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Aspirational District Programme

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ASPIRATIONAL
DISTRICTS
PROGRAMME:
AN APPRAISAL
United Nations Development Programme December 2020
UNDP partners with people at all levels of society to help build nations that can
withstand crisis, and drive and sustain the kind of growth that improves the quality of
life for everyone. On the ground in nearly 170 countries and territories, we ofer
global perspective and local insight to help empower lives and build resilient nations.
Copyright @UNDP India 2020.
All rights reserved
This publication in its entirety may not be reproduced or transmitted in any form or
by any means, electronic or mechanical, including photocopy, recording or any
information storage and retrieval system now known or to be invented, without
written permission from the publisher.
The team that authored this report includes;
Dr. Basudeb Guha Khasnobis (Development Economist)
Mr. Jaimon C Uthup (Policy Specialist – SDGs)
Ms. Sruti Mohanty (Consultant)
Ms. Upasana Sikri (Technical Expert – ADP)
Mr. Digvijay Singh (Social Protection Specialist)
Mr. Daksh Baheti (Research & Data Analytics Expert)
Mr. Suvir Chandna (Research & Data Analytics Expert)
Ms. Anjali Bansal (Research & Data Analytics Expert)
Ms. Pallavi Kashyap (Coordination Support)
Ms. Kaavya Singh (Coordination Support)
Published in India
United Nations Development Programme,
55 Lodhi Estate,
New Delhi
110003
The authors of the report would like to warmly acknowledge the contributions made
by those listed below.
Design, Illustrations & Layout - Thinkstr Consultancy Pvt. Ltd
Aspirational Map designed by Rouge Communications
Photographs – Mr. Biju Boro, Mr. Gaganjit Singh, Mr. Pelevizo Meyase
Aspirational Districts Programme:
An Appraisal ASPIRATIONAL
DISTRICTS
PROGRAMME:
AN APPRAISAL
United Nations Development Programme LIST OF
ASPIRATIONAL
DISTRICTS Jammu & Kashmir
1.   Kupwara
2. Baramula
Himachal Pradesh
3. Chamba
Punjab
4. Moga
104. Firozpur
Uttarakhand
5.   Udham Singh Nagar
6.   Haridwar
Haryana
7.   Mewat
Rajasthan
8.   Dholpur
9.   Karauli
10.   Jaisalmer
11.   Sirohi
12.   Baran
Uttar Pradesh
13.   Chitrakoot
14.   Fatehpur
15.   Bahraich
16.   Shrawasti
17.   Balrampur
18.   Siddharthnagar
19.   Chandauli
20.   Sonebhadra
Bihar
21.   Sitamarhi
22.   Araria
23.   Purnia
24.   Katihar
25.   Muzafarpur
26.   Begusarai
27.   Khagaria
28.   Banka
29.   Sheikhpura
30.   Aurangabad
31.   Gaya
32.   Nawada
33.   Jamui
Sikkim
34.   West Sikkim
Nagaland
35.   Kiphire
Manipur
36.   Chandel
Mizoram
37.   Mamit
Tripura
38.   Dhalai
Meghalaya
39.   Ribhoi
Assam
40.   Goalpara
41.   Barpeta
42.   Hailakandi
43.   Baksa
44.   Darrang
45.   Udalguri
109. Dhubri
Jharkhand
46.   Garhwa
47.   Chatra
48.   Giridih
49.   Godda
50.   Sahibganj
51.   Pakur
52.   Bokaro
53.   Lohardaga
54.   Purbi Singhbhum
55.   Palamu
56.   Latehar
57.   Hazaribagh
58.   Ramgarh
59.   Dumka
60.   Ranchi
61.   Khunti
62.   Gumla
63.   Simdega
64.   Pashchimi Singhbhum
Odisha
65.   Dhenkanal
66.   Gajapati
67.   Kandhamal
68.   Balangir
69.   Kalahandi
70.   Rayagada
71.   Koraput
72.   Malkangiri
73.   Nawarangpur
74.   Nuapada
Chhattisgarh
75.   Korba
76.   Rajnandgaon
77. Mahasamund
78.   Kanker
79.   Narayanpur
80.   Dantewada
81.   Bijapur
105. Bastar
106. Kondagaon
107.  Sukma
Madhya Pradesh
82.   Chhatarpur
83.   Damoh
84.   Barwani
85.   Rajgarh
86.   Vidisha
87.   Guna
88.   Singrauli
89.   Khandwa
Gujarat
90.   Dahod
91.   Narmada
Maharashtra
92.   Nandurbar
93.   Washim
94.   Gadchiroli
95.   Osmanabad
Andhra Pradesh
96.   Vizianagaram
97.   Visakhapatnam
98.   Y.S.R. Kadapa
Karnataka
99.   Raichur
100. Yadgir
Kerala
101.  Wayanad
Tamil Nadu
102. Virudhunagar
103. Ramanathapuram
Arunachal Pradesh
108. Namsai
Telangana
110.   Asifabad (Komaram Bheem)
111.   Jayashankar Bhupalpally
112.   Bhadradri kothagudem MESSAGE
The Asia- Paciﬁc region is an economic powerhouse, a driver of
innovation and invention, and is endowed with abundant human
capacity, societal energies and natural resources. Carrying diverse
and complex developmental issues, the region is challenged by
deep rooted inequalities and pockets of instability that threaten
peaceful progress.
The 2030 Agenda can only be achieved with a level of scale and
ambition in collaboration and commitment across all levels of governments,
the many partners and stakeholders involved. Sub-national and local
governments have an essential role to play in localizing the global goals,
translating and delivering them as integrated programmes and services that
work to improve people’s lives. This is where impact will matter most.
The Aspirational District Programme in India is designed along these lines. It
is an efort to demonstrate that governments and stakeholders can advance
sustainable development by designing and implementing together. While
targeting a set of speciﬁc areas of improvement that have been identiﬁed by
the communities themselves, it carries rigorous monitoring and data driven
decision making approach to keep it on course. The overall success of the
programme will be measured by its ability to inﬂuence and sustain a more
inclusive and locally informed approach to tackling local development.
While the initiative remains at an early stage, the initial ﬁndings are on the
right track. There will be much to be learnt and improved along the way. This
openness to learning and to adapt and grow as needed, will keep the efort
honest and accountable to those it serves. I am pleased to see UNDP’s
engagement in this initiative in India, partnering with Niti Aayog and all
stakeholders.
Kanni Wignaraja,
Assistant Secretary-General,
Assistant Administrator and
Director of the Regional Bureau for Asia and the Paciﬁc MESSAGE
The Aspirational Districts Programme, anchored by NITI Aayog,
aims to transform the socioeconomic status of these priority
districts. The programme’s focus on 3 Cs: Convergence (ofcentral
and state schemes), Collaboration (between Centre, State, District
and Citizens) and Competition (among the districts in key
performance indicators) is proving to be a successful model for
stimulating local development.
Focused at district level and instituted by states, the programme hinges on
the strengths of local governments to accelerate the realisation of SDG
aspirations for communities, households, and individuals, particularly to
those at risk of falling behind. It achieves this in big part through e-monitoring
the real-time data.
The importance of partnerships and collective action is another hallmark of
the Aspirational District Programme, bringing in diferent development
partners with varied expertise to support the district administrations. These
partnerships re-emphasise the importance of consolidating our strengths to
make the spirit of Agenda 2030 spring to life for all people. UNDP greatly
values such partnerships to guide strategic priorities and spur concerted
action to deliver on shared objectives.
These and other attributes make the Aspirational District Programme a
global example in enlisting sub-national government, with multi-stakeholder
partnerships, to ensure that SDG progress becomes real in the eyes of
people in their daily lives. The programme is not only replicable within India,
but also across the globe.
This report presents an appraisal of the Aspirational Districts Programme .
UNDP is committed to closely working with Government of India, and NITI
Aayog in particular, along with other partners, to fully achieve the
programme’s noble objectives.
Renata Dessallien
UN Resident Coordinator in India The Government of India launched the Aspirational Districts
Programme in January 2018 to accelerate improvement in key
development parameters in the most backward districts of the
country. The programme marks a paradigm shift from pursuing
economic growth towards reducing deep spatial inequalities. The
initiative pivots on the Government’s motto of ‘Sabka Saath, Sabka
Vikas’, which mirrors the principle of ‘Leaving No One Behind’ to
achieve the Agenda 2030.
The Programme applies innovative techniques by supporting collaboration
among multiple levels of governance as well as through public-private
partnerships. It applies the 3C principle - Convergence, Competition and
Collaboration – and a well-designed system of incentives for good
performance which is monitored by a set of pre-determined common
indicators. India has been a global leader in advancing the SDG agenda,
and it is heartening to see the country’s initiative on Local Economic
Development (LED) delivering strong results. It merits replication in other
parts of the developing world.
As we publish this appraisal of the Aspirational Districts Programme , the
world is grappling with the devastating consequences of the Covid-19
pandemic and the unravelling of economic recession. Transformative
approaches are needed for progress, including in the Aspirational Districts.
The social protection architecture can be strengthened further to impart
more resilience to backward regions especially at times of crises.
My special appreciation goes to the Policy Unit of UNDP India, who drove
the whole process for this evaluation study.
Shoko Noda
Resident Representative
UNDP India
FOREWORD TABLE OF CONTENTS
INTRODUCTION AND
BACKGROUND TO
THE PROGRAMME
1
1.1. Institutional Structure and Sectoral
Focus: A Transformative Approach
1.2. Data Driven Governance – The Key
to Programme Efciency
LITERATURE
REVIEW
2
2.1. Similar Programmes
EVALUATION
CRITERIA
3
3.1. Key Research Questions
QUANTITATIVE DATA
COLLECTION AND
ANALYSIS
4
4.1. Net Resilience Index
4.1.1.  Methodology
4.1.2. Findings
4.2. Diference-in-Diference Method
4.2.1. Methodology
4.2.2. Findings
QUALITATIVE
DATA
COLLECTION
AND ANALYSIS
5
5.1.   Respondents and Sampling for
Qualitative Data Collection
5.2. Findings
5.2.1. Mapping Sector-wise growth
5.3.  Governance, Administration and
Capacity building
5.3.1. The 3Cs Approach
5.3.2. Targeting the low hanging fruits
5.3.3. Monitoring and Measurement
Methods
5.3.4. Capacity building
5.4. The role of Champions of Change
(CoC) Dashboard in data driven
decision making
THE IMPACT OF
ASPIRATIONAL
DISTRICTS
PROGRAMME AND
WHAT SETS IT APART
6
RECOMMENDATIONS
FOR THE WAY
FORWARD:
COUNTERING THE
EXISTING GAPS AND
CHALLENGES
7
BEST PRACTICES
8
8.1. Health and Nutrition
8.2. Education
8.3. Agriculture and Water Resources
8.4. Basic Infrastructure
8.5. Skill Development and Financial
Inclusion
8.6. Scalability
EXECUTIVE
SUMMARY
TABLE OF CONTENTS
159
131525
353739 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
List of Box & Tables
List of Tables
Table 1: Sectors, weightage and areas
of focus  (pg. 7)
Table 2: Evaluation criteria   (pg. 14)
Table 3: Summary Statistics for Net Resilience
exercise - 2018 and 2020   (pg. 17)
Table 4: CoC and HMIS Data Matching
for H&N Indicators   (pg. 20)
Table 5: Diference-in-diference
results for H&N   (pg. 23)
Table 6: Diference-in-diference
results for FI  (pg. 24)
Table 7: Framework for qualitative analysis   (pg. 26)
Table 8: Sampling used for qualitative
interviews  (pg. 27)
List of Appendix Tables
Table A.1: Data Points Used for Net
Resilience index   (pg. 46)
Table A.2: Ranking of districts based on
change in net resilience since
March 2018 to March 2020   (pg. 56)
Table A.3: List of Aspirational Districts (Treatment
Group for DiD approach)   (pg. 59)
Table A.4: Control Group for DiD approach
for Health and Nutrition sector   (pg. 61)
Table A.5: Control group for DiD approach for
Financial Inclusion indicators (pg. 63)
Table A.6: Comparison of means of treatment
and control group for H&N sector   (pg. 65)
Table A.7: Comparison of means of treatment
and control group for FI sector   (pg. 65)
List of Equations
Equation 1:   Standardization formulae   (pg. 16)
Equation 2: Diference-in-diference estimation   (pg. 22)
List of Figures
Figure 1:   Comparison of resilience and
vulnerability among districts since
inception (2018) of ADP   (pg. 17)
Figure 2:   Top 5 districts with maximum
change in resilience since 2018   (pg. 18)
Figure 3: Comparison of top 5 and bottom
5 districts based on performance
in net  resilience and net
vulnerability index   (pg. 18)
List of Boxes
Box 1:   ADP as a model of Local Area
Development (pg. 3)
Box 2:   Delta Ranking  (pg. 8)
Box 3:   Resilience Score Interpretation   (pg. 16)
Box 4:   Skill Development - Washim   (pg. 30)
Box 5:   EAP-SDG  (pg. 31)
Box 6:   Goalpara Two Pronged Strategy (pg. 32)
Box 7:   Ranchi Low Hanging Fruits   (pg. 32)
Box 8: Technical Support Unit   (pg. 33)
Box 10: Data Driven Development (pg. 36)
Box 11:   Malaria Mukt Bastar Abhiyan,
Bijapur and Dantewada   (pg. 40)
Box 12:  Poshan App, Ranchi   (pg. 41)
Box 13:  Hamara Vidyalaya Program,
Namsai  (pg. 41)
Box 14:   Gyanodaya Project, Godda   (pg. 41)
Box 15:   GoalMart Initiative, Goalpara   (pg. 42)
Box 16:  Recharge pits, Washim   (pg. 43)
Box 17:   Black Rice Initiative, Chandauli   (pg. 43)
Box 18:   Green Technologies Initiative,
Goalpara  (pg. 43)
Box 19: Yuva BPO Initiative, Dantewada   (pg. 44)
Box 20:   Bank Sakhis, Ranchi   (pg. 44)
Box 21: Scaling of Best Practices (pg. 45) ACRONYMS & ABBREVIATIONS
Acronyms & Abbreviations
ADP - Aspirational Districts Programme
ADs - Aspirational Districts
ADFs - Aspirational District Fellows
APY - Atal Pension Yojana
BRAC - Bangladesh Rural Advancement Committee
BDP - Bangladesh Development Program
BRGF - Backward Regions Grant Fund
C4C - Champions for Change
CoC -  Champions of Change
3Cs -  Convergence, Competition and Collaboration
CSOs - Civil Society Organisations
CSR- Corporate Social Responsibility
DAC - Development Assistance Committee
DC - District Commissioners
DFS - Department of Financial Services
DMs - District Magistrates
DiD - Diference-in-Diference
EAP-SDGs - Externally Aided Programme on Sustainable Development Goals
FI - Financial Inclusion
FHP - Farm Harvest Price
H&N - Health & Nutrition
HLPF - High Level Political Forum
HMIS - Heath Management Information System
ICDS - Integrated Child Development Services
JICA - Japan International Cooperation Agency
LNOB - Leave No One Behind
LWE - Left Wing Extremist
MSP- Minimum Support Price
MTCs - Malnourishment Treatment Centres
MTSF - Medium-Term Strategic Framework
Non - ADs - Non-Aspirational Districts
NDP - National Development Plan
NGOs - Non-Government Orgnaisations
ODA - Ofcial Development Assistance
OECD - Organization for Economic Cooperation and Development
PMU - Project Management Unit
PMJJBY - Pradhan Mantri Jeevan Jyoti Beema Yojana
PMSBY - Pradhan Mantri Swasthya Beema Yojana
PMJDY - Pradhan Mantri Jan Dhan Yojana
PMFBY - Pradhan Mantri Fasal Bima Yojana
PMGSY - Pradhan Mantri Gram Sadak Yojana
POs - Prabhari Ofcers
SBA - Skilled Birth Attendant
SDGs - Sustainable Development Goals
TSU - Technical Support Unit
TUP - Targeting the Ultra-poor Programme
UNA - United Nation Agencies
UNDP - United Nation Development Programme
UNVs - UN Volunteers
VO - Village Organisation
VHSND - Village Health Sanitation and Nutrition Day Executive
Summary 2
This appraisal of the Aspirational Districts Programme is
aimed to assess the efectiveness of the ﬂagship
Programme of the Government of India and generate
evidence-based documentation which can be used to
support NITI Aayog and other stakeholders in their eforts
to address existing gaps, evidence-based planning and
decision making. It is also expected to provide guidance
for district administrations, development partners,
knowledge partners and any other stakeholders in
achieving the vision and targets set out for the ADP. In
addition, the evaluation also aimed to analyze the speciﬁc
impact of ADP across the diferent districts, especially in
relation to known issues of development challenges
among the aspirational districts. The ﬁndings of this
evaluation conﬁrm that signiﬁcant progress has been
made since the inception of programme. The key ﬁndings
of the programme are mentioned below:
♦ Sector wise growth:
The Aspirational District Programme focuses on
development across 5 sectors of Healthcare and
Nutrition, Education, Agriculture and Water
Resources, Basic Infrastructure, and Skill
Development and Financial Inclusion. A sector wise
analysis of the impact of ADP highlights two chief
ﬁndings. First, the programme has served as a
catalyst for expediting development among
Aspirational districts. Stakeholders interviewed
mentioned several successful initiatives that are
being carried out in the districts. Second, certain
sectors such as Healthcare and Nutrition, Education,
and to an extent Agriculture and Water Resources
have seen some major changes. This is encouraging
as these are crucial areas for assessing development.
Other sectors of Basic Infrastructure, Financial
Inclusion and Skill Development also achieved
improvement in indicators since the inception of the
programme and ofer scope for further strengthening.
♦ Better governance through convergence:
Among the three approaches of Convergence,
Competition and Collaboration, most stakeholders
who were interviewed credited Convergence as a
crucial approach for the better performance of the
districts. The stakeholders emphasised the
importance of convergence that fostered moving
away from working in silos towards synchronised
planning and governance to achieve the targets of
the programme.
♦ Expediting growth through competitive
federalism:
The Competition aspect of the 3Cs was also seen to
be a helpful method in promoting better monitoring
and creating healthy competiton to achieve targets of
the programme. This has also served as a motivating
factor for districts to increase their eforts and track
progress.
♦ Collaboration:
Although this aspect of ADP has helped ensure
systematic and targeted eforts  among diferent
organizations, it can be further accentuated. This may
especially be helpful as an alternative solution to
bridge certain gaps of technical expertise that
districts face. The diferent development partners
interviewed also expressed interest in expanding
work and collaborating further with government and
non-government organization for the programme.
♦ Commitment of the top most political leadership:
A remarkable feature of the programme that has
greatly contributed in its success, is the commitment
shown by the top most political leadership of the
country to bring about rapid progress in the
under-developed pockets in India. This includes
regular monitoring of the programme at the level of
Shri Narendra Modi, Hon’ble Prime Minister of India,
who has motivated and enthused District Collectors
to deliver their best at the ﬁeld level.

