%
5.71%
5.95%
1.71%
5.84%
3.74%
3.39%
5.15%
16.30%
11.25%
12.22%
9.41%
0.99%
1.47%
10.85%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Bagalkote
Belagavi
Bidar
Chamarajanagara
Chikkaballapura
Chikkamagaluru
Chitradurga
Dakshina Kannada
Davangere
Dharwad
Gadag
Hassan
Haveri
Kodagu
Kolar
Koppal
Mandya
Mysuru
Raichur
Ramanagara
Shivamogga
Tumakuru
Udupi
Uttara Kannada
Yadgir
District
KARNATAKA MPI: PROGRESS REVIEW 2023
134
Bangalore
(Bengaluru Urban)
Bangalore Rural
(Bengaluru Rural )
Bijapur (Vijayapura)
Gulbarga (Kalaburagi)
Bellary (Ballari)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Kerala
KERALA
Overview
Kerala's Headcount Ratio, Intensity and MPI
Kerala: Indicator Contribution to the MPI
Percentage contribution of each indicator to Kerala's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
0.55%2019-21 0.00236.92%
0.70%2015-16 0.00338.99%
Rural
Headcount Ratio Intensity MPI
0.0030.76% 37.14%
Urban
Headcount Ratio Intensity MPI
0.0010.32% 36.36%
0.0040.95% 39.76% 0.0020.43% 37.06%
Multidimensional Poverty in Kerala's Rural and Urban Areas
2019-21
2015-16
Year
Nutrition: 36.61%
Child & Adolescent Mortality: 0.44%
Maternal Health: 8.16%
Years of Schooling: 13.58%
School Attendance: 5.15%
Cooking Fuel: 10.03%
Sanitation: 2.12%
Drinking Water: 2.50%
Electricity: 2.86%
Housing: 8.80%
Assets: 6.54%
Bank Account: 3.21%
Nutrition: 34.12%
Child & Adolescent Mortality: 0.14%
Maternal Health: 4.50%
Years of Schooling: 11.20%
School Attendance: 13.66%
Cooking Fuel: 10.01%
Sanitation: 5.17%
Drinking Water: 2.27%
Electricity: 3.47%
Housing: 6.90%
Assets: 5.58%
Bank Account: 2.98%
136

Percentage of total population who are deprived in each indicator
Kerala: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Kerala: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
0.45%
0.56%
0.01%
0.00%
0.20%
0.15%
0.17%
0.18%
0.06%
0.22%
0.43%
0.58%
0.09%
0.30%
0.11%
0.13%
0.12%
0.20%
0.38%
0.40%
0.28%
0.32%
0.14%
0.17%
16.44%
15.29%
0.20%
0.19%
3.30%
1.73%
2.49%
1.78%
0.25%
0.54%
28.12%
43.89%
1.27%
1.83%
5.40%
5.56%
0.41%
0.74%
16.67%
10.76%
3.05%
2.94%
3.22%
4.32%
137
KERALAMPI: PROGRESS REVIEW 2023

Kerala
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Kerala for 2019-21.
Up to 0.001 0.002 to 0.0030.004 to 0.0050.006 to 0.0070.008 to 0.0090.010 to 0.011 0.012 and above
KERALA MPI: PROGRESS REVIEW 2023
138

Kerala
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Kerala
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Kerala, based on values for 2015-16. Both
the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.001 0.002 to 0.0030.004 to 0.0050.006 to 0.0070.008 to 0.0090.010 to 0.011 0.012 and above
139
KERALAMPI: PROGRESS REVIEW 2023

Kerala: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
00.0%.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%2.0%2.2%2.4%2.6%2.8%3.0%3.2%3.4%3.6%3.8%
3.48%
2.82%
0.94%
1.70%
0.62%
1.34%
1.65%
1.11%
1.11%
0.85%
0.26%
0.68%
1.08%
0.52%
0.83%
0.42%
0.00%
0.14%
0.71%
0.10%
0.72%
0.04%
0.33%
0.03%
0.44%
0.03%
0.10%
0.00%
Wayanad
Kasaragod
Palakkad
Idukki
Malappuram
Kozhikode
Thiruvananthapuram
Pathanamthitta
Kottayam
Alappuzha
Kollam
Thrissur
Kannur
Ernakulam
District
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
KERALA MPI: PROGRESS REVIEW 2023
140

Kerala: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
-0.8-0.7-0.6-0.5-0.4-0.3-0.2-0.10.0 0.10.20.30.4 0.50.6 0.70.8
% point change in proportion of multidimensionally poor population
0.75
0.72
0.42
0.14
-0.10
-0.26
-0.30
-0.41
-0.42
-0.54
-0.56
-0.62
-0.66
-0.68Kollam
Wayanad
Alappuzha
Thiruvananthapuram
Idukki
Pathanamthitta
Kannur
Thrissur
Malappuram
Ernakulam
Kottayam
Kozhikode
Palakkad
Kasaragod
141
KERALAMPI: PROGRESS REVIEW 2023

Kerala: Overview of Districts
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
0.014
0.001
0.004
0.004
0.002
0.004
0.001
0.000
0.003
0.005
0.002
0.006
0.000
0.003
40.94%
37.12%
37.40%
42.48%
37.04%
36.64%
37.31%
42.76%
52.30%
36.04%
37.53%
38.10%
37.34%
3.48%
0.33%
1.08%
0.83%
0.62%
1.11%
0.26%
0.00%
0.72%
0.94%
0.44%
1.65%
0.10%
0.71%
0.011
0.000
0.002
0.001
0.005
0.003
0.002
0.001
0.000
0.006
0.000
0.004
0.000
0.000
39.33%
35.71%
37.51%
35.16%
36.76%
34.70%
35.66%
38.61%
40.48%
37.88%
35.71%
39.29%
34.52%
2.82%
0.03%
0.52%
0.42%
1.34%
0.85%
0.68%
0.14%
0.04%
1.70%
0.03%
1.11%
0.00%
0.10%Alappuzha
Ernakulam
Idukki
Kannur
Kasaragod
Kollam
Kottayam
Kozhikode
Malappuram
Palakkad
Pathanamthitta
Thiruvananthapuram
Thrissur
Wayanad
District
KERALA MPI: PROGRESS REVIEW 2023
142
–
–

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Madhya Pradesh
MADHYA PRADESH
Overview
Madhya Pradesh's Headcount Ratio, Intensity and MPI
Madhya Pradesh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Madhya Pradesh's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
20.63%2019-21 0.09043.70%
36.57%2015-16 0.17347.25%
Rural
Headcount Ratio Intensity MPI
0.11125.32% 43.82%
Urban
Headcount Ratio Intensity MPI
0.0307.10% 42.51%
0.21845.90% 47.57% 0.06113.72% 44.62%
Multidimensional Poverty in Madhya Pradesh's Rural and Urban Areas
2019-21
2015-16
Year
Nutrition: 27.97%
Child & Adolescent Mortality: 1.31%
Maternal Health: 10.05%
Years of Schooling: 13.50%
School Attendance: 7.07%
Cooking Fuel: 9.60%
Sanitation: 9.13%
Drinking Water: 4.78%
Electricity: 1.78%
Housing: 9.00%
Assets: 3.76%
Bank Account: 2.02%
Nutrition: 28.54%
Child & Adolescent Mortality: 1.35%
Maternal Health: 10.45%
Years of Schooling: 14.68%
School Attendance: 8.98%
Cooking Fuel: 9.81%
Sanitation: 7.03%
Drinking Water: 4.50%
Electricity: 0.46%
Housing: 9.27%
Assets: 4.20%
Bank Account: 0.72%
144

Percentage of total population who are deprived in each indicator
Madhya Pradesh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Madhya Pradesh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
15.44%
29.00%
1.46%
2.72%
11.31%
20.85%
7.94%
14.00%
4.86%
7.34%
18.57%
34.85%
13.32%
33.13%
8.52%
17.36%
0.86%
6.46%
17.56%
32.68%
7.96%
13.64%
1.37%
7.35%
34.63%
45.49%
2.32%
3.60%
21.40%
29.38%
12.14%
16.07%
6.76%
8.38%
60.88%
71.24%
35.51%
65.15%
21.73%
29.25%
1.57%
8.95%
54.65%
64.38%
16.05%
19.31%
3.84%
11.15%
145
MADHYA PRADESHMPI: PROGRESS REVIEW 2023

146
Madhya Pradesh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
MADHYA PRADESHMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Madhya Pradesh for 2019-21.
Up to 0.053 0.054 to 0.0850.086 to 0.1160.117 to 0.1480.149 to 0.1790.180 to 0.210 0.211 and above

Madhya Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Madhya Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
147
MADHYA PRADESH MPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Madhya Pradesh, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.099 0.100 to 0.1500.151 to 0.2010.202 to 0.2520.253 to 0.3040.305 to 0.355 0.356 and above

Madhya Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
District
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
68.86%
49.62%
71.31%
40.25%
49.72%
38.00%
56.23%
34.57%
61.60%
33.52%
52.68%
33.07%
42.63%
32.43%
51.92%
31.32%
36.94%
28.88%
48.09%
28.28%
46.09%
27.92%
46.31%
27.39%
42.78%
27.29%
48.78%
27.23%
41.77%
24.04%
43.47%
23.51%
47.19%
23.16%
45.45%
23.14%
45.67%
23.09%
32.36%
22.95%
42.55%
22.37%
40.07%
22.26%
39.94%
21.84%
47.52%
21.19%
34.50%
21.18%
41.48%
20.68%
41.65%
20.04%
34.12%
19.01%
36.94%
18.60%
33.00%
18.38%
40.28%
18.31%
35.80%
17.99%
33.18%
17.61%
34.24%
15.45%
42.53%
15.15%
30.55%
15.11%
24.72%
14.85%
15.01%
19.50%
14.78%
33.45%
14.70%
29.58%
14.38%
30.14%
14.37%
34.52%
13.78%
40.06%
13.59%
33.11%
12.92%
29.67%
12.90%
26.69%
12.28%
31.87%
9.88%
22.38%
9.75%
12.66%
6.75%
10.76%
5.83%
Jhabua
Alirajpur
Sheopur
Dindori
Barwani
Sidhi
Indore
Bhopal
Gwalior
Neemuch
Sehore
Dewas
Harda
Balaghat
Raisen
Chhindwara
Ujjain
Shajapur
Jabalpur
Narmadapuram
Agar Malwa
Narsinghpur
Khandwa (East Nimar)
Datia
Bhind
Khargone (West Nimar)
Dhar
Mandsaur
Burhanpur
Satna
Anuppur
Ratlam
Betul
Tikamgarh
Katni
Sagar
Seoni
Morena
Guna
Umaria
Vidisha
Shahdol
Rajgarh
Chhatarpur
Ashoknagar
Damoh
Shivpuri
Mandla
Rewa
Singrauli
Panna
NFHS-5 (2019-21) NFHS-4 (2015-16)
MADHYA PRADESH MPI: PROGRESS REVIEW 2023
148

Madhya Pradesh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
-34.0-32.0-30.0-28.0-26.0-24.0-22.0-20.0-18.0-16.0-14.0-12.0-10.0-8.0-6.0-4.0-2.00.0
-4.72
-4.93
-5.91
-8.05
-9.42
-9.87
-10.19
-11.72
-12.64
-13.33
-14.42
-14.61
-15.11
-15.20
-15.44
-15.49
-15.56
-15.77
-16.77
-17.73
-17.81
-17.81
-18.11
-18.17
-18.34
-18.79
-18.92
-19.24
-19.61
-19.81
-19.96
-20.19
-20.19
-20.60
-20.74
-20.80
-21.55
-21.62
-21.66
-21.96
-21.98
-22.31
-22.58
-24.03
-26.33
-26.48
-27.38
-28.08
-31.05Alirajpur
Barwani
Khandwa (East Nimar)
Balaghat
Tikamgarh
Vidisha
Guna
Umaria
Neemuch
Dhar
Dindori
Anuppur
Chhatarpur
Ratlam
Raisen
Singrauli
Harda
Seoni
Shahdol
Mandla
Sidhi
Jhabua
Damoh
Datia
Burhanpur
Shivpuri
Katni
Sagar
Khargone (West Nimar)
Rajgarh
Dewas
Chhindwara
Bhind
Ashoknagar
Narsinghpur
Ujjain
Satna
Mandsaur
Sehore
Betul
Gwalior
Sheopur
Panna
Narmadapuram
Morena
Rewa
Bhopal
Indore
Jabalpur
% point change in proportion of multidimensionally poor population
149
MADHYA PRADESHMPI: PROGRESS REVIEW 2023

Madhya Pradesh: Overview of District
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
0.230
0.209
0.135
0.218
0.264
0.254
0.214
0.247
0.153
0.202
0.189
0.124
0.150
0.178
0.165
0.201
0.192
0.155
0.204
0.143
0.136
0.145
0.149
0.227
0.176
0.202
0.181
0.385
0.089
0.049
0.109
0.154
0.099
0.216
0.266
0.199
0.138
0.151
0.215
0.139
0.236
0.193
0.057
0.146
0.162
0.353
0.174
0.202
0.189
0.407
48.64%
46.06%
45.65%
45.88%
50.76%
48.18%
46.38%
49.59%
45.74%
46.41%
44.50%
46.50%
43.96%
44.52%
44.76%
48.43%
45.95%
44.76%
47.93%
44.88%
44.63%
44.86%
45.18%
47.20%
49.15%
47.59%
45.25%
55.96%
45.39%
45.29%
44.06%
46.57%
44.26%
47.31%
47.28%
49.34%
46.41%
44.17%
46.31%
46.01%
48.42%
52.22%
45.16%
44.06%
46.90%
57.27%
43.54%
47.19%
45.26%
57.06%
47.19%
45.45%
29.58%
47.52%
51.92%
52.68%
46.09%
49.72%
33.45%
43.47%
42.55%
26.69%
34.12%
40.07%
36.94%
41.48%
41.77%
34.52%
42.63%
31.87%
30.55%
32.36%
33.00%
48.09%
35.80%
42.53%
39.94%
68.86%
19.50%
10.76%
24.72%
33.11%
22.38%
45.67%
56.23%
40.28%
29.67%
34.24%
46.31%
30.14%
48.78%
36.94%
12.66%
33.18%
34.50%
61.60%
40.06%
42.78%
41.65%
71.31%
0.102
0.098
0.061
0.089
0.144
0.145
0.126
0.183
0.058
0.099
0.094
0.049
0.080
0.092
0.133
0.091
0.107
0.057
0.143
0.041
0.063
0.099
0.074
0.119
0.082
0.067
0.091
0.243
0.057
0.023
0.062
0.058
0.041
0.105
0.145
0.077
0.054
0.065
0.120
0.060
0.116
0.086
0.031
0.076
0.093
0.167
0.055
0.119
0.082
0.192
0.062
44.07%
42.54%
42.29%
42.17%
46.06%
43.87%
44.97%
48.17%
39.43%
42.30%
41.90%
40.12%
42.26%
41.46%
46.10%
44.09%
44.48%
41.62%
44.24%
41.93%
41.70%
43.15%
40.44%
42.10%
45.65%
44.27%
41.47%
48.91%
38.68%
39.60%
41.94%
44.67%
42.43%
45.53%
41.92%
42.30%
42.16%
41.89%
43.72%
41.75%
42.71%
46.40%
46.36%
43.10%
43.71%
49.74%
40.79%
43.62%
40.76%
47.77%
41.35%
23.16%
23.14%
14.38%
21.19%
31.32%
33.07%
27.92%
38.00%
14.70%
23.51%
22.37%
12.28%
19.01%
22.26%
28.88%
20.68%
24.04%
13.78%
32.43%
9.88%
15.11%
22.95%
18.38%
28.28%
17.99%
15.15%
21.84%
49.62%
14.78%
5.83%
14.85%
12.92%
9.75%
23.09%
34.57%
18.31%
12.90%
15.45%
27.39%
14.37%
27.23%
18.60%
6.75%
17.61%
21.18%
33.52%
13.59%
27.29%
20.04%
40.25%
15.01%Agar Malwa
Alirajpur
Anuppur
Ashoknagar
Balaghat
Barwani
Betul
Bhind
Bhopal
Burhanpur
Chhatarpur
Chhindwara
Damoh
Datia
Dewas
Dhar
Dindori
Guna
Gwalior
Harda
Indore
Jabalpur
Jhabua
Katni
Khandwa (East Nimar)
Khargone (West Nimar)
Mandla
Mandsaur
Morena
Narsinghpur
Neemuch
Panna
Raisen
Rajgarh
Ratlam
Rewa
Sagar
Satna
Sehore
Seoni
Shahdol
Shajapur
Sheopur
Shivpuri
Sidhi
Singrauli
Tikamgarh
Ujjain
Umaria
Vidisha
District
MADHYA PRADESH MPI: PROGRESS REVIEW 2023
150
– – –
Hoshangabad
(Narmadapuram)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Maharashtra
MAHARASHTRA
Overview
Maharashtra's Headcount Ratio, Intensity and MPI
Maharashtra: Indicator Contribution to the MPI
Percentage contribution of each indicator to Maharashtra's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2019-21
2015-16
Rural
Headcount Ratio Intensity MPI
Urban
Headcount Ratio Intensity MPI
Multidimensional Poverty in Maharashtra's Rural and Urban Areas
2019-21
2015-16
Year
7.81% 0.03341.77%
14.80% 0.06543.76%
0.04811.49% 41.94% 0.0133.07% 40.96%
0.10022.74% 43.98% 0.0245.54% 42.69%
Nutrition: 32.66%
Child & Adolescent Mortality: 1.27%
Maternal Health: 10.61%
Years of Schooling: 14.23%
School Attendance: 6.69%
Cooking Fuel: 7.66%
Sanitation: 7.78%
Drinking Water: 3.39%
Electricity: 1.37%
Housing: 7.81%
Assets: 4.73%
Bank Account: 1.78%
Nutrition: 31.76%
Child & Adolescent Mortality: 1.06%
Maternal Health: 9.15%
Years of Schooling: 10.96%
School Attendance: 7.63%
Cooking Fuel: 9.13%
Sanitation: 9.16%
Drinking Water: 3.71%
Electricity: 2.30%
Housing: 7.43%
Assets: 4.92%
Bank Account: 2.79%
152

Percentage of total population who are deprived in each indicator
Maharashtra: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Maharashtra: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
32.29%
36.10%
1.11%
1.42%
15.32%
15.95%
5.91%
6.54%
2.35%
4.20%
20.07%
39.49%
28.33%
47.94%
9.53%
12.61%
2.29%
6.59%
24.02%
27.90%
10.04%
13.97%
4.96%
10.35%
6.40%
12.34%
0.50%
0.82%
4.16%
7.11%
2.79%
4.26%
1.31%
2.96%
5.25%
12.42%
5.33%
12.46%
2.33%
5.04%
0.94%
3.13%
5.36%
10.11%
3.24%
6.69%
1.22%
3.79%
153
MAHARASHTRAMPI: PROGRESS REVIEW 2023

Maharashtra
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Maharashtra for 2019-21.
Up to 0.024 0.025 to 0.0460.047 to 0.0670.068 to 0.0880.089 to 0.1100.111 to 0.131 0.132 and above
MAHARASHTRA MPI: PROGRESS REVIEW 2023
154

Maharashtra
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Maharashtra
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Maharashtra, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.051 0.052 to 0.0890.090 to 0.1270.128 to 0.1650.166 to 0.2030.204 to 0.241 0.242 and above
155
MAHARASHTRAMPI: PROGRESS REVIEW 2023

Maharashtra: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%
52.12%
33.17%
33.23%
21.44%
23.28%
14.92%
22.53%
14.54%
28.65%
14.12%
18.31%
13.46%
18.60%
13.39%
13.30%
22.49%
13.06%
20.58%
12.75%
27.37%
12.33%
28.05%
12.26%
23.54%
10.48%
14.22%
9.45%
18.22%
8.77%
18.75%
8.49%
10.04%
8.38%
12.24%
8.22%
18.47%
7.64%
15.40%
7.19%
12.60%
7.16%
17.79%
6.25%
13.38%
6.09%
8.19%
5.85%
17.65%
5.70%
15.24%
5.62%
15.39%
5.59%
17.75%
5.34%
11.02%
4.73%
10.17%
4.48%
5.29%
2.92%
8.82%
2.39%
10.18%
2.14%
6.72%
1.25%
3.59%
1.21%
4.65%
1.15%
District
Nandurbar
Dhule
Parbhani
Washim
Jalna
Nashik
Jalgaon
Palghar
Beed
Gadchiroli
Nanded
Hingoli
Yavatmal
Aurangabad
Buldhana
Gondia
Raigad
Amravati
Ratnagiri
Ahmednagar
Solapur
Latur
Akola
Bhandara
Chandrapur
Thane
Sindhudurg
Osmanabad
Satara
Kolhapur
Pune
Wardha
Sangli
Nagpur
Mumbai
Mumbai Suburban
MAHARASHTRA MPI: PROGRESS REVIEW 2023
156

Maharashtra: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-1.66
-2.34
-2.38
-2.38
-3.50
-4.02
-4.77
-4.85
-5.21
-5.44
-5.47
-5.69
-6.30
-6.42
-7.29
-7.83
-7.99
-8.03
-8.21
-8.37
-9.43
-9.46
-9.79
-10.25
-10.83
-11.53
-11.78
-11.95
-12.41
-13.06
-14.53
-15.04
-15.79
-18.95
% point change in proportion of multidimensionally poor population
Nandurbar
Hingoli
Nanded
Jalna
Yavatmal
Osmanabad
Chandrapur
Dhule
Latur
Ratnagiri
Gondia
Sindhudurg
Buldhana
Beed
Parbhani
Ahmednagar
Sangli
Washim
Gadchiroli
Akola
Wardha
Satara
Kolhapur
Nagpur
Solapur
Jalgaon
Nashik
Aurangabad
Amravati
Mumbai
Suburban
Mumbai
Pune
Bhandara
Raigad
District
157
MAHARASHTRAMPI: PROGRESS REVIEW 2023

Maharashtra: Overview of Districts
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
0.103
0.094
0.036
0.071
0.054
0.061
0.044
0.041
0.075
0.046
0.021
0.094
0.074
0.083
0.280
0.113
0.026
0.020
0.014
0.073
0.041
0.122
0.085
0.119
0.074
0.086
0.167
0.076
0.079
0.097
0.032
0.061
0.051
0.054
0.067
43.56%
41.67%
40.48%
46.87%
42.55%
39.86%
40.33%
40.67%
40.77%
45.58%
39.45%
40.35%
41.66%
45.39%
53.76%
41.45%
38.02%
42.97%
39.73%
41.29%
40.38%
42.55%
45.58%
42.27%
39.31%
41.66%
50.13%
43.00%
43.60%
43.14%
38.79%
42.74%
41.90%
40.01%
43.63%
23.54%
22.53%
8.82%
15.24%
12.60%
15.39%
11.02%
10.18%
18.47%
10.04%
5.29%
23.28%
17.75%
18.31%
52.12%
27.37%
6.72%
4.65%
3.59%
17.79%
10.17%
28.65%
18.60%
28.05%
18.75%
20.58%
33.23%
17.65%
18.22%
22.49%
8.19%
14.22%
12.24%
13.38%
15.40%
0.043
0.061
0.010
0.024
0.030
0.021
0.019
0.008
0.029
0.038
0.012
0.061
0.062
0.021
0.057
0.153
0.051
0.004
0.004
0.005
0.025
0.017
0.059
0.058
0.048
0.032
0.050
0.099
0.024
0.037
0.051
0.022
0.039
0.033
0.024
0.027
41.42%
41.68%
40.95%
42.48%
42.09%
37.63%
40.84%
38.39%
37.92%
45.22%
40.05%
41.08%
46.91%
39.46%
42.06%
46.22%
41.18%
34.18%
35.74%
37.84%
39.40%
36.87%
41.85%
43.18%
39.32%
38.21%
39.09%
46.34%
41.78%
42.51%
39.35%
37.78%
41.78%
39.89%
38.76%
37.21%
10.48%
14.54%
2.39%
5.62%
7.16%
5.59%
4.73%
2.14%
7.64%
8.38%
2.92%
14.92%
13.30%
5.34%
13.46%
33.17%
12.33%
1.25%
1.15%
1.21%
6.25%
4.48%
14.12%
13.39%
12.26%
8.49%
12.75%
21.44%
5.70%
8.77%
13.06%
5.85%
9.45%
8.22%
6.09%
7.19%Ahmednagar
Akola
Amravati
Aurangabad
Bhandara
Bid (Beed)
Buldhana
Chandrapur
Dhule
Gadchiroli
Gondia
Hingoli
Jalgaon
Jalna
Kolhapur
Latur
Mumbai
Mumbai Suburban
Nagpur
Nanded
Nandurbar
Nashik
Osmanabad
Palghar
Parbhani
Pune
Raigad
Ratnagiri
Sangli
Satara
Sindhudurg
Solapur
Thane
Wardha
Washim
Yavatmal
District
–––
MAHARASHTRA MPI: PROGRESS REVIEW 2023
158

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Manipur
MANIPUR
Overview
Manipur's Headcount Ratio, Intensity and MPI
Manipur: Indicator Contribution to the MPI
Percentage contribution of each indicator to Manipur's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
8.10%2019-21 0.03441.91%
16.96%2015-16 0.07644.61%
Rural
Headcount Ratio Intensity MPI
0.04610.95% 42.20%
Urban
Headcount Ratio Intensity MPI
0.0143.43% 40.42%
0.10122.33% 45.11% 0.0368.49% 42.51%
Multidimensional Poverty in Manipur's Rural and Urban Areas
2019-21
2015-16
Year
Nutrition: 27.86%
Child & Adolescent Mortality: 1.03%
Maternal Health: 10.98%
Years of Schooling: 10.11%
School Attendance: 3.79%
Cooking Fuel: 9.84%
Sanitation: 6.90%
Drinking Water: 7.29%
Electricity: 2.15%
Housing: 10.29%
Assets: 4.31%
Bank Account: 5.44%
Nutrition: 29.35%
Child & Adolescent Mortality: 1.15%
Maternal Health: 11.49%
Years of Schooling: 13.45%
School Attendance: 4.76%
Cooking Fuel: 8.71%
Sanitation: 5.26%
Drinking Water: 7.11%
Electricity: 0.98%
Housing: 10.74%
Assets: 5.42%
Bank Account: 1.57%
160

Percentage of total population who are deprived in each indicator
Manipur: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Manipur: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
17.87%
23.57%
1.66%
1.80%
12.26%
17.66%
4.59%
5.35%
2.33%
2.36%
28.75%
58.92%
35.23%
47.54%
26.77%
38.50%
1.94%
7.31%
75.50%
81.49%
12.63%
13.92%
4.04%
21.53%
5.98%
12.65%
0.47%
0.93%
4.68%
9.97%
2.74%
4.59%
0.97%
1.72%
6.21%
15.64%
3.75%
10.97%
5.07%
11.59%
0.70%
3.42%
7.66%
16.36%
3.87%
6.85%
1.12%
8.64%
161
MANIPURMPI: PROGRESS REVIEW 2023

Manipur
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Manipur for 2019-21.
Up to 0.018 0.019 to 0.028 0.029 to 0.039 0.040 to 0.049 0.050 to 0.060 0.061 to 0.070 0.071 and above
MANIPUR MPI: PROGRESS REVIEW 2023
162

Manipur
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Manipur
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Manipur, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.050 0.051 to 0.071 0.072 to 0.092 0.093 to 0.114 0.115 to 0.135 0.136 to 0.156 0.157 and above
163
MANIPURMPI: PROGRESS REVIEW 2023

NFHS-5 (2019-21) NFHS-4 (2015-16)
MANIPUR MPI: PROGRESS REVIEW 2023
164
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
37.38%
18.50%
28.52%
17.87%
33.58%
15.60%
20.86%
15.35%
26.89%
14.74%
16.74%
7.46%
13.72%
6.91%
12.87%
5.19%
7.27%
2.12%
Manipur: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
Tamenglong
Ukhrul
Senapati
Churachandpur
Chandel
Thoubal
Bishnupur
Imphal East
Imphal West
District

165
MANIPURMPI: PROGRESS REVIEW 2023
-20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-5.15
-5.52
-6.81
-7.68
-9.28
-10.65
-12.15
-17.98
-18.88
Manipur: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
% point change in proportion of multidimensionally poor population
Tamenglong
Ukhrul
Senapati
Churachandpur
Chandel
Thoubal
Bishnupur
Imphal East
Imphal West
District

Intensity MPI Intensity MPI
0.133
0.072
0.179
0.154
0.029
0.057
0.099
0.123
0.056
46.71%
42.74%
47.84%
45.78%
40.24%
44.26%
47.47%
45.55%
41.02%
28.52%
16.74%
37.38%
33.58%
7.27%
12.87%
20.86%
26.89%
13.72%
0.075
0.030
0.082
0.068
0.008
0.022
0.065
0.061
0.030
42.12%
39.86%
44.20%
43.30%
38.63%
42.35%
42.49%
41.45%
42.98%
17.87%
7.46%
18.50%
15.60%
2.12%
5.19%
15.35%
14.74%
6.91%
Headcount Ratio Headcount Ratio
Manipur: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)
Bishnupur
Chandel
Churachandpur
Imphal East
Imphal West
Senapati
Tamenglong
Thoubal
Ukhrul
MANIPUR MPI: PROGRESS REVIEW 2023
166
District

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Meghalaya
MEGHALAYA
Overview
Meghalaya's Headcount Ratio, Intensity and MPI
Meghalaya: Indicator Contribution to the MPI
Percentage contribution of each indicator to Meghalaya's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
27.79%2019-21 0.13348.01%
32.54%2015-16 0.15648.08%
Rural
Headcount Ratio Intensity MPI
0.15632.43% 48.17%
Urban
Headcount Ratio Intensity MPI
0.0378.14% 45.40%
0.18638.49% 48.39% 0.0368.41% 42.43%
Multidimensional Poverty in Meghalaya's Rural and Urban Areas
168
2019-21
2015-16
Year
Nutrition: 25.29%
Child & Adolescent Mortality: 1.12%
Maternal Health: 11.95%
Years of Schooling: 17.75%
School Attendance: 5.66%
Cooking Fuel: 9.65%
Sanitation: 5.64%
Drinking Water: 4.07%
Electricity: 1.95%
Housing: 7.09%
Assets: 5.89%
Bank Account: 3.94%
Nutrition: 27.05%
Child & Adolescent Mortality: 1.42%
Maternal Health: 12.75%
Years of Schooling: 17.27%
School Attendance: 7.96%
Cooking Fuel: 9.44%
Sanitation: 2.63%
Drinking Water: 3.39%
Electricity: 2.07%
Housing: 7.17%
Assets: 7.17%
Bank Account: 1.69%

Percentage of total population who are deprived in each indicator
Meghalaya: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Meghalaya: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
169
MEGHALAYAMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
34.72%
37.05%
2.99%
3.10%
31.39%
31.70%
16.70%
19.71%
7.41%
6.15%
67.63%
77.08%
17.10%
38.56%
23.10%
31.77%
8.24%
8.18%
53.40%
50.40%
37.07%
29.88%
9.01%
19.91%
21.66%
23.74%
2.27%
2.11%
20.42%
22.43%
13.83%
16.66%
6.38%
5.32%
26.46%
31.70%
7.37%
18.53%
9.50%
13.36%
5.79%
6.41%
20.09%
23.30%
20.08%
19.35%
4.74%
12.94%

Meghalaya
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Meghalaya for 2019-21.
Up to 0.068 0.069 to 0.103 0.104 to 0.139 0.140 to 0.174 0.175 to 0.209 0.210 to 0.245 0.246 and above
MEGHALAYA MPI: PROGRESS REVIEW 2023
170

Meghalaya
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Meghalaya
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Meghalaya, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.074 0.075 to 0.102 0.103 to 0.129 0.130 to 0.157 0.158 to 0.184 0.185 to 0.212 0.213 and above
171
MEGHALAYAMPI: PROGRESS REVIEW 2023

NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%
39.59%
52.48%
52.08%
46.07%
43.79%
40.98%
46.31%
31.67%
23.39%
24.10%
18.27%
41.78%
14.96%
13.26%
11.27%
9.77%
27.29%
8.00%
Meghalaya: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
West Khasi Hills
South West Khasi Hills
South West Garo Hills
East Garo Hills
North Garo Hills
South Garo Hills
West Garo Hills
East Khasi Hills
Ri Bhoi
West Jaintia Hills
East Jaintia Hills
District
MEGHALAYA MPI: PROGRESS REVIEW 2023
172

-16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0
0.71
-1.50
-14.64
Meghalaya: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
% point change in proportion of multidimensionally poor population
Ri Bhoi
South Garo Hills
East Khasi Hills
District
173
MEGHALAYAMPI: PROGRESS REVIEW 2023

0.185
0.128
0.047
0.231
0.108
0.241
0.200
46.67%
46.74%
42.05%
49.83%
46.28%
52.24%
47.83%
39.59%
27.29%
11.27%
46.31%
23.39%
46.07%
41.78%
0.252
0.281
0.034
0.183
0.081
0.041
0.152
0.058
0.112
0.220
0.067
48.07%
53.97%
42.28%
44.62%
44.40%
41.69%
47.93%
43.81%
46.34%
50.20%
44.72%
52.48%
52.08%
8.00%
40.98%
18.27%
9.77%
31.67%
13.26%
24.10%
43.79%
14.96%
Meghalaya: Overview of Districts
Headcount Ratio, Intensity and MPI
East Garo Hills
East Jaintia Hills
East Khasi Hills
North Garo Hills
Ri Bhoi
South Garo Hills
South West Garo Hills
South West Khasi Hills
West Garo Hills
West Jaintia Hills
West Khasi Hills
District
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
–––
–––
–––
–––
MEGHALAYA MPI: PROGRESS REVIEW 2023
174

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Mizoram
MIZORAM
Overview
Mizoram's Headcount Ratio, Intensity and MPI
Mizoram: Indicator Contribution to the MPI
Percentage contribution of each indicator to Mizoram's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
5.30%2019-21 0.02445.62%
9.78%2015-16 0.04647.42%
Rural
Headcount Ratio Intensity MPI
0.04910.77% 45.86%
Urban
Headcount Ratio Intensity MPI
0.0020.58% 41.68%
0.09820.45% 47.95% 0.0061.40% 41.39%
Multidimensional Poverty in Mizoram's Rural and Urban Areas
176
2019-21
2015-16
Year
Nutrition: 23.29%
Child & Adolescent Mortality: 0.93%
Maternal Health: 11.80%
Years of Schooling: 21.00%
School Attendance: 9.62%
Cooking Fuel: 8.26%
Sanitation: 2.80%
Drinking Water: 3.15%
Electricity: 2.04%
Housing: 8.76%
Assets: 7.38%
Bank Account: 0.97%
Nutrition: 22.28%
Child & Adolescent Mortality: 1.13%
Maternal Health: 10.74%
Years of Schooling: 19.59%
School Attendance: 8.29%
Cooking Fuel: 8.90%
Sanitation: 5.82%
Drinking Water: 2.85%
Electricity: 3.06%
Housing: 7.79%
Assets: 6.81%
Bank Account: 2.74%

Percentage of total population who are deprived in each indicator
Mizoram: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Mizoram: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
177
MIZORAMMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
15.63%
21.38%
0.93%
2.30%
11.32%
16.11%
6.79%
7.92%
2.50%
3.75%
17.06%
32.17%
4.66%
15.81%
4.82%
7.79%
1.92%
4.08%
30.70%
24.18%
12.35%
13.94%
3.30%
5.81%
3.38%
6.20%
0.27%
0.63%
3.42%
5.97%
3.05%
5.45%
1.40%
2.31%
4.19%
8.66%
1.42%
5.67%
1.60%
2.78%
1.03%
2.98%
4.45%
7.59%
3.75%
6.63%
0.49%
2.67%

Mizoram
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Mizoram for 2019-21.
Up to 0.017 0.018 to 0.032 0.033 to 0.046 0.047 to 0.061 0.062 to 0.075 0.076 to 0.089 0.090 and above
MIZORAM MPI: PROGRESS REVIEW 2023
178

Mizoram
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Mizoram
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Mizoram, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.028 0.029 to 0.0500.051 to 0.0720.073 to 0.0940.095 to 0.1160.117 to 0.138 0.139 and above
179
MIZORAMMPI: PROGRESS REVIEW 2023

NFHS-5 (2019-21) NFHS-4 (2015-16)
MIZORAM MPI: PROGRESS REVIEW 2023
180
30.45%
21.63%
25.29%
9.29%
12.69%
6.90%
8.44%
5.25%
10.16%
5.15%
3.45%
3.30%
10.09%
1.51%
1.76%
0.87%
Lawngtlai
Mamit
Saiha
Kolasib
Lunglei
Serchhip
Champhai
Aizawl
Mizoram: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
District
2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0% 22.0% 24.0% 26.0% 28.0% 30.0% 32.0%0.0%

-18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-0.14
-0.89
-3.19
-5.01
-5.79
-8.57
-8.82
-16.00
Mizoram: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
% point change in proportion of multidimensionally poor population
Mamit
Saiha
Lawngtlai
Champhai
Lunglei
Aizawl
Kolasib
Serchhip
District
181
MIZORAMMPI: PROGRESS REVIEW 2023

0.014
0.054
0.128
0.045
0.161
0.041
0.040
0.007
40.24%
42.21%
50.58%
43.92%
52.72%
48.69%
39.83%
39.01%
3.45%
12.69%
25.29%
10.16%
30.45%
8.44%
10.09%
1.76%
0.013
0.029
0.041
0.022
0.105
0.024
0.006
0.004
38.74%
41.45%
44.55%
42.98%
48.46%
46.34%
42.04%
42.85%
3.30%
6.90%
9.29%
5.15%
21.63%
5.25%
1.51%
0.87%
Mizoram: Overview of Districts
Headcount Ratio, Intensity and MPI
Aizawl
Champhai
Kolasib
Lawngtlai
Lunglei
Mamit
Saiha
Serchhip
District
MIZORAM MPI: PROGRESS REVIEW 2023
182
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015–16) NFHS-5 (2019–21)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Nagaland
NAGALAND
Overview
Nagaland's Headcount Ratio, Intensity and MPI
Nagaland: Indicator Contribution to the MPI
Percentage contribution of each indicator to Nagaland's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
15.43%2019-21 0.06642.61%
25.16%2015-16 0.11646.29%
Rural
Headcount Ratio Intensity MPI
0.08519.88% 42.67%
Urban
Headcount Ratio Intensity MPI
0.0266.14% 42.20%
0.15332.73% 46.65% 0.04710.70% 44.23%
Multidimensional Poverty in Nagaland's Rural and Urban Areas
Nutrition: 27.32%
Child & Adolescent Mortality: 0.94%
Child & Adolescent Mortality: 0.98%
Maternal Health: 14.48%
Years of Schooling: 14.96%
School Attendance: 6.59%
Cooking Fuel: 10.30%
Sanitation: 2.43%
Drinking Water: 2.32%
Electricity: 0.51%
Housing: 10.47%
Assets: 7.24%
Bank Account: 2.43%
Nutrition: 24.52%
Maternal Health: 13.09%
Years of Schooling: 16.11%
School Attendance: 5.26%
Cooking Fuel: 9.77%
Sanitation: 3.52%
Drinking Water: 2.72%
Electricity: 1.02%
Housing: 9.77%
Assets: 6.79%
Bank Account: 6.45%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
184

Percentage of total population who are deprived in each indicator
Nagaland: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Nagaland: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
185
NAGALANDMPI: PROGRESS REVIEW 2023
20.61%
24.49%
1.42%
2.06%
22.15%
33.05%
10.49%
13.61%
4.45%
4.81%
56.48%
69.28%
12.24%
23.18%
10.47%
19.26%
1.46%
3.25%
64.60%
70.97%
29.53%
33.90%
7.04%
28.67%
10.78%
17.14%
0.74%
1.37%
11.42%
18.29%
5.90%
11.26%
2.60%
3.67%
14.21%
23.91%
3.36%
8.62%
3.20%
6.65%
0.71%
2.49%
14.46%
23.91%
10.00%
16.62%
3.36%
15.77%

Nagaland
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Nagaland for 2019-21.
Up to 0.038 0.039 to 0.0530.054 to 0.0680.069 to 0.0820.083 to 0.0970.098 to 0.111 0.112 and above
NAGALAND MPI: PROGRESS REVIEW 2023
186

Nagaland
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Nagaland
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Ngaland, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.058 0.059 to 0.0860.087 to 0.1130.114 to 0.1410.142 to 0.1680.169 to 0.196 0.197 and above
187
NAGALANDMPI: PROGRESS REVIEW 2023

Nagaland: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NAGALAND MPI: PROGRESS REVIEW 2023
188
% of population who are multidimensionally poor
District
Tuensang
Kiphire
Longleng
Mon
Zunheboto
Peren
Wokha
Phek
Dimapur
Mokokchung
Kohima
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
38.33%
29.21%
37.33%
28.19%
33.88%
26.90%
45.56%
22.95%
23.61%
20.31%
24.58%
17.46%
27.25%
17.28%
15.35%
11.99%
17.33%
7.23%
7.92%
7.22%
11.03%
6.50%
NFHS-5 (2019-21) NFHS-4 (2015-16)

Nagaland: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Mon
Dimapur
Phek
Kiphire
Tuensang
Peren
Longleng
Kohima
Wokha
Zunheboto
Mokokchung
-24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-0.70
-3.30
-3.37
-4.53
-6.99
-7.12
-9.12
-9.14
-9.97
-10.09
-22.61
% point change in proportion of multidimensionally poor population
189
NAGALANDMPI: PROGRESS REVIEW 2023

Nagaland: Overview of Districts
Headcount Ratio, Intensity and MPI
0.101
0.065
0.179
0.116
0.115
0.224
0.032
0.151
0.045
0.165
0.086
42.81%
42.39%
46.62%
42.52%
46.61%
49.23%
39.89%
44.65%
41.15%
44.32%
49.50%
23.61%
15.35%
38.33%
27.25%
24.58%
45.56%
7.92%
33.88%
11.03%
37.33%
17.33%
0.086
0.047
0.126
0.072
0.078
0.098
0.028
0.118
0.025
0.127
0.032
42.21%
39.13%
43.26%
41.55%
44.55%
42.77%
39.45%
43.91%
38.21%
44.97%
43.98%
20.31%
11.99%
29.21%
17.28%
17.46%
22.95%
7.22%
26.90%
6.50%
28.19%
7.23%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Dimapur
Kiphire
Kohima
Longleng
Mokokchung
Mon
Peren
Phek
Tuensang
Wokha
Zunheboto
District
NAGALAND MPI: PROGRESS REVIEW 2023
190

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Odisha
ODISHA
Overview
Odisha's Headcount Ratio, Intensity and MPI
Odisha: Indicator Contribution to the MPI
Percentage contribution of each indicator to Odisha's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
15.68%2019-21 0.07044.50%
29.34%2015-16 0.13646.42%
Rural
Headcount Ratio Intensity MPI
0.07917.72% 44.58%
Urban
Headcount Ratio Intensity MPI
0.0235.42% 43.15%
0.15232.64% 46.44% 0.05712.32% 46.11%
Multidimensional Poverty in Odisha's Rural and Urban Areas
Nutrition: 29.37%
Child & Adolescent Mortality: 1.02%
Child & Adolescent Mortality: 0.92%
Maternal Health: 8.59%
Years of Schooling: 19.79%
School Attendance: 6.32%
Cooking Fuel: 10.18%
Sanitation: 7.56%
Drinking Water: 2.78%
Electricity: 1.26%
Housing: 8.26%
Assets: 4.31%
Bank Account: 0.57%
Nutrition: 27.42%
Maternal Health: 7.82%
Years of Schooling: 16.85%
School Attendance: 5.29%
Cooking Fuel: 10.06%
Sanitation: 9.48%
Drinking Water: 3.43%
Electricity: 3.12%
Housing: 8.69%
Assets: 4.65%
Bank Account: 2.27%
192
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account

Percentage of total population who are deprived in each indicator
Odisha: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Odisha: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
193
ODISHAMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
30.77%
37.27%
1.57%
2.23%
14.83%
19.49%
13.44%
16.64%
3.92%
4.95%
65.94%
80.94%
39.85%
70.32%
13.55%
20.61%
3.04%
13.36%
40.70%
55.80%
12.30%
19.22%
2.53%
10.94%
12.30%
22.41%
0.85%
1.51%
7.19%
12.77%
8.29%
13.77%
2.65%
4.32%
14.91%
28.76%
11.08%
27.11%
4.08%
9.82%
1.85%
8.93%
12.10%
24.86%
6.31%
13.30%
0.84%
6.49%

Odisha
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Odisha for 2019-21.
Up to 0.042 0.043 to 0.0720.073 to 0.1020.103 to 0.1320.133 to 0.1620.163 to 0.192 0.193 and above
ODISHA MPI: PROGRESS REVIEW 2023
194

Odisha
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Odisha
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Odisha, based on values for 2015-16. Both
the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.083 0.084 to 0.1200.121 to 0.1580.159 to 0.1950.196 to 0.2330.234 to 0.270 0.271 and above
195
ODISHAMPI: PROGRESS REVIEW 2023

Odisha: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
ODISHA MPI: PROGRESS REVIEW 2023
196
District
0.0%5.0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%55.0%60.0%65.0%
58.66%
45.01%
48.14%
34.03%
51.14%
33.54%
59.32%
33.45%
45.06%
30.57%
38.76%
28.14%
41.69%
26.76%
44.75%
25.30%
37.80%
20.19%
47.28%
19.47%
28.32%
16.60%
37.10%
16.56%
33.03%
16.27%
29.98%
15.98%
24.76%
14.77%
24.43%
14.21%
20.75%
14.10%
24.57%
13.87%
24.77%
11.51%
24.37%
10.05%
27.49%
9.52%
21.82%
8.90%
28.05%
8.68%
18.41%
7.09%
20.49%
6.63%
21.88%
6.31%
14.97%
6.31%
15.50%
3.95%
11.83%
3.53%
11.64%
3.29%
Malkangiri
Rayagada
Koraput
Nabarangpur
Mayurbhanj
Gajapati
Kendujhar
Kandhamal
Nuapada
Kalahandi
Bhadrak
Deogarh
Boudh
Dhenkanal
Sundargarh
Baleshwar
Jajapur
Anugul
Bargarh
Sambalpur
Balangir
Kendrapara
Sonepur
Jharsuguda
Nayagarh
Ganjam
Cuttack
Khordha
Jagatsinghapur
Puri
% of population who are multidimensionally poor

Odisha: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Kalahandi
Nabarangpur
Deogarh
Kandhamal
Sonepur
Balangir
Nuapada
Koraput
Boudh
Ganjam
Kendujhar
Mayurbhanj
Sambalpur
Rayagada
Dhenkanal
Nayagarh
Malkangiri
Bargarh
Kendrapara
Bhadrak
Khordha
Jharsuguda
Anugul
Gajapati
Baleshwar
Sundargarh
Cuttack
Puri
Jagatsinghapur
Jajapur
-30.0-28.0-26.0-24.0-22.0-20.0-18.0-16.0-14.0-12.0-10.0-8.0 -6.0 -4.0 -2.0 0.0
-6.65
-8.30
-8.35
-8.66
-9.99
-10.22
-10.62
-10.70
-11.32
-11.55
-11.72
-12.92
-13.27
-13.65
-13.86
-14.00
-14.10
-14.32
-14.50
-14.93
-15.57
-16.76
-17.59
-17.61
-17.97
-19.37
-19.45
-20.55
-25.87
-27.81
% point change in proportion of multidimensionally poor population
197
ODISHAMPI: PROGRESS REVIEW 2023

0.112
0.116
0.105
0.244
0.046
0.173
0.091
0.302
0.211
0.309
0.265
0.069
0.209
0.092
0.210
0.226
0.079
0.092
0.049
0.098
0.183
0.133
0.177
0.065
0.123
0.145
0.106
0.110
0.124
0.107
45.29%
41.47%
43.10%
50.78%
39.56%
45.67%
44.42%
50.87%
46.90%
52.64%
51.77%
44.75%
50.25%
42.20%
46.99%
47.86%
42.67%
44.12%
41.38%
44.92%
47.24%
44.52%
47.60%
43.12%
43.39%
43.89%
42.96%
44.85%
45.11%
43.44%
24.76%
28.05%
24.37%
48.14%
11.64%
37.80%
20.49%
59.32%
45.06%
58.66%
51.14%
15.50%
41.69%
21.82%
44.75%
47.28%
18.41%
20.75%
11.83%
21.88%
38.76%
29.98%
37.10%
14.97%
28.32%
33.03%
24.77%
24.43%
27.49%
24.57%
0.062
0.035
0.041
0.165
0.013
0.087
0.028
0.157
0.140
0.223
0.157
0.017
0.128
0.036
0.113
0.083
0.028
0.060
0.015
0.029
0.128
0.068
0.070
0.025
0.066
0.068
0.045
0.061
0.040
0.061
42.13%
40.36%
40.90%
48.42%
40.47%
43.17%
42.60%
46.92%
45.73%
49.50%
46.90%
42.55%
48.01%
40.78%
44.54%
42.44%
39.00%
42.79%
41.60%
45.35%
45.60%
42.82%
42.41%
39.26%
39.80%
41.88%
39.29%
43.04%
42.01%
43.94%
14.77%
8.68%
10.05%
34.03%
3.29%
20.19%
6.63%
33.45%
30.57%
45.01%
33.54%
3.95%
26.76%
8.90%
25.30%
19.47%
7.09%
14.10%
3.53%
6.31%
28.14%
15.98%
16.56%
6.31%
16.60%
16.27%
11.51%
14.21%
9.52%
13.87%
Odisha: Overview of Districts
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Anugul
Balangir
Baleshwar
Bargarh
Baudh (Boudh)
Bhadrak
Cuttack
Deogarh
Dhenkanal
Gajapati
Ganjam
Jagatsinghapur
Jajapur
Jharsuguda
Kalahandi
Kandhamal
Kendrapara
Kendujhar
Khordha
Koraput
Malkangiri
Mayurbhanj
Nabarangpur
Nayagarh
Nuapada
Puri
Rayagada
Sambalpur
Sonepur
Sundargarh
District
ODISHA MPI: PROGRESS REVIEW 2023
198

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Punjab
PUNJAB
Overview
Punjab's Headcount Ratio, Intensity and MPI
Punjab: Indicator Contribution to the MPI
Percentage contribution of each indicator to Punjab's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
4.75%2019-21 0.02041.22%
5.57%2015-16 0.02443.74%
Rural
Headcount Ratio Intensity MPI
0.0204.74% 41.19%
Urban
Headcount Ratio Intensity MPI
0.0204.76% 41.27%
0.0286.38% 43.21% 0.0194.32% 44.95%
Multidimensional Poverty in Punjab's Rural and Urban Areas
Nutrition: 31.46%
Child & Adolescent Mortality: 1.93%
Child & Adolescent Mortality: 1.72%
Maternal Health: 11.32%
Years of Schooling: 22.65%
School Attendance: 10.15%
Cooking Fuel: 6.68%
Sanitation: 5.40%
Drinking Water: 0.89%
Electricity: 0.19%
Housing: 6.62%
Assets: 1.41%
Bank Account: 1.30%
Nutrition: 30.13%
Maternal Health: 10.53%
Years of Schooling: 23.24%
School Attendance: 9.63%
Cooking Fuel: 8.25%
Sanitation: 5.88%
Drinking Water: 0.56%
Electricity: 0.43%
Housing: 6.47%
Assets: 1.16%
Bank Account: 2.01%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
200

Percentage of total population who are deprived in each indicator
Punjab: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Punjab: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
201
PUNJABMPI: PROGRESS REVIEW 2023
20.80%
22.11%
1.32%
1.39%
14.24%
12.70%
6.72%
7.28%
2.77%
2.59%
25.33%
36.40%
13.69%
17.28%
1.84%
1.54%
0.34%
0.39%
21.96%
19.30%
1.60%
1.72%
3.88%
3.71%3.70%
4.41%
0.45%
0.50%
2.66%
3.08%
2.66%
3.40%
1.19%
1.41%
2.75%
4.23%
2.22%
3.01%
0.37%
0.29%
0.08%
0.22%
2.72%
3.31%
0.58%
0.59%
0.53%
1.03%

Punjab
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Punjab for 2019-21.
Up to 0.009 0.010 to 0.0130.014 to 0.0180.019 to 0.0220.023 to 0.0270.028 to 0.031 0.032 and above
PUNJAB MPI: PROGRESS REVIEW 2023
202

Punjab
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Punjab
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Punjab, based on values for 2015-16. Both
the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.0120.013 to 0.0170.018 to 0.0220.023 to 0.0270.028 to 0.0310.032 to 0.036 0.037 and above
203
PUNJABMPI: PROGRESS REVIEW 2023

Punjab: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
PUNJAB MPI: PROGRESS REVIEW 2023
204
District
% of population who are multidimensionally poor
0.0%1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%11.0%
8.45%
9.42%
8.42%
5.62%
8.03%
8.32%
7.83%
9.99%
7.80%
7.71%
7.57%
2.96%
6.04%
8.01%
5.65%
5.81%
5.49%
5.11%
5.08%
5.08%
5.02%
3.83%
4.59%
3.26%
3.58%
7.12%
3.53%
7.42%
3.37%
5.05%
3.35%
3.49%
3.10%
3.75%
2.95%
2.01%
2.88%
2.35%
4.49%
1.50%
3.56%
1.31%
Fazilka
Ferozepur
Bathinda
Tarn Taran
Mansa
Sri Muktsar Sahib
Faridkot
Moga
Barnala
Gurdaspur
Kapurthala
Ludhiana
Jalandhar
Sangrur
Amritsar
S.A.S Nagar
Fatehgarh Sahib
Patiala
Rupnagar
Pathankot
Hoshiarpur
Shahid Bhagat
Singh Nagar
NFHS-5 (2019-21) NFHS-4 (2015-16)

Punjab: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
% point change in proportion of multidimensionally poor population
-4.5%-4.0-3.5-3.0-2.5-2.0-1.5-1.0-0.50.0 0.51.0 1.5 2.0 2.5 3.0 3.5
3.09
2.41
0.87
0.75
0.33
-0.05
-0.14
-0.32
-0.38
-0.49
-0.80
-1.70
-2.20
-2.25
-2.37
-2.99
-3.59
-4.05Amritsar
Sangrur
Hoshiarpur
Moga
Mansa
Patiala
Tarn Taran
Fatehgarh Sahib
Barnala
Sri Muktsar Sahib
Kapurthala
Jalandhar
Ludhiana
Rupnagar
Bathinda
Faridkot
Shahid Bhagat
Singh Nagar
S.A.S. Nagar
205
PUNJABMPI: PROGRESS REVIEW 2023

0.038
0.014
0.031
0.024
0.009
0.016
0.034
0.034
0.042
0.017
0.024
0.013
0.020
0.022
0.041
0.015
0.013
0.025
0.025
0.033
45.30%
39.56%
43.20%
48.42%
42.76%
41.82%
44.55%
42.41%
41.91%
45.35%
47.86%
39.15%
44.97%
43.68%
43.22%
43.61%
42.66%
43.77%
43.39%
44.92%
8.32%
3.56%
7.12%
5.05%
2.01%
3.75%
7.71%
8.01%
9.99%
3.83%
5.08%
3.26%
4.49%
5.11%
9.42%
3.49%
2.96%
5.62%
5.81%
7.42%
0.032
0.005
0.014
0.014
0.012
0.012
0.009
0.032
0.022
0.035
0.017
0.021
0.015
0.006
0.022
0.037
0.036
0.012
0.025
0.033
0.023
0.014
41.44%
39.41%
38.61%
40.83%
42.49%
40.18%
37.21%
42.61%
39.43%
44.44%
37.78%
41.90%
42.47%
40.33%
43.35%
43.38%
42.80%
38.90%
41.93%
40.59%
42.66%
40.75%
7.83%
1.31%
3.53%
3.35%
2.88%
2.95%
2.35%
7.57%
5.65%
7.80%
4.59%
5.02%
3.58%
1.50%
5.08%
8.42%
8.45%
3.10%
6.04%
8.03%
5.49%
3.37%
Punjab: Overview of Districts
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Amritsar
Barnala
Bathinda
Faridkot
Fatehgarh Sahib
Fazilka
Ferozepur
Gurdaspur
Hoshiarpur
Jalandhar
Kapurthala
Ludhiana
Mansa
Moga
Pathankot
Patiala
Rupnagar
Sangrur
Shahid Bhagat Singh
Tarn Taran
Nagar
District
–––
–––
PUNJAB MPI: PROGRESS REVIEW 2023
206
Muktsar
(Sri Muktsar Sahib)
Sahibzada Ajit Singh
Nagar (S.A.S Nagar)

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Rajasthan
RAJASTHAN
Overview
Rajasthan's Headcount Ratio, Intensity and MPI
Rajasthan: Indicator Contribution to the MPI
Percentage contribution of each indicator to Rajasthan's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
15.31%2019-21 0.06542.70%
28.86%2015-16 0.13747.34%
Rural
Headcount Ratio Intensity MPI
0.08018.62% 42.80%
Urban
Headcount Ratio Intensity MPI
0.0194.54% 41.39%
0.16434.53% 47.60% 0.05011.21% 44.79%
Multidimensional Poverty in Rajasthan's Rural and Urban Areas
Nutrition: 31.12%
Child & Adolescent Mortality: 1.49%
Child & Adolescent Mortality: 1.26%
Maternal Health: 11.94%
Years of Schooling: 15.72%
School Attendance: 7.31%
Cooking Fuel: 10.02%
Sanitation: 6.63%
Drinking Water: 2.61%
Electricity: 0.83%
Housing: 8.39%
Assets: 3.49%
Bank Account: 0.43%
Nutrition: 27.88%
Maternal Health: 10.26%
Years of Schooling: 16.15%
School Attendance: 8.79%
Cooking Fuel: 9.47%
Sanitation: 8.51%
Drinking Water: 3.61%
Electricity: 2.28%
Housing: 6.46%
Assets: 4.57%
Bank Account: 0.76%
208
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account

Percentage of total population who are deprived in each indicator
Rajasthan: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Rajasthan: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
209
RAJASTHANMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
34.09%
42.62%
2.14%
2.95%
21.17%
26.33%
10.06%
17.09%
4.25%
8.48%
60.56%
69.94%
29.03%
53.90%
10.24%
19.18%
1.86%
8.73%
45.73%
35.55%
10.77%
20.50%
2.18%
4.03%
12.20%
22.85%
1.17%
2.07%
9.37%
16.82%
6.17%
13.24%
2.87%
7.21%
13.76%
27.17%
9.09%
24.40%
3.58%
10.36%
1.14%
6.54%
11.52%
18.53%
4.79%
13.12%
0.59%
2.19%

Rajasthan
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Rajasthan for 2019-21.
Up to 0.038 0.039 to 0.0540.055 to 0.0700.071 to 0.0850.086 to 0.1010.102 to 0.117 0.118 and above
RAJASTHAN MPI: PROGRESS REVIEW 2023
210

Rajasthan
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Rajasthan
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Rajasthan, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.087 0.088 to 0.1190.120 to 0.1510.152 to 0.1830.184 to 0.2160.217 to 0.248 0.249 and above
211
RAJASTHANMPI: PROGRESS REVIEW 2023

Rajasthan: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
% of population who are multidimensionally poor
District
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%60.0%
52.33%
29.68%
50.97%
27.45%
39.82%
26.65%
39.90%
26.25%
40.00%
23.88%
50.07%
22.36%
29.96%
20.93%
53.54%
20.64%
41.71%
19.62%
33.07%
18.88%
33.25%
18.66%
22.21%
17.70%
44.69%
17.44%
22.79%
17.32%
26.77%
17.02%
40.41%
17.02%
32.40%
16.29%
32.74%
16.07%
27.23%
14.93%
25.23%
14.72%
47.53%
14.45%
27.29%
14.32%
27.72%
13.11%
20.79%
13.08%
29.42%
12.96%
19.08%
10.08%
17.90%
9.87%
18.23%
9.43%
23.62%
9.41%
12.76%
9.32%
14.27%
9.09%
15.07%
7.40%
13.30%
5.59%
Pratapgarh
Banswara
Dholpur
Karauli
Bharatpur
Jaisalmer
Alwar
Barmer
Sirohi
Bundi
Sawai Madhopur
Bikaner
Dungarpur
Churu
Jodhpur
Jalore
Baran
Jhalawar
Bhilwara
Tonk
Udaipur
Dausa
Chittorgarh
Nagaur
Rajsamand
Hanumangarh
Ajmer
Ganganagar
Pali
Jhunjhunu
Sikar
Jaipur
Kota
NFHS-5 (2019-21) NFHS-4 (2015-16)
RAJASTHAN MPI: PROGRESS REVIEW 2023
212

Rajasthan: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Udaipur
Barmer
Jaisalmer
Dungarpur
Banswara
Jalore
Pratapgarh
Sirohi
Jhalawar
Rajsamand
Bharatpur
Baran
Chittorgarh
Sawai Madhopur
Pali
Bundi
Karauli
Dholpur
Dausa
Bhilwara
Tonk
Jodhpur
Alwar
Hanumangarh
Ganganagar
Ajmer
Nagaur
Kota
Jaipur
Churu
Sikar
Bikaner
Jhunjhunu
% point change in proportion of multidimensionally poor population
-36.0 -32.0 -28.0 -24.0 -20.0 -16.0 -12.0 -8.0 -4.0 0.0
-3.44
-4.51
-5.18
-5.47
-7.67
-7.70
-7.71
-8.02
-8.81
-9.01
-9.03
-9.74
-10.50
-12.30
-12.97
-13.16
-13.65
-14.19
-14.21
-14.59
-14.61
-16.11
-16.12
-16.46
-16.67
-22.09
-22.66
-23.39
-23.52
-27.25
-27.71
-32.90
-33.08
213
RAJASTHANMPI: PROGRESS REVIEW 2023

0.250
0.108
0.211
0.062
0.151
0.135
0.263
0.109
0.097
0.060
0.183
0.128
0.055
0.154
0.200
0.264
0.063
0.087
0.077
0.220
0.183
0.117
0.100
0.131
0.153
0.105
0.126
0.191
0.281
0.144
0.254
0.135
0.080
52.51%
42.82%
50.59%
43.30%
45.53%
45.91%
50.28%
46.04%
46.46%
45.23%
45.89%
47.77%
43.50%
47.04%
49.61%
52.72%
41.96%
45.46%
42.13%
49.31%
46.03%
42.89%
44.03%
47.31%
46.21%
47.25%
46.39%
47.73%
52.52%
44.50%
49.93%
45.16%
44.71%
47.53%
25.23%
41.71%
14.27%
33.25%
29.42%
52.33%
23.62%
20.79%
13.30%
39.90%
26.77%
12.76%
32.74%
40.41%
50.07%
15.07%
19.08%
18.23%
44.69%
39.82%
27.29%
22.79%
27.72%
33.07%
22.21%
27.23%
40.00%
53.54%
32.40%
50.97%
29.96%
17.90%
0.063
0.059
0.093
0.038
0.080
0.055
0.134
0.040
0.054
0.023
0.113
0.077
0.035
0.068
0.071
0.105
0.028
0.040
0.039
0.075
0.115
0.058
0.072
0.055
0.083
0.078
0.064
0.105
0.087
0.071
0.124
0.089
0.041
43.63%
40.27%
47.36%
41.70%
42.69%
42.17%
45.09%
42.24%
41.04%
41.61%
43.18%
45.07%
37.85%
42.26%
41.91%
46.84%
38.19%
40.13%
41.39%
42.74%
43.02%
40.21%
41.49%
42.18%
43.72%
44.05%
42.91%
43.87%
42.07%
43.80%
45.00%
42.76%
41.28%
14.45%
14.72%
19.62%
9.09%
18.66%
12.96%
29.68%
9.41%
13.08%
5.59%
26.25%
17.02%
9.32%
16.07%
17.02%
22.36%
7.40%
10.08%
9.43%
17.44%
26.65%
14.32%
17.32%
13.11%
18.88%
17.70%
14.93%
23.88%
20.64%
16.29%
27.45%
20.93%
9.87%
Rajasthan: Overview of Districts
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Ajmer
Alwar
Banswara
Baran
Barmer
Bharatpur
Bhilwara
Bikaner
Bundi
Chittorgarh
Churu
Dausa
Dholpur
Dungarpur
Ganganagar
Hanumangarh
Jaipur
Jaisalmer
Jalore
Jhalawar
Jhunjhunu
Jodhpur
Karauli
Kota
Nagaur
Pali
Pratapgarh
Rajsamand
Sawai Madhopur
Sikar
Sirohi
Tonk
Udaipur
District
RAJASTHAN MPI: PROGRESS REVIEW 2023
214

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Sikkim
SIKKIM
Overview
Sikkim's Headcount Ratio, Intensity and MPI
Sikkim: Indicator Contribution to the MPI
Percentage contribution of each indicator to Sikkim’s MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2.60%2019-21 0.01141.02%
3.82%2015-16 0.01641.20%
Rural
Headcount Ratio Intensity MPI
0.0153.75% 41.22%
Urban
Headcount Ratio Intensity MPI
0.0020.51% 38.44%
0.0184.25% 41.15% 0.0122.80% 41.36%
2019-21
2015-16
Multidimensional Poverty in Sikkim's Rural and Urban Areas
Nutrition: 30.44%
Child & Adolescent Mortality: 1.31%
Maternal Health: 9.28%
Years of Schooling: 26.33%
School Attendance: 3.79%
Cooking Fuel: 8.76%
Sanitation: 3.42%
Drinking Water: 0.57%
Electricity: 0.22%
Housing: 6.98%
Assets: 5.58%
Bank Account: 3.33%
Nutrition: 27.18%
Child & Adolescent Mortality: 0.99%
Maternal Health: 7.40%
Years of Schooling: 24.15%
School Attendance: 5.28%
Cooking Fuel: 8.97%
Sanitation: 3.29%
Drinking Water: 3.24%
Electricity: 1.15%
Housing: 7.98%
Assets: 8.00%
Bank Account: 2.36%
Year
216

Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
SanitationDrinking
Water
ElectricityHousing Assets Bank
Account
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
Percentage of total population who are deprived in each indicator
Sikkim: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Sikkim: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
SanitationDrinking
Water
ElectricityHousing Assets Bank
Account
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
1.74%
2.87%
0.13%
0.25%
0.95%
1.75%
1.55%
2.48%
0.34%
0.36%
2.01%
2.89%
0.74%
1.13%
0.73%
0.19%
0.26%
0.07%
1.79%
2.30%
1.80%
1.84%
0.53%
1.10%
10.36%
13.32%
0.26%
1.00%
6.72%
5.42%
8.59%
8.20%
1.15%
1.42%
24.50%
42.20%
12.71%
10.36%
7.84%
2.24%
0.77%
0.65%
24.15%
26.71%
14.42%
9.52%
5.99%
8.38%
217
SIKKIMMPI: PROGRESS REVIEW 2023

Sikkim
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
SIKKIM MPI: PROGRESS REVIEW 2023
218

Sikkim
Sikkim
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
219
SIKKIMMPI: PROGRESS REVIEW 2023
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Sikkim: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
Sikkim: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
NFHS-5 (2019-21) NFHS-4 (2015-16)
Mangan
Mangan
Namchi
Namchi
Gyalshing
Gyalshing
Gangtok
Gangtok
0.0%0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5%
% of population who are multidimensionally poor
5.17%
2.90%
2.77%
2.12%
4.47%
2.74%
4.66%
3.90%
-2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8-0.6-0.4 -0.2 0.0 0.2 0.4 0.6 0.81.0
% point change in proportion of multidimensionally poor population
District
District
-1.89
-1.78
0.16
0.70
SIKKIM MPI: PROGRESS REVIEW 2023
220

Sikkim: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Gangtok 3.90% 40.96% 0.016 2.12% 39.46% 0.008
Gyalshing 4.66% 42.39% 0.020 2.77% 40.83% 0.011
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
Mangan 4.47% 41.57% 0.019 5.17% 41.38% 0.021
Namchi 2.74% 39.94% 0.011 2.90% 43.27% 0.013
District
221
SIKKIMMPI: PROGRESS REVIEW 2023

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Tamil Nadu
TAMIL NADU
Overview
Tamil Nadu's Headcount Ratio, Intensity and MPI
Tamil Nadu: Indicator Contribution to the MPI
Percentage contribution of each indicator to Tamil Nadu's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2.20%2019-21 0.00938.70%
4.76%2015-16 0.01939.97%
Rural
Headcount Ratio Intensity MPI
0.0112.90% 38.84%
Urban
Headcount Ratio Intensity MPI
0.0051.41% 38.37%
0.0297.18% 40.21% 0.0092.37% 39.25%
Multidimensional Poverty in Tamil Nadu's Rural and Urban Areas
Nutrition: 27.24%
Child & Adolescent Mortality: 1.28%
Child & Adolescent Mortality: 1.33%
Maternal Health: 4.32%
Years of Schooling: 25.76%
School Attendance: 8.20%
Cooking Fuel: 7.71%
Sanitation: 9.02%
Drinking Water: 2.17%
Electricity: 1.34%
Housing: 6.38%
Assets: 4.63%
Bank Account: 1.97%
Nutrition: 31.02%
Maternal Health: 7.14%
Years of Schooling: 19.62%
School Attendance: 3.97%
Cooking Fuel: 8.94%
Sanitation: 11.05%
Drinking Water: 2.39%
Electricity: 1.07%
Housing: 6.45%
Assets: 3.36%
Bank Account: 3.65%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
222

Percentage of total population who are deprived in each indicator
Tamil Nadu: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Tamil Nadu: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
19.17%
24.77%
0.84%
1.15%
3.31%
6.70%
8.53%
6.61%
1.30%
1.03%
15.10%
24.06%
27.95%
47.55%
5.71%
6.05%
0.67%
0.97%
11.37%
20.17%
3.89%
3.38%
2.56%
6.35%1.39%
3.54%
0.13%
0.30%
0.44%
1.63%
1.32%
2.24%
0.42%
0.45%
1.38%
3.57%
1.61%
4.42%
0.39%
0.96%
0.24%
0.43%
1.14%
2.58%
0.83%
1.34%
0.35%
1.46%
223
TAMIL NADUMPI: PROGRESS REVIEW 2023

Tamil Nadu
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Tamil Nadu for 2019-21.
Up to 0.004 0.005 to 0.0060.007 to 0.0080.009 to 0.0110.012 to 0.0130.014 to 0.015 0.016 and above
TAMIL NADU MPI: PROGRESS REVIEW 2023
224

Tamil Nadu
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Tamil Nadu
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Tamil Nadu, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.009 0.010 to 0.0140.015 to 0.0200.021 to 0.0260.027 to 0.0310.032 to 0.037 0.038 and above
225
TAMIL NADUMPI: PROGRESS REVIEW 2023

Tamil Nadu: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0%1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%11.0%12.0%
11.14%
4.63%
4.16%
4.52%
8.64%
3.96%
6.28%
3.75%
9.29%
3.62%
8.71%
3.53%
8.18%
3.31%
4.40%
3.11%
7.99%
3.02%
3.73%
2.92%
6.79%
2.76%
7.90%
2.69%
6.53%
2.47%
7.23%
2.45%
5.92%
2.37%
4.90%
2.19%
5.26%
2.10%
5.52%
1.96%
6.56%
1.94%
3.11%
1.94%
4.76%
1.91%
5.82%
1.88%
7.61%
1.78%
2.73%
1.40%
2.59%
1.39%
2.03%
1.33%
2.97%
1.23%
2.53%
1.18%
3.16%
1.09%
0.93%
1.03%
2.29%
0.96%
1.52%
0.67%
% of population who are multidimensionally poor
District
Dindigul
Pudukkottai
Sivaganga
Cuddalore
Villupuram
Nagapattinam
Ariyalur
Karur
Virudhunagar
Tiruchirappalli
Thiruvarur
Tuticorin
Ramanathapuram
Thanjavur
Tiruvannamalai
Krishnagiri
Dharmapuri
Tirunelveli
Salem
Tiruppur
Theni
Madurai
Perambalur
Erode
Namakkal
The Nilgiris
Kanchipuram
Vellore
Thiruvallur
Chennai
Coimbatore
Kanniyakumari
TAMIL NADU MPI: PROGRESS REVIEW 2023
226