♦ What gets measured gets done:
In addition to the 3Cs approach, the study also found
that the ADP’s focus on constant real-time monitoring
and data driven decision-making has been a chief
contributor to better governance. This has especially
helped district administrations in identifying the
Executive Summary
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
Shri Narendra Modi, Hon'ble Prime Minister of India, launching
the Aspirational Districts Programme- January 2018 3 EXECUTIVE SUMMARY
strengths or weaknesses of a district, resulting in
more strategic and informed approaches for
development.
♦ Capacity building:
While the ADP has strengthened the technical and
administrative capacities of the districts, interviews
with diferent stakeholders highlighted the need to
focus on this aspect even more. Findings mainly
highlight the need for strengthening of internal
capacities. Some methods suggested by the
stakeholders for addressing this concern were to
appoint dedicated personnel such as Aspirational
District Fellows or Technical Support Units across all
the districts or to collaborate with development
partners for providing technical expertise. Other
suggestions include provision of skills training for
ofcials and staf, increased ﬂexibility in hiring
processes, and increase in incentives for promoting
recruitment in these districts.
♦ Role of delta rankings:
The delta ranking provided on the Champions of
Change (CoC) dashboard is a unique and dynamic
feature of the ADP. All districts interviewed admitted
to having used the dashboard to check their rankings
and progress, especially in the initial months of the
programme. However, a few stakeholders suggested
that rankings be done on a quarterly or annual basis.
This would give districts sufcient time to focus on
outcomes that require long-term planning and work.
♦ Addition or revision of Sectors/Indicators:
While stakeholders credited the use of monitoring
methods and the use of a pre-determined set of
indicators for measuring performance, some
highlighted the need to revise a few indicators which
are close to being saturated or met by most districts
such as “electriﬁcation of households” as an indicator
of basic infrastructure, or improvement in indicators
related to micro-irrigation under the sector of
Agriculture and Water Resources. Similar to the
suggestion of delta rankings, district administrations
suggested that more indicators be measured on a
quarterly or annual basis rather than monthly basis, as
it would help to implement sustainable and long-term
changes.
♦ Aspirational districts versus non-aspirational
districts:
Based on the interviews with diferent stakeholders, it
was found that one of the chief advantages of the
ADP is that it has given attention to districts otherwise
neglected due to their lower performances. This
aspect has aided most districts to demand the
necessary support required for their districts.
♦ Efectiveness of the ADP:
This evaluation found that a key feature that sets the
ADP apart from other development programmes is
the clear and comprehensive framework it provides
to the districts. This framework has provided efective
guidance for districts to focus their eforts on
achieving the targets of the programme. In fact, the
framework is an efective method of ensuring that
eforts are synchronised with the wider goals of the
country and are not arbitrary in nature.
♦ Motivation for the way forward:
Interviews with diferent stakeholders highlighted that
while the initial stages of the ADP helped propel
notable changes within the districts and the
programme’s pre-eminence should be maintained.
Therefore, as the programme has completed 3 years,
it is crucial that eforts be made to motivate districts
and reinforce the programme in all respects.
Overall, while the programme may have encountered
certain challenges, especially related to capacity building
there is no doubt that it has been immensely successful in
propelling development among the backward districts. It
must be noted most Aspirational Districts are located in
remote areas, and some even plagued with Left Wing
Extremist (LWE) conﬂicts. These factors continue to hinder
their growth and make it more difcult for any
development programmes to be implemented. However,
given the political salience around ADP and the
concerted eforts of diferent government and
non-government organizations, the districts have
experienced more growth and development in the last
three years than ever before. Evidence to support this
ﬁnding can be seen from the diference-in-diference
analysis conducted by the evaluation, as well as
examples documented under the qualitative analysis
section and best practices. Given the positive impact of
the programme, it is necessary to ensure the focus on
development is encouraged further and momentum
gained so far in expediting growth is maintained. Based
on the ﬁndings of the evaluation, it is recommended that
the success of the programme be scaled up and
replicated for other sectors and districts.
Overall, ADP is a very successful model of local area development. It is aligned to the principle of “leave no one
behind” – the vital core of the SDGs. Political commitment at the highest level has resulted in rapid success of the
programme. It should serve as a best practice for several other countries where regional disparities in development
status persist for many reasons. 1
Introduction
and
Background
to the
Programme 6
The Aspirational Districts Program was launched by the
Honorable Prime Minister, Sh. Narendra Modi in 2018,
with the objective of expediting the transformation of 112
most backward districts across 28 states through the
convergence of government programmes and schemes
1
.
The districts were chosen by senior ofcials of the Union
government in consultation with states ofcials. To
shortlist states a composite index of deprivation was
constructed using a range of socio-economic indicators
2
.
A minimum of one district was initially chosen from every
state (except Goa). Predictably, more districts made it to
the list of backward regions from the smaller states or
states ranking lower in the development spectrum such
as Bihar, Odisha, Jharkhand, Chhattisgarh, Uttar Pradesh,
and Madhya Pradesh.
As the programme is a policy priority of the Government
of India, it is anchored by the NITI Aayog which works in
collaboration with central and state governments for the
programme to streamline the efectiveness and provide
regular checks and guidelines. As a result, ofcers of
Additional Secretary and Joint Secretary ranks have been
nominated as ‘Central Prabhari Ofcers’ of each district,
who together with state nodal ofcers work with the
respective District Collectors/ District Magistrates to drive
change at the grassroots level. Furthermore, an
Empowered Committee – comprising of Secretaries
(Department Heads) of key Central Ministries – has also
been set up under the Chief Executive Ofcer, NITI Aayog
to support the various levels of government. This
institutional structure is based on an inclusive approach to
governance – termed as “Sabka Saath Sabka Vikas”
which aims to facilitate growth and development of the
entire district, rather than any single group of population.
This motto is mirrored in the principle of Leave No One
Behind (LNOB), the central and transformative promise of
the 2030 Agenda for Sustainable Development.
The Aspirational Districts Programme marks an important
shift in the approach towards inclusive development by
focusing on ﬁve critical sectors – i.e. Healthcare,
Education, Agriculture & Water Resources, Financial
Inclusion and Skill Development and Basic Infrastructure.
The selection of these ﬁve themes is based on the fact
that they have a direct bearing on the quality of life and
economic productivity of citizens
3
. Therefore, each of the
sectors have been allocated diferent weightage
4
and
indicators which serve as the basis for measuring
performance. The following is the sector-wise breakup of
indicators:
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
1.1 Institutional Structure and
Sectoral Focus:
A Transformative Approach