Tamil Nadu: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Pudukkottai
Perambalur
Villupuram
Tuticorin
Ariyalur
Virudhunagar
Nagapattinam
Thanjavur
Sivaganga
Salem
Ramanathapuram
Thiruvarur
Madurai
Tirunelveli
Tiruvannamalai
Dharmapuri
Theni
Krishnagiri
Cuddalore
Vellore
Kanchipuram
Thiruvallur
Coimbatore
Erode
Karur
Namakkal
Tiruppur
Kanniyakumari
Tiruchirappalli
The Nilgiris
Chennai
Dindigul
% point change in proportion of multidimensionally poor population
-7.0-6.5-6.0-5.5-5.0-4.5-4.0-3.5-3.0-2.5-2.0-1.5-1.0-0.50.0 0.5 1.0
0.37
0.10
-0.70
-0.81
-0.85
-1.17
-1.21
-1.29
-1.33
-1.34
-1.35
-1.74
-2.08
-2.53
-2.70
-2.85
-3.16
-3.55
-3.57
-3.94
-4.03
-4.06
-4.62
-4.69
-4.78
-4.87
-4.97
-5.18
-5.21
-5.66
-5.82
-6.51
227
TAMIL NADUMPI: PROGRESS REVIEW 2023

Tamil Nadu: Overview of Districts
Headcount Ratio, Intensity and MPI
0.031
0.038
0.012
0.024
0.012
0.022
0.014
0.032
0.027
0.010
0.019
0.008
0.028
0.033
0.029
0.027
0.044
0.030
0.011
0.033
0.023
0.020
0.018
0.005
0.011
0.012
0.016
0.021
0.025
0.009
0.004
0.034
39.26%
40.52%
37.63%
40.35%
38.99%
40.35%
38.06%
40.50%
40.41%
38.74%
39.91%
39.01%
38.16%
38.43%
44.73%
41.46%
39.10%
39.81%
41.35%
40.30%
39.20%
41.13%
39.86%
35.81%
38.76%
42.54%
38.87%
39.68%
39.61%
40.14%
41.86%
38.73%
7.99%
9.29%
3.16%
5.92%
3.11%
5.52%
3.73%
7.90%
6.79%
2.53%
4.76%
2.03%
7.23%
8.64%
6.56%
6.53%
11.14%
7.61%
2.59%
8.18%
5.82%
4.90%
4.40%
1.52%
2.97%
2.73%
4.16%
5.26%
6.28%
2.29%
0.93%
8.71%
0.011
0.015
0.004
0.010
0.007
0.008
0.011
0.011
0.011
0.005
0.007
0.005
0.010
0.015
0.007
0.009
0.018
0.007
0.005
0.013
0.007
0.009
0.012
0.002
0.005
0.005
0.018
0.008
0.014
0.004
0.004
0.014
36.62%
41.41%
35.99%
41.54%
37.22%
38.40%
37.87%
40.01%
38.27%
38.64%
36.39%
37.22%
40.21%
37.97%
38.60%
37.37%
38.30%
39.15%
36.02%
40.19%
38.23%
40.66%
37.61%
36.53%
38.86%
36.21%
40.47%
39.50%
38.26%
37.14%
36.62%
38.90%
3.02%
3.62%
1.09%
2.37%
1.94%
1.96%
2.92%
2.69%
2.76%
1.18%
1.91%
1.33%
2.45%
3.96%
1.94%
2.47%
4.63%
1.78%
1.39%
3.31%
1.88%
2.19%
3.11%
0.67%
1.23%
1.40%
4.52%
2.10%
3.75%
0.96%
1.03%
3.53%
District
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Ariyalur
Chennai
Coimbatore
Cuddalore
Dharmapuri
Dindigul
Erode
Kanchipuram
Kanniyakumari
Karur
Krishnagiri
Madurai
Nagapattinam
Namakkal
Perambalur
Pudukkottai
Ramanathapuram
Salem
Sivaganga
Thanjavur
The Nilgiris
Theni
Thiruvallur
Thiruvarur
Thoothukkudi (Tuticorin)
Tiruchirappalli
Tirunelveli
Tiruppur
Tiruvannamalai
Vellore
Villupuram
Virudhunagar
TAMIL NADU MPI: PROGRESS REVIEW 2023
228

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Telangana
TELANGANA
Overview
Telangana's Headcount Ratio, Intensity and MPI
Telangana: Indicator Contribution to the MPI
Percentage contribution of each indicator to Telangana's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
5.88%2019-21 0.02440.85%
13.18%2015-16 0.05743.29%
Rural
Headcount Ratio Intensity MPI
0.0317.51% 40.88%
Urban
Headcount Ratio Intensity MPI
0.0112.73% 40.70%
0.08519.51% 43.33% 0.0214.92% 43.06%
Multidimensional Poverty in Telangana's Rural and Urban Areas
Nutrition: 34.09%
Child & Adolescent Mortality: 1.64%
Child & Adolescent Mortality: 1.10%
Maternal Health: 9.14%
Years of Schooling: 26.71%
School Attendance: 5.14%
Cooking Fuel: 4.08%
Sanitation: 7.14%
Drinking Water: 1.00%
Electricity: 0.41%
Housing: 6.28%
Assets: 3.63%
Bank Account: 0.73%
Nutrition: 28.57%
Maternal Health: 7.23%
Years of Schooling: 24.22%
School Attendance: 3.32%
Cooking Fuel: 8.49%
Sanitation: 9.77%
Drinking Water: 2.74%
Electricity: 0.70%
Housing: 6.73%
Assets: 4.86%
Bank Account: 2.27%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
230

Percentage of total population who are deprived in each indicator
Telangana: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Telangana: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
28.35%
31.09%
1.15%
1.38%
13.17%
10.87%
14.56%
15.83%
1.35%
2.10%
7.93%
31.67%
24.41%
49.01%
3.36%
10.80%
0.44%
1.23%
20.49%
25.54%
8.51%
12.79%
2.74%
7.46%
4.91%
9.78%
0.47%
0.75%
2.64%
4.95%
3.85%
8.29%
0.74%
1.14%
2.06%
10.17%
3.60%
11.71%
0.50%
3.28%
0.21%
0.84%
3.17%
8.07%
1.83%
5.83%
0.37%
2.71%
231
TELANGANAMPI: PROGRESS REVIEW 2023

Telangana
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Telangana for 2019-21.
Up to 0.016 0.017 to 0.0250.026 to 0.0340.035 to 0.0430.044 to 0.0520.053 to 0.061 0.062 and above
TELANGANA MPI: PROGRESS REVIEW 2023
232

Telangana
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Telangana
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Telangana, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.032 0.033 to 0.0470.048 to 0.0620.063 to 0.0780.079 to 0.0930.094 to 0.108 0.109 and above
233
TELANGANAMPI: PROGRESS REVIEW 2023

Telangana: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
16.59%
15.37%
27.12%
14.24%
12.50%
11.90%
24.72%
10.27%
17.87%
9.34%
8.86%
7.14%
7.09%
21.06%
6.76%
6.45%
6.45%
6.33%
5.95%
4.77%
4.43%
13.35%
4.40%
4.33%
4.24%
3.89%
5.31%
3.83%
3.68%
3.39%
13.75%
3.18%
3.06%
2.91%
4.21%
2.52%
8.65%
2.50%
2.41%
2.17%
12.12%
% of population who are multidimensionally poor
District
Kumuram Bheem Asifabad
Jogulamba Gadwal
Adilabad
Vikarabad
Kamareddy
Mahabubnagar
Medak
Wanaparthy
Nirmal
Sangareddy
Nizamabad
Mahabubabad
Warangal Rural
Jayashankar Bhupalapally
Nagarkurnool
Jagitial
Mancherial
Nalgonda
Bhadradri Kothagudem
Yadadri Bhuvanagiri
Suryapet
Ranga Reddy
Rajanna Sircilla
Siddipet
Khammam
Medchal-Malkajgiri
Jangoan
Hyderabad
Karimnagar
Peddapalli
Warangal
Warangal Urban
20.0% .0%4.0%6.0%8.0%10.0%12.0%14.0%16.0%18.0%20.0%22.0%24.0%26.0%28.0%
TELANGANA MPI: PROGRESS REVIEW 2023
234

Telangana: Overview of Districts
Headcount Ratio, Intensity and MPI
0.049
0.022
0.094
0.059
0.076
0.108
0.058
0.036
0.017
0.125
40.49%
41.94%
44.51%
44.31%
42.46%
43.83%
42.23%
41.20%
40.98%
46.01%
12.12%
5.31%
21.06%
13.35%
17.87%
24.72%
13.75%
8.65%
4.21%
27.12%
0.016
0.010
0.025
0.038
0.050
0.015
0.013
0.030
0.016
0.015
0.008
0.028
0.030
0.017
0.024
0.012
0.037
0.018
0.041
0.025
0.071
0.014
0.010
0.049
0.063
0.027
0.011
0.021
0.009
0.017
0.064
37.19%
39.40%
38.95%
43.06%
39.75%
39.35%
38.29%
42.48%
41.66%
39.57%
37.36%
40.96%
41.69%
39.48%
41.11%
38.56%
39.49%
40.12%
40.35%
38.39%
42.97%
43.80%
39.98%
41.48%
41.24%
42.01%
37.82%
44.58%
36.17%
39.79%
44.82%
4.24%
2.41%
6.45%
8.86%
12.50%
3.89%
3.39%
7.09%
3.83%
3.68%
2.17%
6.76%
7.14%
4.40%
5.95%
3.06%
9.34%
4.43%
10.27%
6.45%
16.59%
3.18%
2.50%
11.90%
15.37%
6.33%
2.91%
4.77%
2.52%
4.33%
14.24%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Adilabad
Bhadradri Kothagudem
Hyderabad
Jagitial
Jangoan
Jayashankar Bhupalapally
Jogulamba Gadwal
Kamareddy
Karimnagar
Khammam
Kumuram Bheem Asifabad
Mahabubabad
Mahabubnagar
Mancherial
Medak
Medchal-Malkajgiri
Nagarkurnool
Nalgonda
Nirmal
Nizamabad
Peddapalli
Rajanna Sircilla
Ranga Reddy
Sangareddy
Siddipet
Suryapet
Vikarabad
Wanaparthy
Warangal
Warangal Rural
Warangal Urban
Yadadri Bhuvanagiri
District
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
–––
235
TELANGANAMPI: PROGRESS REVIEW 2023

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Tripura
TRIPURA
Overview
Tripura's Headcount Ratio, Intensity and MPI
Tripura: Indicator Contribution to the MPI
Percentage contribution of each indicator to Tripura's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
13.11%2019-21 0.05642.68%
16.62%2015-16 0.07545.03%
Rural
Headcount Ratio Intensity MPI
0.07116.47% 42.84%
Urban
Headcount Ratio Intensity MPI
0.0194.69% 41.26%
0.09520.93% 45.34% 0.0235.50% 42.08%
Multidimensional Poverty in Tripura's Rural and Urban Areas
Nutrition: 28.70%
Child & Adolescent Mortality: 1.30%
Child & Adolescent Mortality: 0.98%
Maternal Health: 11.36%
Years of Schooling: 16.76%
School Attendance: 4.37%
Cooking Fuel: 10.20%
Sanitation: 5.32%
Drinking Water: 4.44%
Electricity: 1.05%
Housing: 10.22%
Assets: 5.42%
Bank Account: 0.85%
Nutrition: 26.69%
Maternal Health: 8.68%
Years of Schooling: 18.03%
School Attendance: 3.73%
Cooking Fuel: 9.84%
Sanitation: 7.03%
Drinking Water: 4.62%
Electricity: 2.74%
Housing: 10.28%
Assets: 5.97%
Bank Account: 1.40%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
236

Percentage of total population who are deprived in each indicator
Tripura: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Tripura: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
26.13%
28.02%
1.55%
1.28%
16.07%
13.49%
10.47%
10.79%
2.50%
2.19%
54.75%
65.84%
26.56%
36.36%
13.87%
16.18%
1.75%
7.18%
66.83%
74.66%
14.83%
18.76%
3.02%
3.63%9.64%
11.98%
0.87%
0.88%
7.63%
7.79%
5.63%
8.10%
1.47%
1.67%
11.98%
15.47%
6.25%
11.05%
5.22%
7.26%
1.24%
4.30%
12.01%
16.16%
6.37%
9.38%
1.00%
2.20%
237
TRIPURAMPI: PROGRESS REVIEW 2023

Tripura
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Tripura for 2019-21.
Up to 0.035 0.036 to 0.0470.048 to 0.0590.060 to 0.0720.073 to 0.0840.085 to 0.096 0.097 and above
TRIPURA MPI: PROGRESS REVIEW 2023
238

Tripura
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Tripura
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Tripura, based on values for 2015-16. Both
the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.053 0.054 to 0.0680.069 to 0.0840.085 to 0.0990.100 to 0.1140.115 to 0.129 0.130 and above
239
TRIPURAMPI: PROGRESS REVIEW 2023

Tripura: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
20.0% .0%4.0%6.0%8.0%10.0%12.0%14.0%16.0%18.0%20.0%22.0%24.0%26.0%28.0%30.0%32.0%
24.92%
26.23%
21.89%
30.65%
17.85%
17.27%
12.27%
17.03%
11.89%
9.06%
8.97%
6.00%
% of population who are multidimensionally poor
District
Unakoti
Dhalai
North Tripura
Khowai
Sepahijala
South Tripura
Gomati
West Tripura
NFHS-5 (2019-21) NFHS-4 (2015-16)
TRIPURA MPI: PROGRESS REVIEW 2023
240

Tripura: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Tripura: Overview of Districts
Headcount Ratio, Intensity and MPI
% point change in proportion of multidimensionally poor population
-4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0
-4.34
0.039
0.072
0.146
0.124
43.26%
42.02%
47.49%
47.29%
8.97%
17.03%
30.65%
26.23%
0.024
0.109
0.054
0.049
0.078
0.073
0.037
0.096
40.09%
43.77%
45.43%
39.81%
43.64%
42.12%
41.18%
44.04%
6.00%
24.92%
11.89%
12.27%
17.85%
17.27%
9.06%
21.89%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Dhalai
Gomati
Khowai
North Tripura
Sepahijala
South Tripura
Unakoti
West Tripura
Dhalai
District
–––
–––
–––
–––
241
TRIPURAMPI: PROGRESS REVIEW 2023

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Uttar Pradesh
UTTAR PRADESH
Overview
Uttar Pradesh's Headcount Ratio, Intensity and MPI
Uttar Pradesh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Uttar Pradesh's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
22.93%2019-21 0.10344.83%
37.68%2015-16 0.17947.60%
Rural
Headcount Ratio Intensity MPI
0.11826.35% 44.89%
Urban
Headcount Ratio Intensity MPI
0.05111.57% 44.36%
0.21144.29% 47.66% 0.08417.72% 47.14%
Multidimensional Poverty in Uttar Pradesh's Rural and Urban Areas
Nutrition: 29.91%
Child & Adolescent Mortality: 1.79%
Child & Adolescent Mortality: 1.77%
Maternal Health: 12.94%
Years of Schooling: 14.94%
School Attendance: 12.35%
Cooking Fuel: 8.32%
Sanitation: 5.52%
Drinking Water: 0.43%
Electricity: 2.31%
Housing: 9.06%
Assets: 1.95%
Bank Account: 0.49%
Nutrition: 28.25%
Maternal Health: 11.71%
Years of Schooling: 13.98%
School Attendance: 9.26%
Cooking Fuel: 9.09%
Sanitation: 8.43%
Drinking Water: 0.56%
Electricity: 4.87%
Housing: 8.85%
Assets: 2.35%
Bank Account: 0.88%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
242

Percentage of total population who are deprived in each indicator
Uttar Pradesh
Percentage of total population who are multidimensionally poor and deprived in each indicator
Uttar Pradesh
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
36.43%
44.47%
3.54%
4.97%
30.03%
35.44%
13.18%
17.49%
10.91%
11.91%
52.92%
68.85%
31.61%
63.65%
2.06%
3.66%
9.16%
27.43%
60.09%
67.52%
7.80%
12.44%
2.96%
4.87%
18.45%
30.40%
2.20%
3.81%
15.97%
25.20%
9.21%
15.05%
7.62%
9.96%
17.95%
34.24%
11.91%
31.74%
0.93%
2.09%
4.98%
18.34%
19.56%
33.35%
4.22%
8.86%
1.06%
3.33%
243
UTTAR PRADESHMPI: PROGRESS REVIEW 2023

Uttar Pradesh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Uttar Pradesh for 2019-21.
Up to 0.065 0.066 to 0.1020.103 to 0.1380.139 to 0.1750.176 to 0.2110.212 to 0.248 0.249 and above
UTTAR PRADESH MPI: PROGRESS REVIEW 2023
244

Uttar Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Uttar Pradesh
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
Up to 0.104 0.105 to 0.1550.156 to 0.2060.207 to 0.2570.258 to 0.3080.309 to 0.359 0.360 and above
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Uttar Pradesh, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
245
UTTAR PRADESHMPI: PROGRESS REVIEW 2023

% of population who are multidimensionally poor
Uttar Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
District
69.45%
41.55%
Bahraich
Shravasti
Balrampur
Budaun
Sitapur
Siddharth Nagar
Sambhal
Kheri
Hardoi
Banda
Shahjahanpur
Barabanki
Sonbhadra
Kaushambi
Lalitpur
Gonda
Amethi
Kannauj
Fatehpur
Etah
Farrukhabad
Chandauli
Mahoba
Bareilly
Rampur
Unnao
Sant Kabeer Nagar
Pilibhit
Shamli
Mathura
Hathras
Rae Bareli
Sultanpur
Bhadohi
Mirzapur
Aligarh
Kasganj
Chitrakoot
71.85%
54.44%
74.35%
49.62%
57.10%
40.37%
56.71%
40.15%
57.24%
37.67%
35.06%
59.95%
34.73%
51.16%
34.14%
40.18%
33.80%
50.52%
32.57%
44.77%
31.68%
47.81%
31.43%
52.81%
31.41%
48.35%
30.55%
56.06%
30.31%
35.98%
29.98%
59.26%
29.71%
28.96%
43.50%
28.90%
42.63%
27.38%
38.47%
25.63%
39.18%
25.44%
37.91%
25.41%
35.29%
23.81%
38.58%
23.39%
38.89%
23.03%
40.79%
22.96%
43.79%
22.57%
43.26%
22.54%
22.45%
42.19%
22.04%
33.78%
22.42%
32.35%
22.39%
37.26%
22.00%
42.73%
22.00%
36.23%
22.32%
34.10%
22.37%
NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
UTTAR PRADESH MPI: PROGRESS REVIEW 2023
246

% of population who are multidimensionally poor
Uttar Pradesh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
District
Pratapgarh
Prayagraj
Amroha
Ambedkar Nagar
Kanpur Dehat
Kushi Nagar
Ballia
Basti
Mahrajganj
Auraiya
Firozabad
Ghazipur
Mainpuri
Saharanpur
Moradabad
Ayodhya
Bulandshahr
Hamirpur
Azamgarh
Bijnor
Jaunpur
Deoria
Etawah
Gorakhpur
Varanasi
Jhansi
Baghpat
Mau
Meerut
Muzaffarnagar
Hapur
Gautam
Buddha Nagar
Kanpur Nagar
Lucknow
Ghaziabad
Agra
Jalaun
26.17%
15.83%
36.94%
21.29%
32.77%
21.21%
34.84%
21.11%
34.03%
21.00%
37.98%
20.68%
42.82%
20.54%
37.11%
19.97%
43.26%
19.89%
49.12%
19.47%
29.82%
19.28%
32.03%
19.06%
41.04%
18.22%
32.83%
18.20%
27.64%
18.12%
31.41%
18.03%
28.52%
17.86%
36.85%
17.80%
38.73%
17.79%
32.88%
17.44%
30.92%
17.29%
32.77%
17.14%
29.76%
16.42%
40.78%
16.13%
31.36%
16.09%
27.29%
15.99%
26.00%
15.26%
20.17%
15.14%
21.08%
13.64%
32.63%
13.36%
21.10%
13.24%
29.85%
12.91%
12.60%
15.17%
12.16%
14.32%
9.11%
12.16%
8.48%
16.59%
6.93%
NFHS-5 (2019-21) NFHS-4 (2015-16)
(CONTD.) Uttar Pradesh: Headcount Ratio
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
247
UTTAR PRADESHMPI: PROGRESS REVIEW 2023

Uttar Pradesh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Mahrajganj
Gonda
Balrampur
Kaushambi
Kheri
Shravasti
Jaunpur
Basti
Ghazipur
Kushi Nagar
Chitrakoot
Sant Kabeer Nagar
Ayodhya
Mirzapur
Pilibhit
Bhadohi
Siddharth Nagar
Mau
Shahjahanpur
Unnao
Sonbhadra
Bahraich
Kanpur Dehat
Ballia
Hardoi
Sitapur
Kasganj
Rampur
Pratapgarh
Azamgarh
Bulandshahr
Deoria
Aligarh
Fatehpur
Bareilly
Agra
Kannauj
Farrukhabad
Amroha
Hamirpur
Mainpuri
Bijnor
Barabanki
Ambedkar Nagar
Firozabad
Etah
Chandauli
Prayagraj
Mahoba
Mathura
Etawah
Varanasi
Saharanpur
Auraiya
Gorakhpur
Hathras
Jalaun
Meerut
Baghpat
Banda
Lalitpur
Kanpur Nagar
Jhansi
Lucknow
Gautam Buddha Nagar
% point change in proportion of multidimensionally poor population
-32.0-30.0-28.0-26.0-24.0-22.0-20.0-18.0-16.0-14.0-12.0-10.0-8.0-6.0-4.0-2.0 0.0
-3.01
-3.68
-5.03
-5.20
-5.99
-6.38
-7.44
-7.86
-9.51
-9.96
-10.35
-10.54
-10.66
-10.74
-11.30
-11.36
-11.48
-11.56
-12.50
-12.85
-12.97
-13.03
-13.08
-13.33
-13.38
-13.63
-13.74
-13.74
-14.60
-14.63
-15.19
-15.25
-15.26
-15.27
-15.45
-15.63
-15.65
-15.86
-16.38
-16.56
-17.01
-17.15
-17.30
-17.41
-17.80
-17.84
-17.95
-19.26
-19.57
-20.15
-20.72
-20.73
-20.94
-21.22
-21.40
-22.28
-22.83
-23.36
-24.65
-24.72
-25.23
-25.75
-27.90
-29.55
-29.64
UTTAR PRADESH MPI: PROGRESS REVIEW 2023
248

Uttar Pradesh: Overview of Districts
Headcount Ratio, Intensity and MPI
0.116
0.192
0.171
0.243
0.281
0.286
0.412
0.247
0.192
0.205
0.139
0.193
0.160
0.166
0.207
0.143
0.177
0.201
0.096
0.146
0.150
0.139
0.154
0.225
0.142
0.054
0.159
0.197
0.308
0.291
0.237
0.063
0.167
0.206
0.168
0.089
0.179
0.121
0.246
0.136
0.121
0.301
0.184
0.075
0.067
0.149
0.202
0.188
0.178
0.116
0.175
0.142
0.254
0.168
0.153
0.298
0.134
0.199
0.190
0.221
0.185
0.373
0.167
0.391
0.099
0.142
0.134
0.148
0.153
0.173
0.154
44.69%
47.16%
47.22%
50.19%
49.63%
50.01%
55.35%
48.90%
45.53%
46.77%
48.89%
49.64%
46.87%
45.00%
47.95%
47.84%
48.04%
47.11%
45.59%
44.79%
44.27%
44.16%
43.70%
45.88%
44.01%
44.57%
44.31%
46.07%
51.32%
51.88%
49.55%
43.98%
43.87%
47.26%
48.24%
44.32%
44.01%
43.79%
48.14%
44.09%
46.08%
50.81%
44.95%
45.47%
43.96%
46.66%
47.30%
48.04%
45.84%
42.47%
45.61%
45.43%
48.17%
44.44%
46.59%
52.22%
45.11%
46.04%
49.26%
49.41%
46.08%
53.77%
45.09%
54.38%
47.03%
43.44%
44.92%
43.55%
46.56%
46.55%
46.83%
26.00%
40.79%
36.23%
48.35%
56.71%
57.24%
74.35%
50.52%
42.19%
43.79%
28.52%
38.89%
34.10%
36.94%
43.26%
29.85%
36.85%
42.73%
21.10%
32.63%
33.78%
31.41%
35.29%
49.12%
32.35%
12.16%
35.98%
42.82%
59.95%
56.06%
47.81%
14.32%
37.98%
43.50%
34.84%
20.17%
40.78%
27.64%
51.16%
30.92%
26.17%
59.26%
41.04%
16.59%
15.17%
32.03%
42.63%
39.18%
38.73%
27.29%
38.47%
31.36%
52.81%
37.91%
32.88%
57.10%
29.76%
43.26%
38.58%
44.77%
40.18%
69.45%
37.11%
71.85%
21.08%
32.77%
29.82%
34.03%
32.77%
37.26%
32.83%
0.066
0.107
0.093
0.141
0.189
0.179
0.249
0.102
0.146
0.094
0.101
0.164
0.078
0.103
0.097
0.092
0.098
0.059
0.081
0.095
0.059
0.056
0.097
0.077
0.103
0.085
0.096
0.036
0.131
0.091
0.159
0.144
0.142
0.039
0.091
0.124
0.096
0.063
0.064
0.077
0.155
0.053
0.071
0.069
0.135
0.073
0.030
0.048
0.080
0.123
0.112
0.078
0.064
0.110
0.068
0.141
0.113
0.074
0.188
0.070
0.087
0.106
0.145
0.154
0.209
0.087
0.285
0.060
0.071
0.084
0.131
0.085
0.094
0.095
0.080
43.00%
46.56%
41.80%
46.17%
47.10%
47.64%
50.18%
45.51%
44.84%
42.71%
44.81%
46.63%
43.90%
44.89%
43.53%
43.34%
43.51%
45.49%
45.54%
43.27%
44.88%
41.79%
43.17%
42.43%
43.13%
43.64%
42.84%
41.88%
43.77%
44.44%
45.90%
47.37%
45.14%
43.33%
43.86%
42.84%
45.47%
41.62%
39.91%
42.41%
45.50%
42.44%
41.32%
43.63%
45.54%
40.17%
43.25%
39.82%
42.08%
44.76%
43.86%
43.91%
40.12%
43.03%
42.41%
44.98%
44.50%
42.22%
46.58%
42.57%
43.59%
45.35%
45.88%
45.59%
50.26%
43.35%
52.42%
43.96%
41.68%
43.61%
45.06%
40.42%
44.48%
43.25%
44.16%
15.26%
22.96%
22.32%
30.55%
40.15%
37.67%
49.62%
22.45%
32.57%
22.04%
22.57%
35.06%
17.86%
23.03%
22.37%
21.29%
22.54%
12.91%
17.80%
22.00%
13.24%
13.36%
22.42%
18.03%
23.81%
19.47%
22.39%
8.48%
29.98%
20.54%
34.73%
30.31%
31.43%
9.11%
20.68%
28.90%
21.11%
15.14%
16.13%
18.12%
34.14%
12.60%
17.29%
15.83%
29.71%
18.22%
6.93%
12.16%
19.06%
27.38%
25.44%
17.79%
15.99%
25.63%
16.09%
31.41%
25.41%
17.44%
40.37%
16.42%
19.89%
23.39%
31.68%
33.80%
41.55%
19.97%
54.44%
13.64%
17.14%
19.28%
28.96%
21.00%
21.21%
22.00%
18.20%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Agra
Aligarh
Allahabad (Prayagraj)
Ambedkar Nagar
Amethi
Auraiya
Azamgarh
Baghpat
Bahraich
Ballia
Balrampur
Banda
Barabanki
Bareilly
Basti
Bijnor
Budaun
Bulandshahr
Chandauli
Chitrakoot
Deoria
Etah
Etawah
Faizabad (Ayodhya)
Farrukhabad
Fatehpur
Firozabad
Gautam Buddha Nagar
Ghaziabad
Ghazipur
Gonda
Gorakhpur
Hamirpur
Hapur
Hardoi
Jalaun
Jaunpur
Jhansi
Jyotiba Phule Nagar (Amroha)
Kannauj
Kanpur Dehat
Kanpur Nagar
Kasganj
Kaushambi
Kheri
Kushi Nagar
Lalitpur
Lucknow
Mahamaya Nagar (Hathras)
Mahrajganj
Mahoba
Mainpuri
Mathura
Mau
Meerut
Mirzapur
Moradabad
Muzaffarnagar
Pilibhit
Pratapgarh
Rae Bareli
Rampur
Saharanpur
Sambhal
Sant Kabeer Nagar
Sant Ravidas Nagar (Bhadohi)
Shahjahanpur
Shamli
Shravasti
Siddharth Nagar
Sitapur
Sonbhadra
Sultanpur
Unnao
Varanasi
District
–––
–––
–––
–––
249
UTTAR PRADESHMPI: PROGRESS REVIEW 2023

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in Uttarakhand
UTTARAKHAND
Overview
Uttarakhand's Headcount Ratio, Intensity and MPI
Uttarakhand: Indicator Contribution to the MPI
Percentage contribution of each indicator to Uttarakhand's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
9.67%2019-21 0.04141.99%
17.67%2015-16 0.07844.35%
Rural
Headcount Ratio Intensity MPI
0.04510.84% 41.13%
Urban
Headcount Ratio Intensity MPI
0.0327.00% 45.03%
0.09621.87% 43.75% 0.0469.89% 46.80%
Multidimensional Poverty in Uttarakhand's Rural and Urban Areas
Nutrition: 30.79%
Child & Adolescent Mortality: 1.85% Child & Adolescent Mortality: 1.73%
Maternal Health: 13.27%
Years of Schooling: 16.50%
School Attendance: 10.85%
Cooking Fuel: 8.67%
Sanitation: 5.99%
Drinking Water: 1.65%
Electricity: 0.26%
Housing: 5.93%
Assets: 3.21%
BBank Account: 1.04%
Nutrition: 31.12%
Maternal Health: 13.84%
Years of Schooling: 14.26%
School Attendance: 6.77%
Cooking Fuel: 9.58%
Sanitation: 6.77%
Drinking Water: 1.89%
Electricity: 0.85%
Housing: 7.47%
Assets: 3.77%
Bank Account: 1.95%
250
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account

Percentage of total population who are deprived in each indicator
Uttarakhand: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Uttarakhand: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
251
UTTARAKHANDMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
23.68%
32.85%
1.89%
2.58%
20.42%
28.54%
7.89%
9.76%
4.65%
4.37%
44.13%
52.06%
21.70%
33.93%
6.63%
8.65%
0.39%
2.17%
24.19%
35.58%
9.10%
13.84%
2.89%
6.89%
7.50%
14.64%
0.90%
1.63%
6.47%
13.02%
4.02%
6.70%
2.64%
3.18%
7.39%
15.76%
5.11%
11.14%
1.41%
3.10%
0.22%
1.39%
5.06%
12.30%
2.74%
6.21%
0.88%
3.21%

Uttarakhand
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
UTTARAKHAND MPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Uttarakhand for 2019-21.
Up to 0.019 0.020 to 0.0280.029 to 0.0360.037 to 0.0440.045 to 0.0520.053 to 0.061 0.062 and above
252

Uttarakhand
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Uttarakhand
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of Uttarakhand, based on values for
2015-16. Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.042 0.043 to 0.0550.056 to 0.0670.068 to 0.0790.080 to 0.0910.092 to 0.103 0.104 and above
253
UTTARAKHANDMPI: PROGRESS REVIEW 2023

Uttarakhand: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
20.0% .0%4.0%6.0%8.0%10.0%12.0%14.0%16.0%18.0%20.0%22.0%24.0%26.0%28.0%
24.70%
16.29%
22.98%
11.26%
13.39%
10.09%
22.41%
9.59%
24.28%
9.54%
25.65%
9.47%
11.93%
8.91%
19.53%
7.94%
19.99%
7.50%
16.78%
6.81%
13.96%
6.48%
13.91%
5.14%
6.88%
3.02%
% of population who are multidimensionally poor
District
Haridwar
Udham Singh Nagar
Nainital
Champawat
Uttarkashi
Almora
Pauri Garhwal
Tehri Garhwal
Bageshwar
Chamoli
Pithoragarh
Rudraprayag
Dehradun
NFHS-5 (2019-21) NFHS-4 (2015-16)
UTTARAKHAND MPI: PROGRESS REVIEW 2023
254

Uttarakhand: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Almora
Uttarkashi
Champawat
Bageshwar
Udham Singh Nagar
Tehri Garhwal
Chamoli
Rudraprayag
Haridwar
Pithoragarh
Dehradun
Nainital
Pauri Garhwal
% point change in proportion of multidimensionally poor population
-18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-3.01
-3.31
-3.86
-7.48
-8.41
-8.77
-9.96
-11.60
-11.72
-12.49
-12.82
-14.74
-16.18
255
UTTARAKHANDMPI: PROGRESS REVIEW 2023

Uttarakhand: Overview of Districts
Headcount Ratio, Intensity and MPI
0.108
0.105
0.079
0.056
0.057
0.058
0.117
0.048
0.031
0.100
0.069
0.082
0.103
44.51%
45.63%
40.64%
40.28%
41.06%
43.65%
47.25%
40.17%
45.33%
44.75%
41.32%
41.08%
40.33%
24.28%
22.98%
19.53%
13.91%
13.96%
13.39%
24.70%
11.93%
6.88%
22.41%
16.78%
19.99%
25.65%
0.038
0.049
0.031
0.021
0.025
0.047
0.070
0.034
0.012
0.037
0.025
0.029
0.036
40.24%
43.72%
38.91%
40.03%
38.76%
46.24%
42.81%
38.49%
40.32%
39.00%
36.49%
38.47%
37.49%
9.54%
11.26%
7.94%
5.14%
6.48%
10.09%
16.29%
8.91%
3.02%
9.59%
6.81%
7.50%
9.47%
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
Almora
Bageshwar
Chamoli
Champawat
Dehradun
Garhwal (Pauri Garhwal)
Haridwar
Nainital
Pithoragarh
Rudraprayag
Tehri Garhwal
Udham Singh Nagar
Uttarkashi
District
UTTARAKHAND MPI: PROGRESS REVIEW 2023
256

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Health Education Standard of Living
Health Education Standard of Living
MPI: PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in West Bengal
WEST BENGAL
Overview
West Bengal's Headcount Ratio, Intensity and MPI
West Bengal: Indicator Contribution to the MPI
Percentage contribution of each indicator to West Bengal's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
11.89%2019-21 0.05042.35%
21.29%2015-16 0.09745.50%
Rural
Headcount Ratio Intensity MPI
0.06415.15% 42.26%
Urban
Headcount Ratio Intensity MPI
0.0225.04% 42.92%
0.11625.66% 45.39% 0.05311.56% 46.02%
Multidimensional Poverty in West Bengal's Rural and Urban Areas
Nutrition: 31.01%
Child & Adolescent Mortality: 0.83%
Child & Adolescent Mortality: 0.86%
Maternal Health: 9.12%
Years of Schooling: 20.68%
School Attendance: 4.25%
Cooking Fuel: 10.37%
Sanitation: 7.03%
Drinking Water: 1.09%
Electricity: 1.34%
Housing: 9.37%
Assets: 3.44%
Bank Account: 1.46%
Nutrition: 27.76%
Maternal Health: 8.09%
Years of Schooling: 19.30%
School Attendance: 4.78%
Cooking Fuel: 10.17%
Sanitation: 8.26%
Drinking Water: 2.00%
Electricity: 1.83%
Housing: 9.19%
Assets: 4.24%
Bank Account: 3.50%
2019-21
2015-16
Year
2015-162019-21
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
258

Percentage of total population who are deprived in each indicator
West Bengal: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
West Bengal: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
27.28%
33.62%
1.06%
1.50%
11.43%
14.38%
12.87%
15.84%
2.12%
3.82%
61.25%
73.01%
31.91%
47.81%
4.97%
9.46%
2.50%
5.75%
47.17%
54.25%
8.13%
14.10%
4.44%
13.82%
9.37%
16.14%
0.50%
1.00%
5.51%
9.41%
6.25%
11.22%
1.28%
2.78%
10.96%
20.70%
7.43%
16.82%
1.15%
4.08%
1.41%
3.73%
9.91%
18.70%
3.64%
8.64%
1.55%
7.12%
259
WEST BENGALMPI: PROGRESS REVIEW 2023

West Bengal
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
WEST BENGAL MPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. The legend provides the range of MPI scores of West Bengal for 2019-21.
Up to 0.025 0.026 to 0.0400.041 to 0.0550.056 to 0.0710.072 to 0.0860.087 to 0.101 0.102 and above
260