1
While 117 districts were selected initially, West Bengal never joined the programme. Therefore, there are 112 districts now. Baramula
and Kupwara, although now part of UT (Kashmir) are still aspirational districts.
2
NITI Aayog 2018. Transformation of Aspirational Districts: Baseline Ranking and Real-time Monitoring Dashboard.
3
NITI Aayog, 2018. Deep Dive: Insights from Champions of Change – The Aspirational Districts Dashboard
4
The ability of district administration in making improvements is among the many factors that results in the diferential sectoral
weightage. For example, in domains such as basic infrastructure and ﬁnancial inclusion, much of the progress depends on the
federal programmes and action taken by other ﬁnancial institutions respectively. Thus, these domains have been given a lower
weightage. Progress in health, nutrition, agriculture and education – on the other hand – can be greatly impacted by the district
administration and have therefore been given more weightage.
Introduction and Background to the Programme 7 INTRODUCTION AND BACKGROUND TO THE PROGRAMME
Table 1: Sectors, weightage and areas of focus
Health & Nutrition 30% 31 • Some of the key areas of focus are antenatal
care, postnatal care, contagious diseases,
growth of health infrastructure. Aspects of
childcare such as Severe Acute Malnutrition,
supplementary nutrition under ICDS are also
covered under this.
   • The education sector focusses mostly on
learning outcomes at primary and secondary
level, especially students’ performance in
Mathematics and Language
   • It also focuses on infrastructure pertaining to
education institutions such as girls’ access to
toilets, electricity supply, drinking water, etc.
Agriculture & Water   20% 12 • Indicators for this domain involve improving
Resources   access to water management as well as market
access for farmers, improved agricultural inputs,
livestock, among others.
   • There are six indicators for Financial Inclusion
which include improved access to bank
accounts, especially through major schemes
such as Pradhan Mantri Jan Dhan Yojana,
disbursement of loans under Pradhan Mantri
Mudra Yojana.
10% 16 • Indicators for the skill development includes
both short- and long-term training schemes and
the number of apprentices trained. There are 10
indicators for skill development.
Basic Infrastructure 10% 8 • This domain focusses on access to housing
water, electricity, and road connectivity. It mainly
involves community level infrastructure.
Total 100% 81
Themes Overall Data-points Areas of focus
weightage
Education 30% 14
Financial Inclusion and
Skill Development
At the core of this sectoral development ideology, is the
ADP’s theory of change based on the 3 pillars, popularly
referred to as the 3Cs, i.e. –
♦ Convergence – which is based on the synthesis of
diferent government schemes and authorities
(state, district, block level), and
♦ Collaboration which focuses on partnerships
between civil society organisations, philanthropies
and government for achieving the targets.
♦ Competition – which is expected to foster
competition and accountability among district
governments for achieving the development
targets,
In accordance with this approach, the programme
requires the involvement of central, state and district
government authorities. The programme also involved
collaboration with knowledge partners such as Tata Trusts
and IDinsight for monitoring and data collection purposes,
and several development partners to assist the district
administrations in improving the key performance
indicators. The development partners on-boarded for the
programme are Piramal (Health, Education and Sarwajal),
BMGF, Tata Trusts, Microsave, IdInsight, ITC Ltd, CSBC,
Lupin, Bharatiya Jain Sangathan, Vedanta, Plan India,
Save the Children, L&T, CII and NSE Foundation. In
addition, a Project Management Unit (PMU) has been set
up at NITI Aayog where experts from United Nations
Development Programme and Asian Development Bank
are providing technical support to districts in preparing
proposals to access funds through various sources. This
highlights the collaborative nature of the programme, and
an attempt to converge schemes across the sectors at
the national, state or district levels aiming to improve the
coordination among central and state governments to
improve social development indicators. 8 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
While the core approach of the programme is based on
the 3Cs (Convergence, Competition and Collaboration) a
key component in facilitating these, especially pertaining
to Competition is through the real time data collection and
monitoring undertaken by the NITI Aayog. While district
ofcials are responsible for updating a majority
5
of real
time data against the indicators, NITI Aayog commissions
regular surveys to ensure validity of data entered on the
dashboard.
The baseline assessment for instance, was conducted in
March 2018 upon commencement of the programme and
used 49 indicators (81 data points) to rank the status of the
districts across the ﬁve sectors.  Since then, districts are
ranked on a month-on-month basis, which is displayed on
the Champions of Change (CoC) Dashboard dedicated
solely for the purpose of monitoring data and providing
districts updated information on their performance as
compared to other districts. The CoC dashboard provides
sector wise ranking as well. This is expected to bring in a
sense of competition and accountability, as well as serve
as a mechanism for identifying key development sectors
that may need further handholding and support.
Although the delta rankings are subject to change
frequently, it must be noted that the competitive and
dynamic culture fostered by the programme, has resulted
in several lesser ranked districts (in baseline ranking) in
performing better over the last 3 years. For instance, our
evaluation found districts of Simdega (Jharkhand),
Chanduali (Uttar Pradesh) and Sonbhardra (Uttar Pradesh)
and Rajgarh (Madhya Pradesh) to be among the top
performing districts when progress is measured since the
beginning of the programme.
1.2. Data Driven Governance –
The Key to Programme
Efficiency?
5
While district ofcials are responsible for uploading a majority of data, data on some indicators – for example in the basic
infrastructure and ﬁnancial inclusion domain – are taken from the concerned Central Ministries.
Delta Ranking: The Delta ranking method
measures incremental changes in performance
indicators on a monthly basis. The methodology
adopted by NITI Aayog for this purpose,
employs a mix of self-reported data entered by
districts as well as data points validated by third
party agencies such as Tata Trust and
IDinsights, also referred to as knowledge
partners under the ADP. Literature
Review
2 In order to undertake an in-depth literature review,
several sources of data were studied. However, as the
Aspirational Districts Programme was implemented only
over the last three years, studies conducted by third party
organizations are scarce. Of these, many are focused on
the healthcare and nutrition sector with a particular
emphasis on POSHAN Abhiyan.
A recent report by the Institute of Competitiveness
(2020)
6
  revealed that Health & Nutrition and Education
are among the sectors closest to achieving their target by
2022, while agriculture, ﬁnancial inclusion and skill
development require signiﬁcant attention. Further, the
report also found that sectors apart from Healthcare and
Education had fewer knowledge /development partners
across the districts.
Other studies such as Borah et al. (2020)
7
highlight the
improvement in health and nutrition outcomes in Baksa
district of Assam since the inception of the ADP.
According to the authors, the improvement is also
reﬂected in the district’s change in ranking from 107 out of
the 112 districts since the ADP’s introduction in 2018 to
now being ranked as 26 out 112 aspirational districts for
health and nutrition as of July 2020  (ranking cited from
the CoC portal). This signiﬁcant change in ranking could
be a result of all the major health and nutrition
programmes that the district is currently undertaking.
Other independent studies and evaluation reports
highlighting such facts, along with presentations, articles
available in the public domain, and scholarly databases
have been analyzed for this review. The chief aim of this
is to serve as the backbone of the methodology and
inform the development of the interview guides and
quantitative analysis. By studying existing literature, this
review aims to map programmes like the ADP and
highlight what sets the latter apart.
The BRGF (Backwards Regions Grant Fund) was
implemented in India with the aim of addressing regional
imbalances by converging existing ﬁnancial and
development resources to reduce overall backwardness
and improving livelihood conditions of districts. While
these aspects correspond strongly with the Aspirational
Districts Programme, there are signiﬁcant diferences
between the two in terms of scale, areas of development,
focus, and processes of assessment.
First,  while The BRGF targeted 250 backward districts,
the ADP targets only 112 districts. Second,  while the BRGF
focused primarily on infrastructure and livelihood
programmes, the ADP seeks to categorically improve 5
key sectors. Furthermore,  the BRGF established a
separate funding mechanism for Panchayats to utilise for
development of infrastructure facilities; a concept that
ADP has not adopted. The aim of ADP is to function on
the convergence of central and state schemes at the
grassroots level rather than establishing new and
separate units at each level of governance
9
.
The most signiﬁcant diference, however, is the
monitoring and assessment methods of the two
programmes. While the BRGF hinged on assessing its
outcomes on a yearly or ﬁve year basis, the ADP
outcomes are updated constantly on the CoC portal in
the form of composite score and ranks, along with regular
evaluation and follow up reports published to provide
insights on the progress. This feature of constant
monitoring is undertaken with the expectation of fostering
a sense of accountability and competition among the
districts and also learning from each other’s practices: a
feature that has not been implemented previously by any
government development project/programmes
10
.
In addition to the BRGF in India, the ADP can be
compared to similar programmes in other developing
countries as well. One such project is the Medium-Term
Strategic Framework (MTSF) introduced by the
Government of South Africa from 2014-2019
11
. Like the
ADP, the MTSF aimed to ensure policy coherence,
alignment and coordination across government plans as
well as alignment with their budgeting processes. It was a
part of South Africa’s larger “National Development Plan”
and included performance agreements between the
President and ministers toreﬂect upon the relevant
6
Institute of Competitiveness, 2020. An Assessment of Aspirational Districts Programme.
7
Borah, P.K.; Raj, S.; Sharma, G.K., 2020. Role of Knowledge Management in Transformation of Aspirational Districts Programme –
A Case Study of Health & Nutrition Sector in Baksa District of Assam. Journal of Interdisciplinary Cycle Research, Volume XII, Issue VII.
9
This is complemented by the fact that ADP does not envisage the infusion of large funds as its core strategy.
10
Sinha, S. 2019. Is the Aspirational Districts Programme Merely A Political Device?. EPW. Vol.54, Issue No. 3. Accessed on:
https://www.epw.in/engage/article/is-the-aspirational-districts-programme-merely-a-political-device-development
11
Republic of South Africa, Medium-Term Strategic Framework 2014-2019. Government Programmes: Accessed from
https://www.gov.za/sites/default/ﬁles/gcis_document/201409/mtsf2014-2019.pdf
Literature Review
10 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
2.1. Similar Programmes actions, indicators and targets set out in the MTSF. Some
of the major areas of focus for the programme were
Education, Health, Safety and Security, Economic
Growth and Employment, Skills, Infrastructure, Rural
Development, and Local Governance. Other similarities
include the use of a pre-determined list of outcomes
based on which the progress was to be mapped
12
with
each department expected to develop annual and
quarterly action plans in line with the MTSF outcomes and
multi-stakeholder partnerships criteria.
While no evaluation reports about the impact of the
Medium Term Strategic Framework (MTSF) 2014–2019
are available to understand its impact, of relevance is a
recent study by Haywood et al. (2018)
13
  that examines the
importance of multi-stakeholder partnerships in achieving
South Africa’s SDGs, National Development Plan (NDP)
and Medium Term Strategic Framework (MTSF). [It should
be noted that the NDP and MTSF precede the SDGs plan
of action in South Africa as both the NDP and MTSF serve
as blueprints through which the SDGs can be achieved].
The researchers highlight that both the NDP and
MTSF programmes prioritised the involvement of multi-
stakeholder partnerships and established a strong
foundation at diferent levels of governance within the
country which expected to expedite its transition to a
more inclusive and sustainable growth plan. Among the
types of partnerships examined, the researchers
highlighted that partnership between the 17 UN agencies
in SA and local Civil Society Organisations were among
the strongest linkages with the South African Government
in driving changes. Other forms of partnership such as
business enterprises and academia, although promising,
have not been able to establish strong relations with the
government as yet. This is an area that perhaps ADP can
consider to improve its impact.
Similarly, apart from government-initiated programmes,
there appear to be other relevant programmes which
speciﬁcally target backward regions or populations. The
‘Champions for Change (C4C)’ programme in Nigeria by
the Bill and Melinda Gates Foundation is one such
programme
14
. While the ADP has diversiﬁed into diferent
thematic sectors, the Champions for Change programme
in Nigeria primarily focuses on providing funding to local
Nigerian programmes that improve health of women,
children, and youth. It also invests in visionary Nigerian
civil society leaders, organisations and advocates to
provide them the resources, tools, networks, and support
they need to drive meaningful change. Much like the ADP,
the Champions for Change looks at strengthening
grassroot organisations to drive change.
Other relevant programmes include BRAC’s (Bangladesh
Rural Advancement Committee) Development
Programme (BDP) which targeted the upliftment of the
“ultra-poor” population
15
. The programme especially
focused on livelihood improvement by ensuring
community participation along with participation from
village organisations and other structures. Members
(especially women) were given training for income
generating activities and micro-ﬁnances when they
became a member of the Village Organisation (VO).
However, over time, the programmes’ assessments found
that livelihood trainings and microﬁnance were not
sufcient in upliftment of the ‘target population’, thereby
leading to the introduction of a subsidiary programme of
BDP, called ‘Targeting the Ultra-poor Programme (TUP)
16
.
This revised programme aims to provide transfer of both
cash and assets, access to savings and credit facilities,
and training for longer term (24 months). The short and
medium term impact of this subsidiary programme show
that there has been an increase in income and ownership
of productive assets (assets which are directly linked to
generating income such as land, livestock, farm
equipment, etc.) and non-productive assets (assets not
related to generating income such as home appliances
used for personal use), increased food and non-food
consumption, and a favourable shift in ownership of
assets and hours spent on self-employment. The
programme was also found to positively impact gender
equality and empowerment in the areas.
11 LITERATURE REVIEW
12
Parliamentary Budget Ofce Republic of South Africa.2016. Monitoring of Performance and Expenditure on the outcomes of the
National Development Plan.
13
Haywood, L. K., Funke, N., Audouin, M., Musvoto, C., &Nahman, A. (2018). The Sustainable Development Goals in South Africa:
Investigating the need for multi-stakeholder partnerships. Development Southern Africa, 1–15. doi:10.1080/0376835x.2018.1461611
14
Champions of change. 2015. Saving the Lives of Women Newborns, and Children in Nigeria. Source:
https://www.riseuptogether.org/wp-content/uploads/2016/09/C4C-One-pager-design-10.6.15-ﬁnal-Sunrise.pdf
15
Barua P and Sualiman M. Is the BDP Ultra Poor Approach Working? Survey of some Key issues. Dhaka and Ottawa: BRAC and
Aga Khan Foundation Canada, 2007. (CFPR/TUP Working paper series No. 16).
16
Brito, Roberta. 2018. Bangladesh's TUP programme: Challenges in the design of gender sensitive social protection.
https://socialprotection.org/discover/blog/bangladeshs-tup-programme-challenges-design-gender-sensitive-social-protection Another study - by Hulme and Moore (2007) - of the
University of Manchester highlight similar trends
regarding the TUP
17
. The study highlights that the TUP
performance is monitored by the maintenance of a panel
dataset that tracks key indicators from a sample of
selected ultra-poor households. The authors do not
attribute regular monitoring mechanisms as being the key
to achievements of the programme; however, this feature
relates closely to the ﬁnding that TUP participants - as
compared to non-participants - had a greater rate of asset
accumulation across all domains.
The study also found that the programme has contributed
to the general well-being; especially in terms of improved
food security. Other indicators also show positive results
such as improved access to microﬁnance and
employment, whereby 70% of women were able to repay
their microﬁnance loans. Nutritional outcomes for children
was among the few indicators that did not see signiﬁcant
improvement. The potential reasons included possible
lags associated with changes in such indicators and
non-optimal patterns of intra-household resource
allocation.
Among the key learnings highlighted by this study, and of
relevance to the ADP, is TUP’s revised approach in
working directly with Village Organisations and using
these organisations to gain community support for
development aims and objectives. The chief diference
between the TUP model and other process models lies in
the balancing act of BRAC’s technical analysis along with
beneﬁciary participation and decision making. A study by
International Growth Centre
18
also conﬁrms the success of
the TUP programme and highlights it as a scalable
approach that can be successfully adapted to diferent
contexts. It is worth noting that BRAC has reached over
7000 households in Ethiopia, Ghana, Honduras, India,
Pakistan, and Peru.
Programmes such as the Medium Term Strategic
Framework (MTSF) in South Africa, Champion for Change
(C4C) in Nigeria or BRAC’S IDP and TUP programmes in
Bangladesh signify the importance of speciﬁc and
targeted policies or programmes; speciﬁcally for
improving backward regions. The initiation of the ADP - as
seen in this context - proves to be a step in the right
direction for socio-economic development.
12 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
17
Hulme, D., Moore, K. 2007. Assisting the poorest in Bangladesh: Learning from BRAC’s ‘Targeting the Ultra Poor’ Programme.
University of Manchester, Manchester, United Kingdom
18
  Balboni, C.; Banderia, O; Burgess, R; Kaul; U; 2015. Transforming the economic lives of the ultra-poor. International Growth
Centre. Accessed from: https://www.theigc.org/wp-content/uploads/2015/12/IGCJ2287_Growth_Brief_4_WEB.pdf 3
Evaluation
Criteria 14 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
Evaluation Criteria
The Aspirational Districts Programme (ADP) aims to instil a
culture of change through competition, collaboration and
convergence in some of the most deprived parts of the
country.  In order to evaluate the programme, it is
essential to develop a clear understanding of the current
trends for the diferent sectors and indicators in these
districts. While districts are ranked on their delta
performance on a monthly basis on the Champions of
Change dashboard, this evaluation aims to delve deeper
and study the progress made by these districts since the
beginning of the programme. This evaluation also
highlights the best practices implemented by some
districts which can be replicated in other districts.
The quantitative analysis for this assessment consists of
two parts. In the ﬁrst, districts are ranked on the basis of
their performance since the beginning of the programme
and in the second, a comparison of aspirational and non-
aspirational districts is made using a diference in
diference approach. The qualitative component involves
semi-structured interviews and thematic analysis. Details
for each component are provided in the following
sections.
Table 2: Evaluation criteria
Relevance This examines the relevance of the Aspirational Districts Programme in line with the vision set
forth by the Prime Minister and NITI Aayog. It also examines the current context, sectoral
programmes and interventions being implemented by districts.
Coherence This criterion evaluates the extent to which the means justify the outcome. In particular,
efciency in resource (ﬁnancial and human) allocation. Of other considerations are the quality,
timeliness of the results, partnership strategies, resource mobilization, use of programming
and partnership modalities conducive to the delivery of programme outputs, adequate
oversight and monitoring mechanisms.
Efectiveness Assesses to what extent do strategic partnerships exist with other national and sub national
institutions, CSO/NGOs, UN agencies, CSR agencies, knowledge partners or development
partners to sustain the attained results and to what extent have partners committed to
providing continuing support.
Impact This analyses to what extent the Aspirational Districts Programme has achieved output
results and evidence of their contribution to the outcomes over the last 3 years.
Sustainability This examines the extent to which districts have established mechanisms under the ADP to
ensure the sustainability of the results attained/to be attained.
Criteria   Objectives and themes
The key research questions for this evaluation are:
♦ How have the Aspirational districts performed since
their inception in terms of improving the key
performance indicators of the programme?
♦ What has been the impact of the programme for the
districts? What have been the beneﬁts and
challenges?
♦ How efcient is this programme in efecting change,
and is this model of development sustainable in the
future?
♦ Is the ADP replicable in other districts of India,
and/or in other developing countries?
♦ How can the ADP become even more efective in
accelerating the signiﬁcant progress it has already
made?
In line with the research questions, this review, especially
the qualitative interviews were conducted using the ﬁve
OECD-DAC (Organisation for Economic Co-operation
and Development's Development Assistance Committee)
evaluation criteria of (a) relevance; (b) coherence; (c)
efectiveness, (d) impact; and (e) sustainability of
development results. The rationale for them is explained
below:
3.1. Key Research Questions: Quantitative Data
Collection and
Analysis
4 16 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
19
Data from March 2020 is used so as to avoid capturing the impact of the pandemic. The pandemic would lead to a general
decline in performance in all indicators leading to absolute and relative fall in outcomes, thereby inculcating a bias.
20
Based on the availability of data for the two time periods. Full list of data points used to calculate Net Resilience Index is provided
in Appendix A.1.
21
The 5 sectors are: 1) Agriculture, 2) Health and Nutrition,3) Education, 4) Financial Inclusion and Skill Development and 5) Basic
Infrastructure.
22
  The scores on the y-axis have been multiplied by 100 for ease of visual interpretation
The quantitative analysis for this evaluation comprises of
two components:
i) Net Resilience Index; and
ii) Diference in Diference Analysis
4.1.1. Methodology:
This exercise throws light on the overall performance of
Aspirational Districts since the inception of the
programme. It also aims to highlight the most and least
improved districts since March 2018 till March 2020
19
. 60
data points
20
(for 111 districts) from the Champions of
Change dashboard are used for this exercise and are
divided into two broad categories: resilience and
vulnerability.
Resilience is measured by a set of positive indicators
which reﬂects factors that bolster the development
capacity of the districts. Data points were taken from 5
sectors
21
as monitored by the ADP. A few examples of
data points included are as follows:  Percentage of area
under micro-irrigation (Agriculture), Tuberculosis (TB)
case notiﬁcation rate (Public and Private Institutions) as
against estimated cases (Health and Nutrition),
Percentage of elementary schools complying with RTE
speciﬁed Pupil Teacher Ratio (Education), Pradhan Mantri
Jeevan Jyoti Bima Yojana (PMJJBY): number of
enrolments per 1 lakh population (Financial Inclusion),
Percentage of certiﬁed youth employed to number of
youth trained under short term or long term training (Skill
Development), Percentage of gram panchayats with
internet connection (Basic Infrastructure) etc.
Vulnerability, on the other hand, is measured by a set of
negative indicators. An increase in the vulnerability
indicators hinders districts’ ability to attain their
development goals. All vulnerability indicators are taken
from the Health and Nutrition Sector.  Few examples of
data points included as measures of vulnerability are as
follows: Percentage of low birth weight babies (less than
2500g), Percentage of Severe Acute Malnourishment
(SAM) in children under 6 years to total children under 6
years etc.
To ensure comparability across indicators and districts,
data points for every indicator and district were
standardized using the min-max formula and a simple
average was used to calculate resilience and vulnerability
score for each district.
A higher resilience score represents positive overall status, and sustainable impact of the work undertaken.
A higher vulnerability score on the other hand highlights the need for further attention and scope for improvement.
Equation 1: Standardization Formulae
Where:
s is the standardized score for each data point. It
takes values between 0 and 1,
ﬂ is the value of data point being standardized,
min is the minimum value of the data point being
standardized across all districts,
max is the maximum value of the data point being
standardized across all districts.
Here, a higher resilience score represents more
resilience - and similarly for vulnerability – for any given
district. Finally, resilience and vulnerability scores in
isolation do not provide a holistic picture of the
performance  of aspirational districts. To address this, we
use the diference between resilience and vulnerability
scores to arrive at a measure of net resilience.
4.1.2. Findings
Figure 1
22
shows the average resilience, average
vulnerability and net resilience scores across all districts
for March 2018 and March 2020. From the ﬁgure, it is
evident that the Aspirational Districts have shown an
overall increase in resilience, a corresponding reduction
s =
(x-min)
(max-min)
Quantitative Data Collection and Analysis
4.1. Net Resilience Index Figure 1: Comparison of resilience and vulnerability among
districts since inception (2018) of ADP
20182020
60
50
40
30
20
10
0
ResilienceVulunerabilityNet ResilienceResilienceVulunerabilityNet Resilience
QUANTITATIVE DATA COLLECTION AND ANALYSIS
Table 3: Summary Statistics for Net Resilience exercise - 2018 and 2020
♦ Top and low performing districts
Insights pertaining to the implementation of successful
programmes and best practices can be drawn from
districts that have improved the most since the
programme began. Figure 2 shows the districts that
achieved the largest increases in net resilience
between March 2018 and March 2020.
in vulnerabilities and therefore an overall rise in net
resilience. These results are suggestive of the success of
the programme in improving development outcomes in
some of the most disadvantaged areas of the country.
However, this aggregate picture leaves out essential
diferences among districts.  In order to look at the district
wise diference, the districts which have improved the
most in terms of net resilience between 2018 and 2020
are illustrated in Figure 2.
Mean 49.58 23.89 25.68 58.28 21.88 36.40 10.72
Median 48.97 23.15 27.47 57.97 21.12 36.98 11.17
Min 36.11 0.09 -2 3.29 37.11 0.88 -1 8.67 -5 8.05
Max 63.82 59.99 61.15 70.71 67.04 61.83 58.26
Std Dev 5.97 12.49 15.19 6.27 13.68 15.63 14.16
Average
Resilience
Score
(2018)
Average
Vulnerability
Score
(2018)
Net
Resilience
Score
(2018)
Average
Resilience
Score
(2020)
Average
Vulnerability
Score
(2020)
Net
Resilience
Score
(2020)
Diference
in Net
Resilien
ce Score
17 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
Figure 2: Top 5 districts with maximum change in net resilience since 2018
22
  The scores on the y-axis have been multiplied by 100 for ease of visual interpretation
To gain a deeper understanding of changes in resilience
and vulnerabilities over time, Figure 3 shows the average
resilience and vulnerability scores for the most improved
as well as least improved districts (in terms of net
resilience). Figure 3 indicates that for the most improved
districts, average resilience increased while average
vulnerabilities reduced from 2018 to 2020. However, the
narrative is diferent for the least improved districts.
Except Sitamarhi (Bihar), these districts have witnessed
large increases in vulnerabilities which has pulled down
the net resilience.
Figure 3: Comparison of top 5 and bottom 5 districts based on performance in
net resilience and net vulnerability index.
Ranchi
Chandauli
Simdega
Sonbhadra
Rajgarh
0204060
Change In Net Resilience (2018 to 2020)
Most improved districts based on change in net resilience
41.74
42.09
44.11
33.24
47.37
34.49
38.00
38.72
51.70
34.98
68.7
10.88
64.9
10.24
56.72
9.05
59.44
25.47
66.6
15.57
Ranchi
Chandauli
Sonbhadra
Simdega
Rajgarh
Resilience Vulnerability
20182020
58.26
42.98
34.80
34.69
34.33
18 Overall, ﬁndings from the Net Resilience Index indicate
that the Aspirational Districts, on average, have been on
an upward trajectory since the inception of the
programme. A closer look at the best performers indicates
an improvement in resilience along with a corresponding
reduction in vulnerabilities. On the other hand, the least
improved districts have seen signiﬁcant increases in
vulnerabilities. The latter calls for focused attention on
speciﬁc sectors where these districts have
underperformed. Replicating successful programs and
learnings from top performers might form the basis of the
inclusive growth among the Aspirational Districts.
Note on data collection and ﬁlling missing values:  Data
points in the ADP programme are reported at diferent
frequencies (yearly, half yearly, quarterly and monthly).  For
2018, yearly data was obtained from March 2018, half
yearly data from September 2018, quarterly data from
June 2018 and monthly data from April 2018. For 2020,
data points for all frequencies were obtained from March
2020. Missing values for half yearly data were imputed
from September 2019, missing values for quarterly data
were imputed from December 2019 and missing data for
monthly data were imputed from February 2020
24
. Finally,
the ranking also excludes Kiphire and Khammam since
net resilience could not be calculated due to missing
values in average vulnerability in 2020 for Khammam and
in 2018 for Kiphire. Therefore, the ﬁnal ranking includes 111
districts
25
.
4.2.1. Methodology:
The Diference-in-Diference (DiD) framework for impact
evaluation is a widely used technique that teases out the
actual impact of an intervention from extraneous factors
such as that of natural growth over time. The framework
requires the existence of two sets of groups – the
treatment group which is made up of entities that received
the intervention and the control group that serves as the
counterfactual – and data on both these groups for the
selected indicators on (at least) two time periods. The DiD
method – by comparing the average change over time in
the outcome variable for the treatment group to that of the
control group – teases out the ‘true’ impact of events and
interventions.
This framework is used on two sectors of the Aspirational
Districts Programme: Health & Nutrition (H&N) and
Financial Inclusion (FI). For the H&N indicators, data from
the Heath Management Information System (HMIS) – a
digital initiative under the National Health Mission, Ministry
of Health and Family Welfare, Government of India is used.
DFS (Department of Financial Services, Government of
India) data is used for the FI indicators. While the former is
a portal gateway to a wealth of information related to
health indicators at state and district level (directly
uploaded by the States/ UTs), the latter is a government
entity that monitors the indicators related to FI for the
Aspirational Districts Programme
Data: Two sets of data are taken from these sources: for
March 2018 (which serves as the baseline) and the same
for March 2020 (which is the most recent available data for
pre-Covid period). Since indicators for Health and Nutrition
in Aspirational Districts Programme form a subset of the
indicators reported by the HMIS, an indicator matching
exercise was performed in order to observe the overlap
between the two data sources. The table below
represents this exercise for those indicators that were
found to be either directly or derivatively matching
between the two data sources:
24
Full list of indicators is provided in Appendix Table A.1
25
Full list of rankings based on Net Resilience scores is provided in Appendix Table A.2
4.2. Difference in Difference
Method:
Least improved districts based on change in net resilience
51.95
19.41
42.61
34.71
57.59
30.90
50.26
23.15
44.91
5.53
38.07
16.38
44.74
47.83
60.99
47.43
56.92
49.43
48.36
67
Sitamarhi
Gumla
Bijapur
Dantewada
Nawada
Resilience Vulunerability
20182020
QUANTITATIVE DATA COLLECTION AND ANALYSIS 19 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
Table 4: CoC and HMIS Data Matching for H&N Indicators
Indicator Detail from
the Champions of
Change (CoC)
Dashboard
NITI Aayog
Performance
Indicator
Number
(CoC)
S.
No.
Type of
matching
for H&N
Indicators
HMIS
Indicator
Serial
Number
Indicator Detail
Percentage of Pregnant
Women receiving four or
more antenatal care
check-ups against total ANC
registrations
1.11Derived
(exact
match)
4 divided by 14 – Number of pregnant
women receiving 4 or
more ANC check ups
1 – Total number of
pregnant women
Registered for ANC
Percentage of ANC
registered within the ﬁrst
trimester against total ANC
registrations
1.22Direct 3 % 1st Trimester
registration to Total ANC
Registrations
Percentage of Pregnant
women having severe
anaemia treated against
Pregnant women having
severe anaemia tested cases
3.13Direct 13 % Pregnant women
having severe anaemia
(Hb<7) treated at
institution to women
having hb level<7
Sex Ratio at birth4.14Direct 52 Sex Ratio at birth
(Female Live Births/
Male Live Births *1000)
Percentage of institutional
deliveries out of total
estimated deliveries
4.25Direct
(but not an
exact match)
28 % Institutional
deliveries to Total
Reported Deliveries
Percentage of new-borns
breastfed within one hour
of birth
6.17Direct 51 % New-borns breast fed
within 1 hour of birth to
Total live birth
Percentage of home
deliveries attended by an
SBA (Skilled Birth
Attendance) trained health
worker out of total home
deliveries
56Direct 18 % SBA attended
home deliveries to
Total Reported Home
Deliveries
Percentage of low birth
weight babies (Less than
2500 grams)
6.28Direct 49 % New-borns having
weight less than 2.5 kg to
New-borns weighed at
birth
9Proportion of live babies
weighed at birth
6.3Direct 47 % New-borns weighed at
birth to live birth
Percentage of children
with Diarrhoea treated
with ORS
8.210Derived
(but not an
exact
match)
158 divided
by 157
158 – Diarrhoea treated
in Inpatients in Children
0-5 Years of Age 157 –
Diarrhoea in Children
0-5 Years of Age
20 21
There are two important points to be noted. First,
indicator 8.2 from the CoC Dashboard is matched to a
derived version of two indicators (number 158 and 157)
from the HMIS data. This is not an exact match since the
CoC indicator focuses only on treatment of diarrhoea in
children through ORS whereas the latter is a more
general version of the same. While this prevents a
one-on-one matching, it allows for a broader measure to
be included in the exercise. Second, all indicators except
number 6.2 (Percentage of low birth weights babies) are
positive in nature, i.e., a higher value of an indicator
indicates an improvement in the H&N outcome of the
district. Indicator number 6.2, on the other hand – is a
negative indicator implying that an increase in its value
signiﬁes a deterioration of H&N outcome.
For the indicators under the FI sector, the CoC
Dashboard reports values directly from the data of
Department of Financial Services (DFS). Hence, all
indicators received from the DFS matched directly to
those in the CoC Dashboard except one
26
(which has
been left out of this analysis).
The districts on which data was obtained were
segregated into the treatment and the control group. The
treatment group comprised of all districts that are a part of
the Aspirational Districts Programme. Therefore, the
treatment group for the H&N exercise consists of 113
27
ADs while that for the FI exercise consists of 112
28
ADs.
The creation of the control group, however, is more
nuanced.
In economic theory, a control group is a set of
observations that are exactly similar to their counterparts
in the treatment group except for one crucial aspect: that
those in the treatment group received the treatment and
those in the control group did not receive that treatment.
This ‘almost’ similar control group is often referred to as
the counterfactual: a group that mimics the characteristics
of the treatment group except for the treatment itself.
For the purpose of this evaluation, this means that control
group – in order to be as close to a theoretical
counterfactual – had to consist of non-ADs were matched
with ADs from the same states. More precisely, out of the
remaining districts (after the separation of ADs), the
control group must have consisted of same number of
non-AD’s that display similar characteristics as the AD's. A
weighted proportional method was employed to
construct the control group.
For all non-ADs, data from March 2018 was ﬁrst
normalized. This was then used to create an index by
multiplying the respective indicators with proportional
29