West Bengal
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
West Bengal
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
The colour represents the MPI score of a district. The legend provides the range of MPI scores of West Bengal, based on values for 2015-16.
Both the comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.043 0.044 to 0.0750.076 to 0.1070.108 to 0.1390.140 to 0.1710.172 to 0.203 0.204 and above
261
WEST BENGALMPI: PROGRESS REVIEW 2023

West Bengal: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
WEST BENGAL MPI: PROGRESS REVIEW 2023
262
49.69%
26.84%
42.84%
21.65%
26.99%
18.49%
27.35%
18.27%
23.82%
18.14%
27.23%
16.55%
34.48%
15.57%
14.32%
22.48%
13.37%
14.19%
12.48%
11.20%
28.10%
10.96%
21.90%
10.31%
21.83%
8.85%
11.07%
8.20%
14.93%
7.36%
12.84%
6.06%
11.32%
5.45%
9.80%
4.37%
2.72%
2.56%
20.33%
0.0%5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%
% of population who are multidimensionally poor
District
Puruliya
Dinjapur Uttar
Bankura
Birbhum
Medinipur West
Murshidabad
Maldah
Purba Bardhaman
Dinajpur Dakshin
Medinipur East
Paschim Bardhaman
South 24 Parganas
Coochbehar
Jalpaiguri
Nadia
Hooghly
Howrah
Darjeeling
North 24 Parganas
Kolkata
Barddhaman
NFHS-5 (2019-21) NFHS-4 (2015-16)

West Bengal: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
District
Puruliya
Dinjapur Uttar
Maldah
South 24 Parganas
Jalpaiguri
Coochbehar
Murshidabad
Dinajpur Dakshin
Birbhum
Bankura
Hooghly
Howrah
Darjeeling
Medinipur West
North 24 Parganas
Nadia
Medinipur East
Kolkata
% point change in proportion of multidimensionally poor population
-24.0 -22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
263
WEST BENGALMPI: PROGRESS REVIEW 2023
-22.85
-21.19
-18.91
-17.14
-12.98
-11.60
-10.68
-9.11
-9.08
-8.51
-7.56
-6.78
-5.87
-5.68
-5.43
-2.87
-1.70
-0.16

0.213
0.128
0.236
0.061
0.104
0.041
0.047
0.125
0.158
0.012
0.099
0.099
0.066
0.058
0.051
0.099
0.125
0.096
0.121
49.79%
45.71%
47.44%
42.68%
43.50%
41.37%
42.79%
45.94%
45.74%
45.77%
45.28%
45.53%
44.15%
45.02%
44.84%
44.18%
45.69%
47.06%
44.74%
42.84%
28.10%
49.69%
14.19%
23.82%
9.80%
11.07%
27.23%
34.48%
2.72%
21.90%
21.83%
14.93%
12.84%
11.32%
22.48%
27.35%
20.33%
26.99%
0.099
0.045
0.117
0.051
0.059
0.077
0.048
0.018
0.033
0.071
0.067
0.010
0.042
0.037
0.030
0.025
0.023
0.057
0.082
0.078
45.61%
41.22%
43.74%
41.16%
41.18%
42.31%
42.89%
40.11%
40.00%
43.18%
42.91%
40.79%
41.17%
41.34%
41.04%
40.99%
42.95%
42.70%
44.67%
42.07%
21.65%
10.96%
26.84%
12.48%
14.32%
18.14%
11.20%
4.37%
8.20%
16.55%
15.57%
2.56%
10.31%
8.85%
7.36%
6.06%
5.45%
13.37%
18.27%
18.49%
West Bengal: Overview of Districts
Headcount Ratio, Intensity and MPI
Headcount Ratio Intensity MPI Headcount Ratio Intensity MPI
NFHS-4 (2015-16) NFHS-5 (2019-21)
District
–––
–––
–––
WEST BENGAL MPI: PROGRESS REVIEW 2023
264
Paschim Medinipur
(Medinipur West)
Purba Medinipur
(Medinipur East)
Uttar Dinajpur
(Dinjapur Uttar)
Bankura
Barddhaman
Birbhum
Darjeeling
Howrah
Hugli (Hooghly)
Jalpaiguri
Koch Bihar (Coochbehar)
Kolkata
Maldah
Murshidabad
Nadia
North 24 Parganas
Paschim Bardhaman
Purba Bardhaman
Puruliya
South 24 Parganas
Dakshin Dinajpur
(Dinajpur Dakshin)

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
A snapshot of multidimensional poverty in
Andaman & Nicobar Islands
ANDAMAN & NICOBAR
ISLANDS
MPI: PROGRESS REVIEW 2023
Overview
Andaman & Nicobar Islands's Headcount Ratio, Intensity and MPI
Andaman & Nicobar Islands: Indicator Contribution to the MPI
Percentage contribution of each indicator to Andaman & Nicobar Islands's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
2.30%2019-21 0.00940.62%
4.29%2015-16 0.01740.50%
Rural
Headcount Ratio Intensity MPI
0.0112.71% 41.55%
Urban
Headcount Ratio Intensity MPI
0.0061.60% 37.96%
0.0286.75% 40.79% 0.0040.97% 37.76%
2019-21
2015-16
Multidimensional Poverty in Andaman & Nicobar Islands's Rural and Urban Area
Nutrition: 26.84%
Child & Adolescent Mortality: 0.56%
Maternal Health: 5.88%
Years of Schooling: 19.62%
School Attendance: 4.47%
Cooking Fuel: 7.96%
Sanitation: 9.10%%
Drinking Water: 4.19%
Electricity: 4.45%
Housing: 8.85%
Assets: 7.59%
Bank Account: 0.46%
Nutrition: 32.73%
Child & Adolescent Mortality: 1.46%
Maternal Health: 4.89%
Years of Schooling: 18.59%
School Attendance: 2.56%
Cooking Fuel: 8.59%
Sanitation: 8.17%
Drinking Water: 3.04%
Electricity: 4.24%
Housing: 9.58%
Assets: 5.85%
Bank Account: 0.30%
Year
266

Percentage of total population who are deprived in each indicator
Andaman & Nicobar Islands: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Andaman & Nicobar Islands: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
Health Education Standard of Living
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Nutrition Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
SanitationDrinking
Water
ElectricityHousing Assets Bank
Account
Nutrition Child &
Adolescent
Mortality
Maternal
Health
Years of
Schooling
School
Attendance
Cooking
Fuel
SanitationDrinking
Water
ElectricityHousing Assets Bank
Account
15.09%
22.05%
0.91%
0.83%
4.02%
5.11%
5.93%
4.87%
0.63%
0.92%
15.73%
24.53%
12.12%
24.37%
5.04%
5.65%
2.47%
2.72%
30.10%
33.61%
7.90%
7.10%
2.57%
1.57%
1.50%
3.41%
0.06%
0.31%
0.66%
1.02%
1.10%
1.94%
0.25%
0.27%
1.56%
3.14%
1.78%
2.98%
0.82%
1.11%
0.87%
1.55%
1.73%
3.50%
1.49%
2.14%
0.09%
0.11%
ANDAMAN & NICOBAR ISLANDSMPI: PROGRESS REVIEW 2023
267

Andaman & Nicobar Islands
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
ANDAMAN & NICOBAR ISLANDS MPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
268

Andaman & Nicobar Islands
Andaman & Nicobar Islands
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)
ANDAMAN & NICOBAR ISLANDSMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
269

Andaman & Nicobar Islands: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Nicobars
North & Middle
Andaman
South Andamans
5.28% 36.80% 0.019 3.40% 39.42% 0.013
9.32% 41.82% 0.039 4.39% 42.51% 0.019
2.20% 39.23% 0.009 1.15% 37.66% 0.004
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
District
Andaman & Nicobar Islands: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
District
District
Andaman & Nicobar Islands: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
% point change in proportion of multidimensionally poor population
North & Middle
Andaman
South Andamans
Nicobars
-4.93
-1.88
-1.05
-0.0-0.5-1.0-1.5-2.0-2.5-3.0-3.5-4.0-4.5-5.0
0.0%1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%
North & Middle
Andaman
South Andamans
Nicobars
2.20%
4.39%
3.40%
1.15%
5.28%
9.32%
% of population who are multidimensionally poor
270

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
A snapshot of multidimensional poverty in
Chandigarh
CHANDIGARH
MPI: PROGRESS REVIEW 2023
Overview
Chandigarh's Headcount Ratio, Intensity and MPI
Chandigarh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Chandigarh's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
3.52%2019-21 0.01747.41%
5.97%2015-16 0.02643.39%
Rural
Headcount Ratio Intensity MPI
0.0153.88% 38.10%
Urban
Headcount Ratio Intensity MPI
0.0173.51% 47.55%
0.08918.56% 47.88% 0.0235.45% 42.76%
2019-21
2015-16
Multidimensional Poverty in Chandigarh's Rural and Urban Area
Nutrition: 31.53%
Child & Adolescent Mortality: 1.67%
Maternal Health: 10.30%
Years of Schooling: 20.74%
School Attendance: 9.38%
Cooking Fuel: 5.99%
Sanitation: 8.90%
Drinking Water: 2.38%
Electricity: 0.88%
Housing: 4.55%
Assets: 2.20%
Bank Account: 1.48%
Nutrition: 23.68%
Child & Adolescent Mortality: 2 .51%
Maternal Health: 8.19%
Years of Schooling: 21 .26%
School Attendance: 23.20%
Cooking Fuel: 4 .56%
Sanitation: 7.03%
Drinking Water: 3.74%
Electricity: 0.00%
Housing: 5.60%
Assets: 0.23%
Bank Account: 0.00%
272
Year

Percentage of total population who are deprived in each indicator
Chandigarh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Chandigarh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
273
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
21.57%
23.11%
1.17%
1.16%
7.32%
11.05%
4.54%
5.83%
3.97%
1.76%
5.23%
4.85%
17.82%
19.04%
3.23%
1.85%
0.04%
0.48%
4.33%
6.40%
0.59%
2.71%
1.75%
3.97%2.37%
4.90%
0.50%
0.52%
1.64%
3.20%
2.13%
3.23%
2.32%
1.46%
1.60%
3.26%
2.46%
4.84%
1.31%
1.30%
0.00%
0.48%
1.96%
2.48%
0.08%
1.20%
0.00%
0.81%
CHANDIGARHMPI: PROGRESS REVIEW 2023

Chandigarh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
CHANDIGARH MPI: PROGRESS REVIEW 2023
274

Chandigarh
Chandigarh
CHANDIGARHMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
275
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Chandigarh: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Chandigarh 5.97% 43.39% 0.026 3.52% 47.41% 0.017
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
District
Chandigarh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0%0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0% 6.5%
Chandigarh
3.52%
5.97%
District
Chandigarh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
Chandigarh
District
-2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.20.0
-2.46
% point change in proportion of multidimensionally poor population
% of population who are multidimensionally poor
CHANDIGARH MPI: PROGRESS REVIEW 2023
276

2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
A snapshot of multidimensional poverty in
Dadra & Nagar Haveli & Daman & Diu
DADRA & NAGAR
HAVELI & DAMAN & DIU
MPI: PROGRESS REVIEW 2023
Overview
Dadra & Nagar Haveli & Daman & Diu's Headcount Ratio, Intensity and MPI
Dadra & Nagar Haveli & Daman & Diu: Indicator Contribution to the MPI
Percentage contribution of each indicator to Dadra & Nagar Haveli & Daman & Diu's MPI Score
Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
9.21%2019-21 0.03942.15%
19.58%2015-16 0.08744.23%
Rural
Headcount Ratio Intensity MPI
0.05112.27% 41.48%
Urban
Headcount Ratio Intensity MPI
0.0255.67% 43.85%
0.15935.74% 44.42% 0.0255.72% 43.20%
2019-21
2015-16
Multidimensional Poverty in Dadra & Nagar Haveli & Daman & Diu's Rural and Urban Area
Nutrition: 35.01%
Child & Adolescent Mortality: 1.47%
Maternal Health: 5.82%
Years of Schooling: 17.36%
School Attendance: 10.29%
Cooking Fuel: 6.17%
Sanitation: 6.60%
Drinking Water: 1.79%
Electricity: 0.31%
Housing: 7.38%
Assets: 6.13%
Bank Account: 1.68%
Nutrition: 33.00%
Child & Adolescent Mortality: 0.92%
Maternal Health: 6.42%
Years of Schooling: 10.47%
School Attendance: 10.35%
Cooking Fuel: 8.65%
Sanitation: 9.97%
Drinking Water: 2.47%
Electricity: 0.72%
Housing: 9.10%
Assets: 5.08%
Bank Account: 2.85%
278
Year
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%

Percentage of total population who are deprived in each indicator
Dadra & Nagar Haveli & Daman & Diu: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Dadra & Nagar Haveli & Daman & Diu: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
37.81%
36.71%
1.73%
1.63%
8.06%
13.80%
8.16%
7.53%
3.29%
6.71%
22.54%
33.68%
34.59%
56.32%
5.74%
9.69%
0.35%
1.73%
31.61%
40.15%
16.79%
18.68%
6.65%
11.40%
279
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
8.15%
17.15%
0.68%
0.96%
2.71%
6.67%
4.04%
5.44%
2.39%
5.38%
5.02%
15.74%
5.38%
18.15%
1.46%
4.49%
0.25%
1.32%
6.01%
16.55%
4.99%
9.25%
1.37%
5.18%
DADRA & NAGAR HAVELI & DAMAN & DIUMPI: PROGRESS REVIEW 2023

280
DADRA & NAGAR HAVELI & DAMAN & DIU MPI: PROGRESS REVIEW 2023
Dadra & Nagar Haveli & Daman & Diu
Dadra & Nagar Haveli & Daman & Diu
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Dadra & Nagar Haveli & Daman & Diu (2019-21)
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
Dadra & Nagar Haveli & Daman & Diu: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
281
DADRA & NAGAR HAVELI & DAMAN & DIUMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
20.0% .0%4.0%6.0%8.0%10.0%12.0%14.0%16.0%18.0%20.0%22.0%24.0%26.0%28.0%
4.34%
1.62%
7.15%
6.57%
26.40%
10.79%
Dadra & Nagar
Haveli
Daman
Diu
NFHS-5 (2019-21) NFHS-4 (2015-16)
District
% of population who are multidimensionally poor

Dadra & Nagar Haveli & Daman & Diu: Overview of Districts
Headcount Ratio, Intensity and MPI
Dadra & Nagar Haveli & Daman & Diu:
Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-2.72
-15.61
-17.0-16.0-15.0-14.0-13.0-12.0-11.0
% point change in proportion of multidimensionally poor population
-10.0-9.0-8.0-7.0-6.0-5.0-4.0-3.0-2.0-1.0
-0.58
Dadra & Nagar
Haveli
Diu
Daman
District
0.0
NFHS-4 (2015-16)
Headcount Ratio
26.40% 44.29% 0.117 10.79% 41.82% 0.045
7.15% 44.76% 0.032 6.57% 43.96% 0.029
4.34% 37.64% 0.016 1.62% 39.52% 0.006
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
Dadra & Nagar Haveli
Daman
Diu
District
282
DADRA & NAGAR HAVELI & DAMAN & DIU MPI: PROGRESS REVIEW 2023

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education
Standard of
Living
Health Education Standard of Living
A snapshot of multidimensional poverty in Delhi
DELHI
MPI: PROGRESS REVIEW 2023
Overview
Delhi's Headcount Ratio, Intensity and MPI
Delhi: Indicator Contribution to the MPI
Percentage contribution of each indicator to Delhi's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
3.43%2019-21 0.01441.99%
4.44%2015-16 0.02043.92%
Rural
Headcount Ratio Intensity MPI
0.0112.57% 42.72%
Urban
Headcount Ratio Intensity MPI
0.0143.45% 41.98%
0.0102.39% 39.74% 0.0204.46% 43.94%
2019-21
2015-16
Multidimensional Poverty in Delhi's Rural and Urban Areas
Nutrition: 30.95%
Child & Adolescent Mortality: 3.05%
Maternal Health: 13.00%
Years of Schooling: 21.31%
School Attendance: 9.68%
Cooking Fuel: 1.39%
Sanitation: 8.20%
Drinking Water: 1.42%
Electricity: 0.15%
Housing: 3.50%
Assets: 4.24%
Bank Account: 3.10%
Nutrition: 30.93%
Child & Adolescent Mortality: 1.90%
Maternal Health: 10.49%
Years of Schooling: 21.50%
School Attendance: 16.29%
Cooking Fuel: 1.10%
Sanitation: 7.72%
Drinking Water: 0.58%
Electricity: 0.14%
Housing: 2.80%
Assets: 3.97%
Bank Account: 2.58%
284
Year

Percentage of total population who are deprived in each indicator
Delhi: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Delhi: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
285
DELHIMPI: PROGRESS REVIEW 2023
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
2.67%
3.62%
0.33%
0.71%
1.81%
3.04%
1.86%
2.50%
1.41%
1.13%
0.33%
0.57%
2.33%
3.36%
0.18%
0.58%
0.04%
0.06%
0.85%
1.43%
1.20%
1.74%
0.78%
1.27%
20.38%
23.41%
1.38%
1.91%
10.07%
15.20%
4.37%
5.93%
2.77%
2.63%
0.91%
2.21%
19.21%
26.41%
1.92%
4.45%
0.14%
0.28%
6.25%
10.80%
4.42%
5.54%
5.78%
8.36%

Delhi
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
DELHI MPI: PROGRESS REVIEW 2023
286

Delhi
Delhi
DELHIMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
287
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Delhi: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.5%1.0%1.5%2.0%2.5%3.0%3.5%4.0%4.5%5.0%5.5%6.0%6.5%7.0%7.5%8.0%
2.41%
6.26%
4.16%
4.83%
2.29%
4.68%
3.84%
3.88%
7.35%
3.69%
2.17%
3.45%
4.06%
2.79%
2.69%
6.84%
2.05%
1.52%
4.69%
1.29%
North
New Delhi
West
Central
North East
South West
East
South East
North West
Shahdara
South
% of population who are multidimensionally poor
District
NFHS-5 (2019-21) NFHS-4 (2015-16)
0.0%
DELHI MPI: PROGRESS REVIEW 2023
288

Delhi: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
3.84% 43.30% 0.017 3.88% 42.30% 0.016
4.06% 41.99% 0.017 2.79% 43.64% 0.012
4.16% 42.99% 0.018 4.83% 39.61% 0.019
2.41% 41.65% 0.010 6.26% 41.64% 0.026
7.35% 42.70% 0.031 3.69% 41.39% 0.015
6.84% 46.38% 0.032 2.05% 40.50% 0.008
1.52% 41.84% 0.006
4.69% 41.64% 0.020 1.29% 51.36% 0.007
2.69% 39.10% 0.011
2.17% 45.16% 0.010 3.45% 43.71% 0.015
Central
East
New Delhi
North
North East
North West
Shahdara
South
South East
South West
West 2.29% 44.66% 0.010 4.68% 42.43% 0.020
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
District
–––
–––
DELHIMPI: PROGRESS REVIEW 2023
289

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
A snapshot of multidimensional poverty in
Jammu & Kashmir
JAMMU & KASHMIR
MPI: PROGRESS REVIEW 2023
Overview
Jammu & Kashmir's Headcount Ratio, Intensity and MPI
Jammu & Kashmir: Indicator Contribution to the MPI
Percentage contribution of each indicator to Jammu & Kashmir's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
4.80%2019-21 0.02042.11%
12.56%2015-16 0.05544.17%
Rural
Headcount Ratio Intensity MPI
0.0266.10% 42.29%
Urban
Headcount Ratio Intensity MPI
0.0041.09% 39.26%
0.07316.37% 44.34% 0.0153.51% 42.30%
2019-21
2015-16
Multidimensional Poverty in Jammu & Kashmir's Rural and Urban Areas
Nutrition: 29.11%
Child & Adolescent Mortality: 1.28%
Maternal Health: 9.13%
Years of Schooling: 13.45%
School Attendance: 7.55%
Cooking Fuel: 9.68%
Sanitation: 9.07%
Drinking Water: 4.27%
Electricity: 1.44%
Housing: 8.05%
Assets: 5.75%
Bank Account: 1.24%
Nutrition: 25.94%
Child & Adolescent Mortality: 0.64%
Maternal Health: 8.48%
Years of Schooling: 17.85%
School Attendance: 10.93%
Cooking Fuel: 9.28%
Sanitation: 7.67%
Drinking Water: 4.88%
Electricity: 0.48%
Housing: 8.84%
Assets: 4.37%
Bank Account: 0.65%
Year
290

Percentage of total population who are deprived in each indicator
Jammu & Kashmir: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Jammu & Kashmir: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
3.15%
9.69%
0.15%
0.85%
2.06%
6.08%
2.17%
4.48%
1.33%
2.51%
3.94%
11.27%
3.25%
10.57%
2.07%
4.97%
0.20%
1.68%
3.75%
9.38%
1.86%
6.70%
0.27%
1.44%
15.52%
25.88%
0.73%
1.85%
7.58%
12.73%
4.25%
6.83%
2.94%
3.74%
32.23%
45.38%
24.30%
46.23%
10.37%
13.77%
0.76%
2.80%
25.36%
28.65%
8.03%
16.24%
2.93%
3.98%
JAMMU & KASHMIRMPI: PROGRESS REVIEW 2023
291

292
Jammu & Kashmir
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
JAMMU & KASHMIR MPI: PROGRESS REVIEW 2023

Jammu & Kashmir
Jammu & Kashmir
293
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
JAMMU & KASHMIRMPI: PROGRESS REVIEW 2023
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Jammu & Kashmir: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
35.26%
14.86%
21.92%
11.40%
24.29%
10.59%
26.83%
10.23%
27.41%
8.07%
28.71%
7.71%
7.06%
6.68%
24.27%
6.65%
11.07%
5.36%
6.84%
5.20%
16.08%
4.97%
7.43%
3.97%
8.36%
3.07%
13.08%
2.70%
9.67%
2.30%
3.79%
2.09%
6.51%
1.54%
1.51%
1.34%
6.97%
0.49%
% of population who are multidimensionally poor
District
NFHS-5 (2019-21) NFHS-4 (2015-16)
Ramban
Reasi
Kishtwar
Udhampur
Rajouri
Doda
Baramulla
Poonch
Bandipora
Budgam
Kupwara
Kulgam
Ganderbal
Anantnag
Kathua
Samba
Pulwama
Shopian
Srinagar
Jammu
3.46%
7.82%
294
JAMMU & KASHMIR MPI: PROGRESS REVIEW 2023

Jammu & Kashmir: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-22.0 -20.0 -18.0 -16.0 -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0
-0.17
-0.38
-1.64
-1.69
-3.46
-4.37
-4.97
-5.28
-5.71
-6.48
-7.38
-10.38
-10.52
-11.11
-13.71
-16.61
-17.62
-19.33
-20.40
-21.00
% point change in proportion of multidimensionally poor
District
Doda
Ramban
Rajouri
Poonch
Udhampur
Kishtwar
Kupwara
Reasi
Kathua
Samba
Jammu
Bandipora
Anantnag
Shopian
Ganderbal
Kulgam
Pulwama
Budgam
Baramulla
Srinagar
295
JAMMU & KASHMIRMPI: PROGRESS REVIEW 2023

Jammu & Kashmir: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Anantnag 8.36% 46.54% 0.039 3.07% 45.72% 0.014
Badgam (Budgam) 6.84% 41.74% 0.029 5.20% 38.99% 0.020
Bandipora 11.07% 45.41% 0.050 5.36% 44.40% 0.024
Baramulla 7.06% 43.09% 0.030 6.68% 41.75% 0.028
Doda 28.71% 47.07% 0.135 7.71% 41.82% 0.032
Ganderbal 7.82% 43.18% 0.034 3.46% 42.76% 0.015
Jammu 6.97% 43.33% 0.030 0.49% 51.29% 0.003
Kathua 13.08% 43.46% 0.057 2.70% 41.48% 0.011
Kishtwar 24.29% 45.52% 0.111 10.59% 42.16% 0.045
Kulgam 7.43% 43.79% 0.033 3.97% 45.40% 0.018
Kupwara 16.08% 41.57% 0.067 4.97% 40.59% 0.020
Pulwama 3.79% 42.07% 0.016 2.09% 40.36% 0.008
Punch (Poonch) 24.27% 43.48% 0.106 6.65% 39.43% 0.026
Rajouri 27.41% 44.72% 0.123 8.07% 41.88% 0.034
Ramban 35.26% 46.29% 0.163 14.86% 42.90% 0.064
Reasi 21.92% 44.41% 0.097 11.40% 42.87% 0.049
Samba 9.67% 42.77% 0.041 2.30% 41.43% 0.010
Shopian 6.51% 43.32% 0.028 1.54% 37.06% 0.006
Srinagar 1.51% 39.64% 0.006 1.34% 37.23% 0.005
Udhampur 26.83% 43.59% 0.117 10.23% 43.10% 0.044
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
District
296
JAMMU & KASHMIR MPI: PROGRESS REVIEW 2023

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
A snapshot of multidimensional poverty in Ladakh
LADAKH
Overview
Ladakh's Headcount Ratio, Intensity and MPI
Ladakh: Indicator Contribution to the MPI
Percentage contribution of each indicator to Ladakh's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
3.53%2019-21 0.01541.20%
12.70%2015-16 0.05140.37%
Rural
Headcount Ratio Intensity MPI
0.0163.89% 41.44%
Urban
Headcount Ratio Intensity MPI
0.0082.00% 39.27%
0.06516.20% 40.27% 0.0133.02% 41.82%
2019-21
2015-16
Multidimensional Poverty in Ladakh's Rural and Urban Areas
Nutrition: 32.94%
Child & Adolescent Mortality: 1.43%
Maternal Health: 11.31%
Years of Schooling: 10.84%
School Attendance: 3.52%
Cooking Fuel: 8.18%
Sanitation: 11.75%
Drinking Water: 4.36%
Electricity: 0.68%
Housing: 11.36%
Assets: 2.96%
Bank Account: 0.68%
Nutrition: 28.65%
Child & Adolescent Mortality: 1.58%
Maternal Health: 8.66%
Years of Schooling: 18.61%
School Attendance: 12.44%
Cooking Fuel: 4.47%
Sanitation: 9.93%
Drinking Water: 3.77%
Electricity: 0.25%
Housing: 9.36%
Assets: 1.53%
Bank Account: 0.73%
Year
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298

Percentage of total population who are deprived in each indicator
Ladakh: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Ladakh: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
% of population deprived
2.50%
10.13%
0.28%
0.88%
1.51%
6.95%
1.62%
3.33%
1.09%
1.08%
1.37%
8.80%
3.03%
12.64%
1.15%
4.69%
0.08%
0.73%
2.86%
12.22%
0.47%
3.19%
0.22%
0.74%
LADAKHMPI: PROGRESS REVIEW 2023
299
14.40%
26.72%
0.91%
2.11%
7.07%
11.62%
4.08%
7.04%
2.86%
2.24%
24.52%
34.80%
57.40%
82.56%
15.41%
22.41%
0.50%
1.38%
56.98%
88.20%
3.32%
9.10%
3.88%
1.84%

Ladakh
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
LADAKH MPI: PROGRESS REVIEW 2023
300

Ladakh
Ladakh
LADAKHMPI: PROGRESS REVIEW 2023
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
301
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Ladakh: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Kargil 19.29% 40.78% 0.079 3.70% 41.68% 0.015
Leh Ladakh
5.37% 38.74% 0.021 3.35% 40.64% 0.014
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
Ladakh: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
Kargil
Leh Ladakh
Ladakh: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
Kargil
Leh Ladakh
-15.59
-2.02
0.0%2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0%
% of population who are multidimensionally poor
3.70%
5.37%
19.29%
3.35%
-17.0-16.0-15.0-14.0-13.0-12.0-11.0-10.0-9.0-8.0-7.0-6.0-5.0-4.0-3.0-2.0-1.00.0
% point chage in proportion of multidimensionally poor population
District
District
District
LADAKH MPI: PROGRESS REVIEW 2023
302

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education
Standard of
Living
PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in
Lakshadweep
LAKSHADWEEP
Overview
Lakshadweep's Headcount Ratio, Intensity and MPI
Lakshadweep: Indicator Contribution to the MPI
Percentage contribution of each indicator to Lakshadweep's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
1.11%2019-21 0.00436.47%
1.82%2015-16 0.00735.80%
Rural
Headcount Ratio Intensity MPI
0.0010.36% 35.71%
Urban
Headcount Ratio Intensity MPI
0.0051.32% 36.52%
0.0051.16% 42.86% 0.0072.00% 34.69%
2019-21
2015-16
Multidimensional Poverty in Lakshadweep's Rural and Urban Areas
Nutrition: 46.56%
Child & Adolescent Mortality: 6.71%
Maternal Health: 10.99%
Years of Schooling: 0.00%
School Attendance: 16.37%
Cooking Fuel: 8.08%
Sanitation: 0.99%
Drinking Water: 3.17%
Electricity: 0.00%
Housing: 2.34%
Assets: 0.99%
Bank Account: 3.81%
Nutrition: 42.09%
Maternal Health: 8.63%
Years of Schooling: 5.58%
School Attendance: 23.31%
Cooking Fuel: 7.43%
Sanitation: 0.00%
Drinking Water: 5.84%
Electricity: 0.00%
Housing: 5.19%
Assets: 0.90%
Bank Account: 1.03%
MPI: PROGRESS REVIEW 2023
304
Year
Child & Adolescent Mortality: 0.00%

Percentage of total population who are deprived in each indicator
Lakshadweep: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Lakshadweep: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
LAKSHADWEEPMPI: PROGRESS REVIEW 2023
305
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
24.63%
31.47%
0.32%
1.96%
2.11%
6.50%
1.84%
0.95%
0.84%
1.43%
35.31%
58.15%
0.20%
0.44%
7.27%
9.17%
0.22%
0.05%
11.32%
1.54%
1.70%
1.02%
3.10%
5.62%
1.02%
1.82%
0.00%
0.53%
0.42%
0.86%
0.14%
0.00%
0.57%
0.64%
0.63%
1.11%
0.00%
0.14%
0.50%
0.43%
0.00%
0.00%
0.44%
0.32%
0.08%
0.14%
0.09%
0.52%

Lakshadweep
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
LAKSHADWEEP MPI: PROGRESS REVIEW 2023
306

Lakshadweep
Lakshadweep
LAKSHADWEEPMPI: PROGRESS REVIEW 2023
307
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Lakshadweep: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Lakshadweep 1.82% 35.80% 0.007 1.11% 36.47% 0.004
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
Lakshadweep: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
NFHS-5 (2019-21) NFHS-4 (2015-16)
Lakshadweep
Lakshadweep
Lakshadweep: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
-0.71
1.11%
1.82%
0.1%0.2%0.3%0.4%0.5%0.6%0.7%0.8%0.9%1.0%1.1%1.2%1.3%1.4%1.5%1.6%1.7%1.8%1.9%
% of population who are multidimensionally poor
District
District
0.0%
-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0
% point chage in proportion of multidimensionally poor population
District
LAKSHADWEEP MPI: PROGRESS REVIEW 2023
308

Nutrition
Child & Adolescent Mortality
Maternal Health
Years of Schooling School Attendance
Cooking Fuel
Sanitation Drinking Water
Electricity
Housing Assets
Bank Account
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
2015-162019-21
Health Education Standard of Living
Health Education Standard of Living
PROGRESS REVIEW 2023
A snapshot of multidimensional poverty in
Puducherry
PUDUCHERRY
Overview
Puducherry's Headcount Ratio, Intensity and MPI
Puducherry: Indicator Contribution to the MPI
Percentage contribution of each indicator to Puducherry's MPI Score
Headcount Ratio (H)Year MPI (HxA)Intensity (A)
0.85%2019-21 0.00338.03%
1.71%2015-16 0.00738.55%
Rural
Headcount Ratio Intensity MPI
0.0030.71% 38.90%
Urban
Headcount Ratio Intensity MPI
0.0030.91% 37.72%
0.0123.33% 36.74% 0.0040.98% 41.33%
2019-21
2015-16
Multidimensional Poverty in Puducherry's Rural and Urban Areas
Nutrition: 33.33%
Child & Adolescent Mortality: 3.56%
Maternal Health: 5.50%
Years of Schooling: 22.39%
School Attendance: 0.22%
Cooking Fuel: 9.72%
Sanitation: 10.21%
Drinking Water: 0.73%
Electricity: 0.77%
Housing: 7.91%
Assets: 3.04%
Bank Account: 2.64%
Nutrition: 29.01%
Child & Adolescent Mortality: 0.37%
Maternal Health: 2.86%
Years of Schooling: 20.90%
School Attendance: 19.50%
Cooking Fuel: 4.37%
Sanitation: 10.27%
Drinking Water: 1.12%
Electricity: 0.69%
Housing: 5.96%
Assets: 3.83%
Bank Account: 1.14%
Year
MPI: PROGRESS REVIEW 2023
310

Percentage of total population who are deprived in each indicator
Puducherry: Uncensored Headcount Ratio
Percentage of total population who are multidimensionally poor and deprived in each indicator
Puducherry: Censored Headcount Ratio
Health (2015-16)
Health (2019-21)
Education (2015-16)
Education (2019-21)
Standard of living (2015-16)
Standard of living (2019-21)
Dimension
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population multidimensionally poor and deprived
Health Education Standard of Living
Nutrition Child &
Adolescent
Mortality
Years of
Schooling
Cooking
Fuel
Drinking
Water
Housing Bank
Account
Maternal
Health
School
Attendance
Sanitation Electricity Assets
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
% of population deprived
0.56%
1.32%
0.01%
0.28%
0.11%
0.44%
0.40%
0.89%
0.38%
0.01%
0.30%
1.35%
0.69%
1.42%
0.08%
0.10%
0.05%
0.11%
0.40%
1.10%
0.26%
0.42%
0.08%
0.37%
13.88%
21.87%
0.23%
0.66%
3.32%
4.13%
3.41%
3.29%
1.67%
1.21%
4.73%
13.51%
15.25%
35.06%
2.20%
2.03%
0.13%
0.24%
11.31%
17.59%
2.10%
1.65%
2.11%
5.35%
PUDUCHERRYMPI: PROGRESS REVIEW 2023
311

Puducherry
Multidimensional Poverty Index Score (District-wise): NFHS-5 (2019-21)
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores for 2019-21.
Up to 0.009 0.010 to 0.0180.019 to 0.0270.028 to 0.0360.037 to 0.0450.046 to 0.0540.055 and above
PUDUCHERRY MPI: PROGRESS REVIEW 2023
312

Puducherry
Puducherry
The colour represents the MPI score of a district. 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 legend provides the range of MPI scores, based on values for 2015-16. Both the
comparative maps use the same legend to represent the change in MPI scores between 2015-16 and 2019-21.
Up to 0.023 0.024 to 0.0460.047 to 0.0690.070 to 0.0920.093 to 0.1160.117 to 0.1390.140 and above
PUDUCHERRYMPI: PROGRESS REVIEW 2023
313
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-4(2015-16)
Comparative view of the Multidimensional Poverty Index Score (District-wise): NFHS-5(2019-21)

Puducherry: Headcount Ratio
Percentage of population who are multidimensionally poor in each district
Puducherry: Changes over time for Headcount Ratio
District-wise percentage point change in the headcount ratio between 2015-16 and 2019-21
NFHS-5 (2019-21) NFHS-4 (2015-16)
Yanam
Yanam
Puducherry
Puducherry
Mahe
Mahe
Karaikal
Karaikal
0.0%0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5%
% of population who are multidimensionally poor
5.06%
3.95%
2.28%
1.30%
0.30%
3.13%
0.08%
-0.99
0.11
0.19%
-1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2
% point chage in proportion of multidimensionally poor population
-0.86
District
District
-1.10
PUDUCHERRY MPI: PROGRESS REVIEW 2023
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Puducherry: Overview of Districts
Headcount Ratio, Intensity and MPI
NFHS-4 (2015-16)
Headcount Ratio
Karaikal 3.13% 35.97% 0.011 2.28% 37.62% 0.009
Mahe 0.08% 35.71% 0.000 0.19% 33.33% 0.001
Puducherry 1.30% 39.28% 0.005 0.30% 39.36% 0.001
Yanam 5.06% 41.54% 0.021 3.95% 37.54% 0.015
Intensity MPI Headcount Ratio Intensity MPI
NFHS-5 (2019-21)
District
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SECTION
IV
Technical Notes
& Data Tables

SECTION 4
TECHNICAL NOTES
Estimation Details
4.1 Policy for treatment of Missing Values
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
subsequently in this section.
4.2 Policy for the indicator on Bank Accounts
In the case of the indicator for bank accounts, a certain
percentage of individuals in the NFHS have responded
“don’t know” when asked if they have a bank account.
For the national MPI, the individuals who responded
with “don’t know” 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 account, then it may be
considered analogous to them not having a bank
account to begin with.
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. On the other
hand, individuals who are already multidimensionally
poor by virtue of other indicators will be retained in the
final estimation sample.
4.3 Policy for the indicator on Maternal Health
The indicator for maternal health is comprised 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
treatment 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.
318
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.
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.

319
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 maternal health. A total
number of 6,087 unweighted observations from the
NFHS-4 dataset and 8,763 observations from the
NFHS-5 dataset fall in outcome 8 and there are no
observations in outcome 7.
If the policy for treatment of missing values is to be
applied, then these 6,087 observations from NFHS-4
and 8,763 observations from NFHS-5 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 NFHS-4 dataset and 8,763
observations from the NFHS-5 in the final MPI
estimation sample. In its place a different policy has
been utilized, the policy is as follows:
4.3.1 There are four steps involved to implement
this policy. They have been outlined in the
following paragraphs using the example of
how the policy was implemented in practice
for the NFHS-4 dataset.
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 in the NFHS-4 dataset 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.
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 ≥ k) 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,
4,921 observations are not multidimensionally poor,
1,058 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,
3,784 observations are not multidimensionally poor,
2,195 are multidimensionally poor and 108 have
missing values in other indicators and have been
dropped from the sample.

Outcome Number Deprived in Antenatal Care Deprived in Assisted DeliveryDeprived in Maternal Health
1 No No No
2 Yes No Yes
3 No Yes Yes
4 Yes Yes Yes
5 Yes Info Missing Yes
6 Info Missing Yes Yes
7 No Info Missing ?
8 Info Missing No ?
9 Info Missing Info Missing Observation is Dropped
MPI Poor Frequency Percent
No 4,921 80.84
Yes 1,058 17.38
Missing 108 1.77
Total 6,087 100.00
MPI Poor Frequency Percent
No 3,784 62.17
Yes 2,195 36.06
Missing 108 1.77
Total 6,087 100.00
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 MPI only if their deprivation score
is higher than the second order cutoff and irrespective of
the value taken by the indicator on maternal health.
TECHNICAL NOTESMPI: PROGRESS REVIEW 2023

iii Step 3
Identify observations whose deprivation status remains
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.
4,842 (3,784 not deprived and 1,058 deprived)
observations remain common across both the scenarios
i.e., their deprivation status (c
i
≥ k or c
i
< k) remains
unchanged irrespective of the value taken by the
maternal health indicator.
iv. Step 4
Of the 6,087 identified ambiguous observations, it can
be determined with absolute certainty that 4,842
observations will remain multidimensionally poor or not
regardless of the value taken by the maternal health
indicator. Therefore, these 4,842 observations will be
retained in the estimation sample of the national MPI.
As a result of the application of this policy, 6,985
observations from the NFHS-5, and 4,842 observations
from the NFHS-4 were retained in the overall estimation
sample for the national MPI.
4.4 Changes in the definitions of the indicators
The following are the indicators that have undergone
changes in their definitions in the NFHS-5 when
compared to NFHS-4:
4.4.1 Sanitation

According to the NFHS-4, improved sanitation
facilities include any non-shared toilet of the
following types: flush / pour flush toilets to piped
sewer systems, septic tanks, and pit latrines;
ventilated improved pit (VIP) / biogas latrines; pit
latrines with slabs; and twin pit / composting toilets.
However, according to the NFHS-5, improved toilet
facilities include any non-shared toilet of the
following types: flush / pour flush toilets to piped
sewer systems, septic tanks, pit latrines, or an
unknown destination; ventilated improved pit (VIP) /
biogas latrines; pit latrines with slabs; and twin pit /
composting toilets. To allow for comparability between
the 2 surveys, households with toilet flush to unknown
destination are considered as having access to
improved sanitation facility in this report.
4.4.2 Drinking water
According to the NFHS-4, improved sources of
drinking water include piped water, public taps,
standpipes, tube wells, boreholes, protected dug
wells and springs, rainwater, and community
reverse osmosis (RO) plants. According to the
NFHS-5, improved sources of drinking water
include piped water, public taps, standpipes, tube
wells, boreholes, protected dug wells and springs,
rainwater, tanker truck, cart with small tank, bottled
water, and community reverse osmosis (RO)
plants. To allow for comparability between the 2
surveys, households with access to drinking water
through tanker truck, cart with small tank or bottled
water are considered as having access to improved
drinking water source in this report.
4.5 Changes over time
The methodology for the calculation of the national
MPI, its indicators and its partial indices remain
unchanged across both the time periods. The
changes over time may be viewed as a simple
difference of the deprivation levels across two time
periods.
To verify the robustness of the changes over time
estimates, tests of significance have been carried out
at two levels on the estimates provided in this report:
1. Each point estimate for an indicator (i.e., the estimate
for that indicator in a single time period) has been
tested for significance at 95% level of confidence.
2. Each estimate for changes over time for an indicator
(i.e., the estimate for simple difference in the value of
the indicator for two time periods) has also been
tested for significance at 95% level of confidence.
There can be scenarios where the point estimate
for an indicator may be statistically significant, but
the change over time may not be so. Further details
are provided in the appendix. The report presents
estimates for the Headcount Ratio and Intensity
rounded off to two decimal points. Similarly,
estimates for the MPI value have been rounded off
to three decimal places and correspondingly their
changes over time estimates.
4.6 Sample size
The estimates for the baseline of the National MPI
based on the NFHS-4 utilized 26,98,699
unweighted observations as its estimation sample,
consisting of de jure household members for whom
the data for all twelve indicators of the national MPI
were present. Thus, from the 28,01,958
unweighted observations for de jure household
members present in the NFHS-4 microdata,
1,03,259 observations (3.69%) were dropped due
to data missing for one or more component indicators of the national MPI. Thus, the baseline estimates for the national MPI are based on 96.31% of the total unweighted sample of de jure household members in the NFHS-4 dataset.
The estimates for this edition of the national MPI
based on NFHS-5 utilized 26,66,529 unweighted observations as its estimation sample, consisting of de jure household members for whom the data for all twelve indicators of the national MPI are present. Thus, from the 27,95,894 unweighted observations for de jure household members present in the NFHS-5 microdata, 1,29,365 observations (4.63%) were dropped due to data missing for one or more component indicators of the national MPI. Therefore, the updated estimates of the national MPI are based on 95.37% of the total unweighted sample of de jure household members in the NFHS-5 dataset.
4.7 Major sample drop across districts
The districts given below have had at least 25% of
observations (i.e., individuals) dropped from the estimation sample in the given period due to missing or incomplete information in one or more indicators of the national MPI. It should be noted that because of this sample loss, the estimates for these districts may not be completely representative. Discretion during the interpretation of the results for these districts is advised.
The districts with at least 25% sample drop for the
estimates based on NFHS-5 (2019-21) are given below:
1. Agar Malwa (Madhya Pradesh) 2. Bhopal (Madhya Pradesh) 3. Raisen (Madhya Pradesh) 4. Mumbai Suburban (Maharashtra) 5. Hyderabad (Telangana) 6. Ghaziabad (Uttar Pradesh) 7. Khandwa (East Nimar) (Madhya Pradesh)
The districts with at least 25% sample drop for the
estimates based on NFHS-4 (2015-16) are given below:
1. Central Delhi (Delhi)
2. North Delhi (Delhi)
3. North West Delhi (Delhi)
4. South West Delhi (Delhi)
5. East Delhi (Delhi)
4.8 Micro-data Extraction, Treatment, and
Visualization
The micro-data for the NFHS-4 and NFHS-5 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, IABR74DL), Individual Recode
(IAIR74FL, IAIR74DL), Men's Recode (IAMR74FL,
IAMR74DL), and Person's Recode (IAPR74FL,
IAPR74DL). Extraction of data, adjustments for survey
design and application of sample weights was
completed adhering to the procedures stated in the
Standard Recode Manual. The processing of the
data and computation of point estimates and
estimate variance was carried out in STATA-17
(MP). The final point estimates and standard errors
were exported to Microsoft Excel for visualization.
The choropleth maps were constructed in Tableau
and QGIS using shapefiles obtained from the
Survey of India for the NFHS-4 (2015-16) estimates
and DHS Program Spatial Data Repository (DHS
2020) for the NFHS-5 (2019-21) estimates.
4.9 Estimation: Number of MPI Poor
This report provides data on the individuals who
have escaped multidimensional poverty, both at the
national and state levels. In this report, the
estimation is derived by multiplying the headcount
ratio of the respective years with the estimated
population size for the year 2021 in each region
(National and State/UT)
1
. This estimation approach
assumes that the rate of population growth remains
consistent with the changes in poverty levels.
This report uses population projections for India and
States for the period 2011- 2036 prepared by a
Technical Group under the chairmanship of
Registrar General of India, constituted by the
National Commission on Population (NCP) under
Ministry of Health and Family Welfare (MoHFW).
These estimates are based on data from 2011
Census and Sample Registration System (SRS).
These remain the best available estimates of
population given the fact that the Population Census
is a decennial exercise and cannot provide yearly
changes in the population. The latest Census in
India was conducted in 2011.

320
TECHNICAL NOTES MPI: PROGRESS REVIEW 2023

iii Step 3
Identify observations whose deprivation status remains
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.
4,842 (3,784 not deprived and 1,058 deprived)
observations remain common across both the scenarios
i.e., their deprivation status (c
i
≥ k or c
i
< k) remains
unchanged irrespective of the value taken by the
maternal health indicator.
iv. Step 4
Of the 6,087 identified ambiguous observations, it can
be determined with absolute certainty that 4,842
observations will remain multidimensionally poor or not
regardless of the value taken by the maternal health
indicator. Therefore, these 4,842 observations will be
retained in the estimation sample of the national MPI.
As a result of the application of this policy, 6,985
observations from the NFHS-5, and 4,842 observations
from the NFHS-4 were retained in the overall estimation
sample for the national MPI.
4.4 Changes in the definitions of the indicators
The following are the indicators that have undergone
changes in their definitions in the NFHS-5 when
compared to NFHS-4:
4.4.1 Sanitation

According to the NFHS-4, improved sanitation
facilities include any non-shared toilet of the
following types: flush / pour flush toilets to piped
sewer systems, septic tanks, and pit latrines;
ventilated improved pit (VIP) / biogas latrines; pit
latrines with slabs; and twin pit / composting toilets.
However, according to the NFHS-5, improved toilet
facilities include any non-shared toilet of the
following types: flush / pour flush toilets to piped
sewer systems, septic tanks, pit latrines, or an
unknown destination; ventilated improved pit (VIP) /
biogas latrines; pit latrines with slabs; and twin pit /
composting toilets. To allow for comparability between
the 2 surveys, households with toilet flush to unknown
destination are considered as having access to
improved sanitation facility in this report.
4.4.2 Drinking water
According to the NFHS-4, improved sources of
drinking water include piped water, public taps,
standpipes, tube wells, boreholes, protected dug
wells and springs, rainwater, and community
reverse osmosis (RO) plants. According to the
NFHS-5, improved sources of drinking water
include piped water, public taps, standpipes, tube
wells, boreholes, protected dug wells and springs,
rainwater, tanker truck, cart with small tank, bottled
water, and community reverse osmosis (RO)
plants. To allow for comparability between the 2
surveys, households with access to drinking water
through tanker truck, cart with small tank or bottled
water are considered as having access to improved
drinking water source in this report.
4.5 Changes over time
The methodology for the calculation of the national
MPI, its indicators and its partial indices remain
unchanged across both the time periods. The
changes over time may be viewed as a simple
difference of the deprivation levels across two time
periods.
To verify the robustness of the changes over time
estimates, tests of significance have been carried out
at two levels on the estimates provided in this report:
1. Each point estimate for an indicator (i.e., the estimate
for that indicator in a single time period) has been
tested for significance at 95% level of confidence.
2. Each estimate for changes over time for an indicator
(i.e., the estimate for simple difference in the value of
the indicator for two time periods) has also been
tested for significance at 95% level of confidence.
There can be scenarios where the point estimate
for an indicator may be statistically significant, but
the change over time may not be so. Further details
are provided in the appendix. The report presents
estimates for the Headcount Ratio and Intensity
rounded off to two decimal points. Similarly,
estimates for the MPI value have been rounded off
to three decimal places and correspondingly their
changes over time estimates.
4.6 Sample size
The estimates for the baseline of the National MPI
based on the NFHS-4 utilized 26,98,699
unweighted observations as its estimation sample,
consisting of de jure household members for whom
the data for all twelve indicators of the national MPI
were present. Thus, from the 28,01,958
unweighted observations for de jure household
members present in the NFHS-4 microdata,
1,03,259 observations (3.69%) were dropped due
to data missing for one or more component indicators
of the national MPI. Thus, the baseline estimates for
the national MPI are based on 96.31% of the total
unweighted sample of de jure household members in
the NFHS-4 dataset.
The estimates for this edition of the national MPI
based on NFHS-5 utilized 26,66,529 unweighted
observations as its estimation sample, consisting of
de jure household members for whom the data for all
twelve indicators of the national MPI are present.
Thus, from the 27,95,894 unweighted observations
for de jure household members present in the
NFHS-5 microdata, 1,29,365 observations (4.63%)
were dropped due to data missing for one or more
component indicators of the national MPI. Therefore,
the updated estimates of the national MPI are based
on 95.37% of the total unweighted sample of de jure
household members in the NFHS-5 dataset.
4.7 Major sample drop across districts
The districts given below have had at least 25% of
observations (i.e., individuals) dropped from the
estimation sample in the given period due to missing or
incomplete information in one or more indicators of the
national MPI. It should be noted that because of this
sample loss, the estimates for these districts may not
be completely representative. Discretion during the
interpretation of the results for these districts is advised.
The districts with at least 25% sample drop for the
estimates based on NFHS-5 (2019-21) are given below:
1. Agar Malwa (Madhya Pradesh)
2. Bhopal (Madhya Pradesh)
3. Raisen (Madhya Pradesh)
4. Mumbai Suburban (Maharashtra)
5. Hyderabad (Telangana)
6. Ghaziabad (Uttar Pradesh)
7. Khandwa (East Nimar) (Madhya Pradesh)
The districts with at least 25% sample drop for the
estimates based on NFHS-4 (2015-16) are given below:
1. Central Delhi (Delhi)
2. North Delhi (Delhi)
3. North West Delhi (Delhi)
4. South West Delhi (Delhi)
5. East Delhi (Delhi)
4.8 Micro-data Extraction, Treatment, and
Visualization
The micro-data for the NFHS-4 and NFHS-5 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, IABR74DL), Individual Recode
(IAIR74FL, IAIR74DL), Men's Recode (IAMR74FL,
IAMR74DL), and Person's Recode (IAPR74FL,
IAPR74DL). Extraction of data, adjustments for survey
design and application of sample weights was
completed adhering to the procedures stated in the
Standard Recode Manual. The processing of the
data and computation of point estimates and
estimate variance was carried out in STATA-17
(MP). The final point estimates and standard errors
were exported to Microsoft Excel for visualization.
The choropleth maps were constructed in Tableau
and QGIS using shapefiles obtained from the
Survey of India for the NFHS-4 (2015-16) estimates
and DHS Program Spatial Data Repository (DHS
2020) for the NFHS-5 (2019-21) estimates.
4.9 Estimation: Number of MPI Poor
This report provides data on the individuals who
have escaped multidimensional poverty, both at the
national and state levels. In this report, the
estimation is derived by multiplying the headcount
ratio of the respective years with the estimated
population size for the year 2021 in each region
(National and State/UT)
1
. This estimation approach
assumes that the rate of population growth remains
consistent with the changes in poverty levels.
This report uses population projections for India and
States for the period 2011- 2036 prepared by a
Technical Group under the chairmanship of
Registrar General of India, constituted by the
National Commission on Population (NCP) under
Ministry of Health and Family Welfare (MoHFW).
These estimates are based on data from 2011
Census and Sample Registration System (SRS).
These remain the best available estimates of
population given the fact that the Population Census
is a decennial exercise and cannot provide yearly
changes in the population. The latest Census in
India was conducted in 2011.

321
TECHNICAL NOTESMPI: PROGRESS REVIEW 2023
1
There are multiple ways of estimating the number of people who have escaped poverty depending on the assumptions used. Alternatives can include using 
the year(s) of each survey multiplied by incidence, to obtain the number of poor persons in each period, then take the dierence between these numbers. 
If surveys are done over several years, computations could use the frst year, the most recent year, the simple average of the years' population fgures, or the 
weighted average according to the share of interviews collected in each year as well.

REFERENCES MPI: PROGRESS REVIEW 2023
REFERENCES
Alkire, S. (2020). Multidimensional Poverty Measures as Policy Tools. In 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., and Suppa, N. (2022). ‘A methodological note on the global Multidimensional Poverty Index (MPI)
2022 changes over time results for 84 countries’, OPHI MPI Methodological Note 54, Oxford Poverty and Human Development
Initiative (OPHI), University of Oxford.
Alkire, S., Kanagaratnam, U., & Suppa, N. (2019). The Global Multidimensional Poverty Index (MPI) 2019. OPHI MPI
Methodological Note 47, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.
Chakravarty, S. R. (2009). Inequality, Polarization, and Poverty: Advances in Distributional Analysis. 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 Dimensions of Poverty: Measurement, Epistemic Injustices, Activism (p.
229). Springer.
Gaur S. & Rao N. S. (2020). Poverty Measurement in India: A Status Update. Working Paper No. 1/2020. New Delhi: Ministry of
Rural Development
Godinot, X., & Walker, R. (2020). Poverty in All Its Forms: Determining the Dimensions of Poverty Through Merging Knowledge. In
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. (2020). Health And Family Welfare Statistics in India 2019-20. Government of India.
National Commission on Population, Ministry of Health & Family Welfare. (2020). Population Projections for India and States 2011
- 2036. 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.
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.
UNDP. (2018). What Does it Mean to Leave No One Behind? A UNDP discussion paper and framework for implementation
UNDP. (2010). Human Development Report 2010. New York: Palgrave Macmillan.
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.
322

INDEX OF TABLESMPI: PROGRESS REVIEW 2023
Table 1 - State/UT-Wise: Headcount Ratio, Intensity, MPI
Table 2 - State/UT-Wise: Headcount Ratio, Intensity, MPI (Rural)
Table 3 - State/UT-Wise: Headcount Ratio, Intensity, MPI (Urban)
Table 4 - State/UT-wise: Uncensored Headcount Ratio
Table 5 - State/UT-wise: Uncensored Headcount Ratio (Rural)
Table 6 - State/UT-wise: Uncensored Headcount Ratio (Urban)
Table 7 - State/UT-wise: Censored Headcount Ratio
Table 8 - State/UT-wise: Censored Headcount Ratio (Rural)
Table 9 - State/UT-wise: Censored Headcount Ratio (Urban)
Table 10 - State/UT-wise: Indicator Contribution to the MPI Score
Table 11 - State/UT-wise: Indicator Contribution to the MPI Score (Rural)
Table 12 - State/UT-wise: Indicator Contribution to the MPI Score (Urban)
Table 13 - Standard Errors: State/UT-wise - Headcount Ratio, Intensity, MPI
Table 14 - Standard Errors: State/UT-wise - Headcount Ratio, Intensity, MPI (Rural)
Table 15 - Standard Errors: State/UT-wise - Headcount Ratio, Intensity, MPI (Urban)
Table 16 - Standard Errors: State/UT-wise - Uncensored Headcount Ratio
Table 17 - Standard Errors: State/UT-wise - Uncensored Headcount Ratio (Rural)
Table 18 - Standard Errors: State/UT-wise - Uncensored Headcount Ratio (Urban)
Table 19 - Standard Errors: State/UT-wise - Censored Headcount Ratio
Table 20 - Standard Errors: State/UT-wise - Censored Headcount Ratio (Rural)
Table 21 - Standard Errors: State/UT-wise - Censored Headcount Ratio (Urban)
INDEX OF TABLES
323

Table 1 - State/UT-Wise: Headcount Ratio, Intensity, MPI
State/UT
Headcount Ratio (H)
2015-16 (x)2019-21 (y)
Change
(y-x)
Intensity (A)
2015-16 (x)2019-21 (y)
Change
(y-x)
MPI
2015-16 (x)2019-21 (y)
Change
(y-x)
Andhra Pradesh 11.77% 6.06% -5.71% 43.28% 41.12% -2.16% 0.051 0.025 -0.026
Arunachal Pradesh 24.23% 13.76% -10.48% 47.25% 43.04% -4.21% 0.115 0.059 -0.055
Assam 32.65% 19.35% -13.30% 47.88% 44.41% -3.47% 0.156 0.086 -0.070
Bihar 51.89% 33.76% -18.13% 51.01% 47.40% -3.61% 0.265 0.160 -0.105
Chhattisgarh 29.90% 16.37% -13.53% 44.64% 42.61% -2.03% 0.133 0.070 -0.064
Goa 3.76% 0.84% -2.92% 40.13% 38.69% -1.44% 0.015 0.003 -0.012
Gujarat 18.47% 11.66% -6.81% 44.97% 43.25% -1.72% 0.083 0.050 -0.033
Haryana 11.88% 7.07% -4.81% 44.40% 43.34% -1.06% 0.053 0.031 -0.022
Himachal Pradesh 7.59% 4.93% -2.65% 39.44% 40.22% 0.78% 0.030 0.020 -0.010
Jharkhand 42.10% 28.81% -13.29% 47.92% 45.59% -2.33% 0.202 0.131 -0.070
Karnataka 12.77% 7.58% -5.20% 42.76% 41.21% -1.55% 0.055 0.031 -0.023
Kerala 0.70% 0.55% -0.15% 38.99% 36.92% -2.06% 0.003 0.002 -0.001
Madhya Pradesh 36.57% 20.63% -15.94% 47.25% 43.70% -3.55% 0.173 0.090 -0.083
Maharashtra 14.80% 7.81% -6.99% 43.76% 41.77% -1.98% 0.065 0.033 -0.032
Manipur 16.96% 8.10% -8.86% 44.61% 41.91% -2.69% 0.076 0.034 -0.042
Meghalaya 32.54% 27.79% -4.75% 48.08% 48.01% -0.07% 0.156 0.133 -0.023
Mizoram 9.78% 5.30% -4.48% 47.42% 45.62% -1.81% 0.046 0.024 -0.022
Nagaland 25.16% 15.43% -9.73% 46.29% 42.61% -3.69% 0.116 0.066 -0.051
Odisha 29.34% 15.68% -13.65% 46.42% 44.50% -1.92% 0.136 0.070 -0.066
Punjab 5.57% 4.75% -0.82% 43.74% 41.22% -2.52% 0.024 0.020 -0.005
Rajasthan 28.86% 15.31% -13.56% 47.34% 42.70% -4.63% 0.137 0.065 -0.071
Sikkim 3.82% 2.60% -1.21% 41.20% 41.02% -0.18% 0.016 0.011 -0.005
Tamil Nadu 4.76% 2.20% -2.56% 39.97% 38.70% -1.27% 0.019 0.009 -0.011
Telangana 13.18% 5.88% -7.30% 43.29% 40.85% -2.44% 0.057 0.024 -0.033
Tripura 16.62% 13.11% -3.50% 45.03% 42.68% -2.36% 0.075 0.056 -0.019
Uttar Pradesh 37.68% 22.93% -14.75% 47.60% 44.83% -2.77% 0.179 0.103 -0.077
Uttarakhand 17.67% 9.67% -8.00% 44.35% 41.99% -2.36% 0.078 0.041 -0.038
West Bengal 21.29% 11.89% -9.41% 45.50% 42.35% -3.14% 0.097 0.050 -0.047
Andaman & Nicobar Islands 4.29% 2.30% -1.99% 40.50% 40.62% 0.13% 0.017 0.009 -0.008
Chandigarh 5.97% 3.52% -2.46% 43.39% 47.41% 4.02% 0.026 0.017 -0.009
Dadra & Nagar Haveli & Daman & Diu 19.58% 9.21% -10.38% 44.23% 42.15% -2.08% 0.087 0.039 -0.048
Delhi 4.44% 3.43% -1.02% 43.92% 41.99% -1.93% 0.020 0.014 -0.005
Jammu & Kashmir 12.56% 4.80% -7.76% 44.17% 42.11% -2.06% 0.055 0.020 -0.035
Ladakh 12.70% 3.53% -9.17% 40.37% 41.20% 0.83% 0.051 0.015 -0.037
Lakshadweep 1.82% 1.11% -0.71% 35.80% 36.47% 0.67% 0.007 0.004 -0.002
Puducherry 1.71% 0.85% -0.87% 38.55% 38.03% -0.53% 0.007 0.003 -0.003
India 24.85% 14.96% -9.89% 47.14% 44.39% -2.75% 0.117 0.066 -0.051
State UT
DATA TABLES MPI: PROGRESS REVIEW 2023
324

Table 2 - State/UT-Wise: Headcount Ratio, Intensity, MPI (Rural)
State/UT
Headcount Ratio (H)
2015-16 (x)2019-21 (y)
Change
(y-x)
Intensity (A)
2015-16 (x)2019-21 (y)
Change
(y-x)
MPI
2015-16 (x)2019-21 (y)
Change
(y-x)
Andhra Pradesh 14.72% 7.71% -7.01% 43.32% 41.41% -1.91% 0.064 0.032 -0.032
Arunachal Pradesh 29.20% 15.14% -14.05% 47.59% 43.15% -4.45% 0.139 0.065 -0.074
Assam 36.14% 21.41% -14.73% 48.06% 44.50% -3.56% 0.174 0.095 -0.078
Bihar 56.00% 36.95% -19.05% 51.14% 47.52% -3.62% 0.286 0.176 -0.111
Chhattisgarh 35.73% 19.71% -16.02% 44.83% 42.67% -2.16% 0.160 0.084 -0.076
Goa 4.44% 1.90% -2.54% 39.25% 39.15% -0.10% 0.017 0.007 -0.010
Gujarat 27.25% 17.15% -10.10% 45.11% 43.47% -1.63% 0.123 0.075 -0.048
Haryana 14.61% 8.41% -6.20% 44.29% 43.42% -0.87% 0.065 0.037 -0.028
Himachal Pradesh 8.21% 5.23% -2.98% 39.29% 39.46% 0.17% 0.032 0.021 -0.012
Jharkhand 50.92% 34.93% -15.99% 48.26% 45.76% -2.50% 0.246 0.160 -0.086
Karnataka 18.45% 10.33% -8.12% 42.87% 41.36% -1.51% 0.079 0.043 -0.036
Kerala 0.95% 0.76% -0.18% 39.76% 37.14% -2.62% 0.004 0.003 -0.001
Madhya Pradesh 45.90% 25.32% -20.58% 47.57% 43.82% -3.75% 0.218 0.111 -0.107
Maharashtra 22.74% 11.49% -11.25% 43.98% 41.94% -2.04% 0.100 0.048 -0.052
Manipur 22.33% 10.95% -11.38% 45.11% 42.20% -2.91% 0.101 0.046 -0.055
Meghalaya 38.49% 32.43% -6.06% 48.39% 48.17% -0.22% 0.186 0.156 -0.030
Mizoram 20.45% 10.77% -9.68% 47.95% 45.86% -2.09% 0.098 0.049 -0.049
Nagaland 32.73% 19.88% -12.85% 46.65% 42.67% -3.98% 0.153 0.085 -0.068
Odisha 32.64% 17.72% -14.92% 46.44% 44.58% -1.86% 0.152 0.079 -0.073
Punjab 6.38% 4.74% -1.64% 43.21% 41.19% -2.02% 0.028 0.020 -0.008
Rajasthan 34.53% 18.62% -15.91% 47.60% 42.80% -4.80% 0.164 0.080 -0.085
Sikkim 4.25% 3.75% -0.50% 41.15% 41.22% 0.06% 0.018 0.015 -0.002
Tamil Nadu 7.18% 2.90% -4.29% 40.21% 38.84% -1.37% 0.029 0.011 -0.018
Telangana 19.51% 7.51% -12.00% 43.33% 40.88% -2.46% 0.085 0.031 -0.054
Tripura 20.93% 16.47% -4.47% 45.34% 42.84% -2.50% 0.095 0.071 -0.024
Uttar Pradesh 44.29% 26.35% -17.94% 47.66% 44.89% -2.76% 0.211 0.118 -0.093
Uttarakhand 21.87% 10.84% -11.03% 43.75% 41.13% -2.62% 0.096 0.045 -0.051
West Bengal 25.66% 15.15% -10.50% 45.39% 42.26% -3.13% 0.116 0.064 -0.052
Andaman & Nicobar Islands 6.75% 2.71% -4.04% 40.79% 41.55% 0.76% 0.028 0.011 -0.016
Chandigarh 18.56% 3.88% -14.67% 47.88% 38.10% -9.79% 0.089 0.015 -0.074
Dadra & Nagar Haveli & Daman & Diu 35.74% 12.27% -23.47% 44.42% 41.48% -2.95% 0.159 0.051 -0.108
Delhi 2.39% 2.57% 0.18% 39.74% 42.72% 2.98% 0.010 0.011 0.001
Jammu & Kashmir 16.37% 6.10% -10.28% 44.34% 42.29% -2.05% 0.073 0.026 -0.047
Ladakh 16.20% 3.89% -12.31% 40.27% 41.44% 1.16% 0.065 0.016 -0.049
Lakshadweep 1.16% 0.36% -0.81% 42.86% 35.71% -7.14% 0.005 0.001 -0.004
Puducherry 3.33% 0.71% -2.62% 36.74% 38.90% 2.16% 0.012 0.003 -0.009
India 32.59% 19.28% -13.31% 47.38% 44.55% -2.83% 0.154 0.086 -0.068
State UT
DATA TABLESMPI: PROGRESS REVIEW 2023
325

State UTTable 3 - State/UT-Wise: Headcount Ratio, Intensity, MPI (Urban)
State/UT
Headcount Ratio (H)
2015-16 (x)2019-21 (y)
Change
(y-x)
Intensity (A)
2015-16 (x)2019-21 (y)
Change
(y-x)
MPI
2015-16 (x)2019-21 (y)
Change
(y-x)
Andhra Pradesh 4.63% 2.20% -2.43% 42.97% 38.77% -4.20% 0.020 0.009 -0.011
Arunachal Pradesh 8.08% 5.90% -2.17% 43.24% 41.53% -1.71% 0.035 0.025 -0.010
Assam 9.94% 6.88% -3.06% 43.57% 42.61% -0.97% 0.043 0.029 -0.014
Bihar 23.85% 16.67% -7.18% 49.02% 45.95% -3.07% 0.117 0.077 -0.040
Chhattisgarh 10.17% 4.59% -5.58% 42.34% 41.69% -0.65% 0.043 0.019 -0.024
Goa 3.34% 0.12% -3.22% 40.84% 33.94% -6.90% 0.014 0.000 -0.013
Gujarat 6.49% 3.81% -2.69% 44.19% 41.79% -2.40% 0.029 0.016 -0.013
Haryana 7.52% 4.26% -3.26% 44.74% 43.00% -1.75% 0.034 0.018 -0.015
Himachal Pradesh 1.46% 2.96% 1.50% 47.61% 49.27% 1.66% 0.007 0.015 0.008
Jharkhand 15.04% 8.67% -6.36% 44.32% 43.24% -1.08% 0.067 0.038 -0.029
Karnataka 4.92% 3.22% -1.70% 42.22% 40.47% -1.74% 0.021 0.013 -0.008
Kerala 0.43% 0.32% -0.11% 37.06% 36.36% -0.69% 0.002 0.001 0.000
Madhya Pradesh 13.72% 7.10% -6.61% 44.62% 42.51% -2.11% 0.061 0.030 -0.031
Maharashtra 5.54% 3.07% -2.47% 42.69% 40.96% -1.72% 0.024 0.013 -0.011
Manipur 8.49% 3.43% -5.07% 42.51% 40.42% -2.09% 0.036 0.014 -0.022
Meghalaya 8.41% 8.14% -0.27% 42.43% 45.40% 2.97% 0.036 0.037 0.001
Mizoram 1.40% 0.58% -0.82% 41.39% 41.68% 0.29% 0.006 0.002 -0.003
Nagaland 10.70% 6.14% -4.56% 44.23% 42.20% -2.03% 0.047 0.026 -0.021
Odisha 12.32% 5.42% -6.89% 46.11% 43.15% -2.97% 0.057 0.023 -0.033
Punjab 4.32% 4.76% 0.44% 44.95% 41.27% -3.68% 0.019 0.020 0.000
Rajasthan 11.21% 4.54% -6.67% 44.79% 41.39% -3.40% 0.050 0.019 -0.031
Sikkim 2.80% 0.51% -2.29% 41.36% 38.44% -2.92% 0.012 0.002 -0.010
Tamil Nadu 2.37% 1.41% -0.96% 39.25% 38.37% -0.88% 0.009 0.005 -0.004
Telangana 4.92% 2.73% -2.19% 43.06% 40.70% -2.36% 0.021 0.011 -0.010
Tripura 5.50% 4.69% -0.80% 42.08% 41.26% -0.82% 0.023 0.019 -0.004
Uttar Pradesh 17.72% 11.57% -6.15% 47.14% 44.36% -2.78% 0.084 0.051 -0.032
Uttarakhand 9.89% 7.00% -2.89% 46.80% 45.03% -1.76% 0.046 0.032 -0.015
West Bengal 11.56% 5.04% -6.52% 46.02% 42.92% -3.10% 0.053 0.022 -0.032
Andaman & Nicobar Islands 0.97% 1.60% 0.63% 37.76% 37.96% 0.20% 0.004 0.006 0.002
Chandigarh 5.45% 3.51% -1.94% 42.76% 47.55% 4.79% 0.023 0.017 -0.007
Dadra & Nagar Haveli & Daman & Diu 5.72% 5.67% -0.05% 43.20% 43.85% 0.64% 0.025 0.025 0.000
Delhi 4.46% 3.45% -1.01% 43.94% 41.98% -1.96% 0.020 0.014 -0.005
Jammu & Kashmir 3.51% 1.09% -2.42% 42.30% 39.26% -3.04% 0.015 0.004 -0.011
Ladakh 3.02% 2.00% -1.02% 41.82% 39.27% -2.55% 0.013 0.008 -0.005
Lakshadweep 2.00% 1.32% -0.68% 34.69% 36.52% 1.84% 0.007 0.005 -0.002
Puducherry 0.98% 0.91% -0.08% 41.33% 37.72% -3.61% 0.004 0.003 -0.001
India 8.65% 5.27% -3.38% 45.27% 43.10% -2.17% 0.039 0.023 -0.016
326
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 4 - State/UT-wise: Uncensored Headcount Ratio % of total population deprived in each indicator
State/UT Andhra Pradesh 26.38% 22.94% -3.44% 1.82% 1.27% -0.55% 9.66% 10.77% 1.11% 16.90% 15.81% -1.09%
Arunachal Pradesh 21.05% 17.10% -3.95% 1.97% 1.10% -0.86% 28.34% 22.21% -6.13% 17.75% 14.22% -3.53%
Assam 39.67% 31.83% -7.83% 2.90% 1.77% -1.13% 25.44% 21.40% -4.04% 16.18% 12.35% -3.83%
Bihar 51.87% 42.20% -9.68% 4.58% 4.14% -0.45% 45.61% 37.21% -8.40% 26.26% 22.29% -3.97%
Chhattisgarh 43.02% 35.12% -7.90% 3.32% 2.33% -1.00% 24.70% 20.21% -4.49% 13.47% 10.57% -2.89%
Goa 24.65% 20.23% -4.42% 0.57% 0.39% -0.18% 7.14% 1.88% -5.26% 4.70% 2.53% -2.17%
Gujarat 41.37% 38.09% -3.27% 2.21% 1.81% -0.40% 14.77% 12.72% -2.05% 9.82% 7.94% -1.88%
Haryana 32.34% 26.19% -6.15% 2.17% 1.85% -0.32% 23.86% 16.83% -7.03% 7.09% 5.51% -1.58%
Himachal Pradesh 27.18% 22.98% -4.20% 1.66% 1.07% -0.59% 17.42% 12.51% -4.90% 3.78% 4.63% 0.85%
Jharkhand 48.02% 40.32% -7.69% 3.32% 2.57% -0.75% 33.07% 29.75% -3.33% 18.30% 16.17% -2.13%
Karnataka 33.56% 29.97% -3.60% 1.34% 1.29% -0.05% 12.36% 12.58% 0.22% 8.69% 7.15% -1.54%
Kerala 15.29% 16.44% 1.14% 0.19% 0.20% 0.01% 1.73% 3.30% 1.57% 1.78% 2.49% 0.72%
Madhya Pradesh 45.49% 34.63% -10.86% 3.60% 2.32% -1.28% 29.38% 21.40% -7.98% 16.07% 12.14% -3.92%
Maharashtra 36.10% 32.29% -3.81% 1.42% 1.11% -0.31% 15.95% 15.32% -0.63% 6.54% 5.91% -0.63%
Manipur 23.57% 17.87% -5.70% 1.80% 1.66% -0.14% 17.66% 12.26% -5.40% 5.35% 4.59% -0.77%
Meghalaya 37.05% 34.72% -2.33% 3.10% 2.99% -0.11% 31.70% 31.39% -0.31% 19.71% 16.70% -3.01%
Mizoram 21.38% 15.63% -5.75% 2.30% 0.93% -1.37% 16.11% 11.32% -4.78% 7.92% 6.79% -1.13%
Nagaland 24.49% 20.61% -3.88% 2.06% 1.42% -0.64% 33.05% 22.15% -10.90% 13.61% 10.49% -3.13%
Odisha 37.27% 30.77% -6.50% 2.23% 1.57% -0.66% 19.49% 14.83% -4.66% 16.64% 13.44% -3.20%
Punjab 22.11% 20.80% -1.31% 1.39% 1.32% -0.07% 12.70% 14.24% 1.54% 7.28% 6.72% -0.56%
Rajasthan 42.62% 34.09% -8.53% 2.95% 2.14% -0.81% 26.33% 21.17% -5.16% 17.09% 10.06% -7.03%
Sikkim 13.32% 10.36% -2.96% 1.00% 0.26% -0.74% 5.42% 6.72% 1.30% 8.20% 8.59% 0.39%
Tamil Nadu 24.77% 19.17% -5.60% 1.15% 0.84% -0.31% 6.70% 3.31% -3.38% 6.61% 8.53% 1.92%
Telangana 31.09% 28.35% -2.74% 1.38% 1.15% -0.23% 10.87% 13.17% 2.30% 15.83% 14.56% -1.26%
Tripura 28.02% 26.13% -1.89% 1.28% 1.55% 0.28% 13.49% 16.07% 2.58% 10.79% 10.47% -0.33%
Uttar Pradesh 44.47% 36.43% -8.04% 4.97% 3.54% -1.43% 35.44% 30.03% -5.41% 17.49% 13.18% -4.31%
Uttarakhand 32.85% 23.68% -9.17% 2.58% 1.89% -0.69% 28.54% 20.42% -8.12% 9.76% 7.89% -1.87%
West Bengal 33.62% 27.28% -6.33% 1.50% 1.06% -0.43% 14.38% 11.43% -2.95% 15.84% 12.87% -2.96%
Andaman & Nicobar Islands 22.05% 15.09% -6.96% 0.83% 0.91% 0.08% 5.11% 4.02% -1.09% 4.87% 5.93% 1.05%
Chandigarh 23.11% 21.57% -1.55% 1.16% 1.17% 0.00% 11.05% 7.32% -3.73% 5.83% 4.54% -1.29%
Dadra & Nagar Haveli & Daman & Diu 36.71% 37.81% 1.10% 1.63% 1.73% 0.10% 13.80% 8.06% -5.74% 7.53% 8.16% 0.63%
Delhi 23.41% 20.38% -3.02% 1.91% 1.38% -0.53% 15.20% 10.07% -5.12% 5.93% 4.37% -1.56%
Jammu & Kashmir 25.88% 15.52% -10.36% 1.85% 0.73% -1.11% 12.73% 7.58% -5.15% 6.83% 4.25% -2.58%
Ladakh 26.72% 14.40% -12.32% 2.11% 0.91% -1.20% 11.62% 7.07% -4.55% 7.04% 4.08% -2.95%
Lakshadweep 31.47% 24.63% -6.84% 1.96% 0.32% -1.64% 6.50% 2.11% -4.39% 0.95% 1.84% 0.89%
Puducherry 21.87% 13.88% -7.99% 0.66% 0.23% -0.43% 4.13% 3.32% -0.81% 3.29% 3.41% 0.12%
India 37.60% 31.52% -6.07% 2.69% 2.06% -0.63% 22.58% 19.17% -3.42% 13.86% 11.40% -2.46%
State UT
Nutrition
Child & Adolescent Mortality
Years of Schooling
Maternal Health
HealthEducation
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
327
DATA TABLESMPI: PROGRESS REVIEW 2023