weights (as used in the H&N and FI Index by NITI Aayog).
A district wise ranking was created next. Starting from the
bottom of the ranking
30
, non-ADs were matched with ADs
from the same states
31
. For example, if Andhra Pradesh
has 3 districts in the ADP, then the bottom 3 non-ADs from
Andhra Pradesh were inserted in the control group (and
similarly for other states). However, since Jharkhand has
19 ADs (as opposed to a total of 23 districts), the
state-wise matching could not be strictly fulﬁlled.
To overcome this issue, the remaining 14 non-AD districts
26
Indicator titled: “Total Disbursement of Mudra loan (in rupees) per 1 Lakh population” has not been used since data on this
indicator was not recieved.
27
117 districts were selected for Aspirational Districts Programme by NITI Aayog. However, 5 districts of West Bengal never
joined the programme. Also, Khammam in Telangana was replaced by Bhadradri Kothagudem as an Aspirational District. For
the purpose of this exercise, both the districts have been kept in the treatment group making total number of districts as 113.
28
Since data on Bhadradri Kothagudem was missing from the FI data, it was dropped therefore making the total number of
treatment districts 112.
29
The proportional weightage takes into account missing values and weights the available data based on a proportionate
scale so that the individual weights for each data points are preserved along with the overall weightage.
30
The selection of the AD’s was such that districts performing poorly on socio-economic indicators were selected for the
programme as compared to relatively better performing districts. In order to maintain the same spirit and consistency, the
selection process for the control group is started from the bottom.. This also ensures that the most accurate comparison group
possible is being captured.
31
Using proxy districts  that share the same boundary or belong to the same state is a common practice in literature because it
is more likely that a boundary sharing district better resembles a particular AD – along several characteristics – as compared to
districts that do not share a boundary or do not belong to the same state.
QUANTITATIVE DATA COLLECTION AND ANALYSIS Equation 2: Difference in Difference Estimation
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL22
32
Data characteristics include comparing the state-wise means of select indicators with those of Jharkhand along with
demographic and occupational characteristic matching. The states with the closest characteristics were selected and then the
same process (as outlined above) was followed to choose the districts that would proxy as a control for the remaining districts
from Jharkhand.
33
To maintain consistency, the last three chosen districts from Uttar Pradesh, Odisha and Chhattisgarh are again compared and
the two districts with lowest rankings are included.
34
Following from footnote 30, it can be observed that the selection of the counterfactual group is such that the districts within
this group are the ‘immediate’ competitors of the AD’s.
35
The details are attached as tables in the appendix.
36
In order to compensate for the positive bias shown by HMIS data during its initial years, check mechanisms – such as third part
surveying and continuous review by ofcials and Central Prabhari Ofcers – was put in place for Aspirational Districts. This a)
ensured that the data was reﬂective of the ground realities and b) that – by means of continual review – the quality of data was
regularly improving for the Aspirational Districts. However, the same check mechanism was not ensured for non-Aspirational
Districts therefore leading to a positive bias in the latter’s performance. Therefore, it is likely that the diference-in-diference
results reported are under-estimates for the actual improvement.
(to be mapped to Jharkhand) were selected - using
the same method - from states that share similar data
characteristics
32
(such as Uttar Pradesh, Chattisgarh and
Odisha)
33
. This ensured that the control group consisted
of 113 non-AD’s for the H&N exercise and 112 non-ADs for
the FI exercise; those that resemble  the ADs as closely as
possible on the respective set of indicators
34
.
In order to check the validity of this construction, the
means of selected variables between the treatment and
the control group for both sectors were compared. It was
found that the two groups are similar along all indicators
(at the baseline) hence strengthening the validity and
comparability of our control group35.
With the treatment group and control groups formulated
for all selected indicators for the two time periods, the
following equation was used for the diference-in-
diferencea estimate(s):
DID Estimate
i,t
= ( I
ADP, 2020
– I
ADP, 2018
) – ( I
Non-ADP, 2020
– I
Non-ADP, 2018
)
where the left-hand side denotes the diference-in-
diference (mean and median) estimate for indicator i of
type t. The right-hand side denotes the diference
between the average changes across the two time
periods between the treatment and control groups. A
positive DID Estimate is – by virtue of the
diference-in-diference framework – interpretable as the
‘true’ impact of the Aspirational Districts Programme.
4.2.2. Findings
Health and Nutrition (H&N) is a key focus area of the
Aspirational Districts Programme which takes up 30%
weightage in the overall index used by NITI Aayog. The
results - as computed using the aforementioned
methodology of the diference in diference framework -
indicate that AD’s have outperformed non-AD’s by virtue
of being selected for – and receiving the beneﬁts of – the
Aspirational Districts Programme. Table 5 presents the
mean and median diference-in-diference estimates for
the Health and Nutrition sector. The interpretation of
coefcients follows.
Before moving on to indicator speciﬁc interpretation, note
that all indicators except 4.1 and median estimate for 1.1 are
consistent with the hypothesis that AD’s have
outperformed the control group. All positive indicators –
except sex ratio at birth – show positive coefcients as
well as the negative indicator (6.2) shows negative
coefcient. This broad pattern allows us to interpret – at
ﬁrst glance – that the Aspirational District Programme has
indeed helped the chosen districts outperform those that
were not selected for this programme
36
. 23
Table 5: Difference-in-difference results for H&N
Owing to the construction of the coefcient estimates
according to the diference-in-diference methodology,
each of them is interpretable as the average impact that
being in the ADP provides while taking into account the
natural growth over time in comparison to non-ADP
districts. For example, being in the Aspirational District
Programme has provided – on average across the
sample – an additional 4.5 percentage increase in 1st
trimester registration to total ANC registrations to the AD’s
as compared to the control group. Other coefcients can
be interpreted in a similar manner. Among the noteworthy
increases are that of indicators 1.2, 3.1, 5 and 8.2. The
negative coefcients (-0.29 and -1.20) on indicator 6.2 -
percentage of new-borns having weight less than 2.5 kg
to new-borns weighed at birth – also imply that being in
the ADP has resulted in an improvement in this outcome.
Similar to the Health and Nutrition results, the estimates
for the Financial Inclusion Sector also indicate that ADP
has had a positive impact on the chosen indicators. The
following table presents the mean and median
diference-in-diference estimates for the FI sector:
IndicatorCoC Indicator
Matching
Mean
Estimate
Median
Estimate
Percentage of Pregnant Women receiving four or
more antenatal care check-ups against total ANC
registrations
1.10.23 -1.77
Percentage of ANC registered within the ﬁrst
trimester against total ANC registrations
1.24.55 5.80
Percentage of Pregnant women having severe
anaemia treated against PW having severe anaemia
tested cases
3.15.82 20.60
Sex Ratio at birth (Female Live Births/ Male Live
Births *1000)
4.1-3.39 -7.00
Percentage of institutional deliveries out of total
estimated deliveries
4.20.65 0.50
Percentage of home deliveries attended by an
SBA (Skilled Birth Attendance) trained health
worker out of total home deliveries
59.63 14.90
Percentage of new-borns breastfed within one
hour of birth
6.10.85 0.10
Percentage of low birth weight babies (Less than
2500 grams)
6.2-0.29 -1.20
Proportion of live babies weighed at birth6.30.80 0.80
Percentage of children with Diarrhoea treated8.24.80 1.79
QUANTITATIVE DATA COLLECTION AND ANALYSIS 24 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
Table 6: Difference-in-difference results for FI
IndicatorIndicator
Number
Mean
Estimate
Median
Estimate
PMJJBY enrolments per 1 Lakh population2406.48 411.20
PMSBY enrolments per 1 Lakh population3847.45 715.36
APY beneﬁciaries  per 1 Lakh population448.53 105.37
% of accounts seeded with Aadhaar5-0.61 -1.70
PMJDY Accounts opened per lakh of population61580.48 2482.00
The interpretation for the DiD coefcients for FI slightly
difers from those of Health and Nutrition. The coefcient
on indicator number 2 (in the FI table) indicates that being
in the Aspirational District Programme has provided an
additional 406.48 people per lakh PMJJBY enrolments –
on average across the sample – in the ADs as compared
to the control group. All indicators except indicator 5 –
percentage of accounts seeded with Aadhaar – attest to
the success of the Aspirational Districts Programme.
Overall, after preforming a Diference-in-Diference
analysis on select H&N and FI indicators using
appropriately constructed counterfactuals, the results
indicate that ADs have outperformed non-ADs by the
virtue of being selected for – and receiving the beneﬁts of
– the Aspirational Districts Programme by substantial
margins within the Health & Nutrition and Financial
Inclusion domain. These results not only quantify the
signiﬁcant progress made by districts under the
Aspirational Districts Programme, but also highlight the
various uses of data collection mechanisms under the
Aspirational District Programme that make this analysis
possible. 5
Qualitative
Data Collection
and Analysis ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
All information obtained from the interviews was
thematically analyzed and fed into content analysis
framework using the OECD-DAC criteria and the key
research questions. Thematic coding was employed for
the analysis, as it was deemed most suitable for this
evaluation to identify and group information into themes
or ideas. Since our aim for this evaluation was to identify
patterns across districts, some of the major themes used
were successes, challenges, knowledge gaps, support
required, replicability and acceptability of interventions,
administration capacities, and governance approaches.
As the study focuses on district level implementation, the
stakeholders for this evaluation comprised of district level
ofcials, such as district magistrates, district collectors, or
district commissioners who are in charge of the overall
functioning of the district and hence responsible for the
efective administration of these programmes and have
in-depth knowledge of the revenue and funding
processes for the states. Similarly, we also interviewed
Prabhari ofcers who serve as a key point of contacts and
facilitators between district and the centre.  In addition to
this, DMs from non-aspirational districts were added to
the sample to provide comparative insights on the
functioning of ADP. Non-governmental stakeholders
included knowledge partners, development partners, UN
volunteers, and ADFs working in these districts. The
sampling frame mentioned in Table 8, was adopted to
provide a thorough understanding of the ADP along with
on ground examples and case studies for our evaluation.
Semi- structured interviews were conducted with District
Magistrates or District Collectors, Prabhari ofcers,
knowledge partners, development partners, and
Aspirational District Fellows (ADFs) and UN Volunteers
(UNVs) working in these districts. A few interviews were
also conducted with district magistrates of non-ADs so as
to gain useful insights for facilitating comparisons of best
practices in these districts. For each interview, the
following approach was adopted:
Table 7: Framework for qualitative analysis
Programmatic
Level
Administrative Level
(Implementation
level)
Each qualitative interview aimed to:
• Document the interventions in the 5 core sectors of ADP and their programme
model.
• Identify best practices deployed by the programmes, including intervention
models, local partnerships, stakeholder engagement, and community participation.
• Capture challenges encountered in programme life cycle and how they were
resolved.
• Assess the scalability and replicability of the programme across the country/other
districts.
These qualitative interview sought to:
• Understand which interventions are being implemented, and how they align with
ADP’s goals, objectives and vision.
• Explore the rationale behind undertaking speciﬁc interventions or their processes.
• Capture details about internal capacities, strengths, limitation, with regards to
implementation and funding of the programme.
• Understand how administrative capacities plan to improve their work in the core
areas
• Determine the scope of further engagement opportunities with central and state
level organizations, NITI Aayog and knowledge partners.
5.1. Respondents and
Sampling for Qualitative
Data Collection
Qualitative Data Collection
and Analysis
26 District Magistrates
(DMs) / District
Collectors/ District
Commissioners(DC)
of Aspirational
Districts
District Magistrates
(DMs)/ District
Collectors (DCs)
of non-ADs
Prabhari
Ofcers (POs)
Knowledge
Partners and
Development
partners
Aspirational
District Fellows
(ADFs) and United
Nations Volunteers
(UNVs)
Total
• DMs/DCs/DOs are crucial to the functioning of any programme
in the districts as they are responsible for decision making and
overall administration of the districts.
• Interviews focused on inquiring about the district’s
administrative and internal capacities, support required, themes
and programmes being focused on currently. They also inquired
about the strengths, weaknesses, and areas of improvements
required.
• The aim of conducting interviews with government ofcials from
non-ADP districts was to inquire about the processes and focus
on what sets non-ADs in a more favorable position over ADs.
• We also inquired about successful interventions and
governance approaches that could be borrowed from the
non-ADs .
• As a key feature of the ADP is the supervision and support
provided by Prabhari Ofcers, these interviews were helpful to
inquire about their perceptions of ADs, especially regarding
sustainability and replicability of the programme.
• We also inquired about state and district capabilities and the
support required to drive change.
• Knowledge Partners and Development Partners are important
as they work at the grassroots level and possess a good
understanding of the requirements and perception for
implementation of programmes. Therefore, interviews focused
on understanding the role of civil society organizations in
supporting ADP goals and visions. We also inquired about the
ease of coordinating with diferent levels of government and
support received or challenges encountered while working in
the ADs.
• As these organizations work in multiple districts, group
interviews were conducted for some organizations with
members of diferent teams and ﬁeld ofces participating in
each interview.
• Interviews with ADFs and UNVs focused on implementation of
the ADP at the grassroots level. Focus was also laid on
understanding the capacities and requirements of the districts.
• Group interviews were conducted for ADFs and written forms
submitted from UNVs of diferent districts.
A total of 47stakeholders provided their insights and
experiences on working with the programme.
Respondent Number of   Rationale / Areas of Focus or Inquiry
stakeholders
participated*
11
2
4
10
20
47*
Table 8: Sampling used for qualitative interviews
QUALITATIVE DATA COLLECTION AND ANALYSIS27 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
A majority of the work undertaken across the sectors has
been in the areas of Health and Nutrition, Education, and
Agriculture and Water Conservation. Almost all districts
ofcials interviewed mentioned a number of programmes
and activities implemented across these three sectors
and identiﬁed them as better performing areas or
strengths in some cases. They are also the three largest
sectors within the Aspirational districts programme, and
together constitute 80% of the programme weightage.
Therefore, improvements in these sectors may be
viewed as a positive indication of meeting the
programme’s targets of development.
However, while most districts have mentioned that a
majority of their eforts were focused across the sectors of
Health and Nutrition, Education, and even Agriculture and
Water resources, the sectors of Skill Development and
Financial Inclusion require immediate prioritisation among
the ADs to reach their full goals. This trend in sectoral
disparity was observed across all the districts interviewed.
As per the ﬁndings of the interviews, stakeholders
mentioned a number of initiatives in the sector of Basic
Infrastructure indicating signiﬁcant improvements.
However, there is still scope for further improvement in
the sector. This is especially the case with the more
remote districts among the Aspirational districts, and
those plagued with the double burden of countering LWE
activities. Districts located in more favourable geographic
areas, such as proximity to national highways or cities
have been able to reap more beneﬁts and implement
more infrastructural projects than those in very remote
areas.
5.2.1. Mapping Sector-wise growth
Health and Nutrition:
Findings of the qualitative interviews indicate that
signiﬁcant improvements have been made in the sector.
In fact, almost all the district ofcials interviewed,
mentioned some of the major programmes implemented
over the last three years to have been in the area of
Healthcare and Nutrition. The most common
achievements among these initiatives involve setting up
model anaganwadi centres, eforts to increase the
number of institutional deliveries, reduction in Severe
Acute Malnutrition (SAM) among infants and children,
improving ANC coverage for pregnant women and
improving service delivery in PHCs in remote areas. For
instance, a common example given by district ofcials
during the interviews was improvements in the method of
measuring and recording infants’ weight and height using
standardised index and protocol at Anganwadi centres
rather than staf using their own judgement to determine
if infants were malnourished or underweight .This change
according to the district ofcials has come about due to
two reasons; ﬁrst, better monitoring of these indicators as
required by the Aspirational districts programme and;
second, the prioritization of these sectors has led to
better identiﬁcation of gaps and requirements such as
training for staf or better medical equipment at the
centres.
Additionally, the fact that some of the districts admitted to
coping better with the COVID-19 situation due to better
healthcare infrastructure introduced through ADP, is also
an indication that Aspirational Districts Programme is
contributing to strengthening of healthcare and nutrition
services. For instance, the district of Malkangiri in Odisha,
which is located in close proximity to both neighbouring
states of Chhattisgarh and Andhra Pradesh, became an
entry point for several migrant workers returning back to
the state during the initial phases of lockdown in India.
The district ofcial in this case, claimed to have used their
new infrastructure facilities (both in healthcare and
otherwise) to serve as institutional quarantine centres for
the migrants. Other districts such as Goalpara in Assam
saw more pro-active and synchronised eforts of diferent
departments due to existing foundations of convergence
model laid by the programme. A similar example was
provided by a development partner, Piramal Health which
works across 25 Aspirational districts in the area of
Healthcare and Nutrition. While the development partner
faced severe setbacks in projects during the initial 30-40
days of the pandemic (mainly during the nationwide
lockdown), they soon leveraged their prior engagement
with District Commissioners, panchayats, and community
leaders to build a strong COVID-19 response and cope
with the challenges of the pandemic. The development
partner especially credited the role played by religious
leaders within the community in contributing towards
creating better awareness and understanding of health
issues over the last three years.

Education:
The Education sector has also experienced substantial
improvement among the aspirational districts. The credit
lies in the initiatives taken by several districts to adapt and
innovate, leading to the development of bespoke
programmes best suited for their district’s requirement. A
suitable example of this is the development of
Gyanodaya app and Rath in Godda district of Jharkhand.
Inspired by the award winning Unanyan Banka App
37

developed in Banka district of Bihar, the Gyanodaya app
aims to promote digital learning by converting the
Jharkhand Academic Council (JAC) Board’s approved
syllabus into smart classes format for over 260 schools
and covering over 70,000 students. According to the
ofcials, this initiative was the chief reason for the
5.2. Findings
37
Unnayan Banka’ is an initiative that envisages ‘quality education for all’, using latest technologies.  It is a multi-platform model, where students receive
educational content on various technology platforms like LCD/LED TVs, projectors, laptops and especially on mobile phones. The initiative won the
Commonwealth Association for Public Administration and Management Award (CAPAM) in 2018.
28 signiﬁcant improvement in the district’s performance in
the delta rankings. Another example of technology and
innovation includes the ‘HamaraVidhyalaya’ in Namsai
district of Arunachal Pradesh, which is adapted from the
HamaraVidhyalaya model developed in Ahmedabad. As
per the initiative, a school prabhari is appointed for each
school in the district to ensure monitoring, performance
assessment, and guidance for the school. As a result of
this initiative, the district witnessed tremendous
improvements in the learning outcomes and overall
teaching practices. Both these initiatives are examples of
successful use of technology and innovation. More
importantly,  it is also an example of replication of best
practices across districts, which is a key tenet of the
Aspirational Districts Programme.
Agriculture and Water Resources:
Given that most of the rural areas depend on agriculture
for income, it is no surprise that many districts have been
making considerable eforts to improve services and
infrastructure within this sector. Interviews with district
ofcials provided a varied range of initiatives being
undertaken. For instance, while districts like Washim have
collaborated with private organisations to develop cost
efective methods of better irrigation and water resources
such as recharge pits, others like Chanduali (Uttar
Pradesh), Simdega (Jharkhand) and Godda (Jharkhand)
have used their unique topographic features to harvest
crops best suited for their regions. Many of these are high
value crops that can be exported or used in diferent
industries, such as the production of lemongrass in
Godda. Still other districts such as Goalpara in Assam,
have used technology to develop a digital platform,
called ‘Goalmart’ for local producers to sell their products
online instead of being conﬁned to physical market
spaces.
However, while district ofcials may have mentioned an
impressive set of initiatives, development partners as well
as ﬁndings from other studies
38
highlight the scope for
further improvement in the sector. An interesting
suggestion received from development partners was that
the sector of Agriculture and Water sanitation, should be
allotted the same amount of weightage as Health and
Education under the ADP. Reason given for this, was that
agriculture directly impacts socio-economic conditions of
beneﬁciaries which in-turn, leads to higher investments in
education, or increased health and nutrition priorities of
households. Another suggestion by development
partners was collaboration among the diferent
development partners in providing services across
sectors, while specialising in one area, much like the
convergence model being used for district administration.
Basic Infrastructure:
Although this sector has lesser weightage within the ADP,
it has nevertheless witnessed substantial focus. In fact,
interviews with district ofcials of remote areas suggested
that basic infrastructure is a priority as it is essential for
improving connectivity in their districts. For instance,
districts such as Bijapur (Chhattisgarh) and Malkangiri
(Odisha) have improved their roadways and infrastructure
projects as an attempt to reduce LWE activities. Other
districts such as Goalpara (Assam) have signiﬁcantly
improved their roadways in the last 3 years, resulting in an
addition of 234 kms of new roads which coincidently is
the same number of roads constructed in last 18 years.
This is a clear indication of the impact of Aspirational
Districts in bringing about swift and efective sector wise
growth. Similarly, the district of Namsai (Arunachal
Pradesh) has achieved 100% household electricity and
90% road connectivity under the PMGSY scheme.
Instances such as these, indicate towards the increased
focus on sectors such as basic infrastructure in remote
areas, which may have been neglected previously.
However, according to district ofcials the challenges for
this sector lie with the fact that infrastructure projects
especially for districts with forest reserves require
additional approvals and clearance procedures. This was
cited as one of the reasons for delays in a number of
projects implemented in the sector. Another potential
challenge is the lack of sufcient technical capacity
leading to complete reliance on the state for all the
38
Haque, T., & Joshi, P. K. (2018). Comparative analysis of districts in Bihar: agricultural transformation in aspirational districts of India. Economic and Political
Weekly, 53(51).
QUALITATIVE DATA COLLECTION AND ANALYSIS29 development work. For districts that may not be
technically strong or lack human resource capacity, this
absence of development or CSR partners poses more
difculties.
Financial Inclusion and Skill Development:
Among the Aspirational Districts, the sectors of Financial
Inclusion and Skill Development require more focus.
Although the two sectors comprise only 10% of
weightage under the Aspirational Districts Programme,
development in these sectors is the need for the future.
Discussions with Prabhari ofcers, knowledge partners
and development partners provided useful insights for
the potential lag in these sectors. One of the chief
reasons highlighted for the sectors progressing at a
slower pace has been the lack of  dedicated departments
for the two sectors at the district level,  unlike in the case
of all other sectors. This implies that activities related to
the two sectors must be coordinated with diferent
departments within the district, with no one department to
claim ownership for the responsibilities. This lack of
coordination at the district level has undoubtedly created
a gap or inconsistency in the provision of services.
Development partners such as Microsave, mentioned
during the interviews that they have tried to resolve this
issue by appointing dedicated personnel to coordinate
among the diferent administrative departments.
Although the development partner mentioned this has
been a successful strategy, they also highlighted the
need for a dedicated department at district level as the
ideal way forward.
In the case of skill development, feedback from
stakeholders points to the lack of supplementary factors
such as absence of market demand for skills, or lack of
suitable employment opportunities at appropriate
industries within a district, despite the training provided.
This results in either migration of residents to bigger cites
in search of skilled job opportunities, or lesser uptake of
the skills training programme due to lack of opportunities.
Therefore, indicators developed for skills training must be
revised to suit the requirements of each district. The
quote below by a previous district commissioner, best
explains this situation:

Furthermore, according to stakeholders, sustainable and
actual improvements in Financial Inclusion (and not just
registration of bank accounts) is linked to socio-economic
factors such as low literacy and income levels among
many rural households, both of which may require
initiatives that bear fruit only after a few years and not in a
period of 2-3 years. Additionally, banking services are
often sparse in rural and remote areas, which is the case
with most Aspirational Districts. More importantly, even if
these factors are addressed, a crucial reason highlighted
by development partners was the general lack of trust
among beneﬁciaries in availing banking services and the
lower priority for availing banking services over other
services such as healthcare or education.
There is a need for better outreach programmes on
sectors such as ﬁnancial inclusion and skills training in
order for it to gain priority among both beneﬁciaries and
service providers. Development partners such as
Microsave seem to be already implementing such
strategies by providing counselling services on ﬁnancial
inclusion and establishing a network of bank agents to
create awareness and help in accessing the services.
Another efective solution could be introducing bespoke
programmes based on the needs of each district, just as
it has been done in the districts for the sectors of health,
education and agriculture.
5.3.1. The 3Cs Approach:
As mentioned earlier, a core ideology of the ADP’s is the
triple approach of Convergence, Competition, and
Collaboration in achieving the targets.  Discussion with
diferent stakeholders presented varied insights into the
merits of these three approaches:
30
39
  While the localized nature of skilling programmes cannot be ignored, skilling schemes – such as Deen Dayal Gramin Kaushal Yojana – are relevant for all
districts across the country and therefore would require homogenous measurement indicators.
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
5.3. Governance,
Administration and
Capacity building
“Washim is an agrarian district. So, in this district if
we provide training for beauty parlours or IT sector,
there is no industry to support those jobs in the
district. So, for skill development indicators we
need to do much better”
-Former District Commissioner of Washim district
39 ♦ Convergence: Almost all the ofcials interviewed
mentioned that the Convergence approach has been
one of the positive efects of the ADP. The approach is
said to have bolstered better administration and has
helped transition to a synchronised method of working
rather than in silos. However, other stakeholders such
as development partners, Aspirational District Fellows
(ADFs) and United Nations Volunteers (UNVs) working
in the districts shared a slightly diferent viewpoint.
Although, these stakeholders acknowledged the
improvements in governance through the
convergence model, they also highlighted that they
continue to face difculties in navigating through the
many administrative and bureaucratic processes. This
is especially the case for sectors such as Agriculture
and Water Resources, as they comprise an
amalgamation of multiple departments (from
horticulture to animal husbandry) making coordination
among diferent departments and approval processes
time consuming. Another aspect which seemed to
pose minor difculties for both district ofcials and
development partners was the mismatch of priorities or
thematic areas of focus set by states and those
mandated by the ADP. This mismatch hinders the
growth for ADPs, as district ofcials are required to
strike a balance between the two.
For development partners such mismatch often
results in delays for approvals and programme
implementation. Overall, in spite of the issues, all
stakeholders agreed that the convergence model has
been one of the positive contributions of the ADP and
must be propagated further.
♦ Collaboration: Although most states stressed on the
success of convergence, collaboration was seen as a
promising approach moving forward. Districts
appreciated the collaborative eforts of diferent
development partners in providing sector speciﬁc
technical expertise. It should be noted that the list of
partners collaborated with do not just include
development partners and knowledge partners
commissioned by NITI Aayog, but also include local
NGOs and CSO organisations. In addition to the
expertise ofered by diferent organisations, district
administrations especially credited the constant
support received from Aspirational Districts Fellows
(ADFs) for the programme. In fact, a key suggestion
provided by district administrations and development
partners was the appointment of dedicated personnel
like ADFs in each district to support day to day project
implementation activities. Overall, the collaboration
model has potential to be explored further under the
ADP, as many districts highlighted the need for more
partners or Technical Support Unit (TSU) deployed in
the district. This ﬁnding although consistent among all
districts, is more relevant for those located in remote
areas as they face larger gaps in human resources
capacities. In fact, the engagement of development
partners, especially local and smaller CSOs may be a
useful method for building capacities among the ADP
districts.
♦ Competition: This approach seemed to espouse
mixed opinions from stakeholders. While all
stakeholders were of the belief that competition has
increased districts’ eforts to perform better and
enabled better monitoring mechanisms, it however
may not be the best approach in assessing
development eforts. This view was consistent among
the diferent stakeholders - district ofcials, Prabhari
ofcials, knowledge partners, development partners
and UNVs.

One of the chief reasons cited for this was that, despite
Aspirational Districts being grouped together on the
criteria of lower performance, they nevertheless
comprise districts that difer on geographic, political,
economic and cultural contexts
40
. These variations
may pose several internal challenges such as
countering LWE conﬂicts or even geographic or
topographic diferences leading to economic or
infrastructural challenges. Other concerns raised were
around excessive reliance on competition and
QUALITATIVE DATA COLLECTION AND ANALYSIS
40
While districts are diferent and state policies also vary, it may be noted that all the KPIs except agriculture are equally relevant in all districts. Furthermore, the
delta ranking mechanism has – so far – calculated ranks on the basis of movement in percent points. This automatically favours the lesser developed districts
as progress from a lower base appears more striking. However, the matter of incorporation of diferential contexts is worth consideration for reﬁnement.
41
It is, however, important to note that with the possible exception of law and order, the current indicators nevertheless indicate holistic improvement in districts.
Amongst Development Partners, a few of them have
been outstanding and stand out in terms of the
manpower deployed in Aspirational Districts, like
Piramal Foundation deployed its team in 27
Aspirational Districts to support the District
Administration in Health, Nutrition, Education and
Water Resources Management. Similarly
Microsave (through BMGF) placed teams in these
Districts for supporting Financial Inclusion. Such
collaborations are unique examples of Public
Private Partnership (PPP) in the area of core
governance.
31 rankings leading to improvements centred only on
indicators being measured instead of achieving
sustainable or holistic growth that may be most
relevant to the district
41
. Still others pointed to the
possibility of data discrepancies and misreporting
caused due to excessive competition. Therefore,
several stakeholders suggested that competition be
used only to promote monitoring mechanisms and not
serve as an indicator of development.
5.3.2. Targeting the low hanging
fruits:

In addition to sectoral disparities, there exists signiﬁcant
disparity in strategy adopted by the districts. This is
expected in a federal set up where states have signiﬁcant
autonomy in policy choices. The KPIs provide an
over-arching but non-prescriptive framework which can
facilitate planning and policy prioritization at the
implementing level. While one of the reasons for the
disparity could be due to the difculties posed by
geographic and socio-political reasons, other potential
reasons could be the employment of successful
strategies used by some of best performing districts. For
instance, a key reason for these signiﬁcant improvements
in the areas of Healthcare, Education and Agriculture
among some of the best performing districts can be
attributed to the pre-existing schemes and facilities within
the sectors, making it possible for the districts to adopt
the strategy of “achieving the low hanging fruits” ﬁrst.
Other efcient strategies were constant monitoring and
innovation. The quotes below, from ofcials of some of
the best performing districts best illustrate this:

ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
Government of India launched the Externally Aided
Programme on Sustainable Development Goals
(EAP-SDG) in 2019 for rapid socio-economic
transformation of Aspirational Districts. The
programme is funded by Ofcial Development
Assistance (ODA) from Japan International
Cooperation Agency (JICA) for approximately 15
billion Yen. The additional allocation under challenge
method is allocated to districts on the basis of rank
declared every month on Champions of Change
Dashboard. The districts which rank 1 and 2 in the
overall ranking get Rs. 10 crores and Rs. 5 crores
respectively and districts ranking ﬁrst in sectoral
ranking get Rs. 3 crores each. Organizations such as
UNDP and ADB are providing technical support to
districts in formulating proposals for this scheme and
thereby facilitating access to these funds. By
November 2020, proposals from approximately 65
districts have been approved under this allocation
window. This has proven to be a successful strategy
in incentivising districts to compete and score more in
the Key Performance Indicators.
“We have been following a two pronged strategy: one,
in terms of setting achievable goals, focusing on low
hanging fruits, putting in place Data Driven systematic
systemic improvements and the other in terms of Big
Bang interventions and innovations”.
- District Magistrate, Goalpara (Assam)
“There were a lot of low hanging fruits in the district,
which we knew existed but could never be prioritised.
The Aspirational Districts Programme has provided a
direction to place focus on the low hanging fruits by
seamlessly incorporating them into to the
programme's indicators especially across the priority
sectors of health, nutrition and education which has
enabled us to achieve these indicators with work
pending in those which require long term structural
changes such as  RTI Mechanisms in schools”
- Team member of District Magistrate’s Team
for Ranchi (Jharkhand)
32 5.3.3. Monitoring and Measurement
Methods:

All stakeholders interviewed strongly agreed that
monitoring has helped improve and identify internal
capacities and activities within the districts. In fact, to
quote the District Magistrate of Goalpara, (one of the top
performing districts) on the topic, “What gets measured,
gets done”. Interviews with district ofcials revealed that
constant monitoring and training for measurement
methods have been key to improving the indicators. Of
importance is also the focus on trainings provided to
many stakeholders on measurement and data collection
methods as stakeholders faced confusions ADP
indicators in the initial stages. In fact, the interviews with
the stakeholders highlighted the need for regular training
sessions, and most importantly the need for dedicated
personnel for the programme. Many district ofcials
mentioned that Aspirational District Fellows have been
instrumental in this, providing technical skills and
documentation, support for the programme, especially
since district ofcials are likely to change during the
course of the entire programme. Given such instances,
having a dedicated ofce or a set of personnel for the
ADP was seen as the best way forward.
5.3.4. Capacity building:
There is no doubt that the ADP programme has helped
districts improve their internal capacities across sectors
and departments. In addition to sectoral improvements
mentioned earlier in the report, instances of internal
capacity building comprise of examples ranging from
training of frontline healthcare workers in using
appropriate measurement methods, providing schools
with technology enabled interactive platforms to even
supporting junior administrative ofcials in using online
project management and data collection tools such as
google forms. Additionally, it even includes providing
support and guidance to district magistrates from
experienced Prabhari ofcers to facilitate better planning
and policy implementation. However, despite these
positive contributions, many districts continue to struggle
with insufcient human resources to achieve their full
potential. This need for capacity building is more
prominent among districts located in remote and
challenging areas as they lack connectivity and facilities
common to urban pockets. This, according to many
district ofcials has been the chief barrier in attracting
suitable human resources leading up to 40% vacant
posts. Therefore, despite the three-pronged approach of
the 3Cs, or successful strategies of achieving the low
hanging fruits, most districts continue to stay
incapacitated from achieving their full potential. Some
suggestions received from diferent stakeholders in
countering this issue are:
♦ Dedicated Personnel or unit: The ADP designates
the District Magistrates or District Collectors as directly
responsible for their districts’ performance. While this
is an efective strategy to focus the attention of district
administrations on ADP goals, it is also important to
note that DMs and DOs are tasked with several other
responsibilities. Therefore, this strategy faces the risk
of becoming a person-centred approach and poses
challenges when ofcial appointments are subject to
frequent changes as in the case in India. Hence,
appointing a set of dedicated personnel (such as
Aspirational District Fellows) or a Technical Support
Unit within each district was suggested by many
stakeholders as an efective solution to countering
both issues of human resources and moving from a
person driven model.
♦ Flexibility in recruitment policies: Discussions with
many of the ofcials highlighted the need for relaxing
hiring policies so that vacancies can be ﬁlled. Ofcials
also suggested the use of better incentives to attract
suitable persons for remote districts.
♦ Learning programmes for administrative ofcers
and ADP fellows: Another important suggestion
provided by many Prabhari ofcers and district
ofcials was to introduce learning programmes to
share best practices. These could be visits to best
performing districts to learn about the successful
strategies, best practices and methods.
♦ Technical skills trainings: Ofcials expressed need
for technical training requirements at block and district
levels. Some of the skills mentioned are digitalisation,
data analysis, bid writing skills, and coordination at the
grassroots level. Currently the Aspirational District
Fellows and UNVs provide some of the skills, but there
is need for further technical expertise and hand
holding support. In fact, one of the major capacity
building requirements mentioned was bid/proposal
development, as traditionally this is not a task
executed at the district level.
Data driven decision making has been one of the key
features of the Aspirational Districts Programme, be it for
the purpose of competition or self-monitoring activities.
5.4. The role of Champions of
Change (CoC) Dashboard in
data driven decision making
Hence, appointing a set of dedicated personnel (such
as Aspirational District Fellows) or a Technical
Support Unit within each district was suggested by
many stakeholders as an effective solution to
countering both issues of human resources and
moving from a person driven model.
QUALITATIVE DATA COLLECTION AND ANALYSIS
33 The Champions of Change (CoC) dashboard was
developed solely for the purpose of tracking and
measuring growth. Qualitative interviews with
stakeholders found that most districts use the portal for
both data entry (as mandated under the programme), and
also for basic data analysis, as it displays monthly
progress on the indicators. The district of Ranchi for
instance, has developed its own dashboard enabling a
more in-depth data analysis and tracking of indicators at
the block level. This is yet another example of how the
ADP has successfully brought in a culture of
accountability and transparency among the districts.
However, this data driven aspect is not without its
disadvantages and stakeholders highlighted a few
features that may need improvement. These are as
follows:
Relevance of Delta rankings: Although most
stakeholders admitted to using the Champions of
Change (CoC) portal, they also mentioned that their
usage of the portal for data analysis had decreased over
time. The chief reason cited for this was the frequent and
drastic changes in delta rankings leading to
inconsistencies. This has led to districts developing their
own platforms for data analysis. In line with this,
stakeholders suggested that updates be monitored
quarterly or bi-annually rather than on a monthly basis as
very few improvements can be achieved through 30 days
period.
Data analysis and reporting: In addition to the
unpredictability of delta rankings, stakeholders
mentioned that discrepancies in data existed due to
possible misinterpretations or misreporting of indicators.
For instance, errors such as annual estimates instead of
monthly indicators were entered by many districts in the
initial days of the programme. Although the districts have
gained better understanding of the indicators over time,
some errors and misreporting practices are still reported
to exist. A possible solution suggested by stakeholders
was frequent training programmes on indicators.
Efectiveness of indicators: Among the issues
highlighted by stakeholders, some were regarding the
need for revision of some indicators. Development
partners suggested the removal of certain indicators that
have reached saturation for most districts, such as
“electriﬁcation of households”. Revision maybe required
for such indicators and new indicators need to be added
to the list. Development partners also highlighted that
there is a need to move from input-based indicators to
outcome indicators. Within the education sector,
stakeholders suggested the inclusion of indicators on
girl’s education, co-curricular and vocational programmes
as they need to be implemented in aspirational districts,
and even community engagement in education activities
as it is an inﬂuencing factor. However, inclusion of such
indicators is likely to be afected by practicality and
availability of data at the district level on frequent intervals.
Many of the suggestions provided were pertaining to the
sector of Agriculture and Water resources. For example, it
was highlighted that micro irrigation indicator has an
in-built disadvantage for some geographical areas as it is
recorded only for locations where irrigated land is
available. Therefore, it does not present the ground
realities. In line with this issue, one of stakeholders
suggested that the “Ideal denominator should be total
irrigated land in a district, and then the numerator can be
the micro irrigated land of the district”.
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL34 6
The Impact of
Aspirational Districts
Programme and
What Sets It Apart 36
The Impact of Aspirational Districts
Programme and What Sets It Apart
Based on the insights of the diferent stakeholders, it is
evident that Aspirational Districts Programme has
resulted in sectoral growth and improvements in
governance and administration. Discussions with
stakeholders illustrate the fact that a key feature that sets
the ADP apart from other development programmes is
the framework it provides to the districts through the
categorical focus on sectors and a pre-determined set of
indicators to be achieved. District administration ofcials
with experience of serving in both aspirational as well
non-aspirational districts especially highlighted the fact
that the set of pre-determined indicators provided by the
programme has helped them focus on speciﬁc targets
and sectors instead of broad government schemes or
new initiatives as in the case of previous programmes.
Furthermore, a chief ﬁnding on the diference between
Aspirational and non-Aspirational districts programme
has been the political salience given to aspirational
districts. This could be due to the pressures faced by
states and districts to perform well in the ranking system,
or simply due to the support provided by diferent
components of the programme. For instance, while
certain diferences in priorities or focus areas exist among
diferent states and the aspirational districts, overall it was
found that the level of political support has increased for
the districts as states also face the pressure of displaying
better results and do not want their districts to be ranked
low. Moreover, discussions with district ofcials revealed
that the appointment of Prabhari ofcers for districts and
regular support from NITI Aayog are beneﬁcial elements
that previous programmes and non-Aspirational Districts
lack. This was especially highlighted by district ofcials
with experience in serving in both ADP and non-ADP
districts.
More importantly, the programme was launched with the
objective of reducing inter and intra-state disparities and it
is on track of achieving it. The unique features of
introducing competition, handholding support from the
centre and state and collaboration with various agencies
is proving successful in realising the vision of holistic
development. This is clearly demonstrated by the
Diference-in-Diference methodology adopted in this
evaluation. When compared with other districts with
similar socio-economic indicators, aspirational districts
have fared much better on all development indicators
since the launch of the programme.
However, stakeholders such as Prabhari ofcers and
development partners also warned that the momentum
gained at the inception of the programme is starting to
diminish and eforts must be made to motivate the
districts. In fact, as the programme has completed 3 years,
it may be advisable to introduce re-training and learning
programmes on best practices among the districts to
regain momentum and work towards achieving the
remaining targets.
District administration officials with experience of serving in both aspirational as well
non-aspirational districts especially highlighted the fact that the set of pre-determined indicators
provided by the programme has helped them focus on specific targets and sectors instead of broad
government schemes or new programmes as in the case of previous initiatives.
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL 7
Recommendations
for the Way Forward:
countering the
existing gaps and
challenges A useful suggestion from the Aspirational District Fellows (ADFs) who
work closely with the programme was to include additional sectors or
themes cantered around key topics of  environment and gender. This,
according to the stakeholders, should not just be targeted for the
beneﬁciaries of the programmes, but also integrated within the
governance model as indicators of inclusive and sustainable growth.
The commencement of ADP brought with it few challenges relating to
monitoring and data collection, one of which is the discrepancy in data
collected and recorded. Discussions with diferent stakeholders have
highlighted the need for revising indicators, as well as reduced focus on
a competitive approach, as they are likely to result in misreporting of data
by districts. Apart from this there is also the need for further trainings and
learning programmes.
While it is evident that the ADP has positively impacted
the development targets, it should be noted that there
are still some challenges and issues that need to be
addressed. While some of the challenges have been
mentioned in the sections above, this section provides
a compilation of the challenges.
Recommendations for the Way Forward:
countering the existing gaps and
challenges
While the Aspirational districts programme has helped strengthen
crucial Healthcare and Education sectors, those with lesser weightage
need signiﬁcant focus and improvement. A realignment of sectors and
focus is therefore required.
Disparities
among
sectors
Disparities
among
districts
Scope
for
collaboration
As mentioned earlier, one of the disadvantages of the Aspirational
Districts has been the disparities among districts which does not facilitate
fair competition and comparisons. In order to counter these issues,
districts could be further grouped together based on their common
characteristics and be supported accordingly.
Addition
of sectors
or themes
Given the disparities in sectors, districts and also capacities, furthering
collaboration with diferent organisations may provide the immediate
and required support to districts. This can especially be provided for
districts located in remote and challenging areas.
Data
discrepancies
and
adverse effects
of competition
One of the major issues highlighted across the districts irrespective of
performance has been the lack of human resources and technical
capacities at the district and block level. Even though districts have been
provided support from the Prabhari ofcers and NITI Aayog, there is a
need for capacity building at the grassroots level. This can be resolved
by providing districts with dedicated personnel such as Aspirational
District Fellows or representatives of the programme. This would bring in
additional accountability and ownership for the programme, while also
providing support to DMs and DOs, as they are already tasked with
several responsibilities. Adopting more ﬂexible methods of the hiring
was also suggested as potential solution for improving capacities.
Lack of
human
resources
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL38 8
Best
Practices 40
Much of the work undertaken under the Aspirational
districts programme has been focused on the Healthcare
and Nutrition sector. Initiatives range from improving
infrastructure at Anganwadi centers to ensuring the
availability of ambulance services in remote areas,
designating speciﬁc days for work on VHSND (Village
Health Sanitation and Nutrition Day) or ensuring an
increase in institutional deliveries. Some districts have
even developed apps for tracking progress in the
nutrition sector. The best practices listed in this report are
only a selected few and the ones that show potential for
scalability and replicability. There are other initiatives as
well which have performed well.
1. Ensuring community well-being though the
‘Malaria Mukt Bastar Abhiyan’ - Bijapur and
Dantewada districts (Chhattisgarh)

The Malaria Mukt Bastar Abhiyan is a program
implemented by the National Health Mission and
covers all the districts of Bastar, Kanker and
Kondagaon regions. Given that approximately 72% of
all malaria cases in the country are diagnosed in the
Bastar region
42
, this large-scale project and its
successful implementation was mentioned during our
interviews with two districts’ DMs – Bijapur and
Dantewada. It should be noted that both Bijapur and
Dantewada are located in remote areas and are
severely afected by Left Wing Extremist (LWE)
activities. Needless to say, such factors make
programme implementation more challenging,
especially if using door to door campaigning as
required under the programme. However, despite
these challenges and the Covid-19 pandemic, health
workers covered 100% of the area, which involves
6,000 villages to conduct malaria tests. As
asymptomatic malaria is known to cause anaemia and
malnutrition, testing is a crucial method for early
diagnosis and treatment. As a result of the
programme, the region saw a 65% year-on-year
decline in the total cases of malaria recorded
43
, and by
the ﬁnal phase of testing, malaria incidences in Bijapur
had been reported to reduce by 71.3% and 54% in
Dantewada.
2. Model Anganwadis for holistic child development --
West Singhbhum district (Jharkhand)