DATA TABLES MPI: PROGRESS REVIEW 2023
State UTTable 4 - State/UT-wise: Uncensored Headcount Ratio % of total population deprived in each indicator
State/UT Andhra Pradesh 2.34% 1.35% -1.00% 37.90% 16.09% -21.81% 46.38% 22.84% -23.54% 12.33% 9.14% -3.19%
Arunachal Pradesh 8.15% 5.19% -2.96% 57.78% 48.05% -9.73% 38.55% 17.13% -21.41% 14.81% 6.62% -8.20%
Assam 6.54% 4.31% -2.23% 77.12% 59.33% -17.78% 51.19% 31.58% -19.61% 17.43% 14.91% -2.52%
Bihar 12.53% 10.61% -1.91% 82.92% 63.30% -19.62% 73.49% 50.78% -22.70% 2.12% 1.64% -0.48%
Chhattisgarh 5.38% 5.50% 0.12% 78.04% 66.85% -11.19% 65.37% 23.16% -42.21% 18.14% 8.37% -9.77%
Goa 0.96% 0.70% -0.26% 14.91% 2.57% -12.34% 21.38% 12.26% -9.12% 3.34% 1.52% -1.82%
Gujarat 6.68% 5.06% -1.62% 48.79% 34.74% -14.05% 37.09% 26.05% -11.04% 7.71% 5.31% -2.40%
Haryana 3.82% 4.31% 0.49% 51.24% 43.93% -7.31% 19.19% 15.11% -4.08% 6.63% 6.71% 0.09%
Himachal Pradesh 0.89% 0.91% 0.02% 67.90% 52.74% -15.16% 27.63% 18.27% -9.36% 7.72% 5.14% -2.58%
Jharkhand 8.19% 8.45% 0.26% 82.14% 69.12% -13.02% 75.32% 43.36% -31.95% 30.32% 18.61% -11.71%
Karnataka 3.53% 2.50% -1.03% 45.54% 21.47% -24.07% 42.67% 25.65% -17.02% 9.44% 7.06% -2.38%
Kerala 0.54% 0.25% -0.29% 43.89% 28.12% -15.77% 1.83% 1.27% -0.56% 5.56% 5.40% -0.16%
Madhya Pradesh 8.38% 6.76% -1.63% 71.24% 60.88% -10.36% 65.15% 35.51% -29.63% 29.25% 21.73% -7.52%
Maharashtra 4.20% 2.35% -1.86% 39.49% 20.07% -19.42% 47.94% 28.33% -19.61% 12.61% 9.53% -3.08%
Manipur 2.36% 2.33% -0.03% 58.92% 28.75% -30.17% 47.54% 35.23% -12.31% 38.50% 26.77% -11.73%
Meghalaya 6.15% 7.41% 1.25% 77.08% 67.63% -9.45% 38.56% 17.10% -21.45% 31.77% 23.10% -8.67%
Mizoram 3.75% 2.50% -1.25% 32.17% 17.06% -15.12% 15.81% 4.66% -11.14% 7.79% 4.82% -2.97%
Nagaland 4.81% 4.45% -0.36% 69.28% 56.48% -12.79% 23.18% 12.24% -10.93% 19.26% 10.47% -8.78%
Odisha 4.95% 3.92% -1.03% 80.94% 65.94% -15.00% 70.32% 39.85% -30.47% 20.61% 13.55% -7.06%
Punjab 2.59% 2.77% 0.18% 36.40% 25.33% -11.07% 17.28% 13.69% -3.59% 1.54% 1.84% 0.29%
Rajasthan 8.48% 4.25% -4.23% 69.94% 60.56% -9.38% 53.90% 29.03% -24.88% 19.18% 10.24% -8.94%
Sikkim 1.42% 1.15% -0.26% 42.20% 24.50% -17.71% 10.36% 12.71% 2.35% 2.24% 7.84% 5.60%
Tamil Nadu 1.03% 1.30% 0.27% 24.06% 15.10% -8.96% 47.55% 27.95% -19.60% 6.05% 5.71% -0.34%
Telangana 2.10% 1.35% -0.75% 31.67% 7.93% -23.74% 49.01% 24.41% -24.60% 10.80% 3.36% -7.43%
Tripura 2.19% 2.50% 0.31% 65.84% 54.75% -11.09% 36.36% 26.56% -9.80% 16.18% 13.87% -2.31%
Uttar Pradesh 11.91% 10.91% -0.99% 68.85% 52.92% -15.93% 63.65% 31.61% -32.04% 3.66% 2.06% -1.60%
Uttarakhand 4.37% 4.65% 0.28% 52.06% 44.13% -7.93% 33.93% 21.70% -12.22% 8.65% 6.63% -2.01%
West Bengal 3.82% 2.12% -1.70% 73.01% 61.25% -11.76% 47.81% 31.91% -15.90% 9.46% 4.97% -4.50%
Andaman & Nicobar Islands 0.92% 0.63% -0.29% 24.53% 15.73% -8.80% 24.37% 12.12% -12.25% 5.65% 5.04% -0.61%
Chandigarh 1.76% 3.97% 2.21% 4.85% 5.23% 0.37% 19.04% 17.82% -1.22% 1.85% 3.23% 1.38%
Dadra & Nagar Haveli & Daman & Diu 6.71% 3.29% -3.42% 33.68% 22.54% -11.14% 56.32% 34.59% -21.73% 9.69% 5.74% -3.94%
Delhi 2.63% 2.77% 0.14% 2.21% 0.91% -1.30% 26.41% 19.21% -7.20% 4.45% 1.92% -2.53%
Jammu & Kashmir 3.74% 2.94% -0.81% 45.38% 32.23% -13.14% 46.23% 24.30% -21.92% 13.77% 10.37% -3.39%
Ladakh 2.24% 2.86% 0.62% 34.80% 24.52% -10.28% 82.56% 57.40% -25.16% 22.41% 15.41% -6.99%
Lakshadweep 1.43% 0.84% -0.59% 58.15% 35.31% -22.84% 0.44% 0.20% -0.24% 9.17% 7.27% -1.90%
Puducherry 1.21% 1.67% 0.45% 13.51% 4.73% -8.78% 35.06% 15.25% -19.80% 2.03% 2.20% 0.16%
India 6.40% 5.27% -1.13% 58.47% 43.90% -14.58% 51.88% 30.13% -21.75% 10.92% 7.32% -3.60%
(CONTD.) STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO
School Attendance
2015-16 (x)2019-21 (y)
Change
(y-x)
Cooking Fuel
2015-16 (x)2019-21 (y)
Change
(y-x)
Drinking Water
2015-16 (x)2019-21 (y)
Change
(y-x)
Sanitation
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living Education
328

State UTTable 4 - State/UT-wise: Uncensored Headcount Ratio % of total population deprived in each indicator
State/UT Andhra Pradesh 0.77% 0.56% -0.21% 17.55% 14.67% -2.88% 10.96% 8.11% -2.85% 4.73% 3.56% -1.17%
Arunachal Pradesh 11.83% 5.25% -6.58% 76.14% 74.34% -1.80% 23.35% 14.31% -9.04% 15.40% 7.38% -8.03%
Assam 21.77% 7.44% -14.33% 75.89% 69.37% -6.53% 19.94% 15.02% -4.92% 15.38% 3.65% -11.73%
Bihar 39.86% 3.67% -36.19% 73.73% 65.37% -8.36% 24.32% 20.25% -4.07% 26.00% 3.90% -22.10%
Chhattisgarh 3.64% 1.19% -2.45% 63.31% 55.06% -8.25% 14.92% 10.51% -4.41% 5.74% 4.55% -1.19%
Goa 0.18% 0.00% -0.18% 16.16% 9.50% -6.66% 2.97% 1.77% -1.20% 4.02% 2.71% -1.31%
Gujarat 3.75% 2.44% -1.31% 24.24% 23.30% -0.94% 13.59% 11.37% -2.23% 9.42% 4.40% -5.02%
Haryana 1.06% 0.40% -0.66% 24.26% 23.95% -0.31% 4.65% 5.21% 0.56% 8.17% 3.56% -4.61%
Himachal Pradesh 0.49% 0.54% 0.06% 29.30% 23.73% -5.57% 7.52% 6.75% -0.77% 2.69% 2.11% -0.59%
Jharkhand 18.80% 5.67% -13.12% 61.78% 56.93% -4.85% 21.37% 15.48% -5.89% 8.97% 3.97% -5.00%
Karnataka 1.71% 0.89% -0.82% 37.30% 36.20% -1.10% 10.05% 7.31% -2.74% 8.83% 4.97% -3.85%
Kerala 0.74% 0.41% -0.33% 10.76% 16.67% 5.90% 2.94% 3.05% 0.11% 4.32% 3.22% -1.10%
Madhya Pradesh 8.95% 1.57% -7.38% 64.38% 54.65% -9.73% 19.31% 16.05% -3.26% 11.15% 3.84% -7.31%
Maharashtra 6.59% 2.29% -4.30% 27.90% 24.02% -3.88% 13.97% 10.04% -3.92% 10.35% 4.96% -5.39%
Manipur 7.31% 1.94% -5.36% 81.49% 75.50% -5.99% 13.92% 12.63% -1.29% 21.53% 4.04% -17.49%
Meghalaya 8.18% 8.24% 0.07% 50.40% 53.40% 3.00% 29.88% 37.07% 7.19% 19.91% 9.01% -10.90%
Mizoram 4.08% 1.92% -2.17% 24.18% 30.70% 6.52% 13.94% 12.35% -1.59% 5.81% 3.30% -2.50%
Nagaland 3.25% 1.46% -1.80% 70.97% 64.60% -6.38% 33.90% 29.53% -4.37% 28.67% 7.04% -21.63%
Odisha 13.36% 3.04% -10.32% 55.80% 40.70% -15.10% 19.22% 12.30% -6.92% 10.94% 2.53% -8.41%
Punjab 0.39% 0.34% -0.05% 19.30% 21.96% 2.66% 1.72% 1.60% -0.12% 3.71% 3.88% 0.17%
Rajasthan 8.73% 1.86% -6.87% 35.55% 45.73% 10.18% 20.50% 10.77% -9.73% 4.03% 2.18% -1.85%
Sikkim 0.65% 0.77% 0.13% 26.71% 24.15% -2.56% 9.52% 14.42% 4.90% 8.38% 5.99% -2.39%
Tamil Nadu 0.97% 0.67% -0.30% 20.17% 11.37% -8.80% 3.38% 3.89% 0.50% 6.35% 2.56% -3.78%
Telangana 1.23% 0.44% -0.80% 25.54% 20.49% -5.05% 12.79% 8.51% -4.28% 7.46% 2.74% -4.71%
Tripura 7.18% 1.75% -5.43% 74.66% 66.83% -7.83% 18.76% 14.83% -3.93% 3.63% 3.02% -0.62%
Uttar Pradesh 27.43% 9.16% -18.27% 67.52% 60.09% -7.43% 12.44% 7.80% -4.64% 4.87% 2.96% -1.91%
Uttarakhand 2.17% 0.39% -1.78% 35.58% 24.19% -11.39% 13.84% 9.10% -4.74% 6.89% 2.89% -3.99%
West Bengal 5.75% 2.50% -3.25% 54.25% 47.17% -7.08% 14.10% 8.13% -5.97% 13.82% 4.44% -9.38%
Andaman & Nicobar Islands 2.72% 2.47% -0.24% 33.61% 30.10% -3.51% 7.10% 7.90% 0.80% 1.57% 2.57% 1.01%
Chandigarh 0.48% 0.04% -0.44% 6.40% 4.33% -2.07% 2.71% 0.59% -2.12% 3.97% 1.75% -2.22%
Dadra & Nagar Haveli & Daman & Diu 1.73% 0.35% -1.38% 40.15% 31.61% -8.54% 18.68% 16.79% -1.89% 11.40% 6.65% -4.74%
Delhi 0.28% 0.14% -0.13% 10.80% 6.25% -4.55% 5.54% 4.42% -1.13% 8.36% 5.78% -2.58%
Jammu & Kashmir 2.80% 0.76% -2.04% 28.65% 25.36% -3.28% 16.24% 8.03% -8.21% 3.98% 2.93% -1.05%
Ladakh 1.38% 0.50% -0.88% 88.20% 56.98% -31.23% 9.10% 3.32% -5.78% 1.84% 3.88% 2.04%
Lakshadweep 0.05% 0.22% 0.17% 1.54% 11.32% 9.79% 1.02% 1.70% 0.68% 5.62% 3.10% -2.52%
Puducherry 0.24% 0.13% -0.11% 17.59% 11.31% -6.29% 1.65% 2.10% 0.45% 5.35% 2.11% -3.23%
India 12.16% 3.27% -8.89% 45.65% 41.37% -4.27% 13.97% 10.16% -3.81% 9.66% 3.69% -5.97%
Electricity
2015-16 (x)2019-21 (y)
Change
(y-x)
Housing
2015-16 (x)2019-21 (y)
Change
(y-x)
Bank Account
2015-16 (x)2019-21 (y)
Change
(y-x)
Assets
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living
(CONTD.) STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO
DATA TABLESMPI: PROGRESS REVIEW 2023
329

DATA TABLES MPI: PROGRESS REVIEW 2023
Table 5 - State/UT-wise: Uncensored Headcount Ratio (Rural) % of total population deprived in each indicator by Rural Areas
State/UT Andhra Pradesh 29.49% 24.93% -4.56% 2.10% 1.43% -0.67% 10.58% 11.55% 0.98% 19.82% 18.74% -1.09%
Arunachal Pradesh 22.55% 17.33% -5.21% 2.20% 1.07% -1.13% 30.43% 23.26% -7.17% 21.04% 15.71% -5.34%
Assam 41.67% 33.30% -8.36% 3.13% 1.89% -1.24% 27.39% 22.87% -4.52% 17.74% 13.30% -4.44%
Bihar 53.68% 43.93% -9.75% 4.80% 4.34% -0.46% 47.38% 38.58% -8.80% 28.12% 24.12% -4.01%
Chhattisgarh 46.32% 37.34% -8.99% 3.66% 2.59% -1.07% 27.30% 21.57% -5.73% 15.48% 12.15% -3.33%
Goa 31.95% 22.30% -9.65% 0.39% 0.41% 0.02% 9.31% 1.60% -7.71% 3.47% 3.69% 0.22%
Gujarat 48.81% 44.45% -4.35% 2.76% 2.35% -0.41% 18.03% 15.15% -2.88% 12.72% 10.34% -2.38%
Haryana 35.70% 28.79% -6.91% 2.28% 2.12% -0.16% 26.34% 18.37% -7.97% 7.18% 5.96% -1.22%
Himachal Pradesh 27.94% 24.36% -3.58% 1.68% 1.17% -0.51% 18.16% 13.02% -5.15% 3.90% 4.90% 1.00%
Jharkhand 52.16% 44.53% -7.62% 3.83% 2.94% -0.89% 37.29% 32.50% -4.79% 21.78% 19.09% -2.69%
Karnataka 37.53% 34.06% -3.48% 1.66% 1.49% -0.17% 13.48% 13.96% 0.48% 11.88% 8.98% -2.91%
Kerala 15.65% 17.69% 2.04% 0.16% 0.20% 0.03% 1.51% 3.13% 1.62% 2.04% 3.03% 0.99%
Madhya Pradesh 49.18% 37.09% -12.09% 4.03% 2.52% -1.50% 32.62% 23.02% -9.60% 19.14% 14.43% -4.71%
Maharashtra 42.66% 36.40% -6.25% 1.49% 1.24% -0.25% 17.94% 16.94% -1.00% 8.57% 7.95% -0.62%
Manipur 25.74% 19.58% -6.16% 2.20% 1.95% -0.24% 22.92% 14.67% -8.24% 7.07% 6.13% -0.94%
Meghalaya 39.24% 37.29% -1.94% 3.62% 3.42% -0.20% 36.88% 35.48% -1.40% 23.37% 19.64% -3.73%
Mizoram 26.00% 19.48% -6.51% 3.25% 1.21% -2.04% 26.21% 17.46% -8.75% 15.29% 12.37% -2.92%
Nagaland 26.62% 21.61% -5.01% 2.58% 1.87% -0.71% 38.06% 25.84% -12.22% 18.12% 13.21% -4.91%
Odisha 39.62% 32.86% -6.76% 2.43% 1.70% -0.73% 20.61% 15.46% -5.15% 18.18% 14.93% -3.25%
Punjab 23.73% 21.58% -2.15% 1.53% 1.51% -0.02% 13.19% 15.29% 2.10% 8.21% 6.70% -1.51%
Rajasthan 45.49% 36.17% -9.32% 3.30% 2.29% -1.01% 28.82% 22.66% -6.16% 19.46% 11.59% -7.87%
Sikkim 13.83% 12.21% -1.62% 1.20% 0.40% -0.80% 5.10% 7.61% 2.52% 8.98% 11.02% 2.04%
Tamil Nadu 29.28% 22.49% -6.79% 1.45% 1.08% -0.36% 7.43% 3.44% -3.99% 8.95% 11.13% 2.18%
Telangana 36.24% 31.04% -5.20% 1.55% 1.26% -0.29% 11.90% 13.40% 1.51% 21.29% 18.40% -2.89%
Tripura 29.86% 27.72% -2.14% 1.55% 1.88% 0.33% 15.70% 18.22% 2.52% 13.01% 12.24% -0.78%
Uttar Pradesh 47.98% 38.88% -9.09% 5.52% 3.73% -1.79% 38.83% 32.02% -6.81% 18.84% 14.05% -4.79%
Uttarakhand 34.97% 24.42% -10.55% 2.67% 2.08% -0.59% 31.02% 21.83% -9.19% 10.09% 8.07% -2.02%
West Bengal 37.21% 30.49% -6.72% 1.78% 1.17% -0.61% 16.12% 12.70% -3.42% 18.31% 15.55% -2.76%
Andaman & Nicobar Islands 24.49% 13.81% -10.68% 1.04% 0.73% -0.32% 6.49% 3.61% -2.88% 6.86% 7.22% 0.36%
Chandigarh 51.55% 18.45% -33.10% 0.00% 3.88% 3.88% 4.12% 22.33% 18.21% 18.56% 0.00% -18.56%
Dadra & Nagar Haveli & Daman & Diu 51.59% 44.69% -6.90% 2.68% 1.92% -0.76% 16.05% 4.80% -11.25% 10.72% 9.84% -0.87%
Delhi 26.93% 20.18% -6.75% 0.00% 1.91% 1.91% 22.29% 17.03% -5.26% 2.58% 3.06% 0.47%
Jammu & Kashmir 29.21% 16.50% -12.71% 2.10% 0.82% -1.29% 15.83% 8.19% -7.64% 7.47% 4.79% -2.69%
Ladakh 27.61% 14.71% -12.90% 2.21% 0.80% -1.41% 14.13% 6.67% -7.46% 7.14% 4.06% -3.09%
Lakshadweep 34.33% 32.24% -2.09% 1.93% 1.48% -0.45% 6.95% 1.00% -5.95% 2.31% 1.16% -1.15%
Puducherry 23.28% 15.76% -7.52% 0.82% 0.31% -0.52% 5.79% 4.05% -1.74% 3.51% 4.80% 1.30%
India 42.36% 34.98% -7.37% 3.20% 2.39% -0.81% 26.49% 21.85% -4.64% 16.96% 13.77% -3.19%
State UT
Nutrition
Child & Adolescent Mortality
Years of Schooling
Maternal Health
HealthEducation
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
330

DATA TABLESMPI: PROGRESS REVIEW 2023
Table 5 - State/UT-wise: Uncensored Headcount Ratio (Rural) % of total population deprived in each indicator by Rural Areas
State/UT Andhra Pradesh 2.52% 1.49% -1.03% 49.42% 21.84% -27.58% 56.18% 28.04% -28.14% 15.26% 11.71% -3.56%
Arunachal Pradesh 8.67% 5.32% -3.34% 71.46% 54.50% -16.96% 42.43% 16.61% -25.82% 17.59% 7.45% -10.14%
Assam 7.22% 4.67% -2.55% 85.39% 66.96% -18.42% 53.49% 31.74% -21.74% 18.56% 15.89% -2.67%
Bihar 13.03% 11.21% -1.82% 89.55% 70.66% -18.90% 77.99% 54.40% -23.59% 2.16% 1.70% -0.46%
Chhattisgarh 5.93% 5.97% 0.04% 92.60% 80.28% -12.33% 74.80% 26.41% -48.39% 21.31% 9.87% -11.45%
Goa 1.18% 0.91% -0.26% 28.46% 5.67% -22.79% 17.62% 14.16% -3.45% 5.58% 2.43% -3.15%
Gujarat 8.23% 6.15% -2.08% 74.69% 55.31% -19.37% 53.88% 36.71% -17.16% 12.27% 8.17% -4.10%
Haryana 4.09% 4.62% 0.53% 73.03% 59.82% -13.21% 21.28% 15.45% -5.83% 9.58% 8.95% -0.63%
Himachal Pradesh 0.85% 0.85% 0.00% 73.63% 59.56% -14.07% 28.42% 18.68% -9.74% 7.81% 5.45% -2.36%
Jharkhand 9.56% 9.88% 0.33% 93.56% 80.99% -12.57% 86.74% 49.24% -37.49% 34.35% 21.41% -12.94%
Karnataka 4.17% 2.90% -1.27% 67.70% 31.62% -36.08% 57.32% 31.60% -25.73% 12.68% 8.83% -3.84%
Kerala 0.58% 0.25% -0.33% 50.35% 33.38% -16.97% 2.37% 1.49% -0.89% 6.62% 6.79% 0.17%
Madhya Pradesh 9.90% 7.61% -2.29% 90.19% 76.18% -14.01% 78.83% 41.10% -37.74% 37.18% 26.98% -10.20%
Maharashtra 5.11% 2.56% -2.56% 66.77% 33.10% -33.67% 54.91% 30.58% -24.34% 21.55% 15.89% -5.66%
Manipur 2.94% 2.74% -0.19% 72.39% 37.94% -34.45% 46.48% 32.56% -13.92% 48.46% 34.38% -14.08%
Meghalaya 7.21% 8.58% 1.37% 89.69% 78.29% -11.40% 40.81% 16.74% -24.06% 36.76% 26.75% -10.01%
Mizoram 5.99% 3.95% -2.04% 65.58% 34.55% -31.03% 25.92% 6.76% -19.16% 12.89% 8.01% -4.87%
Nagaland 5.50% 5.14% -0.36% 86.52% 74.70% -11.82% 20.28% 9.56% -10.72% 20.49% 12.17% -8.33%
Odisha 5.20% 4.29% -0.91% 89.30% 74.42% -14.88% 76.24% 42.17% -34.07% 22.62% 15.21% -7.42%
Punjab 2.48% 2.41% -0.07% 53.46% 35.74% -17.73% 19.79% 14.12% -5.68% 2.14% 2.28% 0.15%
Rajasthan 9.52% 4.68% -4.83% 85.57% 74.99% -10.58% 62.82% 34.07% -28.76% 23.73% 12.77% -10.96%
Sikkim 1.46% 1.19% -0.27% 58.85% 36.77% -22.08% 5.25% 10.75% 5.51% 2.93% 10.51% 7.59%
Tamil Nadu 1.26% 1.23% -0.04% 38.62% 23.77% -14.85% 65.27% 36.77% -28.51% 7.37% 7.03% -0.34%
Telangana 2.01% 1.37% -0.63% 50.51% 11.25% -39.26% 60.59% 27.23% -33.37% 15.62% 4.29% -11.33%
Tripura 2.80% 2.81% 0.01% 79.54% 66.92% -12.62% 38.49% 28.46% -10.04% 21.23% 18.15% -3.08%
Uttar Pradesh 12.34% 11.33% -1.01% 83.98% 64.89% -19.09% 74.80% 35.22% -39.57% 4.24% 2.26% -1.98%
Uttarakhand 4.32% 4.61% 0.29% 71.58% 59.41% -12.17% 38.72% 22.39% -16.33% 12.65% 8.81% -3.85%
West Bengal 4.05% 2.30% -1.75% 89.07% 80.24% -8.83% 51.90% 35.09% -16.82% 10.14% 5.87% -4.27%
Andaman & Nicobar Islands 1.12% 0.75% -0.37% 41.05% 24.25% -16.79% 33.16% 11.97% -21.20% 9.81% 6.71% -3.09%
Chandigarh 7.22% 10.68% 3.46% 22.68% 5.83% -16.86% 69.07% 52.43% -16.64% 0.00% 0.00% 0.00%
Dadra & Nagar Haveli & Daman & Diu 9.70% 3.22% -6.48% 66.79% 39.57% -27.22% 76.15% 37.00% -39.15% 14.51% 7.90% -6.61%
Delhi 0.00% 1.29% 1.29% 24.80% 1.94% -22.87% 11.78% 12.36% 0.58% 4.16% 2.22% -1.94%
Jammu & Kashmir 4.46% 3.38% -1.09% 60.68% 41.85% -18.83% 52.32% 27.79% -24.53% 18.47% 13.36% -5.11%
Ladakh 2.23% 3.05% 0.83% 45.65% 29.86% -15.79% 89.54% 64.85% -24.69% 25.50% 17.67% -7.83%
Lakshadweep 1.55% 0.00% -1.55% 73.64% 65.72% -7.92% 0.77% 0.00% -0.77% 7.68% 13.67% 5.99%
Puducherry 1.43% 0.00% -1.43% 28.29% 9.84% -18.44% 53.67% 25.93% -27.74% 1.09% 1.44% 0.35%
India 7.52% 6.09% -1.43% 77.41% 58.60% -18.81% 62.82% 35.14% -27.67% 13.97% 9.31% -4.65%
State UT
School Attendance
2015-16 (x)2019-21 (y)
Change
(y-x)
Cooking Fuel
2015-16 (x)2019-21 (y)
Change
(y-x)
Drinking Water
2015-16 (x)2019-21 (y)
Change
(y-x)
Sanitation
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living Education
(CONTD.) STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
331

Table 5 - State/UT-wise: Uncensored Headcount Ratio (Rural) % of total population deprived in each indicator by Rural Areas
State/UT Andhra Pradesh 0.91% 0.60% -0.31% 22.30% 18.12% -4.17% 13.48% 9.89% -3.59% 4.08% 3.36% -0.72%
Arunachal Pradesh 15.24% 6.09% -9.15% 85.41% 77.92% -7.49% 28.34% 16.00% -12.33% 18.11% 7.46% -10.65%
Assam 24.47% 8.51% -15.96% 81.85% 74.96% -6.89% 21.71% 16.04% -5.67% 16.59% 3.68% -12.91%
Bihar 44.01% 3.66% -40.35% 79.94% 72.30% -7.64% 25.66% 21.76% -3.90% 27.45% 3.86% -23.59%
Chhattisgarh 4.56% 1.37% -3.19% 75.53% 64.37% -11.16% 17.85% 12.14% -5.71% 6.20% 4.20% -2.00%
Goa 0.11% 0.00% -0.11% 26.81% 10.52% -16.30% 4.04% 1.98% -2.06% 4.96% 2.66% -2.29%
Gujarat 5.55% 3.75% -1.80% 37.96% 35.52% -2.44% 19.20% 16.35% -2.85% 10.43% 4.06% -6.36%
Haryana 1.50% 0.50% -1.01% 33.01% 29.62% -3.39% 5.63% 5.93% 0.29% 7.48% 3.18% -4.31%
Himachal Pradesh 0.47% 0.46% -0.01% 31.59% 26.25% -5.34% 7.81% 6.94% -0.86% 2.70% 1.98% -0.72%
Jharkhand 23.97% 7.10% -16.87% 75.78% 68.93% -6.85% 25.42% 18.18% -7.24% 10.48% 3.45% -7.03%
Karnataka 2.34% 0.97% -1.37% 50.51% 43.91% -6.60% 14.37% 9.33% -5.04% 10.27% 4.47% -5.80%
Kerala 1.05% 0.68% -0.37% 13.00% 16.35% 3.35% 3.78% 3.72% -0.07% 4.54% 3.16% -1.38%
Madhya Pradesh 11.92% 1.91% -10.01% 79.15% 65.83% -13.32% 24.75% 19.58% -5.17% 12.80% 3.67% -9.12%
Maharashtra 8.51% 3.28% -5.23% 45.12% 36.47% -8.65% 20.00% 13.71% -6.30% 10.68% 4.37% -6.31%
Manipur 9.56% 2.55% -7.00% 88.85% 83.93% -4.92% 18.66% 15.48% -3.18% 25.09% 4.43% -20.66%
Meghalaya 9.90% 9.48% -0.42% 56.27% 60.06% 3.79% 35.19% 42.25% 7.06% 22.19% 9.43% -12.76%
Mizoram 8.96% 3.59% -5.37% 38.37% 44.18% 5.81% 26.56% 22.75% -3.81% 9.68% 3.39% -6.29%
Nagaland 4.74% 1.98% -2.76% 82.23% 75.43% -6.80% 45.94% 38.81% -7.12% 38.43% 7.72% -30.71%
Odisha 14.94% 3.45% -11.49% 61.96% 45.49% -16.48% 21.45% 13.75% -7.70% 11.46% 2.34% -9.12%
Punjab 0.37% 0.39% 0.01% 27.16% 28.27% 1.11% 1.70% 1.66% -0.04% 3.50% 3.45% -0.05%
Rajasthan 11.10% 2.33% -8.78% 43.62% 51.92% 8.30% 25.13% 13.09% -12.05% 4.11% 1.98% -2.12%
Sikkim 0.44% 0.88% 0.45% 33.57% 29.93% -3.65% 11.73% 16.98% 5.25% 7.97% 3.50% -4.47%
Tamil Nadu 1.31% 0.92% -0.39% 26.20% 15.42% -10.78% 4.94% 5.32% 0.38% 6.77% 2.37% -4.41%
Telangana 1.85% 0.56% -1.29% 38.45% 27.17% -11.28% 18.40% 10.84% -7.56% 6.97% 2.12% -4.84%
Tripura 9.63% 2.20% -7.43% 86.18% 76.37% -9.82% 23.28% 18.48% -4.80% 4.29% 3.08% -1.21%
Uttar Pradesh 34.84% 11.14% -23.70% 80.27% 70.70% -9.57% 13.90% 8.51% -5.39% 4.89% 2.81% -2.08%
Uttarakhand 2.99% 0.45% -2.55% 48.72% 30.90% -17.83% 18.56% 11.19% -7.37% 6.93% 2.70% -4.23%
West Bengal 7.18% 3.36% -3.82% 67.30% 59.46% -7.84% 16.77% 10.06% -6.71% 14.77% 4.79% -9.98%
Andaman & Nicobar Islands 4.45% 3.61% -0.84% 50.83% 42.35% -8.48% 10.96% 10.20% -0.75% 1.68% 2.92% 1.24%
Chandigarh 0.00% 0.00% 0.00% 15.46% 0.00% -15.46% 1.03% 1.94% 0.91% 1.03% 1.94% 0.91%
Dadra & Nagar Haveli & Daman & Diu 3.45% 0.53% -2.92% 68.80% 53.18% -15.61% 25.46% 17.85% -7.62% 13.79% 3.23% -10.56%
Delhi 0.00% 0.07% 0.07% 6.45% 7.36% 0.91% 6.49% 2.32% -4.17% 6.09% 6.73% 0.64%
Jammu & Kashmir 3.83% 1.00% -2.83% 36.73% 30.81% -5.92% 20.97% 10.22% -10.74% 4.40% 2.88% -1.52%
Ladakh 1.88% 0.41% -1.47% 90.94% 61.95% -28.98% 9.94% 3.84% -6.10% 1.59% 4.09% 2.50%
Lakshadweep 0.00% 0.00% 0.00% 2.60% 17.46% 14.86% 0.90% 5.23% 4.33% 10.72% 2.05% -8.68%
Puducherry 0.60% 0.32% -0.28% 31.86% 21.01% -10.85% 2.91% 2.97% 0.06% 3.36% 0.95% -2.41%
India 16.80% 4.30% -12.50% 59.52% 52.63% -6.89% 17.82% 12.59% -5.22% 10.73% 3.43% -7.31%
State UT
Electricity
2015-16 (x)2019-21 (y)
Change
(y-x)
Housing
2015-16 (x)2019-21 (y)
Change
(y-x)
Bank Account
2015-16 (x)2019-21 (y)
Change
(y-x)
Assets
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living
(CONTD.) STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (RURAL)
332
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 6 - State/UT-wise: Uncensored Headcount Ratio (Urban) % of total population deprived in each indicator by Urban Areas
State/UT Andhra Pradesh 18.86% 18.28% -0.57% 1.16% 0.91% -0.26% 7.44% 8.95% 1.50% 9.84% 8.97% -0.87%
Arunachal Pradesh 16.19% 15.78% -0.41% 1.19% 1.26% 0.07% 21.51% 16.21% -5.30% 7.04% 5.79% -1.25%
Assam 26.65% 22.94% -3.71% 1.41% 1.03% -0.38% 12.75% 12.50% -0.24% 6.00% 6.56% 0.56%
Bihar 39.52% 32.89% -6.63% 3.13% 3.02% -0.11% 33.56% 29.86% -3.70% 13.56% 12.52% -1.04%
Chhattisgarh 31.84% 27.31% -4.52% 2.19% 1.39% -0.80% 15.91% 15.43% -0.48% 6.65% 5.04% -1.61%
Goa 20.26% 18.83% -1.43% 0.68% 0.37% -0.31% 5.84% 2.07% -3.77% 5.43% 1.75% -3.69%
Gujarat 31.22% 28.97% -2.25% 1.46% 1.05% -0.41% 10.32% 9.24% -1.08% 5.86% 4.49% -1.37%
Haryana 27.00% 20.75% -6.24% 1.99% 1.26% -0.72% 19.91% 13.61% -6.30% 6.96% 4.57% -2.39%
Himachal Pradesh 19.67% 13.66% -6.01% 1.43% 0.34% -1.08% 10.06% 9.10% -0.97% 2.54% 2.78% 0.24%
Jharkhand 35.31% 26.48% -8.83% 1.75% 1.37% -0.38% 20.16% 20.70% 0.54% 7.64% 6.57% -1.06%
Karnataka 28.07% 23.49% -4.58% 0.91% 0.98% 0.06% 10.80% 10.39% -0.41% 4.27% 4.26% -0.01%
Kerala 14.89% 15.06% 0.17% 0.22% 0.21% -0.01% 1.98% 3.49% 1.51% 1.47% 1.90% 0.43%
Madhya Pradesh 36.47% 27.55% -8.92% 2.57% 1.73% -0.84% 21.44% 16.74% -4.71% 8.55% 5.56% -2.99%
Maharashtra 28.44% 26.98% -1.46% 1.34% 0.95% -0.39% 13.62% 13.22% -0.39% 4.17% 3.28% -0.89%
Manipur 20.13% 15.05% -5.08% 1.17% 1.19% 0.02% 9.35% 8.29% -1.06% 2.64% 2.06% -0.58%
Meghalaya 28.18% 23.83% -4.35% 0.96% 1.16% 0.20% 10.68% 14.04% 3.36% 4.87% 4.23% -0.64%
Mizoram 17.75% 12.30% -5.45% 1.55% 0.69% -0.86% 8.17% 6.02% -2.15% 2.13% 1.97% -0.16%
Nagaland 20.42% 18.52% -1.89% 1.07% 0.49% -0.58% 23.47% 14.43% -9.03% 5.00% 4.79% -0.21%
Odisha 25.18% 20.26% -4.92% 1.19% 0.93% -0.26% 13.74% 11.67% -2.07% 8.72% 5.97% -2.75%
Punjab 19.62% 19.45% -0.17% 1.18% 0.98% -0.20% 11.95% 12.40% 0.45% 5.86% 6.77% 0.91%
Rajasthan 33.69% 27.33% -6.35% 1.87% 1.68% -0.19% 18.57% 16.31% -2.26% 9.70% 5.07% -4.63%
Sikkim 12.14% 6.97% -5.17% 0.53% 0.00% -0.53% 6.16% 5.09% -1.07% 6.40% 4.15% -2.25%
Tamil Nadu 20.31% 15.37% -4.94% 0.85% 0.56% -0.29% 5.97% 3.17% -2.80% 4.29% 5.56% 1.27%
Telangana 24.36% 23.16% -1.20% 1.17% 0.96% -0.22% 9.53% 12.73% 3.20% 8.69% 7.15% -1.54%
Tripura 23.28% 22.12% -1.15% 0.57% 0.73% 0.15% 7.79% 10.66% 2.86% 5.08% 6.01% 0.93%
Uttar Pradesh 33.88% 28.30% -5.58% 3.30% 2.90% -0.40% 25.19% 23.42% -1.76% 13.41% 10.30% -3.11%
Uttarakhand 28.91% 22.00% -6.91% 2.40% 1.44% -0.96% 23.95% 17.18% -6.76% 9.15% 7.47% -1.68%
West Bengal 25.59% 20.56% -5.03% 0.86% 0.84% -0.01% 10.48% 8.76% -1.72% 10.32% 7.28% -3.04%
Andaman & Nicobar Islands 18.74% 17.26% -1.47% 0.55% 1.23% 0.69% 3.23% 4.72% 1.49% 2.19% 3.74% 1.55%
Chandigarh 21.94% 21.61% -0.33% 1.21% 1.13% -0.08% 11.33% 7.11% -4.22% 5.30% 4.60% -0.70%
Dadra & Nagar Haveli & Daman & Diu 23.94% 29.85% 5.91% 0.73% 1.51% 0.78% 11.87% 11.83% -0.04% 4.80% 6.21% 1.41%
Delhi 23.38% 20.39% -2.99% 1.93% 1.37% -0.56% 15.14% 9.90% -5.24% 5.96% 4.40% -1.56%
Jammu & Kashmir 17.97% 12.72% -5.25% 1.23% 0.49% -0.74% 5.38% 5.84% 0.46% 5.31% 2.73% -2.58%
Ladakh 24.28% 13.08% -11.20% 1.81% 1.37% -0.44% 4.66% 8.76% 4.10% 6.74% 4.19% -2.55%
Lakshadweep 30.69% 22.53% -8.16% 1.97% 0.00% -1.97% 6.38% 2.41% -3.97% 0.58% 2.03% 1.45%
Puducherry 21.24% 13.03% -8.21% 0.59% 0.20% -0.39% 3.39% 3.00% -0.39% 3.20% 2.78% -0.42%
India 27.63% 23.76% -3.87% 1.62% 1.33% -0.29% 14.41% 13.15% -1.26% 7.37% 6.09% -1.29%
State UT
Nutrition
Child & Adolescent Mortality
Years of Schooling
Maternal Health
HealthEducation
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
333
DATA TABLESMPI: PROGRESS REVIEW 2023

Table 6 - State/UT-wise: Uncensored Headcount Ratio (Urban) % of total population deprived in each indicator by Urban Areas
State/UT Andhra Pradesh 1.93% 1.02% -0.91% 10.06% 2.67% -7.39% 22.69% 10.69% -12.00% 5.24% 3.14% -2.09%
Arunachal Pradesh 6.47% 4.43% -2.05% 13.23% 11.50% -1.73% 25.91% 20.10% -5.81% 5.77% 1.88% -3.90%
Assam 2.15% 2.12% -0.03% 23.33% 13.17% -10.16% 36.26% 30.57% -5.68% 10.05% 8.97% -1.08%
Bihar 9.07% 7.41% -1.66% 37.67% 23.88% -13.79% 42.76% 31.36% -11.39% 1.84% 1.33% -0.51%
Chhattisgarh 3.52% 3.86% 0.34% 28.71% 19.56% -9.16% 33.44% 11.72% -21.71% 7.38% 3.08% -4.30%
Goa 0.83% 0.56% -0.28% 6.77% 0.48% -6.29% 23.64% 10.98% -12.67% 1.99% 0.90% -1.09%
Gujarat 4.57% 3.49% -1.07% 13.45% 5.24% -8.21% 14.20% 10.78% -3.42% 1.49% 1.20% -0.29%
Haryana 3.39% 3.67% 0.28% 16.47% 10.71% -5.77% 15.85% 14.40% -1.45% 1.92% 2.05% 0.12%
Himachal Pradesh 1.28% 1.32% 0.04% 11.46% 6.60% -4.86% 19.85% 15.49% -4.36% 6.87% 3.08% -3.79%
Jharkhand 3.99% 3.73% -0.26% 47.09% 30.06% -17.03% 40.30% 24.04% -16.26% 17.99% 9.43% -8.56%
Karnataka 2.65% 1.88% -0.78% 14.85% 5.40% -9.45% 22.38% 16.23% -6.15% 4.96% 4.25% -0.70%
Kerala 0.50% 0.25% -0.25% 36.58% 22.32% -14.26% 1.21% 1.03% -0.18% 4.35% 3.86% -0.49%
Madhya Pradesh 4.67% 4.29% -0.38% 24.82% 16.73% -8.09% 31.60% 19.40% -12.20% 9.82% 6.58% -3.24%
Maharashtra 3.15% 2.08% -1.06% 7.66% 3.23% -4.42% 39.81% 25.44% -14.37% 2.17% 1.31% -0.86%
Manipur 1.45% 1.65% 0.20% 37.64% 13.67% -23.97% 49.22% 39.61% -9.61% 22.78% 14.30% -8.48%
Meghalaya 1.84% 2.41% 0.57% 25.95% 22.41% -3.54% 29.44% 18.62% -10.82% 11.55% 7.62% -3.93%
Mizoram 2.00% 1.25% -0.75% 5.95% 1.95% -3.99% 7.87% 2.85% -5.01% 3.79% 2.06% -1.73%
Nagaland 3.49% 3.01% -0.48% 36.31% 18.43% -17.87% 28.71% 17.85% -10.86% 16.90% 6.93% -9.96%
Odisha 3.65% 2.08% -1.58% 37.89% 23.34% -14.55% 39.85% 28.22% -11.63% 10.21% 5.22% -4.99%
Punjab 2.75% 3.38% 0.63% 10.01% 7.09% -2.92% 13.41% 12.95% -0.46% 0.63% 1.05% 0.42%
Rajasthan 5.25% 2.85% -2.40% 21.30% 13.67% -7.63% 26.14% 12.64% -13.50% 5.00% 2.00% -3.00%
Sikkim 1.32% 1.09% -0.23% 3.58% 2.04% -1.54% 22.22% 16.29% -5.93% 0.66% 2.95% 2.29%
Tamil Nadu 0.79% 1.38% 0.59% 9.67% 5.21% -4.47% 30.02% 17.88% -12.15% 4.74% 4.20% -0.55%
Telangana 2.22% 1.30% -0.92% 7.05% 1.51% -5.54% 33.87% 18.97% -14.90% 4.49% 1.58% -2.91%
Tripura 0.61% 1.71% 1.11% 30.58% 24.16% -6.41% 30.87% 21.81% -9.06% 3.18% 3.09% -0.08%
Uttar Pradesh 10.61% 9.54% -1.07% 23.11% 13.18% -9.93% 29.98% 19.63% -10.35% 1.89% 1.39% -0.50%
Uttarakhand 4.48% 4.76% 0.29% 15.84% 9.04% -6.80% 25.03% 20.12% -4.90% 1.21% 1.64% 0.43%
West Bengal 3.32% 1.75% -1.57% 37.19% 21.47% -15.72% 38.69% 25.27% -13.42% 7.95% 3.07% -4.88%
Andaman & Nicobar Islands 0.64% 0.42% -0.22% 2.13% 1.22% -0.91% 12.44% 12.37% -0.07% 0.00% 2.18% 2.18%
Chandigarh 1.53% 3.88% 2.35% 4.12% 5.22% 1.10% 16.97% 17.34% 0.37% 1.93% 3.27% 1.35%
Dadra & Nagar Haveli & Daman & Diu 4.14% 3.37% -0.76% 5.26% 2.84% -2.42% 39.30% 31.81% -7.49% 5.55% 3.25% -2.30%
Delhi 2.65% 2.81% 0.16% 2.02% 0.89% -1.14% 26.53% 19.38% -7.15% 4.45% 1.91% -2.54%
Jammu & Kashmir 2.04% 1.69% -0.35% 9.06% 4.72% -4.34% 31.76% 14.32% -17.44% 2.61% 1.82% -0.79%
Ladakh 2.27% 2.00% -0.27% 4.76% 1.67% -3.09% 63.23% 25.55% -37.68% 13.85% 5.77% -8.07%
Lakshadweep 1.39% 1.07% -0.33% 53.95% 26.90% -27.05% 0.35% 0.26% -0.09% 9.58% 5.50% -4.08%
Puducherry 1.12% 2.42% 1.30% 6.86% 2.42% -4.44% 26.69% 10.43% -16.26% 2.46% 2.54% 0.08%
India 4.04% 3.44% -0.61% 18.84% 10.90% -7.93% 28.96% 18.86% -10.11% 4.55% 2.84% -1.70%
State UT
School Attendance
2015-16 (x)2019-21 (y)
Change
(y-x)
Cooking Fuel
2015-16 (x)2019-21 (y)
Change
(y-x)
Drinking Water
2015-16 (x)2019-21 (y)
Change
(y-x)
Sanitation
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living Education
(CONTD.) STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (URBAN)
334
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 6 - State/UT-wise: Uncensored Headcount Ratio (Urban) % of total population deprived in each indicator by Urban Areas
State/UT Andhra Pradesh 0.44% 0.47% 0.03% 6.06% 6.60% 0.54% 4.86% 3.96% -0.90% 6.30% 4.02% -2.28%
Arunachal Pradesh 0.75% 0.51% -0.23% 45.97% 54.06% 8.09% 7.12% 4.70% -2.42% 6.58% 6.90% 0.32%
Assam 4.21% 0.94% -3.26% 37.15% 35.52% -1.63% 8.47% 8.86% 0.39% 7.56% 3.46% -4.10%
Bihar 11.50% 3.71% -7.79% 31.29% 28.18% -3.11% 15.18% 12.14% -3.04% 16.12% 4.15% -11.97%
Chhattisgarh 0.52% 0.57% 0.05% 21.96% 22.27% 0.32% 5.02% 4.77% -0.25% 4.19% 5.77% 1.58%
Goa 0.23% 0.00% -0.23% 9.76% 8.81% -0.95% 2.32% 1.63% -0.69% 3.46% 2.74% -0.72%
Gujarat 1.29% 0.56% -0.73% 5.53% 5.79% 0.26% 5.94% 4.22% -1.72% 8.04% 4.87% -3.17%
Haryana 0.35% 0.20% -0.15% 10.31% 12.10% 1.79% 3.07% 3.71% 0.64% 9.27% 4.35% -4.91%
Himachal Pradesh 0.69% 1.09% 0.40% 6.73% 6.71% -0.02% 4.68% 5.45% 0.76% 2.63% 2.97% 0.34%
Jharkhand 2.92% 0.97% -1.95% 18.87% 17.50% -1.37% 8.95% 6.62% -2.33% 4.35% 5.67% 1.32%
Karnataka 0.83% 0.76% -0.07% 19.00% 23.98% 4.98% 4.06% 4.12% 0.06% 6.82% 5.77% -1.05%
Kerala 0.38% 0.11% -0.28% 8.23% 17.01% 8.79% 1.99% 2.31% 0.32% 4.08% 3.29% -0.79%
Madhya Pradesh 1.68% 0.59% -1.09% 28.20% 22.38% -5.82% 5.99% 5.86% -0.13% 7.11% 4.31% -2.81%
Maharashtra 4.35% 1.00% -3.35% 7.81% 7.93% 0.13% 6.92% 5.31% -1.61% 9.96% 5.72% -4.24%
Manipur 3.75% 0.94% -2.81% 69.86% 61.68% -8.19% 6.44% 7.96% 1.52% 15.92% 3.42% -12.50%
Meghalaya 1.17% 2.97% 1.80% 26.62% 25.14% -1.48% 8.37% 15.13% 6.77% 10.68% 7.22% -3.46%
Mizoram 0.25% 0.47% 0.22% 13.04% 19.06% 6.02% 4.03% 3.37% -0.65% 2.77% 3.23% 0.46%
Nagaland 0.42% 0.37% -0.06% 49.46% 41.98% -7.48% 10.87% 10.13% -0.74% 10.02% 5.62% -4.39%
Odisha 5.23% 0.98% -4.25% 24.06% 16.61% -7.45% 7.74% 5.01% -2.73% 8.24% 3.47% -4.77%
Punjab 0.40% 0.25% -0.15% 7.16% 10.91% 3.76% 1.75% 1.49% -0.26% 4.04% 4.62% 0.58%
Rajasthan 1.34% 0.34% -1.00% 10.41% 25.58% 15.17% 6.06% 3.24% -2.82% 3.78% 2.82% -0.95%
Sikkim 1.15% 0.58% -0.57% 10.79% 13.59% 2.80% 4.42% 9.74% 5.32% 9.31% 10.54% 1.23%
Tamil Nadu 0.63% 0.39% -0.24% 14.22% 6.75% -7.47% 1.84% 2.25% 0.41% 5.93% 2.79% -3.13%
Telangana 0.42% 0.20% -0.23% 8.66% 7.58% -1.08% 5.46% 4.02% -1.44% 8.10% 3.94% -4.16%
Tripura 0.87% 0.60% -0.27% 45.00% 42.89% -2.12% 7.13% 5.68% -1.46% 1.93% 2.85% 0.92%
Uttar Pradesh 5.03% 2.57% -2.46% 29.00% 24.87% -4.14% 8.03% 5.46% -2.57% 4.83% 3.45% -1.38%
Uttarakhand 0.64% 0.27% -0.37% 11.18% 8.78% -2.40% 5.08% 4.29% -0.79% 6.80% 3.33% -3.47%
West Bengal 2.56% 0.70% -1.86% 25.15% 21.43% -3.71% 8.13% 4.08% -4.05% 11.70% 3.71% -7.98%
Andaman & Nicobar Islands 0.36% 0.55% 0.18% 10.25% 9.22% -1.02% 1.87% 3.97% 2.09% 1.41% 1.98% 0.57%
Chandigarh 0.50% 0.04% -0.46% 6.02% 4.39% -1.64% 2.78% 0.57% -2.21% 4.09% 1.75% -2.35%
Dadra & Nagar Haveli & Daman & Diu 0.25% 0.14% -0.11% 15.56% 6.66% -8.91% 12.85% 15.57% 2.72% 9.34% 10.61% 1.27%
Delhi 0.28% 0.14% -0.13% 10.83% 6.22% -4.61% 5.54% 4.47% -1.07% 8.38% 5.76% -2.62%
Jammu & Kashmir 0.35% 0.06% -0.29% 9.48% 9.78% 0.30% 5.01% 1.74% -3.27% 3.00% 3.09% 0.09%
Ladakh 0.00% 0.89% 0.89% 80.64% 35.71% -44.93% 6.78% 1.12% -5.66% 2.53% 2.97% 0.44%
Lakshadweep 0.06% 0.28% 0.22% 1.25% 9.63% 8.38% 1.05% 0.73% -0.33% 4.23% 3.39% -0.84%
Puducherry 0.08% 0.05% -0.03% 11.18% 6.92% -4.26% 1.08% 1.71% 0.63% 6.24% 2.64% -3.60%
India 2.45% 0.97% -1.48% 16.59% 16.10% -0.50% 5.91% 4.70% -1.21% 7.41% 4.27% -3.14%
State UT
Electricity
2015-16 (x)2019-21 (y)
Change
(y-x)
Housing
2015-16 (x)2019-21 (y)
Change
(y-x)
Bank Account
2015-16 (x)2019-21 (y)
Change
(y-x)
Assets
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living
(CONTD.) STATE/UT-WISE: UNCENSORED HEADCOUNT RATIO (URBAN)
335
DATA TABLESMPI: PROGRESS REVIEW 2023

Table 7 - State/UT-wise: Censored Headcount Ratio % of individuals who are multidimensionally poor and deprived in each indicator
Andhra Pradesh 8.91% 4.87% -4.03% 0.86% 0.44% -0.41% 4.52% 2.82% -1.70% 7.52% 3.85% -3.66%
Arunachal Pradesh 13.80% 8.54% -5.26% 1.19% 0.52% -0.67% 14.86% 9.47% -5.39% 13.45% 7.25% -6.20%
Assam 25.45% 15.19% -10.26% 2.18% 1.07% -1.11% 17.77% 11.84% -5.93% 14.25% 8.50% -5.74%
Bihar 41.59% 26.84% -14.75% 3.92% 2.99% -0.93% 36.50% 25.16% -11.33% 24.70% 17.59% -7.11%
Chhattisgarh 24.04% 13.20% -10.84% 2.25% 1.38% -0.86% 16.96% 9.79% -7.17% 10.91% 5.69% -5.22%
Goa 2.96% 0.75% -2.21% 0.20% 0.17% -0.03% 1.39% 0.04% -1.35% 2.24% 0.62% -1.62%
Gujarat 15.32% 9.63% -5.69% 1.11% 0.95% -0.16% 8.71% 5.14% -3.57% 6.66% 4.38% -2.27%
Haryana 10.05% 5.72% -4.33% 1.19% 0.73% -0.45% 9.11% 4.63% -4.48% 4.59% 2.88% -1.71%
Himachal Pradesh 6.75% 4.16% -2.59% 0.59% 0.48% -0.11% 5.71% 3.52% -2.19% 1.47% 1.45% -0.02%
Jharkhand 34.39% 23.22% -11.16% 2.74% 1.75% -1.00% 26.47% 19.28% -7.19% 16.44% 11.97% -4.47%
Karnataka 9.77% 6.47% -3.30% 0.71% 0.59% -0.12% 5.28% 4.40% -0.88% 5.39% 2.83% -2.56%
Kerala 0.56% 0.45% -0.11% 0.00% 0.01% 0.01% 0.15% 0.20% 0.05% 0.18% 0.17% -0.02%
Madhya Pradesh 29.00% 15.44% -13.56% 2.72% 1.46% -1.26% 20.85% 11.31% -9.54% 14.00% 7.94% -6.06%
Maharashtra 12.34% 6.40% -5.95% 0.82% 0.50% -0.32% 7.11% 4.16% -2.96% 4.26% 2.79% -1.47%
Manipur 12.65% 5.98% -6.67% 0.93% 0.47% -0.47% 9.97% 4.68% -5.29% 4.59% 2.74% -1.85%
Meghalaya 23.74% 21.66% -2.08% 2.11% 2.27% 0.16% 22.43% 20.42% -2.01% 16.66% 13.83% -2.84%
Mizoram 6.20% 3.38% -2.82% 0.63% 0.27% -0.36% 5.97% 3.42% -2.55% 5.45% 3.05% -2.40%
Nagaland 17.14% 10.78% -6.36% 1.37% 0.74% -0.63% 18.29% 11.42% -6.87% 11.26% 5.90% -5.36%
Odisha 22.41% 12.30% -10.11% 1.51% 0.85% -0.65% 12.77% 7.19% -5.58% 13.77% 8.29% -5.48%
Punjab 4.41% 3.70% -0.71% 0.50% 0.45% -0.05% 3.08% 2.66% -0.42% 3.40% 2.66% -0.74%
Rajasthan 22.85% 12.20% -10.65% 2.07% 1.17% -0.90% 16.82% 9.37% -7.45% 13.24% 6.17% -7.08%
Sikkim 2.87% 1.74% -1.13% 0.25% 0.13% -0.12% 1.75% 0.95% -0.80% 2.48% 1.55% -0.94%
Tamil Nadu 3.54% 1.39% -2.15% 0.30% 0.13% -0.17% 1.63% 0.44% -1.19% 2.24% 1.32% -0.92%
Telangana 9.78% 4.91% -4.87% 0.75% 0.47% -0.28% 4.95% 2.64% -2.31% 8.29% 3.85% -4.44%
Tripura 11.98% 9.64% -2.35% 0.88% 0.87% -0.01% 7.79% 7.63% -0.16% 8.10% 5.63% -2.47%
Uttar Pradesh 30.40% 18.45% -11.96% 3.81% 2.20% -1.61% 25.20% 15.97% -9.24% 15.05% 9.21% -5.83%
Uttarakhand 14.64% 7.50% -7.13% 1.63% 0.90% -0.73% 13.02% 6.47% -6.55% 6.70% 4.02% -2.68%
West Bengal 16.14% 9.37% -6.77% 1.00% 0.50% -0.50% 9.41% 5.51% -3.90% 11.22% 6.25% -4.97%
Andaman & Nicobar Islands 3.41% 1.50% -1.91% 0.31% 0.06% -0.24% 1.02% 0.66% -0.36% 1.94% 1.10% -0.84%
Chandigarh 4.90% 2.37% -2.53% 0.52% 0.50% -0.02% 3.20% 1.64% -1.56% 3.23% 2.13% -1.10%
Dadra & Nagar Haveli & Daman & Diu 17.15% 8.15% -9.00% 0.96% 0.68% -0.27% 6.67% 2.71% -3.96% 5.44% 4.04% -1.40%
Delhi 3.62% 2.67% -0.95% 0.71% 0.33% -0.38% 3.04% 1.81% -1.23% 2.50% 1.86% -0.64%
Jammu & Kashmir 9.69% 3.15% -6.54% 0.85% 0.15% -0.70% 6.08% 2.06% -4.02% 4.48% 2.17% -2.31%
Ladakh 10.13% 2.50% -7.63% 0.88% 0.28% -0.60% 6.95% 1.51% -5.44% 3.33% 1.62% -1.71%
Lakshadweep 1.82% 1.02% -0.80% 0.53% 0.00% -0.53% 0.86% 0.42% -0.44% 0.00% 0.14% 0.14%
Puducherry 1.32% 0.56% -0.76% 0.28% 0.01% -0.27% 0.44% 0.11% -0.33% 0.89% 0.40% -0.48%
India 19.79% 11.90% -7.88% 1.87% 1.18% -0.69% 14.64% 9.35% -5.29% 10.67% 6.63% -4.04% State/UT
State UT
Nutrition
Child & Adolescent Mortality
Years of Schooling
Maternal Health
HealthEducation
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
336
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 7 - State/UT-wise: Censored Headcount Ratio % of individuals who are multidimensionally poor and deprived in each indicator
Andhra Pradesh 1.44% 0.64% -0.80% 9.45% 3.31% -6.14% 10.14% 3.73% -6.41% 3.05% 1.51% -1.54%
Arunachal Pradesh 5.90% 3.18% -2.72% 21.26% 10.94% -10.32% 16.49% 4.64% -11.85% 6.14% 2.23% -3.91%
Assam 5.62% 3.11% -2.51% 31.63% 16.71% -14.92% 24.42% 10.64% -13.78% 8.21% 5.13% -3.09%
Bihar 11.63% 8.63% -2.99% 50.19% 28.52% -21.67% 46.53% 24.78% -21.75% 1.58% 0.91% -0.67%
Chhattisgarh 4.31% 3.61% -0.71% 29.14% 15.31% -13.83% 26.62% 7.93% -18.69% 10.14% 3.26% -6.88%
Goa 0.59% 0.19% -0.40% 2.06% 0.21% -1.85% 2.81% 0.36% -2.45% 0.28% 0.00% -0.28%
Gujarat 4.78% 3.16% -1.62% 17.16% 9.74% -7.42% 15.42% 8.12% -7.30% 4.29% 2.03% -2.26%
Haryana 2.82% 2.39% -0.43% 9.97% 5.56% -4.42% 5.98% 2.89% -3.08% 2.19% 1.45% -0.74%
Himachal Pradesh 0.43% 0.47% 0.04% 7.10% 4.31% -2.79% 4.78% 2.64% -2.15% 1.35% 0.95% -0.39%
Jharkhand 7.17% 6.66% -0.51% 41.20% 26.76% -14.44% 39.33% 18.63% -20.70% 17.32% 8.38% -8.94%
Karnataka 2.33% 1.41% -0.92% 11.24% 4.71% -6.53% 10.71% 5.03% -5.69% 2.68% 1.18% -1.50%
Kerala 0.22% 0.06% -0.16% 0.58% 0.43% -0.15% 0.30% 0.09% -0.21% 0.13% 0.11% -0.02%
Madhya Pradesh 7.34% 4.86% -2.48% 34.85% 18.57% -16.27% 33.13% 13.32% -19.81% 17.36% 8.52% -8.84%
Maharashtra 2.96% 1.31% -1.65% 12.42% 5.25% -7.16% 12.46% 5.33% -7.12% 5.04% 2.33% -2.72%
Manipur 1.72% 0.97% -0.75% 15.64% 6.21% -9.43% 10.97% 3.75% -7.22% 11.59% 5.07% -6.52%
Meghalaya 5.32% 6.38% 1.06% 31.70% 26.46% -5.24% 18.53% 7.37% -11.16% 13.36% 9.50% -3.86%
Mizoram 2.31% 1.40% -0.91% 8.66% 4.19% -4.47% 5.67% 1.42% -4.25% 2.78% 1.60% -1.18%
Nagaland 3.67% 2.60% -1.07% 23.91% 14.21% -9.69% 8.62% 3.36% -5.26% 6.65% 3.20% -3.45%
Odisha 4.32% 2.65% -1.67% 28.76% 14.91% -13.85% 27.11% 11.08% -16.04% 9.82% 4.08% -5.74%
Punjab 1.41% 1.19% -0.22% 4.23% 2.75% -1.48% 3.01% 2.22% -0.79% 0.29% 0.37% 0.08%
Rajasthan 7.21% 2.87% -4.34% 27.17% 13.76% -13.41% 24.40% 9.09% -15.31% 10.36% 3.58% -6.78%
Sikkim 0.36% 0.34% -0.02% 2.89% 2.01% -0.88% 1.13% 0.74% -0.39% 0.19% 0.73% 0.54%
Tamil Nadu 0.45% 0.42% -0.03% 3.57% 1.38% -2.19% 4.42% 1.61% -2.80% 0.96% 0.39% -0.57%
Telangana 1.14% 0.74% -0.40% 10.17% 2.06% -8.12% 11.71% 3.60% -8.11% 3.28% 0.50% -2.78%
Tripura 1.67% 1.47% -0.21% 15.47% 11.98% -3.48% 11.05% 6.25% -4.79% 7.26% 5.22% -2.04%
Uttar Pradesh 9.96% 7.62% -2.34% 34.24% 17.95% -16.29% 31.74% 11.91% -19.83% 2.09% 0.93% -1.16%
Uttarakhand 3.18% 2.64% -0.54% 15.76% 7.39% -8.37% 11.14% 5.11% -6.03% 3.10% 1.41% -1.70%
West Bengal 2.78% 1.28% -1.49% 20.70% 10.96% -9.74% 16.82% 7.43% -9.38% 4.08% 1.15% -2.92%
Andaman & Nicobar Islands 0.27% 0.25% -0.02% 3.14% 1.56% -1.58% 2.98% 1.78% -1.20% 1.11% 0.82% -0.29%
Chandigarh 1.46% 2.32% 0.86% 3.26% 1.60% -1.67% 4.84% 2.46% -2.38% 1.30% 1.31% 0.01%
Dadra & Nagar Haveli & Daman & Diu 5.38% 2.39% -2.98% 15.74% 5.02% -10.71% 18.15% 5.38% -12.76% 4.49% 1.46% -3.03%
Delhi 1.13% 1.41% 0.27% 0.57% 0.33% -0.24% 3.36% 2.33% -1.03% 0.58% 0.18% -0.41%
Jammu & Kashmir 2.51% 1.33% -1.19% 11.27% 3.94% -7.34% 10.57% 3.25% -7.31% 4.97% 2.07% -2.90%
Ladakh 1.08% 1.09% 0.00% 8.80% 1.37% -7.44% 12.64% 3.03% -9.61% 4.69% 1.15% -3.54%
Lakshadweep 0.64% 0.57% -0.07% 1.11% 0.63% -0.47% 0.14% 0.00% -0.14% 0.43% 0.50% 0.06%
Puducherry 0.01% 0.38% 0.37% 1.35% 0.30% -1.05% 1.42% 0.69% -0.72% 0.10% 0.08% -0.03%
India 5.22% 3.63% -1.59% 23.03% 12.30% -10.73% 21.20% 9.25% -11.95% 5.05% 2.23% -2.82% State/UT
State UT
School Attendance
2015-16 (x)2019-21 (y)
Change
(y-x)
Cooking Fuel
2015-16 (x)2019-21 (y)
Change
(y-x)
Drinking Water
2015-16 (x)2019-21 (y)
Change
(y-x)
Sanitation
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living Education
(CONTD.) STATE/UT-WISE: CENSORED HEADCOUNT RATIO
337
DATA TABLESMPI: PROGRESS REVIEW 2023