While several Anganwadis among the Aspirational
districts have seen improvement under the
programme, the district of West Singhbhum was
among the ﬁrst to focus on the improvement of
Anganwadis for health and nutrition activities of
children and mothers. One of the key elements of this
has been training of Anganwadi Sevikas (staf) which
included an 80-hour training module regarding holistic
development of each and every child
44
. Salaries of
staf were also increased to serve as an incentive.
Currently, 650 anganwadi centres have been
improved in the West Singhbhum district and include
features such as a mobile science laboratory, digital
literacy, digital literacy workshops and increased
number of healthcare centres. Students have also
been provided with textbooks stationery, learning toys
and classroom accessories. The goal of the initiative is
to reach 1000 Anaganwadis.
8.1. Health and Nutrition
42
Figures citied by Health Department in article by ANI, January 2020. https://www.aninews.in/news/national/general-news/malaria-prevention-to-help-in-
alleviation-of-malnutrition-anaemia-bhupesh-baghel20200125230939/
43
Article in The Print, titled ‘While Covid raged, Chhattisgarh covered over 6,000 villages under ‘Malaria MuktBastar’ project’, November 2020.
44
Article in The New Indian Express on 3rd May 2020 ,titled, ‘This Jharkhand man is changing the face of primary education with innovative ideas’.
Given that approximately 72% of all malaria cases
in the country are diagnosed in the Bastar region,
this large-scale project and its successful
implementation was mentioned during two of our
interviews
Best Practices
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL 41
8.2. Education
3. Tracking nutrition outcomes through the Poshan
App - Ranchi district (Jharkhand)
While many districts have focussed on improving their
anaganwadi centres under the Poshan Abhiyan, the
district of Ranchi has been a step ahead. The Poshan
App was introduced in Ranchi with the aim of
optimizing the resources at Malnourishment
Treatment Centres (MTCs). Keeping with the
Aspirational district programme’s ideology of
monitoring progress, the Poshan App is a
comprehensive real-time data analytics digital
platform which monitors the bed occupancy, child
growth charts and the inventory of each and every
MTC centre in the district. This app also tracks the
attendance of the MTC staf and doctors’ visits are also
tagged to the MTCs. The introduction of the app has
led to the bed occupancy levels increasing over 90%
at healthcare centres, and the inventory being tracked
and managed better.
While the Healthcare sector may have seen an increase
in the number of success stories, it is the education sector
where the most innovative practices have been
implemented. Districts have improved their performance
in this sector by utilizing both technology and monitoring
methods. Examples of the most innovative practices are
mentioned below:
1. Encouraging better school performance through
Hamara Vidyalaya Programmme - Namsai District
(Arunachal Pradesh)
The Hamara Vidyalaya Programme of the Namsai
district in Arunachal Pradesh has been a game
changer programme for a district that was previously
plagued with huge school infrastructure gap, high
dropout rates amongst the lowest socio-economic
groups, high teacher absenteeism, low
parent-teacher coordination and ranked amongst the
lowest three performing districts in learning outcomes
according to NAS. Recognizing these issues, the
district administration initiated this programme with
key features of the Aspirational district programme
itself, i.e. use of a dashboard to constantly monitor
progress among the schools, provide regular
mentoring for schools by an appointed school
Prabhari ofcer and rank schools based on their
performance. Using monitoring and mentoring, the
program aimed to improve teacher and student
absenteeism, increase parent’s engagement in school
management meetings, and encourage students by
identifying good performers for School Olympiad to
be conducted at block level and district level.
Moreover, the program makes use of an online
platform, named “Yathasarvam”, developed by
technology partner–Eckovation, and is linked to a
Mobile app for data entry pertaining to assessment
data, attendance of teachers & students, and the
learning outcome marks by the School Prabhari on a
quarterly basis during the “Hamara Vidyalaya Week”.
The data is then automatically analysed by the
platform and brief reports generated on each criterion,
similar to the Champions of Change dashboard.
2. Improving education through interactive learning
methods by GyanodayaGodda App - Godda district
(Jharkhand)

Inspired by the Unnayan Banka Project in Bihar, the
district administration of Godda implemented the
Gyanodaya Project in the District of Godda to improve
the quality of education. The App provides an
attractive digital learning platform as per Jharkhand
Academic Council (JAC) Board syllabus for grades 6 to
12.  It also involves audio-visual lessons with animated
and contextualized lectures followed by daily
assessments to provide quality education. This was
undertaken to increase students’ access to education
material, as well as improve the performance of
students. The key belief of the programme is that
The key belief of the programme is that teaching alone
is not sufficient to ensure that students have grasped
the concept, hence teaching must be supplemented
with assessments and feedback to improve learning
outcomes.
With key features like a dashboard to constantly
monitor schools’ progress, the Hamara Vidyalaya
Program comprises all the features of the ADP, and in
a way, is the implementation of the ADP programme
itself within the education sector of the district.
Keeping with the ADP’s approach of monitoring
progress, the Poshan App is a comprehensive real-time
data analytics digital platform which monitors the bed
occupancy, child growth charts, and the inventory of
each and every MTC centre
BEST PRACTICES teaching alone is not sufcient to ensure that students
have grasped the concept well, and hence it must be
supplemented with assessments and feedback to
improve learning outcomes. As a result, daily
assessments are completed by students to gain
feedback on improving their learning gap. In fact,
based on the data points generated by the App,
students are provided with AI based
recommendations to help them strengthen their weak
topics. The AI built into the app analyses each
student’s performance while mapping it to the course
curriculum and also benchmarking it with not just that
district, but with the country wide data on the same
curriculum. Further, the AI system generates unique
actionable feedback for each and every student.
Currently the app caters to over 70,000 students
across 260 schools for Maths, Science, Social Science
and Linguistic subjects. The programme also involves
“The Gyanodaya Rath” which identiﬁes 200 best
performing girls and boys from 10th grade in the
district. These students are provided with residential
school facilities and additional preparatory classes in
the last two months leading to the 10th grade board
examinations.
3. ANNIE Smart Classes for visually impaired
students– Ranchi district (Jharkhand)
While most districts have focused on improving their
learning outcomes, teaching methods or infrastructure
facilities in schools, the district of Ranchi adopted a
truly inclusive approach by focusing on improving the
quality of education for diferently abled students as
well. The district administration with support from
private foundation, Thinkerbell labs installed the ﬁrst
smart class for the visually impaired at the
Government School for visually impaired in Ranchi city.
The initiative utilised the District Innovation Fund, and
since the installation it has seen a drastic rise in the
learning outcomes of students in the school as it
enabled Class 5 students to also write in Braille, which
was previously taught only to Class 10 students. The
braille devices installed are enabled with both Hindi
and English as the medium of instruction and also
comes with gamiﬁed content for students’
self-learning.
Agriculture and Water resources is a sector that is fast
gaining importance among the Aspirational districts.
Innovative practices and initiatives among ADs range
from improving irrigation facilities, farmer education, and
to improving yield. Among the many practices mentioned
by the stakeholders, this report has highlighted case
studies from districts have adopted speciﬁc initiatives to
counter their challenges or improve on their strengths.
Although these initiatives may be too speciﬁc to a region
to replicate or scale up among other aspirational districts,
they must nevertheless be applauded for their innovation.
1. Promoting local products through e-commerce
portal  - Goalpara district (Assam)
Similar to the technological initiatives in the education
and healthcare sectors, the GoalMart initiative is an
e-commerce portal set up by the district administration
8.3. Agriculture and
Water Resources
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL
The GoalMart initiative is an e-commerce portal
introduced to promote ethnic and agrarian
products of the district in the national and global
markets.
42 43
45
Government of Goalpara, 2019. ‘Implementation of Green Technologies in Road Construction in Goalpara, Assam’
of Goalpara in Assam. The GoalMart initiative was
introduced to promote rural, ethnic and agrarian
products of the district and to provide a platform for
farmers and retailers to venture into the national and
global markets. The aim is to boost economic growth
of the district. The initiative has been particularly
helpful in Covid 19 times as it relieves the farmers and
retailers from being dependent on a physical
marketplace to sell their products and instead
increase their reach throughout the country or
globally. For instance, Goalpara is one of the districts
producing black rice, which is proﬁtable and in high
demand for exporting in the international market.
While the GoalMart initiative is gaining popularity, it is
deﬁnitely a step in the right direction to improve
access to agricultural markets and opportunities within
the district.
2. Improving irrigation facilities through recharge pits
-  Washim district (Maharashtra)
As part of improving irrigation facilities and water
conservation eforts, the district administration of
Washim in collaboration with private partners
employed a large number of recharge pits in the
district. A ‘recharge pit’ is a closed well like structure,
covered by stones and other material when land is
dug to make pits. Although the concept of recharge
pits is not new, it is a noteworthy initiative in the case of
Washim as it optimizes the use of resources. An
increase in infrastructure development, especially
construction of roadways and highways in the district
led to the opportunity to create recharge pits as a
suitable option for water conservation. The initiative
has proved to be of low cost as well, with
approximately INR 30,000 per structure as they were
constructed by private partners already engaged in
infrastructure development. Given the issues of water
scarcity and cost of developing irrigation facilities, the
concept of recharge pits is proving to be an efective
solution for the district.
3. Enhancing agricultural productivity through high
proﬁt products  - Chandauli district (Uttar Pradesh)

The district of Chanduali is known as the ‘rice bowl’ of
eastern Uttar Pradesh and has a large section of the
population dependent on agriculture for their
livelihood. Therefore, in order to improve agricultural
returns for farmers, the district encouraged farmers to
produce high quality black rice as it provides high
proﬁts. Black rice as such is not native to the area and
is actually produced in high quantities in Manipur.
However, given the increasing demand for the
product in the global markets, the district
administration promoted the product among a small
group of 300 farmers. According to district ofcials
interviewed, per kg of the product is priced at
approximately INR 200, which is double that of normal
rice sold in the local markets. With the success of the
initiative, high quality black rice produced in the district
is now ready to be exported to Australia and New
Zealand and will soon be exported to other countries
as well.
Although Basic Infrastructure comprises only 10%
weightage in the ADP, it is nevertheless a crucial facilitator
of development in the districts, and one which is
interlinked to all other sectors. Best practices in this sector
range from improving connectivity for socio-economic
activities to even ensuring security and safety within the
district. The examples mentioned in this report highlight
these aspects.
1. Utilization of green technologies for better
connectivity – Goalpara district (Assam)
The Goalpara district of Assam has many far-ﬂung
places comprising both plains and some areas of
undulating terrain along the Assam Meghalaya
foothills where rural road connectivity has always
been an issue for the public as well as administration.
In line with this concern the green technologies
initiative is a one-of-a-kind initiative by the district
administration of Goalpara to improve basic
infrastructure by using plastic waste and eco-friendly
methods for the construction work. The initiative is
both unique and environmentally friendly as it is an
example of how single use plastic waste can be
recycled and used for productive endeavors such as
building roads. Along with using recycled plastic
technology, the initiative made use of green
technologies such as cell ﬁlled concrete technology,
geogrid technology, interlocking concrete pavement
blocks, and cold mix technology. In addition to
reducing environment pollution, the initiative is also
said to reduce the cost of the construction. In fact,
Goalpara was the ﬁrst district in India to construct a
‘green road’ and has constructed over 183 kms of
roads built under environment friendly technology
Although the concept of recharge pits is not new, it
is a noteworthy initiative in the case of Washim as it
optimizes the use of resources.
The initiative is both unique and environmentally
friendly as it is an example of how single use
plastic waste can be recycled and used for major
productive endeavors such as building roads.
8.4. Basic Infrastructure
BEST PRACTICES
With the success of the initiative, high quality black
rice produced in the district is now ready to be
exported to Australia and New Zealand; therefore,
bringing in double the profit gained from normal
rice production. 44
8.5. Skill Development and
Financial Inclusion
over the last three years thus providing 433 numbers
of habitations with access to all weather roads since
April 2018
45
. The roads have been built under the
scheme State-Owned Priority Development (SOPD), a
part of the Pradhan Mantri Gram Sadak Yojana
(PMGSY) program.
1. Providing skill development and community
outreach through the YuvaBPO - Dantewada district
(Chhattisgarh)
Dantewada district in Bastar Division of Chhattisgarh is
a district rich in natural resources and cultural diversity.
However, it is also a remote district afected by Left
Wing Extremism activities, and not a location that one
would expect to ﬁnd a BPO centre. However, the Yuva
BPO initiative which provides skill development and
employment opportunities for the youth in the district
and also nearby districts is an outstanding initiative for
its multi-pronged approach in countering several
challenges. While the initiative directly bridges the
gaps of skill development and employment for the
youth, it is also a good means to prevent youth
engagement in LWE activities. However, the most
notable feature of the BPO is its role of information
dissemination on health issues or community
outreach activities.
A key component of the BPO is undertaking
healthcare related outreach activities on behalf of the
district administration. Currently the BPO houses a
separate cell of executives trained to provide
information on maternal health services such as
institutional delivery facilities within the district,
antenatal Care to Immunization activities. The cell was
operationalised using the Innovation Fund under
National Health Mission. The NHM provides the BPO a
list of pregnant women to reach out to for sensitising
them on healthy dietary practice, health check-ups,
precautions etc. On an average 50 calls are made
every day to the pregnant women. In addition, calls
are made to the frontline healthcare workers such as
Anganwadi Workers, ANM, and PRI representatives to
check for any challenges. The BPO cell also
coordinates between the diferent institutions and
beneﬁciaries for improving institutional delivery and
care, ensuring high risk cases are given special
attention such as counselling on delivery and early
childcare, breast feeding etc. In cases where
emergency referral transportation is required, the call
centre also coordinates with ambulance services.
More recently, the BPO was helpful in providing
information and surveillance for during the COVID-19
pandemic as well. The district plans to expand these
services for other sectors as well, such as education.
2. Engagement of community members to improve
ﬁnancial inclusion - Ranchi district (Jharkhand)
In order to promote ﬁnancial inclusion and ﬁnancial
literacy among rural households, the district
administration of Ranchi deployed women SHGs as
‘Bank Sakhis’, or banking correspondents. The aim of
the initiative was to promote ﬁnancial literacy. As part
of the initiative, a Bank Sakhi is placed at a rural bank
branch to assist the local population with their banking
requirements and while also educating them on
various aspects of banking. The initiative found that
rural beneﬁciaries preferred Bank Sakhis to address
their banking queries, due to their existing
interpersonal relationships in rural areas and use of
the local language. The Bank Sakhis conduct regular
evening classes in their villages on ﬁnancial literacy
and on digital banking. The SHGs have conducted
The initiative found that rural beneficiaries
preferred Bank Sakhis to address their banking
queries, due to their existing interpersonal
relationships in rural areas and due to the local
language.
The Yuva BPO is noteworthy for its multi-pronged
approach of providing skill development and
employment opportunities for the youth, as well as
ensuring community engagement and outreach
activities for crucial issues pertaining to health and
well-being.
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL various drives in the village, teaching rural citizens on
the use of UPI and the Bhim App. Rural Women SHGs
have been deployed as banking correspondents in
speciﬁcally those villages where banking systems
were unable to penetrate efectively.
Aspirational Districts Programme aims to promote the
model of cooperative federalism and sharing of best
practices and its subsequent replication by other districts
form the basis of it. Since these districts are plagued with
similar challenges it is not expected that districts reinvent
the wheel, rather they learn from each other and ﬁnd
solutions to common problems. Some of these practices
are so efcient in achieving their goals, they can be
scaled not just in aspirational but other (non-aspirational)
districts as well. Dissemination of such practices can also
happen through international forums like High Level
Political Forum (HLPF) of the United Nations as innovative
approach for local area development in developing
countries.
8.6. Scalability
Some of these practices are so efficient in achieving their goals, they can be scaled not just in aspirational but other
(non-aspirational) districts as well.
BEST PRACTICES
45 46
Appendix
Note:
• Data points marked with asterisks (*) have been omitted from the index. These include price related
indicators in agriculture and caste-subdivision in skill development indicators. These may vary
substantially between districts and distort the analysis due to district level idiosyncrasies.

Sector Total Indicators
(87)
Type of
Indicator
2018 (67) 2020 (68)
Agriculture 1.1) Percentage
of area under
micro-irrigation
Positive 1.1. Percentage
of area under
micro-irrigation
1.1. Percentage of area under
micro-irrigation
Agriculture 1.2) No. of water
bodies
rejuvenated
under MGNREG A
during this period
Positive 1.2. No. of water
bodies
rejuvenated
under MGNREG A
during this period
1.2. No. of water bodies
rejuvenated under MGNREGA
during this period
Ag
riculture 10) Number of
Soil Health Cards
distributed
Positive 10. Number of
Soil Health
Cards distributed
10. Number of Soil Health
Cards distributed
Agriculture 2.1) Crop
Insurance-
Kharif:
Percentage of net
sown area under
Pradhan Mantri
Fasal Bima
Yojana (PMFBY)
Positive

2.1. Crop
Insurance-
Kharif:
Percentage of net
sown area under
Pradhan Mantri
Fasal Bima
Yojana (PMFBY)
Data not available in March
2020
Agriculture 2.2) Crop
Insurance Rabi:
Percentage of net
sown area in Rabi
under Pradhan
Mantri Fasal
Bima Yojana
(PMFBY)
Positive Data not
available in
March-Dec 2018
2.2. Crop Insurance Rabi:
Percentage of net sown area in
Rabi under Pradhan Mantri
Fasal BimaYojana (PMFBY)
Agriculture 3.1) Percentage
increase in
agricultural credit
Positive 3.1. Percentage
increase in
agricultural credit
3.1. Percentage increase in
agricultural credit
Agriculture 3.2) Certiﬁed
quality seed
distribution
Positive 3.2. Certiﬁed
quality seed
distribution
3.2. Certiﬁed quality seed
distribution
Agriculture 4) Number of
Mandis in the
District linked to
Electroni c Market
Positive 4. Number of
Mandis in the
District linked to
Electroni c Market
4. Number of Mandis in the
District linked to Electronic
Market
Agriculture* 5.1) Wheat:
Percentage
Positive 5.1. Wheat:
Percentage
5.1. Wheat: Percentage change
in Price Realizat ion (deﬁned as
Table A.1 Data Points Used for Net Resilience index
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL change in Price
Realization
(deﬁned as the
diference
between Farm
Harvest Price
(FHP) and
Minimum
Suppor t Price
(MSP))
change in Price
Realization
(deﬁned as the
diference
between Farm
Harvest Price
(FHP) and
Minimum
Suppor t Price
(MSP))
the diference between Farm
Harvest Price (FHP) and
Minimum Suppor t Price
(MSP))
Agriculture* 5.2) Paddy
(Common) :
Percentage
change in Price
Realization
(deﬁned as the
diference
between Farm
Harvest Price
(FHP) and
Minimum
Suppor t Price
(MSP))
Positive

5.2. Paddy
(Common) :
Percentage
change in Price
Realization
(deﬁned as the
diference
between Farm
Harvest Price
(FHP) and
Minimum
Suppor t Price
(MSP))
5.2. Paddy (Common) :
Percentage change in Price
Realization (deﬁned as the
diference between Farm
Harvest Price (FHP) and
Minimum Suppor t Price
(MSP))
Agriculture* 5.3) Paddy
(Grade A):
Percentage
change in Price
Realization
(deﬁned as the
diference
between Farm
Harvest Price
(FHP) and
Minimum
Suppor t Price
(MSP))
Positive

5.3. Paddy
(Grade A):
Percentage
change in Price
Realization
(deﬁned as the
diference
between Farm
Harvest Price
(FHP) and
Minimum
Suppor t Price
(MSP))
5.3. Paddy (Grade A):
Percentage change in Price
Realization (deﬁned as the
diference between Farm
Harvest Price (FHP) and
Minimum Suppor t Price
(MSP))
Agriculture 6) Percentage
share of high
value crops to
total sown area in
district
Positive

Data not
available in
March-Dec 2018
6. Percentage share of high
value crops to total sown area
in district
Agriculture 7.1) Agricultural
productivity of
Major Crop1 in
Kharif
Positive

7.1. Agricultural
productivity of
Major Crop1 in
Kharif
Data not available in March
2020
Agriculture 7.2) Agricultural
productivity of
Major Crop2 in
Kharif
Positive