Table 7 - State/UT-wise: Censored Headcount Ratio % of individuals who are multidimensionally poor and deprived in each indicator
Andhra Pradesh 0.60% 0.24% -0.36% 5.45% 2.65% -2.80% 4.66% 1.96% -2.70% 1.66% 0.40% -1.26%
Arunachal Pradesh 7.15% 1.87% -5.28% 23.25% 13.10% -10.15% 12.83% 5.28% -7.55% 9.22% 2.34% -6.88%
Assam 14.70% 4.13% -10.57% 31.38% 18.22% -13.16% 13.90% 7.47% -6.42% 10.49% 1.71% -8.78%
Bihar 28.78% 2.57% -26.22% 47.09% 29.47% -17.62% 18.70% 12.81% -5.89% 19.60% 2.01% -17.59%
Chhattisgarh 2.78% 0.79% -1.99% 26.78% 14.47% -12.30% 10.42% 4.98% -5.45% 3.40% 1.43% -1.97%
Goa 0.00% 0.00% 0.00% 1.83% 0.24% -1.59% 0.86% 0.19% -0.67% 0.79% 0.02% -0.77%
Gujarat 2.89% 1.62% -1.27% 11.34% 7.39% -3.96% 8.18% 5.02% -3.16% 4.32% 1.25% -3.07%
Haryana 0.74% 0.18% -0.56% 7.35% 4.25% -3.10% 2.54% 1.53% -1.01% 2.88% 0.61% -2.27%
Himachal Pradesh 0.24% 0.24% 0.00% 5.22% 3.18% -2.04% 2.16% 1.62% -0.55% 0.65% 0.45% -0.20%
Jharkhand 13.58% 3.74% -9.84% 35.92% 23.88% -12.04% 15.52% 9.31% -6.22% 6.61% 1.76% -4.86%
Karnataka 0.96% 0.37% -0.59% 9.14% 5.06% -4.08% 4.93% 2.13% -2.80% 3.35% 0.88% -2.47%
Kerala 0.20% 0.12% -0.08% 0.40% 0.38% -0.02% 0.32% 0.28% -0.04% 0.17% 0.14% -0.03%
Madhya Pradesh 6.46% 0.86% -5.60% 32.68% 17.56% -15.12% 13.64% 7.96% -5.68% 7.35% 1.37% -5.97%
Maharashtra 3.13% 0.94% -2.20% 10.11% 5.36% -4.75% 6.69% 3.24% -3.45% 3.79% 1.22% -2.57%
Manipur 3.42% 0.70% -2.72% 16.36% 7.66% -8.70% 6.85% 3.87% -2.98% 8.64% 1.12% -7.52%
Meghalaya 6.41% 5.79% -0.62% 23.30% 20.09% -3.21% 19.35% 20.08% 0.73% 12.94% 4.74% -8.20%
Mizoram 2.98% 1.03% -1.95% 7.59% 4.45% -3.14% 6.63% 3.75% -2.88% 2.67% 0.49% -2.17%
Nagaland 2.49% 0.71% -1.78% 23.91% 14.46% -9.45% 16.62% 10.00% -6.62% 15.77% 3.36% -12.41%
Odisha 8.93% 1.85% -7.09% 24.86% 12.10% -12.75% 13.30% 6.31% -6.99% 6.49% 0.84% -5.65%
Punjab 0.22% 0.08% -0.15% 3.31% 2.72% -0.59% 0.59% 0.58% -0.01% 1.03% 0.53% -0.49%
Rajasthan 6.54% 1.14% -5.40% 18.53% 11.52% -7.01% 13.12% 4.79% -8.32% 2.19% 0.59% -1.60%
Sikkim 0.07% 0.26% 0.18% 2.30% 1.79% -0.51% 1.84% 1.80% -0.05% 1.10% 0.53% -0.57%
Tamil Nadu 0.43% 0.24% -0.19% 2.58% 1.14% -1.44% 1.34% 0.83% -0.51% 1.46% 0.35% -1.11%
Telangana 0.84% 0.21% -0.63% 8.07% 3.17% -4.90% 5.83% 1.83% -4.00% 2.71% 0.37% -2.34%
Tripura 4.30% 1.24% -3.07% 16.16% 12.01% -4.15% 9.38% 6.37% -3.01% 2.20% 1.00% -1.19%
Uttar Pradesh 18.34% 4.98% -13.36% 33.35% 19.56% -13.79% 8.86% 4.22% -4.64% 3.33% 1.06% -2.28%
Uttarakhand 1.39% 0.22% -1.17% 12.30% 5.06% -7.24% 6.21% 2.74% -3.48% 3.21% 0.88% -2.33%
West Bengal 3.73% 1.41% -2.32% 18.70% 9.91% -8.79% 8.64% 3.64% -4.99% 7.12% 1.55% -5.58%
Andaman & Nicobar Islands 1.55% 0.87% -0.67% 3.50% 1.73% -1.76% 2.14% 1.49% -0.65% 0.11% 0.09% -0.02%
Chandigarh 0.48% 0.00% -0.48% 2.48% 1.96% -0.52% 1.20% 0.08% -1.12% 0.81% 0.00% -0.81%
Dadra & Nagar Haveli & Daman & Diu 1.32% 0.25% -1.06% 16.55% 6.01% -10.54% 9.25% 4.99% -4.25% 5.18% 1.37% -3.82%
Delhi 0.06% 0.04% -0.02% 1.43% 0.85% -0.59% 1.74% 1.20% -0.54% 1.27% 0.78% -0.49%
Jammu & Kashmir 1.68% 0.20% -1.47% 9.38% 3.75% -5.63% 6.70% 1.86% -4.84% 1.44% 0.27% -1.16%
Ladakh 0.73% 0.08% -0.65% 12.22% 2.86% -9.36% 3.19% 0.47% -2.72% 0.74% 0.22% -0.51%
Lakshadweep 0.00% 0.00% 0.00% 0.32% 0.44% 0.12% 0.14% 0.08% -0.06% 0.52% 0.09% -0.43%
Puducherry 0.11% 0.05% -0.06% 1.10% 0.40% -0.69% 0.42% 0.26% -0.16% 0.37% 0.08% -0.29%
India 8.28% 1.84% -6.45% 20.48% 12.07% -8.41% 8.84% 4.72% -4.12% 5.36% 1.09% -4.27% State/UT
State UT
Electricity
2015-16 (x)2019-21 (y)
Change
(y-x)
Housing
2015-16 (x)2019-21 (y)
Change
(y-x)
Bank Account
2015-16 (x)2019-21 (y)
Change
(y-x)
Assets
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living
(CONTD.) STATE/UT-WISE: CENSORED HEADCOUNT RATIO
338
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 8 - State/UT-wise: Censored Headcount Ratio (Rural) % of individuals who are multidimensionally poor and deprived in each indicator by Rural Areas
Andhra Pradesh 10.95% 6.11% -4.84% 1.08% 0.56% -0.52% 5.78% 3.67% -2.11% 9.28% 4.82% -4.46%
Arunachal Pradesh 16.35% 9.24% -7.11% 1.50% 0.55% -0.95% 17.71% 10.37% -7.34% 16.42% 8.10% -8.32%
Assam 28.13% 16.85% -11.28% 2.40% 1.13% -1.26% 19.75% 13.12% -6.63% 15.81% 9.33% -6.48%
Bihar 44.93% 29.46% -15.47% 4.21% 3.20% -1.01% 39.31% 27.58% -11.73% 26.68% 19.12% -7.56%
Chhattisgarh 28.60% 15.87% -12.73% 2.68% 1.63% -1.05% 20.31% 11.80% -8.52% 12.91% 6.83% -6.08%
Goa 3.82% 1.76% -2.06% 0.12% 0.41% 0.29% 1.52% 0.00% -1.52% 1.56% 1.35% -0.21%
Gujarat 22.59% 14.03% -8.56% 1.67% 1.41% -0.26% 12.95% 7.79% -5.17% 9.14% 6.10% -3.04%
Haryana 12.44% 6.85% -5.59% 1.42% 0.91% -0.51% 11.45% 5.67% -5.78% 4.87% 3.20% -1.67%
Himachal Pradesh 7.30% 4.45% -2.85% 0.62% 0.53% -0.09% 6.18% 3.74% -2.43% 1.56% 1.42% -0.13%
Jharkhand 41.44% 28.07% -13.37% 3.33% 2.08% -1.24% 31.97% 23.24% -8.73% 19.98% 14.53% -5.45%
Karnataka 14.01% 8.81% -5.20% 0.98% 0.80% -0.18% 7.44% 5.97% -1.48% 7.69% 3.87% -3.82%
Kerala 0.72% 0.58% -0.14% 0.01% 0.02% 0.01% 0.17% 0.22% 0.05% 0.26% 0.30% 0.04%
Madhya Pradesh 36.15% 18.83% -17.32% 3.22% 1.65% -1.57% 26.05% 13.76% -12.29% 17.29% 9.77% -7.53%
Maharashtra 18.66% 9.30% -9.36% 0.93% 0.68% -0.25% 10.53% 6.08% -4.45% 6.28% 3.92% -2.36%
Manipur 16.34% 7.92% -8.41% 1.19% 0.73% -0.46% 13.97% 6.46% -7.51% 6.21% 3.91% -2.29%
Meghalaya 27.92% 25.04% -2.89% 2.54% 2.59% 0.05% 26.90% 23.89% -3.00% 19.94% 16.38% -3.56%
Mizoram 12.82% 6.75% -6.07% 1.25% 0.49% -0.75% 12.53% 6.97% -5.56% 11.52% 6.26% -5.25%
Nagaland 21.64% 13.76% -7.88% 1.80% 1.02% -0.79% 23.54% 14.78% -8.76% 15.28% 7.63% -7.65%
Odisha 25.04% 13.88% -11.16% 1.68% 0.98% -0.70% 14.21% 8.19% -6.01% 15.22% 9.38% -5.84%
Punjab 5.17% 3.84% -1.33% 0.54% 0.45% -0.08% 3.49% 2.93% -0.56% 3.73% 2.37% -1.36%
Rajasthan 27.16% 14.82% -12.34% 2.44% 1.38% -1.06% 20.03% 11.36% -8.67% 15.61% 7.38% -8.23%
Sikkim 3.04% 2.42% -0.62% 0.35% 0.20% -0.16% 1.88% 1.31% -0.56% 2.65% 2.33% -0.32%
Tamil Nadu 5.23% 1.72% -3.51% 0.46% 0.18% -0.28% 2.35% 0.55% -1.81% 3.44% 1.83% -1.61%
Telangana 14.30% 6.26% -8.04% 0.94% 0.53% -0.41% 7.23% 3.37% -3.86% 12.19% 4.96% -7.24%
Tripura 14.76% 11.86% -2.90% 1.16% 1.14% -0.02% 9.82% 9.64% -0.17% 10.14% 7.07% -3.06%
Uttar Pradesh 35.93% 21.38% -14.54% 4.39% 2.46% -1.93% 29.91% 18.74% -11.16% 16.74% 10.07% -6.67%
Uttarakhand 18.28% 8.53% -9.75% 1.71% 1.07% -0.64% 16.12% 7.52% -8.60% 6.95% 3.91% -3.04%
West Bengal 19.38% 11.83% -7.55% 1.27% 0.59% -0.68% 11.31% 6.89% -4.42% 13.40% 7.93% -5.47%
Andaman & Nicobar Islands 5.22% 1.45% -3.77% 0.53% 0.10% -0.43% 1.77% 0.54% -1.23% 2.76% 1.40% -1.35%
Chandigarh 17.53% 3.88% -13.64% 0.00% 3.88% 3.88% 4.12% 3.88% -0.24% 14.43% 0.00% -14.43%
Dadra & Nagar Haveli & Daman & Diu 31.61% 11.10% -20.51% 1.68% 0.67% -1.00% 11.04% 2.44% -8.60% 9.20% 5.08% -4.12%
Delhi 2.39% 2.08% -0.31% 0.00% 0.60% 0.60% 2.39% 1.64% -0.75% 0.00% 1.00% 1.00%
Jammu & Kashmir 12.61% 3.96% -8.65% 1.03% 0.20% -0.84% 8.21% 2.73% -5.48% 5.42% 2.67% -2.75%
Ladakh 12.98% 2.73% -10.24% 1.20% 0.28% -0.92% 9.10% 1.60% -7.50% 3.81% 1.71% -2.10%
Lakshadweep 1.16% 0.36% -0.81% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Puducherry 2.60% 0.16% -2.44% 0.36% 0.00% -0.36% 1.12% 0.13% -1.00% 0.94% 0.59% -0.35%
India 25.91% 15.34% -10.56% 2.39% 1.48% -0.91% 19.27% 12.17% -7.10% 13.70% 8.38% -5.32% State/UT
State UT
Nutrition
Child & Adolescent Mortality
Years of Schooling
Maternal Health
HealthEducation
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
339
DATA TABLESMPI: PROGRESS REVIEW 2023

Table 8 - State/UT-wise: Censored Headcount Ratio (Rural) % of individuals who are multidimensionally poor and deprived in each indicator by Rural Areas
Andhra Pradesh 1.65% 0.78% -0.86% 12.43% 4.61% -7.82% 13.12% 4.97% -8.15% 4.08% 2.08% -2.00%
Arunachal Pradesh 6.86% 3.50% -3.36% 26.62% 12.41% -14.21% 20.08% 4.88% -15.20% 7.64% 2.56% -5.08%
Assam 6.29% 3.43% -2.86% 35.46% 18.97% -16.48% 27.04% 11.65% -15.39% 9.11% 5.81% -3.30%
Bihar 12.29% 9.31% -2.97% 54.70% 31.93% -22.77% 50.68% 27.44% -23.25% 1.69% 1.02% -0.67%
Chhattisgarh 4.99% 4.18% -0.81% 35.41% 18.79% -16.62% 32.25% 9.73% -22.52% 12.51% 4.07% -8.43%
Goa 0.44% 0.48% 0.03% 3.68% 0.52% -3.16% 3.24% 0.80% -2.44% 0.75% 0.00% -0.75%
Gujarat 6.41% 4.16% -2.26% 26.63% 15.48% -11.15% 23.91% 12.52% -11.38% 7.08% 3.31% -3.76%
Haryana 3.27% 2.71% -0.56% 13.74% 7.33% -6.42% 7.13% 3.25% -3.88% 3.29% 1.95% -1.34%
Himachal Pradesh 0.43% 0.41% -0.02% 7.80% 4.61% -3.18% 5.14% 2.69% -2.45% 1.43% 0.98% -0.45%
Jharkhand 8.68% 8.08% -0.60% 50.18% 32.85% -17.33% 48.13% 22.78% -25.34% 21.00% 10.35% -10.65%
Karnataka 3.05% 1.83% -1.22% 17.08% 6.89% -10.19% 15.90% 6.92% -8.98% 4.24% 1.68% -2.56%
Kerala 0.25% 0.07% -0.18% 0.83% 0.57% -0.26% 0.47% 0.12% -0.35% 0.20% 0.17% -0.04%
Madhya Pradesh 9.01% 5.78% -3.23% 45.16% 23.48% -21.68% 42.58% 16.48% -26.10% 22.91% 10.94% -11.97%
Maharashtra 4.18% 1.73% -2.46% 21.41% 8.62% -12.79% 19.43% 7.74% -11.69% 8.91% 4.00% -4.90%
Manipur 2.30% 1.27% -1.04% 20.88% 8.57% -12.31% 13.70% 4.70% -9.00% 15.91% 7.23% -8.68%
Meghalaya 6.28% 7.52% 1.24% 37.90% 30.96% -6.94% 21.89% 8.45% -13.44% 16.12% 11.35% -4.77%
Mizoram 4.78% 2.73% -2.04% 18.66% 8.84% -9.82% 12.18% 2.98% -9.20% 6.15% 3.40% -2.75%
Nagaland 4.75% 3.26% -1.48% 32.08% 19.28% -12.81% 9.98% 3.47% -6.50% 8.29% 3.90% -4.39%
Odisha 4.62% 3.00% -1.63% 32.20% 17.01% -15.19% 30.32% 12.40% -17.93% 11.04% 4.65% -6.40%
Punjab 1.24% 0.91% -0.33% 5.44% 3.25% -2.19% 3.52% 2.04% -1.49% 0.36% 0.46% 0.09%
Rajasthan 8.29% 3.30% -4.98% 33.65% 17.29% -16.36% 29.87% 11.36% -18.51% 13.09% 4.59% -8.50%
Sikkim 0.33% 0.45% 0.13% 3.99% 3.05% -0.94% 0.85% 0.99% 0.14% 0.27% 1.13% 0.86%
Tamil Nadu 0.56% 0.40% -0.16% 5.91% 2.05% -3.85% 6.86% 2.35% -4.52% 1.50% 0.56% -0.94%
Telangana 1.36% 0.76% -0.59% 16.12% 2.85% -13.26% 17.63% 4.74% -12.89% 5.14% 0.75% -4.39%
Tripura 2.27% 1.74% -0.52% 19.82% 15.45% -4.36% 13.90% 7.90% -6.00% 9.84% 6.95% -2.89%
Uttar Pradesh 10.74% 8.16% -2.58% 41.95% 21.98% -19.97% 38.80% 14.01% -24.79% 2.55% 1.11% -1.45%
Uttarakhand 3.21% 2.42% -0.80% 20.75% 9.29% -11.47% 14.06% 5.42% -8.64% 4.46% 1.71% -2.75%
West Bengal 3.07% 1.39% -1.68% 25.36% 14.49% -10.87% 20.54% 9.66% -10.87% 4.64% 1.56% -3.08%
Andaman & Nicobar Islands 0.46% 0.40% -0.07% 5.35% 2.20% -3.15% 4.93% 2.22% -2.70% 1.93% 1.12% -0.81%
Chandigarh 7.22% 0.00% -7.22% 16.49% 0.00% -16.49% 18.56% 3.88% -14.67% 0.00% 0.00% 0.00%
Dadra & Nagar Haveli & Daman & Diu 8.45% 2.35% -6.10% 32.89% 8.98% -23.91% 34.03% 6.57% -27.46% 8.87% 2.31% -6.56%
Delhi 0.00% 0.93% 0.93% 2.39% 0.42% -1.97% 2.39% 1.93% -0.46% 0.23% 0.00% -0.23%
Jammu & Kashmir 3.20% 1.62% -1.58% 15.25% 5.23% -10.02% 14.00% 4.22% -9.78% 6.90% 2.71% -4.19%
Ladakh 1.40% 1.34% -0.06% 11.81% 1.60% -10.21% 16.12% 3.37% -12.76% 6.08% 1.29% -4.79%
Lakshadweep 1.16% 0.00% -1.16% 1.16% 0.36% -0.81% 0.00% 0.00% 0.00% 1.16% 0.36% -0.81%
Puducherry 0.00% 0.00% 0.00% 3.20% 0.70% -2.50% 3.17% 0.71% -2.46% 0.12% 0.12% 0.01%
India 6.48% 4.42% -2.06% 31.34% 16.66% -14.68% 28.47% 12.09% -16.37% 6.91% 3.05% -3.86% State/UT
State UT
School Attendance
2015-16 (x)2019-21 (y)
Change
(y-x)
Cooking Fuel
2015-16 (x)2019-21 (y)
Change
(y-x)
Drinking Water
2015-16 (x)2019-21 (y)
Change
(y-x)
Sanitation
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living Education
(CONTD.) STATE/UT-WISE: CENSORED HEADCOUNT RATIO (RURAL)
340
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 8 - State/UT-wise: Censored Headcount Ratio (Rural) % of individuals who are multidimensionally poor and deprived in each indicator by Rural Areas
Andhra Pradesh 0.74% 0.32% -0.42% 7.17% 3.66% -3.51% 6.05% 2.57% -3.48% 1.77% 0.48% -1.29%
Arunachal Pradesh 9.20% 2.18% -7.02% 28.39% 14.45% -13.94% 16.07% 6.04% -10.03% 11.46% 2.65% -8.81%
Assam 16.58% 4.75% -11.83% 34.94% 20.31% -14.63% 15.47% 8.17% -7.29% 11.61% 1.82% -9.79%
Bihar 31.89% 2.61% -29.27% 51.58% 32.90% -18.68% 19.99% 14.11% -5.87% 21.01% 2.18% -18.83%
Chhattisgarh 3.50% 0.95% -2.55% 32.78% 17.81% -14.97% 12.85% 6.08% -6.78% 4.09% 1.60% -2.49%
Goa 0.00% 0.00% 0.00% 3.44% 0.51% -2.93% 1.36% 0.48% -0.88% 0.94% 0.05% -0.89%
Gujarat 4.42% 2.57% -1.85% 18.38% 11.85% -6.54% 12.62% 7.92% -4.70% 5.96% 1.77% -4.19%
Haryana 1.06% 0.22% -0.84% 9.89% 5.31% -4.57% 3.17% 1.78% -1.39% 3.09% 0.67% -2.42%
Himachal Pradesh 0.25% 0.18% -0.07% 5.70% 3.39% -2.31% 2.33% 1.62% -0.70% 0.68% 0.38% -0.30%
Jharkhand 17.49% 4.71% -12.79% 44.94% 29.91% -15.02% 19.17% 11.40% -7.77% 8.07% 2.00% -6.07%
Karnataka 1.36% 0.44% -0.91% 13.80% 7.14% -6.66% 7.41% 2.95% -4.46% 4.90% 1.07% -3.82%
Kerala 0.35% 0.23% -0.12% 0.62% 0.50% -0.13% 0.54% 0.45% -0.09% 0.25% 0.15% -0.11%
Madhya Pradesh 8.63% 1.07% -7.56% 42.42% 22.08% -20.34% 17.94% 10.00% -7.94% 9.08% 1.64% -7.44%
Maharashtra 5.16% 1.50% -3.66% 17.20% 8.65% -8.55% 10.85% 5.03% -5.82% 5.12% 1.50% -3.62%
Manipur 4.76% 0.96% -3.80% 21.68% 10.36% -11.32% 9.79% 5.26% -4.53% 11.30% 1.51% -9.79%
Meghalaya 7.78% 6.73% -1.04% 27.72% 23.73% -3.99% 23.42% 23.60% 0.18% 15.24% 5.55% -9.69%
Mizoram 6.68% 2.23% -4.45% 16.24% 9.17% -7.06% 14.29% 7.91% -6.38% 5.66% 1.01% -4.66%
Nagaland 3.66% 1.04% -2.62% 31.37% 18.65% -12.71% 23.24% 13.67% -9.58% 21.80% 4.20% -17.60%
Odisha 10.00% 2.12% -7.88% 27.94% 13.75% -14.19% 14.94% 7.14% -7.80% 7.04% 0.94% -6.10%
Punjab 0.16% 0.11% -0.06% 4.30% 3.36% -0.94% 0.58% 0.57% -0.01% 1.02% 0.44% -0.59%
Rajasthan 8.38% 1.45% -6.92% 23.29% 14.28% -9.00% 16.41% 6.04% -10.36% 2.47% 0.75% -1.72%
Sikkim 0.05% 0.39% 0.34% 2.91% 2.56% -0.35% 2.39% 2.66% 0.27% 1.32% 0.81% -0.52%
Tamil Nadu 0.71% 0.32% -0.39% 4.09% 1.64% -2.45% 2.22% 1.24% -0.98% 2.16% 0.39% -1.77%
Telangana 1.21% 0.28% -0.93% 12.84% 4.33% -8.51% 9.15% 2.33% -6.82% 3.67% 0.47% -3.20%
Tripura 5.85% 1.64% -4.21% 20.61% 15.24% -5.37% 12.17% 8.38% -3.79% 2.83% 1.31% -1.52%
Uttar Pradesh 23.30% 6.06% -17.24% 40.83% 23.61% -17.22% 10.27% 4.78% -5.50% 3.61% 1.17% -2.44%
Uttarakhand 1.93% 0.21% -1.72% 17.12% 6.08% -11.03% 8.33% 3.15% -5.18% 3.46% 0.73% -2.73%
West Bengal 4.56% 1.95% -2.61% 23.28% 13.04% -10.25% 10.45% 4.73% -5.72% 8.25% 1.96% -6.29%
Andaman & Nicobar Islands 2.48% 1.20% -1.28% 5.72% 2.41% -3.31% 3.61% 1.92% -1.69% 0.19% 0.05% -0.14%
Chandigarh 0.00% 0.00% 0.00% 5.15% 0.00% -5.15% 1.03% 0.00% -1.03% 1.03% 0.00% -1.03%
Dadra & Nagar Haveli & Daman & Diu 2.85% 0.44% -2.41% 33.80% 10.48% -23.32% 17.46% 6.73% -10.73% 8.83% 1.03% -7.79%
Delhi 0.00% 0.07% 0.07% 0.00% 0.91% 0.91% 2.16% 0.88% -1.28% 0.23% 0.85% 0.62%
Jammu & Kashmir 2.26% 0.27% -1.98% 12.76% 4.91% -7.85% 8.97% 2.44% -6.53% 1.85% 0.36% -1.49%
Ladakh 0.99% 0.09% -0.90% 15.64% 3.16% -12.47% 3.84% 0.51% -3.34% 0.79% 0.27% -0.52%
Lakshadweep 0.00% 0.00% 0.00% 0.00% 0.36% 0.36% 0.00% 0.36% 0.36% 0.00% 0.00% 0.00%
Puducherry 0.19% 0.13% -0.05% 2.36% 0.71% -1.65% 0.75% 0.51% -0.24% 0.96% 0.12% -0.84%
India 11.63% 2.46% -9.17% 28.00% 16.21% -11.79% 11.83% 6.21% -5.62% 6.83% 1.31% -5.51% State/UT
State UT
Electricity
2015-16 (x)2019-21 (y)
Change
(y-x)
Housing
2015-16 (x)2019-21 (y)
Change
(y-x)
Bank Account
2015-16 (x)2019-21 (y)
Change
(y-x)
Assets
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living
(CONTD.) STATE/UT-WISE: CENSORED HEADCOUNT RATIO (RURAL)
341
DATA TABLESMPI: PROGRESS REVIEW 2023

Table 9 - State/UT-wise: Censored Headcount Ratio (Urban) % of individuals who are multidimensionally poor and deprived in each indicator by Urban Areas
Andhra Pradesh 3.97% 1.99% -1.97% 0.32% 0.16% -0.16% 1.47% 0.85% -0.62% 3.26% 1.60% -1.66%
Arunachal Pradesh 5.50% 4.61% -0.89% 0.18% 0.38% 0.20% 5.57% 4.38% -1.19% 3.79% 2.47% -1.31%
Assam 7.99% 5.16% -2.82% 0.78% 0.67% -0.11% 4.92% 4.13% -0.79% 4.12% 3.53% -0.59%
Bihar 18.78% 12.76% -6.02% 1.91% 1.85% -0.06% 17.25% 12.21% -5.04% 11.15% 9.35% -1.79%
Chhattisgarh 8.57% 3.77% -4.80% 0.80% 0.53% -0.26% 5.60% 2.69% -2.91% 4.14% 1.68% -2.46%
Goa 2.44% 0.06% -2.38% 0.24% 0.00% -0.24% 1.31% 0.06% -1.25% 2.65% 0.12% -2.53%
Gujarat 5.39% 3.32% -2.08% 0.34% 0.30% -0.04% 2.92% 1.35% -1.56% 3.27% 1.92% -1.35%
Haryana 6.24% 3.37% -2.88% 0.81% 0.37% -0.44% 5.39% 2.47% -2.91% 4.13% 2.22% -1.92%
Himachal Pradesh 1.37% 2.23% 0.86% 0.27% 0.12% -0.15% 1.16% 2.00% 0.84% 0.65% 1.63% 0.98%
Jharkhand 12.75% 7.28% -5.47% 0.95% 0.64% -0.31% 9.60% 6.26% -3.34% 5.58% 3.57% -2.01%
Karnataka 3.90% 2.76% -1.14% 0.34% 0.26% -0.09% 2.29% 1.92% -0.37% 2.19% 1.19% -1.01%
Kerala 0.38% 0.30% -0.08% 0.00% 0.00% 0.00% 0.12% 0.18% 0.06% 0.10% 0.02% -0.08%
Madhya Pradesh 11.50% 5.65% -5.85% 1.50% 0.90% -0.60% 8.09% 4.23% -3.86% 5.93% 2.67% -3.26%
Maharashtra 4.97% 2.64% -2.33% 0.70% 0.26% -0.44% 3.12% 1.67% -1.46% 1.91% 1.33% -0.58%
Manipur 6.82% 2.79% -4.04% 0.53% 0.04% -0.49% 3.66% 1.77% -1.89% 2.04% 0.81% -1.23%
Meghalaya 6.80% 7.34% 0.54% 0.35% 0.89% 0.54% 4.30% 5.68% 1.38% 3.37% 2.97% -0.40%
Mizoram 0.99% 0.46% -0.53% 0.14% 0.08% -0.07% 0.83% 0.36% -0.47% 0.68% 0.27% -0.42%
Nagaland 8.54% 4.56% -3.98% 0.54% 0.17% -0.37% 8.26% 4.42% -3.84% 3.57% 2.30% -1.27%
Odisha 8.87% 4.35% -4.52% 0.64% 0.24% -0.40% 5.39% 2.17% -3.22% 6.27% 2.79% -3.48%
Punjab 3.23% 3.46% 0.23% 0.45% 0.45% 0.00% 2.45% 2.19% -0.26% 2.88% 3.17% 0.28%
Rajasthan 9.44% 3.71% -5.73% 0.89% 0.47% -0.42% 6.80% 2.89% -3.92% 5.86% 2.22% -3.64%
Sikkim 2.48% 0.50% -1.98% 0.00% 0.00% 0.00% 1.46% 0.28% -1.18% 2.09% 0.12% -1.97%
Tamil Nadu 1.87% 1.02% -0.86% 0.15% 0.08% -0.07% 0.92% 0.32% -0.60% 1.06% 0.74% -0.32%
Telangana 3.88% 2.31% -1.57% 0.50% 0.37% -0.13% 1.97% 1.23% -0.74% 3.20% 1.71% -1.48%
Tripura 4.83% 4.04% -0.79% 0.17% 0.21% 0.05% 2.58% 2.56% -0.02% 2.84% 1.99% -0.85%
Uttar Pradesh 13.71% 8.70% -5.01% 2.06% 1.35% -0.71% 11.00% 6.75% -4.25% 9.92% 6.38% -3.54%
Uttarakhand 7.87% 5.15% -2.72% 1.47% 0.52% -0.95% 7.26% 4.05% -3.21% 6.25% 4.28% -1.97%
West Bengal 8.90% 4.22% -4.68% 0.40% 0.33% -0.08% 5.17% 2.61% -2.56% 6.35% 2.73% -3.63%
Andaman & Nicobar Islands 0.97% 1.60% 0.63% 0.00% 0.00% 0.00% 0.00% 0.86% 0.86% 0.83% 0.58% -0.25%
Chandigarh 4.38% 2.35% -2.03% 0.54% 0.46% -0.08% 3.17% 1.61% -1.56% 2.76% 2.16% -0.61%
Dadra & Nagar Haveli & Daman & Diu 4.74% 4.74% 0.00% 0.34% 0.70% 0.36% 2.92% 3.02% 0.10% 2.21% 2.84% 0.63%
Delhi 3.63% 2.69% -0.95% 0.72% 0.32% -0.40% 3.05% 1.82% -1.23% 2.52% 1.88% -0.64%
Jammu & Kashmir 2.76% 0.81% -1.95% 0.41% 0.04% -0.38% 1.01% 0.13% -0.88% 2.25% 0.73% -1.52%
Ladakh 2.25% 1.50% -0.75% 0.00% 0.25% 0.25% 1.01% 1.14% 0.12% 2.00% 1.26% -0.75%
Lakshadweep 2.00% 1.21% -0.79% 0.67% 0.00% -0.67% 1.09% 0.54% -0.56% 0.00% 0.17% 0.17%
Puducherry 0.75% 0.74% 0.00% 0.25% 0.02% -0.23% 0.13% 0.10% -0.02% 0.86% 0.32% -0.54%
India 6.97% 4.18% -2.79% 0.79% 0.52% -0.27% 4.93% 3.02% -1.91% 4.32% 2.71% -1.62% State/UT
State UT
Nutrition
Child & Adolescent Mortality
Years of Schooling
Maternal Health
HealthEducation
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
2015-16 (x)2019-21 (y)
Change
(y-x)
342
DATA TABLES MPI: PROGRESS REVIEW 2023

Table 9 - State/UT-wise: Censored Headcount Ratio (Urban) % of individuals who are multidimensionally poor and deprived in each indicator by Urban Areas
Andhra Pradesh 0.94% 0.31% -0.64% 2.25% 0.29% -1.97% 2.92% 0.84% -2.09% 0.56% 0.21% -0.36%
Arunachal Pradesh 2.79% 1.41% -1.38% 3.80% 2.62% -1.18% 4.78% 3.25% -1.52% 1.24% 0.38% -0.85%
Assam 1.28% 1.14% -0.14% 6.76% 3.02% -3.74% 7.40% 4.54% -2.86% 2.35% 0.97% -1.37%
Bihar 7.09% 4.96% -2.13% 19.45% 10.29% -9.17% 18.18% 10.54% -7.65% 0.82% 0.35% -0.47%
Chhattisgarh 2.02% 1.58% -0.45% 7.88% 3.02% -4.87% 7.57% 1.62% -5.95% 2.14% 0.40% -1.74%
Goa 0.67% 0.00% -0.67% 1.08% 0.00% -1.08% 2.55% 0.06% -2.48% 0.00% 0.00% 0.00%
Gujarat 2.55% 1.73% -0.82% 4.24% 1.50% -2.74% 3.84% 1.80% -2.03% 0.48% 0.19% -0.30%
Haryana 2.10% 1.71% -0.39% 3.96% 1.85% -2.11% 4.13% 2.14% -2.00% 0.42% 0.40% -0.03%
Himachal Pradesh 0.42% 0.89% 0.48% 0.26% 2.25% 2.00% 1.24% 2.28% 1.05% 0.53% 0.78% 0.25%
Jharkhand 2.55% 2.01% -0.54% 13.67% 6.74% -6.93% 12.37% 4.97% -7.40% 6.02% 1.91% -4.12%
Karnataka 1.33% 0.76% -0.57% 3.15% 1.25% -1.90% 3.54% 2.03% -1.50% 0.52% 0.38% -0.14%
Kerala 0.20% 0.05% -0.14% 0.28% 0.27% -0.01% 0.10% 0.06% -0.05% 0.05% 0.04% 0.00%
Madhya Pradesh 3.23% 2.21% -1.01% 9.57% 4.41% -5.16% 9.98% 4.20% -5.77% 3.75% 1.51% -2.24%
Maharashtra 1.54% 0.78% -0.77% 1.92% 0.89% -1.02% 4.32% 2.22% -2.10% 0.54% 0.16% -0.38%
Manipur 0.80% 0.48% -0.32% 7.35% 2.34% -5.01% 6.66% 2.19% -4.48% 4.75% 1.53% -3.22%
Meghalaya 1.40% 1.53% 0.13% 6.54% 7.36% 0.81% 4.88% 2.79% -2.09% 2.18% 1.65% -0.53%
Mizoram 0.37% 0.24% -0.13% 0.82% 0.18% -0.64% 0.56% 0.07% -0.48% 0.13% 0.04% -0.09%
Nagaland 1.62% 1.22% -0.40% 8.27% 3.64% -4.63% 6.03% 3.12% -2.91% 3.52% 1.75% -1.77%
Odisha 2.77% 0.91% -1.87% 11.05% 4.40% -6.66% 10.59% 4.44% -6.14% 3.50% 1.20% -2.31%
Punjab 1.67% 1.69% 0.02% 2.35% 1.88% -0.47% 2.21% 2.55% 0.33% 0.17% 0.21% 0.04%
Rajasthan 3.85% 1.45% -2.40% 6.98% 2.26% -4.72% 7.38% 1.74% -5.64% 1.87% 0.29% -1.58%
Sikkim 0.43% 0.13% -0.30% 0.33% 0.11% -0.22% 1.78% 0.28% -1.50% 0.00% 0.00% 0.00%
Tamil Nadu 0.35% 0.44% 0.09% 1.26% 0.61% -0.65% 2.00% 0.78% -1.22% 0.42% 0.20% -0.23%
Telangana 0.85% 0.70% -0.15% 2.41% 0.52% -1.89% 3.97% 1.40% -2.56% 0.85% 0.04% -0.82%
Tripura 0.15% 0.78% 0.63% 4.26% 3.27% -0.99% 3.71% 2.13% -1.58% 0.60% 0.86% 0.26%
Uttar Pradesh 7.61% 5.84% -1.77% 10.93% 4.59% -6.35% 10.42% 4.96% -5.46% 0.71% 0.36% -0.35%
Uttarakhand 3.13% 3.17% 0.04% 6.49% 3.05% -3.45% 5.74% 4.41% -1.33% 0.59% 0.72% 0.13%
West Bengal 2.12% 1.06% -1.06% 10.30% 3.56% -6.73% 8.51% 2.77% -5.74% 2.81% 0.31% -2.51%
Andaman & Nicobar Islands 0.00% 0.00% 0.00% 0.14% 0.47% 0.33% 0.35% 1.04% 0.69% 0.00% 0.31% 0.31%
Chandigarh 1.22% 2.35% 1.13% 2.71% 1.62% -1.10% 4.28% 2.44% -1.84% 1.35% 1.33% -0.02%
Dadra & Nagar Haveli & Daman & Diu 2.74% 2.45% -0.29% 1.02% 0.45% -0.57% 4.51% 4.01% -0.50% 0.72% 0.47% -0.25%
Delhi 1.14% 1.42% 0.28% 0.55% 0.33% -0.23% 3.37% 2.34% -1.03% 0.58% 0.18% -0.40%
Jammu & Kashmir 0.89% 0.49% -0.40% 1.85% 0.25% -1.60% 2.41% 0.48% -1.93% 0.39% 0.23% -0.16%
Ladakh 0.21% 0.00% -0.21% 0.49% 0.38% -0.11% 3.02% 1.62% -1.40% 0.86% 0.55% -0.31%
Lakshadweep 0.50% 0.72% 0.23% 1.09% 0.71% -0.38% 0.17% 0.00% -0.17% 0.24% 0.54% 0.30%
Puducherry 0.01% 0.55% 0.53% 0.52% 0.11% -0.40% 0.63% 0.69% 0.06% 0.09% 0.05% -0.04%
India 2.58% 1.84% -0.73% 5.63% 2.52% -3.12% 5.98% 2.85% -3.13% 1.16% 0.38% -0.78% State/UT
State UT
School Attendance
2015-16 (x)2019-21 (y)
Change
(y-x)
Cooking Fuel
2015-16 (x)2019-21 (y)
Change
(y-x)
Drinking Water
2015-16 (x)2019-21 (y)
Change
(y-x)
Sanitation
2015-16 (x)2019-21 (y)
Change
(y-x)
Standard of Living Education
(CONTD.) STATE/UT-WISE: CENSORED HEADCOUNT RATIO (URBAN)
343
DATA TABLESMPI: PROGRESS REVIEW 2023

Table 9 - State/UT-wise: Censored Headcount Ratio (Urban) % of individuals who are multidimensionally poor and deprived in each indicator by Urban Areas
Andhra Pradesh 0.27% 0.06% -0.22% 1.30% 0.29% -1.01% 1.30% 0.54% -0.76% 1.41% 0.23% -1.18%
Arunachal Pradesh 0.50% 0.13% -0.36% 6.52% 5.44% -1.07% 2.29% 1.01% -1.28% 1.90% 0.57% -1.33%
Assam 2.49% 0.39% -2.10% 8.24% 5.56% -2.68% 3.69% 3.23% -0.46% 3.23% 1.05% -2.18%
Bihar 7.59% 2.31% -5.28% 16.51% 11.11% -5.39% 9.88% 5.81% -4.06% 9.97% 1.11% -8.86%
Chhattisgarh 0.33% 0.23% -0.10% 6.46% 2.73% -3.72% 2.19% 1.10% -1.09% 1.08% 0.85% -0.23%
Goa 0.00% 0.00% 0.00% 0.87% 0.06% -0.81% 0.56% 0.00% -0.56% 0.70% 0.00% -0.70%
Gujarat 0.81% 0.26% -0.55% 1.74% 1.00% -0.74% 2.11% 0.85% -1.26% 2.08% 0.49% -1