7.2. Agricultural
productivity of
Major Crop2 in
Kharif
Data not available in March
2020
Agriculture 7.3) Agricultural
productivity of
Major Crop1 in
Rabi
Positive

Data not
available in
March-Dec 2018
7.3. Agricultural productivity of
Major Crop1 in Rabi
47 APPENDIX Agriculture 7.4) Agricultural
productivity of
Major Crop2 in
Rabi
Positive Data not
available in
March-Dec 2018
7.4. Agricultural productivity of
Major Crop2 in Rabi
Agriculture 8) Percentage of
animals
vaccinated
Positive 8. Percentage of
animals
vaccinated
8. Percentage of animals
vaccinated
Agriculture 9) Artiﬁcial
insemination
coverage
Positive 9. Artiﬁcial
insemination
coverage
9. Artiﬁcial insemination
coverage
Basic
Infrastructure
1) Percentage of
households with
electricity
connect ion
Positive 1. Percentage of
households with
electricity
connect ion
Data not available in March
2020
Basic
Infrastructure
2) Percentage of
gram panchayats
with internet
connect ion
Positive 2. Percentage of
gram panchayats
with internet
connect ion
2. Percentage of gram
panchayats with internet
connect ion
Basic
Infrastructure
3.1) Percentage
of habitations
with access to all
weather roads
under PMGSY
Positive 3.1. Percentage
of habitations
with access to all
weather roads
under PMGSY
3.1. Percentage of habitations
with access to all weather roads
under PMGSY
Basic
Infrastructure
3.2) Cumulative
number of
kilometers of all-
weather road
work completed
as a percentage of
total sanctioned
kilometers in the
district under
PMGSY
Positive 3.2. Cumulative
number of
kilometers of all-
weather road
work completed
as a percentage
of total sanctioned
kilometers in the
district under
PMGSY
3.2. Cumulative number of
kilometers of all-weather road
work completed as a
percentage of total sanctioned
kilometers in the district under
PMGSY
Basic
Infrastructure
4) Percentage of
households with
individual
household latrines
Positive 4. Percentage of
households with
individual
household latrines
4. Percentage of households
with individual househol d
latrines
Basic
Infrastructure
5) Percentage of
rural habitations
with access to
adequate quantity
of potable water
(40 lpcd) drinking
water
Positive 5. Percentage of
rural habitations
with access to
adequate quantity
of potable water
(40 lpcd) drinking
water
5. Percentage of rural
habitations with access to
adequate quantity of potable
water (40 lpcd) drinking water
Basic
Infrastructure
6) Percentage
coverage of
establishment of
Common Service
Centres at Gram
Panchayat level
Positive 6. Percentage
coverage of
establishment of
Common Service
Centres at Gram
Panchayat level
6. Percentage coverage of
establishment of Common
Service Centres at Gram
Panchayat level
Basic
Infrastructure
7) Percentage of
pucca houses
Positive 7. Percentage of
pucca houses
7. Percentage of pucca houses
constructed for households
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL48 constructed for
households that
are shelterless or
have one room
with kuchha wall
and roof or have
2 rooms with
kuchha wall and
roof
constructed for
households that
are shelterless or
have one room
with kuchha wall
and roof or have
2 rooms with
kuchha wall and
roof
that are shelterless or have one
room with kuchha wall and
roof or have 2 rooms with
kuchha wall and roof
Education 1.1) Transition
rate from primary
to upper primary
school level
Positive

1.1. Transition
rate from primary
to upper primary
school level
Data not available in March
2020
Education 1.2) Transition
rate from upper
primary to
seconda ry school
level
Positive

1.2. Transition
rate from upper
primary to
seconda ry school
level
Data not available in March
2020
Education 2) Toilet access:
percentage
schools with
functional girls’
toilets
Positive

2. Toilet access:
percentage
schools with
functional girls’
toilets
2. Toilet access: percentage
schools with functional girls’
toilets

Education 3.1) Mathematics
performance in
class 3
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 3.2) Language
performance in
class 3
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 3.3) Mathematics
performance in
class 5
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 3.4) Language
performance in
cl
ass 5
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 3.5) Mathematics
performance in
class 8
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 3.6) Language
performance in
class 8
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 4) Female literacy
rate (15+ age
group)
Positive

Data not
available in
March-Dec 2018
Data not available in March
2020
Education 5) Percentage of
schools with
functional
drinking water
facility
Positive

5. Percentage of
schools with
functional
drinking water
facility
5. Percentage of schools with
functional drinking water
facility
Education 6) Percentage of
schools with
functional
electricity facility
at seconda ry level
Positive

6. Percentage of
schools with
functional
electricity facility
at seconda ry level
6. Percentage of schools with
functional electricity facility at
seconda ry level
APPENDIX
49 Education 7) Percentage of
elementary
schools complying
with RTE speciﬁed
Pupil Teacher
Ratio

Positive

7. Percentage of
elementary
schools complying
with RTE speciﬁed
Pupil Teacher
Ratio

7. Percentage of elementary
schools complying with RTE
speciﬁed Pupil Teacher Ratio
Education 8) Percentage of
schools providing
textbooks to
children within 1
month of start of
academic session
Positive

8. Percentage of
schools providing
textbooks to
children within 1
month of start of
academic session
8. Percentage of schools
providing textbooks to children
within 1 month of start of
academic session
Financial
Inclusion
1) Total
disbursement of
Mudra loan (in
Crore rupees ) per
1 lakh population
Positive

Data not
available in
March-Dec 2018
1. Total disbursement of Mudra
loan (in Crore rupees ) per 1
lakh population
Financial
Inclusion
2) Pradhan
Mantri Jeevan
Jyoti Bima
Yojana
(PMJJBY):
number of
enrolments per 1
lakh population
Positive

2. Pradhan
Mantri Jeevan
Jyoti Bima
Yojana
(PMJJBY):
number of
enrolments per 1
lakh population
2. Pradhan Mantri Jeevan Jyoti
Bima Yojana (PMJJBY):
number of enrolments per 1
lakh population
Financial
Inclusion
3) Pradhan
Mantri Suraksha
Bima Yojana
(PMSBY):
number of
enrolments per 1
lakh population
Positive

3. Pradhan
Mantri Suraksha
Bima Yojana
(PMSBY):
number of
enrolments per 1
lakh population
3. Pradhan Mantri Suraksha
Bima Yojana (PMSBY):
number of enrolments per 1
lakh population
Financial
Inclusion
4) Atal Pension
Yojana (APY):
number of
beneﬁciaries per
1 lakh population
Positive

4. Atal Pension
Yojana (APY):
number of
beneﬁciaries per
1 lakh popu lation
4. Atal Pension Yojana (APY):
number of beneﬁciaries per 1
lakh population
Financial
Inclusion
5) Percentage of
account s seeded
with Aadhaar to
total bank
account s
Positive

5. Percentage of
account s seeded
with Aadhaar to
total bank
account s
5. Percentage of accounts
seeded with Aadhaar to total
bank accounts
Financial
Inclusion
6) Number of
account s opened
under Pradhan
Mantri Jan Dhan
Yojana per 1
Lakh population
Positive

6. Number of
account s opened
under Pradhan
Mantri Jan Dhan
Yojana per 1
Lakh population
6. Number of accounts opened
under Pradhan Mantri Jan
Dhan Yojana per 1 Lakh
population
Health and
Nutrition
1.1) Percentage
of pregnant
women receiving
Positive

Data not
available in
March-Dec 2018
1.1. Percentage of pregnant
women receiving 4 or more
antenatal care check-ups to the
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL50 4 or more
antenatal care
check-ups to the
total no. of
pregnant women
registered for
antenatal care
total no. of pregnant women
registered for antenatal care
Health and
Nutrition
1.2) Percentage
of ANC
registered within
the ﬁrst trimester
against Total
ANC Registration
Positive 1.2. Percentage
of ANC
registered within
the ﬁrst trimester
against Total
ANC Registration
1.2. Percentage of ANC
registered within the ﬁrst
trimester against Total ANC
Registration
Health and
Nutrition
1.3) Percentage
of pregnant
women (PWs)
registered for
ANCs to total
estimated
pregnancies
Positive 1.3. Percentage
of pregnant
women (PWs)
registered for
ANCs to total
estimated
pregnancies
1.3. Percentage of pregnant
women (PWs) registered for
ANCs to total estimated
pregnancies
Health and
Nutrition
10.1) Percentage
of Breastfeeding
children receiving
adequate diet
(6-23 months)
Positive Data not
available in
March-Dec 2018
Data not available in March
2020

Health and
Nutrition
10.2) Non-
breastfeeding
children receiving
adequate diet-
(6-23 months)
PositiveData not available in March
2020
Data not
available in
March-Dec 2018
Health and
Nutrition
11) Percentage of
children fully
immuni zed (9-11
months) (BCG+
DPT3 + OPV3
+ Measles1)
Positive 11. Percentage of
children fully
immuni zed
9-11 months) (BCG+
DPT3 + OPV3
+ Measles1)
11. Percentage of children fully
immun
ized (9-11 months)-
(BCG+ DPT3 + OPV3 +
Measles1)
Health and
Nutrition
12.1)
Tuberculosis
(TB) case
notiﬁcation rate
(Public and
Private
Institutions) as
against estimated
cases
Positive 12.1.
Tuberculosis
(TB) case
notiﬁcation rate
(Public and
Private
Institutions) as
against estimated
cases
12.1. Tuberculosis (TB) case
notiﬁcation rate (Public and
Private Institutions) as against
estimated cases
Health and
Nutrition
12.2. TB
treatment success
rate among
notiﬁed TB
patients (public
and private)

Positive 12.2. TB
treatment success
rate among
notiﬁed TB
patients (public
and private)
12.2. TB treatment success rate
among notiﬁed TB patients
(public and private)
Health and
Nutrition
13.1) Proporti on
of sub-
Positive 13.1. Proporti on
of sub-
13.1. Proporti on of of sub-
centers/PHCs converted into
APPENDIX
51 centers/PHCs
converted into
Health &
Wellness Centers
(HWCs)
centers/PHCs
converted into
Health &
Wellness Centers
(HWCs)
Health & Wellness Centers
(HWCs)
Health and
Nutrition
13.2) Percentage
of Primary
Health Centers
compliant to
Indian Public
Health Standards
Positive 13.2. Percentage
of Primary
Health Centers
compliant to
Indian Public
Health Standards
13.2. Percentage of Primary
Health Centers compliant to
Indian Public Health Standards
Health and
Nutrition
13.3) Proporti on
of functional
FRUs (First
Referral Units)
against the norm
of 1 per 500,000
population (1 per
300,000 in hilly
areas)
Positive 13.3. Proporti on
of functional
FRUs (First
Referral Units)
against the norm
of 1 per 500,000
population (1 per
300,000 in hilly
areas)
13.3. Proporti on of functional
FRUs (First Referral Units)
against the norm of 1 per
500,000 population (1 per
300,000 in hilly areas)
Health and
Nutrition
13.4) Proportion
of specialist
services available
in district
hospitals against
IPHS norms
Positive 13.4. Proporti on
of specialist
services available
in district
hospitals against
IPHS norms
13.4. Proporti on of specialist
services available in district
hospitals against IPHS norms
Health and
Nutrition
13.5) Percentage
of
Anganwadis/UP
HCs reported to
have conducted
at least one
Village Health
Sanitation &
Nutrition day /
Urban Health
Sanitation &
Nutrition day
outreach in the
last one month
Positive 13.5. Percentage
of
Anganwadis/UP
HCs reported to
have conducted
at least one
Village Health
Sanitation &
Nutrition day /
Urban Health
Sanitation &
Nutrition day
outreach in the
last one month
13.5. Percentage of
Anganwadis/UPHCs reported
to have conducted at least one
Village Health Sanitation &
Nutrition day / Urban Health
Sanitation & Nutrition day
outreach in the last one month
Health and
Nutrition
13.6) Proporti on
of Anganwadis
with own
buildings
Positive 13.6. Proporti on
of Anganwadis
with own
buildings
13.6. Proporti on of
Anganwadis with own
buildings
Health and
Nutrition
13.7) Percentage
of First Referral
Units (FRU) with
labour rooms and
obstetrics OT
NQAS certiﬁed
(meet
LaQShyaquidelin
es)
Positive 13.7. Percentage
of First Referral
Units (FRU)
with labour
rooms and
obstetrics OT
NQAS certiﬁed
(meet
LaQShyaquidelin
es)
13.7. Percentage of First
Referral Units (FRU) with
labour rooms and obstetrics
OT NQAS certiﬁed (meet
LaQShyaquidelines)
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL52 Health and
Nutrition
3.1) Percentage
of Pregnant
women having
severe anemia
treated, against
PW having severe
anemia tested
cases
Positive 3.1. Percentage
of Pregnant
women having
severe anemia
treated, against
PW having
severe anemia
tested cases
3.1. Percentage of Pregnant
women having severe anemia
treated, against PW having
severe anemia tested cases
Health and
Nutrition
3.2) Percentage
of pregnant
women tested for
Hemoglobin 4 or
more times in
respective ANCs
to total ANC
registration
Positive Data not
available in
March-Dec 2018
3.2. Percentage of pregnant
women tested for Hemoglobin
4 or more times in respective
ANCs to total ANC
registration
Health and
Nutrition
4.1) Sex Ratio at
birth
Positive 4.1. Sex Ratio at
birth
4.1. Sex Ratio at birth
Health and
Nutrition
4.2) Percentage
of institutional
deliveries to total
estimated
deliveries
Positive 4.2. Percentage
of institutional
deliveries to total
estimated
deliveries
4.2. Percentage of institutional
deliveries to total estimated
deliveries
Health and
Nutrition
6.1) Percentage
of newborns
breastfed within
one hour of birth
Positive 6.1. Percentage
of newborns
breastfed within
one hour of birth
6.1. Percentage of newborns
breastfed within one hour of
birth
Health and
Nutrition
6.2) Percentage
of low birth
weight babies
(less than 2500g )
Negative 6.2. Percentage
of low birth
weight babies
(less than 2500g)
6.2. Percentage of low birth
weight babies (less than 2500g )
Health and
Nutrition
6.3) Percentage
of live babies
weighed at birth
Positive 6.3. Percentage
of live babies
weighed at birth
6.3. Percentage of live babies
weighed at birth
Health and
Nutrition
7. Percentage of
underweight
children unde r
6 years
Negative 7. Percentage of
underweight
children unde r
6 years
7. Percentage of underweight
children under 6 years
regularly taking
Supplementary
Nutrition under
the ICDS
programme
Supplementary Nutrition under
the ICDS programme
Health and
Nutrition
2) Percentage of
pregnant women
Positive
regularly taking
Supplementary
Nutrition under
the ICDS
programme
2. Percentage of
pregnant women
2. Percentage of pregnant
women regularly taking
APPENDIX
Health and
Nutrition
Positive 5. Percentage of
deliveries at home
attended by an
SBA (Skilled Birth
Attendance)
trained health
worker to total
home deliveries
5. Percentage of deliveries at
home attended by an SBA
(Skilled Birth Attendance)
trained health worker to total
home deliveries
5. Percentage of
deliveries at home
attended by an
SBA (Skilled Birth
Attendance)
trained health
worker to total
home deliveries
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
53 Health and
Nutrition
8.1) Percentage
of stunted
children under 6
years
Negative Data not
available in
March-Dec 2018
Data not available in March
2020
Health and
Nutrition
8.2) Percentage
of children under
5 years with
Diarrhea treated
with ORS
Positive Data not
available in
March-Dec 2018
Data not available in March
2020
Health and
Nutrition
8.3) Percentage
of children under
5 years with
Diarrhea treated
with Zinc
Positive Data not
available in
March-Dec 2018
Data not available in March
2020
Health and
Nutrition
8.4) Percentage
of children under
5 years with
Acute Respiratory
Infections (ARI)
taken to a health
facility in the last
2 weeks
Positive Data not
available in
March-Dec 2018
Data not available in March
2020
Health and
Nutrition
9.1) Percentage
of Severe Acute
Malnourishment
(SAM) in children
under 6 years to
total children
under 6 years
Negative 9.1. Percentage
of Severe Acute
Malnourishment
(SAM) in children
under 6 years to
total children
under 6 years
9.1. Percentage of Severe Acute
Malnourishment (SAM) in
children under 6 years to total
children under 6 years
Health and
Nutrition
9.2) Percentage
of Moderate
Acute
Malnutrition
(MAM) in
children under 6
years to total
children under 6
years
Negative 9.2. Percentage
of Moderate
Acute
Malnutrition
(MAM) in
children under 6
years to total
children under 6
years
9.2. Percentage of Moderate
Acute Malnutrition (MAM) in
children under 6 years to total
children under 6 years

Skill
Development
1) Percentage of
youth certiﬁed in
short termor
long-term
training schemes
to no. of youth in
district in age
group 15-29*
Positive 7. Percentage of
youth certiﬁed in
short term or
long-term
training schemes
to no. of youth
in district in age
group 15-29*
7. Percentage of youth certiﬁed
in short term or long-term
training schemes to no. of
youth in district in age group
15-29*
2) Percentage of
certiﬁed youth
employed# to
no.
of youth
trained under
short term or
long-term training
Positive 8. Percentage of
certiﬁed youth
employed# to
no. of youth
trained under
short term or
long-term training
8. Percentage of certiﬁed youth
employed# to no. of youth
trained under short term or
long-term training
Skill
Development
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL54 Skill
Development
3) Number of
apprenticeships
completing to
total number of
trainees registered
on the portal
Positive 9. Number of
apprenticeships
completing to
total number of
trainees registered
on the portal
9. Number of apprenticeships
completing to total number of
trainees registered on the portal
Skill
Development
4) No. of people
certiﬁed under
Recognition of
Prior Learning to
non-formally
skilled workforce
Positive 10. No. of
people certiﬁed
under
R
ecognition of
Prior Learning to
non-formally
skilled workforce
10. No. of people certiﬁed
under Recognition of Prior
Learning to non-formally
skilled workforce
Skill
Development
5.1) Percentage
certiﬁed trained:
women
Positive 11.1. Percentage
certiﬁed trained:
women
11.1. Percentage certiﬁed
trained: women
Skill
Development*
5.2) Percentage
certiﬁed trained:
SC
Positive 11.2. Percentage
certiﬁed trained:
SC
11.2. Percentage certiﬁed
trained: SC
Skill
Development*
5.3) Percentage
certiﬁed trained:
ST
Positive 11.3. Percentage
certiﬁed trained:
ST
11.3. Percentage certiﬁed
trained: ST
Skill
Development*
5.4) Percentage
certiﬁed trained:
OBC
Positive 11.4. Percentage
certiﬁed trained:
OBC
11.4. Percentage certiﬁed
trained: OBC
Skill
Development*
5.5) Percentage
certiﬁed trained:
minorities
Positive 11.5. Percentage
certiﬁed trained:
minorities
11.5. Percentage certiﬁed
trained: minorities
Skill
Development*
5.6) Percentage
certiﬁed trained:
diferently abled
Positive 11.6. Percentage
certiﬁed trained:
diferently abled
11.6. Percentage certiﬁed
trained: diferently abled
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
55 APPENDIX 56
Table A.2: Ranking of districts based on change in net resilience since
March 2018 to March 2020
State District  Rank
Jharkhand Ranchi 1
Uttar Pradesh Chandauli 2
Jharkhand Simdega 3
Uttar Pradesh Sonbhadra 4
Madhya Pradesh Rajgarh 5
Assam Goalpara 6
Uttar Pradesh Fatehpur 7
Arunachal Pradesh Namsai 8
Karnataka Raichur 9
Jharkhand Godda 10
Assam Darrang 11
Bihar Muzafarpur 12
Odisha Nabarangapur 13
Bihar Araria 14
Bihar Aurangabad 15
Odisha Rayagada 16
Odisha Koraput 17
Madhya Pradesh Guna 18
Uttar Pradesh Balrampur 19
Manipur Chandel 20
Jharkhand Khunti 21
Bihar Sheikhpura 22
Telangana Bhoopalapalli (Warangal) 23
Rajasthan Karauli 24
Uttar Pradesh Chitrakoot 25
Uttar Pradesh Shrawasti 26
Assam Baksa 27
Jharkhand Latehar 28
Jharkhand Lohardaga 29
Jammu & Kashmir Kupwara 30
Uttarakhand Hardwar 31
Odisha Dhenkanal 32
Rajasthan Sirohi 33
Madhya Pradesh Vidisha 34
Bihar Jamui 35
Mizoram Mamit 36
Tamil Nadu Virudhunagar 37
Meghalaya Ribhoi 38
Uttar Pradesh Siddharthnagar 39
Madhya Pradesh Singrauli 40
Assam Dhubri 41
Bihar Begusarai 42
Jharkhand Pakur 43
Assam Hailakandi 44
Jharkhand Giridih 45
Odisha Gajapati 46
Madhya Pradesh Damoh 47
Uttarakhand Udham Singh Nagar 48
Jharkhand Chatra 49
Kerala Wayanad 50
Tamil Nadu Ramanathapuram 51
Karnataka Yadgir 52
Jharkhand Purbi Singhbhum 53
Jammu & Kashmir Baramula 54
Assam Barpeta 55
Chhattisgarh Sukma 56
Jharkhand Dumka 57
Odisha Kandhamal 58
Punjab Moga 59
Jharkhand Palamu 60
Bihar Purnia 61
Jharkhand Bokaro 62
Odisha Kalahandi 63
Bihar Banka 64
Assam Udalguri 65
Haryana Mewat 66
Jharkhand Hazaribagh 67
Bihar Khagaria 68
Chhattisgarh Rajnandgaon 69
Chhattisgarh Mahasamund 70
Chhattisgarh Uttar Bastar Kanker 71
Andhra Pradesh Visakhapatnam 72
Punjab Firozpur 73
Bihar Katihar 74
Odisha Balangir 75
Odisha Nuapada 76
Telangana Bhadradri-Kothagudem 77
Gujarat Narmada 78
Chhattisgarh Korba 79
Maharashtra Osmanabad 80
Uttar Pradesh Bahraich 81
Andhra Pradesh Y.S.R. 82
Jharkhand Garhwa 83
Gujarat Dohad 84
Himachal Pradesh Chamba 85
Tripura Dhalai 86
Sikkim West District 87
Bihar Gaya 88
Madhya Pradesh Barwani 89
Chhattisgarh Kondagaon 90
Andhra Pradesh Vizianagaram 91
Chhattisgarh Narayanpur 92
Rajasthan Dhaulpur 93
Jharkhand Ramgarh 94
Chhattisgarh Bastar 95
Rajasthan Jaisalmer 96
Maharashtra Nandurbar 97
Madhya Pradesh Khandwa (East Nimar) 98
Rajasthan Baran 99
Jharkhand Sahibganj 100
Maharashtra Gadchiroli 101
Telangana Asifabad (Adilabad) 102
Odisha Malkangiri 103
Maharashtra Washim 104
Madhya Pradesh Chhatarpur 105
Jharkhand Pashchimi Singhbhum 106
Bihar Sitamarhi 107
Jharkhand Gumla 108
Chhattisgarh Dakshin Bastar Dantewada 109
Chhattisgarh Bijapur 110
Bihar Nawada 111
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL State District  Rank
Jharkhand Ranchi 1
Uttar Pradesh Chandauli 2
Jharkhand Simdega 3
Uttar Pradesh Sonbhadra 4
Madhya Pradesh Rajgarh 5
Assam Goalpara 6
Uttar Pradesh Fatehpur 7
Arunachal Pradesh Namsai 8
Karnataka Raichur 9
Jharkhand Godda 10
Assam Darrang 11
Bihar Muzafarpur 12
Odisha Nabarangapur 13
Bihar Araria 14
Bihar Aurangabad 15
Odisha Rayagada 16
Odisha Koraput 17
Madhya Pradesh Guna 18
Uttar Pradesh Balrampur 19
Manipur Chandel 20
Jharkhand Khunti 21
Bihar Sheikhpura 22
Telangana Bhoopalapalli (Warangal) 23
Rajasthan Karauli 24
Uttar Pradesh Chitrakoot 25
Uttar Pradesh Shrawasti 26
Assam Baksa 27
Jharkhand Latehar 28
Jharkhand Lohardaga 29
Jammu & Kashmir Kupwara 30
Uttarakhand Hardwar 31
Odisha Dhenkanal 32
Rajasthan Sirohi 33
Madhya Pradesh Vidisha 34
Bihar Jamui 35
Mizoram Mamit 36
Tamil Nadu Virudhunagar 37
Meghalaya Ribhoi 38
Uttar Pradesh Siddharthnagar 39
Madhya Pradesh Singrauli 40
Assam Dhubri 41
Bihar Begusarai 42
Jharkhand Pakur 43
Assam Hailakandi 44
Jharkhand Giridih 45
Odisha Gajapati 46
Madhya Pradesh Damoh 47
Uttarakhand Udham Singh Nagar 48
Jharkhand Chatra 49
Kerala Wayanad 50
Tamil Nadu Ramanathapuram 51
Karnataka Yadgir 52
Jharkhand Purbi Singhbhum 53
Jammu & Kashmir Baramula 54
Assam Barpeta 55
Chhattisgarh Sukma 56
Jharkhand Dumka 57
Odisha Kandhamal 58
Punjab Moga 59
Jharkhand Palamu 60
Bihar Purnia 61
Jharkhand Bokaro 62
Odisha Kalahandi 63
Bihar Banka 64
Assam Udalguri 65
Haryana Mewat 66
Jharkhand Hazaribagh 67
Bihar Khagaria 68
Chhattisgarh Rajnandgaon 69
Chhattisgarh Mahasamund 70
Chhattisgarh Uttar Bastar Kanker 71
Andhra Pradesh Visakhapatnam 72
Punjab Firozpur 73
Bihar Katihar 74
Odisha Balangir 75
Odisha Nuapada 76
Telangana Bhadradri-Kothagudem 77
Gujarat Narmada 78
Chhattisgarh Korba 79
Maharashtra Osmanabad 80
Uttar Pradesh Bahraich 81
Andhra Pradesh Y.S.R. 82
Jharkhand Garhwa 83
APPENDIX
Gujarat Dohad 84
Himachal Pradesh Chamba 85
Tripura Dhalai 86
Sikkim West District 87
Bihar Gaya 88
Madhya Pradesh Barwani 89
Chhattisgarh Kondagaon 90
Andhra Pradesh Vizianagaram 91
Chhattisgarh Narayanpur 92
Rajasthan Dhaulpur 93
Jharkhand Ramgarh 94
Chhattisgarh Bastar 95
Rajasthan Jaisalmer 96
Maharashtra Nandurbar 97
Madhya Pradesh Khandwa (East Nimar) 98
Rajasthan Baran 99
Jharkhand Sahibganj 100
Maharashtra Gadchiroli 101
Telangana Asifabad (Adilabad) 102
Odisha Malkangiri 103
Maharashtra Washim 104
Madhya Pradesh Chhatarpur 105
Jharkhand Pashchimi Singhbhum 106
Bihar Sitamarhi 107
Jharkhand Gumla 108
Chhattisgarh Dakshin Bastar Dantewada 109
Chhattisgarh Bijapur 110
Bihar Nawada 111
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
57 State District  Rank
Jharkhand Ranchi 1
Uttar Pradesh Chandauli 2
Jharkhand Simdega 3
Uttar Pradesh Sonbhadra 4
Madhya Pradesh Rajgarh 5
Assam Goalpara 6
Uttar Pradesh Fatehpur 7
Arunachal Pradesh Namsai 8
Karnataka Raichur 9
Jharkhand Godda 10
Assam Darrang 11
Bihar Muzafarpur 12
Odisha Nabarangapur 13
Bihar Araria 14
Bihar Aurangabad 15
Odisha Rayagada 16
Odisha Koraput 17
Madhya Pradesh Guna 18
Uttar Pradesh Balrampur 19
Manipur Chandel 20
Jharkhand Khunti 21
Bihar Sheikhpura 22
Telangana Bhoopalapalli (Warangal) 23
Rajasthan Karauli 24
Uttar Pradesh Chitrakoot 25
Uttar Pradesh Shrawasti 26
Assam Baksa 27
Jharkhand Latehar 28
Jharkhand Lohardaga 29
Jammu & Kashmir Kupwara 30
Uttarakhand Hardwar 31
Odisha Dhenkanal 32
Rajasthan Sirohi 33
Madhya Pradesh Vidisha 34
Bihar Jamui 35
Mizoram Mamit 36
Tamil Nadu Virudhunagar 37
Meghalaya Ribhoi 38
Uttar Pradesh Siddharthnagar 39
Madhya Pradesh Singrauli 40
Assam Dhubri 41
Bihar Begusarai 42
Jharkhand Pakur 43
Assam Hailakandi 44
Jharkhand Giridih 45
Odisha Gajapati 46
Madhya Pradesh Damoh 47
Uttarakhand Udham Singh Nagar 48
Jharkhand Chatra 49
Kerala Wayanad 50
Tamil Nadu Ramanathapuram 51
Karnataka Yadgir 52
Jharkhand Purbi Singhbhum 53
Jammu & Kashmir Baramula 54
Assam Barpeta 55
Chhattisgarh Sukma 56
Jharkhand Dumka 57
Odisha Kandhamal 58
Punjab Moga 59
Jharkhand Palamu 60
Bihar Purnia 61
Jharkhand Bokaro 62
Odisha Kalahandi 63
Bihar Banka 64
Assam Udalguri 65
Haryana Mewat 66
Jharkhand Hazaribagh 67
Bihar Khagaria 68
Chhattisgarh Rajnandgaon 69
Chhattisgarh Mahasamund 70
Chhattisgarh Uttar Bastar Kanker 71
Andhra Pradesh Visakhapatnam 72
Punjab Firozpur 73
Bihar Katihar 74
Odisha Balangir 75
Odisha Nuapada 76
Telangana Bhadradri-Kothagudem 77
Gujarat Narmada 78
Chhattisgarh Korba 79
Maharashtra Osmanabad 80
Uttar Pradesh Bahraich 81
Andhra Pradesh Y.S.R. 82
Jharkhand Garhwa 83
Gujarat Dohad 84
Himachal Pradesh Chamba 85
Tripura Dhalai 86
Sikkim West District 87
Bihar Gaya 88
Madhya Pradesh Barwani 89
Chhattisgarh Kondagaon 90
Andhra Pradesh Vizianagaram 91
Chhattisgarh Narayanpur 92
Rajasthan Dhaulpur 93
Jharkhand Ramgarh 94
Chhattisgarh Bastar 95
Rajasthan Jaisalmer 96
Maharashtra Nandurbar 97
Madhya Pradesh Khandwa (East Nimar) 98
Rajasthan Baran 99
Jharkhand Sahibganj 100
Maharashtra Gadchiroli 101
Telangana Asifabad (Adilabad) 102
Odisha Malkangiri 103
Maharashtra Washim 104
Madhya Pradesh Chhatarpur 105
Jharkhand Pashchimi Singhbhum 106
Bihar Sitamarhi 107
Jharkhand Gumla 108
Chhattisgarh Dakshin Bastar Dantewada 109
Chhattisgarh Bijapur 110
Bihar Nawada 111
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
58 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL 59
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
APPENDIX
Table A.3: List of Aspirational Districts (Treatment Group for Difference in
Difference Evaluation) S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL60 S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
61
66 Odisha Cuttack
67 Odisha Puri
68 Odisha Khordha
69 Odisha Sambalpur
70 Odisha Ganjam
71 Odisha Keonjhar
72 Odisha Baleshwar
73 Odisha Mayurbhanj
74 Odisha Nayagarh
75 Punjab Tarn Taran
76 Punjab Faridkot
77 Rajasthan Pratapgarh
78 Rajasthan Udaipur
79 Rajasthan Jodhpur
80 Rajasthan Bikaner
81 Rajasthan Kota
82 Sikkim East
83 Tamil Nadu Dharmapuri
84 Tamil Nadu Thiruvarur
85 Telangana Medak
86 Telangana Hyderabad
87 Telangana Nalgonda
88 Telangana Jogulamba Gadwal
89 Tripura North Tripura
90 Uttar Pradesh Kanpur Nagar
91 Uttar Pradesh Ghaziabad
92 Uttar Pradesh Sambhal
93 Uttar Pradesh Kashi Ram Nagar
94 Uttar Pradesh Gonda
95 Uttar Pradesh Barabanki
96 Uttar Pradesh Farrukhabad
97 Uttar Pradesh Faizabad
98 Uttarakhand Tehri Garhwal
99 Uttarakhand Champawat
100 Uttar Pradesh Etah
101 Uttar Pradesh Rampur
102 Uttar Pradesh Hardoi
103 Uttar Pradesh Lakhimpur Kheri
104 Uttar Pradesh Moradabad
105 Odisha Jharsuguda
106 Odisha Anugul
107 Odisha Jagatsinghpur
108 Odisha Deogarh
109 Odisha Jajapur
110 Chhattisgarh Bilaspur
111 Chhattisgarh Koriya
112 Chhattisgarh Raipur
113 Chhattisgarh Jashpur
Table A.4: Control Group for DiD approach for Health and Nutrition Sector
S.no State District
1 Andhra Pradesh Srikakulam
2 Andhra Pradesh Prakasam
3 Andhra Pradesh East Godavari
4 Arunachal Pradesh Dibang Valley
5 Assam Chirang
6 Assam Dima Hasao
7 Assam Kokrajhar
8 Assam Karimganj
9 Assam Sonitpur
10 Assam Bongaigaon
11 Assam Marigaon
12 Bihar Darbhanga
13 Bihar West Champaran
14 Bihar Jehanabad
15 Bihar Saran
16 Bihar Sheohar
17 Bihar Supaul
18 Bihar Saharsa
19 Bihar Bhagalpur
20 Bihar Kaimur Bhabua
21 Bihar East Champaran
22 Bihar Patna
23 Bihar Arwal
24 Bihar Vaishali
25 Chhattisgarh Surajpur
26 Chhattisgarh Bemetra
27 Chhattisgarh Baloda Bazar
28 Chhattisgarh Kawardha
29 Chhattisgarh Balod
30 Chhattisgarh Surguja
31 Chhattisgarh Balrampur
32 Chhattisgarh Durg
33 Chhattisgarh Gariyaband
34 Chhattisgarh Janjgir Champa
35 Gujarat Gir Somnath
36 Gujarat Anand
37 Haryana Palwal
38 Himachal Pradesh Kangra
39 Jammu & Kashmir Doda
40 Jammu & Kashmir Kishtwar
41 Jharkhand Dhanbad
42 Jharkhand Kodarma
43 Jharkhand Deoghar
44 Jharkhand Saraikela
45 Jharkhand Jamtara
46 Karnataka Bidar
47 Karnataka Davanagere
48 Kerala Kannur
49 Madhya Pradesh Alirajpur
50 Madhya Pradesh Burhanpur
51 Madhya Pradesh Jhabua
52 Madhya Pradesh Sheopur
53 Madhya Pradesh Morena
54 Madhya Pradesh Satna
55 Madhya Pradesh Harda
56 Madhya Pradesh Betul
57 Maharashtra Brihan Mumbai
58 Maharashtra Nashik
59 Maharashtra Thane
60 Maharashtra Chandrapur
61 Manipur Ukhrul
62 Meghalaya East Jaintia Hills
63 Mizoram Saiha
64 Nagaland Tuensang
65 Odisha Sundargarh
APPENDIX S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
66 Odisha Cuttack
67 Odisha Puri
68 Odisha Khordha
69 Odisha Sambalpur
70 Odisha Ganjam
71 Odisha Keonjhar
72 Odisha Baleshwar
73 Odisha Mayurbhanj
74 Odisha Nayagarh
75 Punjab Tarn Taran
76 Punjab Faridkot
77 Rajasthan Pratapgarh
78 Rajasthan Udaipur
79 Rajasthan Jodhpur
80 Rajasthan Bikaner
81 Rajasthan Kota
82 Sikkim East
83 Tamil Nadu Dharmapuri
84 Tamil Nadu Thiruvarur
85 Telangana Medak
86 Telangana Hyderabad
87 Telangana Nalgonda
88 Telangana Jogulamba Gadwal
89 Tripura North Tripura
90 Uttar Pradesh Kanpur Nagar
91 Uttar Pradesh Ghaziabad
92 Uttar Pradesh Sambhal
93 Uttar Pradesh Kashi Ram Nagar
94 Uttar Pradesh Gonda
95 Uttar Pradesh Barabanki
96 Uttar Pradesh Farrukhabad
97 Uttar Pradesh Faizabad
98 Uttarakhand Tehri Garhwal
99 Uttarakhand Champawat
100 Uttar Pradesh Etah
101 Uttar Pradesh Rampur
102 Uttar Pradesh Hardoi
103 Uttar Pradesh Lakhimpur Kheri
104 Uttar Pradesh Moradabad
105 Odisha Jharsuguda
106 Odisha Anugul
107 Odisha Jagatsinghpur
108 Odisha Deogarh
109 Odisha Jajapur
110 Chhattisgarh Bilaspur
111 Chhattisgarh Koriya
112 Chhattisgarh Raipur
113 Chhattisgarh Jashpur
S.no State District
1 Andhra Pradesh Srikakulam
2 Andhra Pradesh Prakasam
3 Andhra Pradesh East Godavari
4 Arunachal Pradesh Dibang Valley
5 Assam Chirang
6 Assam Dima Hasao
7 Assam Kokrajhar
8 Assam Karimganj
9 Assam Sonitpur
10 Assam Bongaigaon
11 Assam Marigaon
12 Bihar Darbhanga
13 Bihar West Champaran
14 Bihar Jehanabad
15 Bihar Saran
16 Bihar Sheohar
17 Bihar Supaul
18 Bihar Saharsa
19 Bihar Bhagalpur
20 Bihar Kaimur Bhabua
21 Bihar East Champaran
22 Bihar Patna
23 Bihar Arwal
24 Bihar Vaishali
25 Chhattisgarh Surajpur
26 Chhattisgarh Bemetra
27 Chhattisgarh Baloda Bazar
28 Chhattisgarh Kawardha
29 Chhattisgarh Balod
30 Chhattisgarh Surguja
31 Chhattisgarh Balrampur
32 Chhattisgarh Durg
33 Chhattisgarh Gariyaband
34 Chhattisgarh Janjgir Champa
35 Gujarat Gir Somnath
36 Gujarat Anand
37 Haryana Palwal
38 Himachal Pradesh Kangra
39 Jammu & Kashmir Doda
40 Jammu & Kashmir Kishtwar
41 Jharkhand Dhanbad
42 Jharkhand Kodarma
43 Jharkhand Deoghar
44 Jharkhand Saraikela
45 Jharkhand Jamtara
46 Karnataka Bidar
47 Karnataka Davanagere
48 Kerala Kannur
49 Madhya Pradesh Alirajpur
50 Madhya Pradesh Burhanpur
51 Madhya Pradesh Jhabua
52 Madhya Pradesh Sheopur
53 Madhya Pradesh Morena
54 Madhya Pradesh Satna
55 Madhya Pradesh Harda
56 Madhya Pradesh Betul
57 Maharashtra Brihan Mumbai
58 Maharashtra Nashik
59 Maharashtra Thane
60 Maharashtra Chandrapur
61 Manipur Ukhrul
62 Meghalaya East Jaintia Hills
63 Mizoram Saiha
64 Nagaland Tuensang
65 Odisha Sundargarh
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL62 S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
S.no State District
1 Andhra Pradesh Chittoor
2 Andhra Pradesh Sri Potti Sriramulu Nellore
3 Andhra Pradesh Kurnool
4 Arunachal Pradesh East Kameng
5 Assam Kokrajhar
6 Assam Karimganj
7 Assam Bongaigaon
8 Assam Tinsukia
9 Assam Dima Hasao
10 Assam Sonitpur
11 Assam Nalbari
12 Bihar Purba Champaran
13 Bihar Darbhanga
14 Bihar Siwan
15 Bihar Madhubani
16 Bihar Saharsa
17 Bihar Madhepura
18 Bihar Jehanabad
19 Bihar Supaul
20 Bihar Gopalganj
21 Bihar Munger
22 Bihar Kaimur (Bhabua)
23 Bihar Pashchim Champaran
24 Bihar Bhagalpur
25 Chhattisgarh Balrampur
26 Chhattisgarh Baloda Bazar
27 Chhattisgarh Bemetara
28 Chhattisgarh Surajpur
29 Chhattisgarh Balod
30 Chhattisgarh Mungeli
31 Chhattisgarh Jashpur
32 Chhattisgarh Gariyaband
33 Chhattisgarh Bilaspur
34 Chhattisgarh Janjgir - Champa
35 Gujarat Mahisagar
36 Gujarat Dawarka Devbhoomi
37 Haryana Jind
38 Himachal Pradesh Lahul & Spiti
39 Jammu & Kashmir Srinagar
40 Jammu & Kashmir Punch
41 Jharkhand Dhanbad
42 Jharkhand Jamtara
43 Jharkhand Kodarma
44 Jharkhand Saraikela-Kharsawan
45 Jharkhand Deoghar
46 Karnataka Chikkaballapura
47 Karnataka Bidar
48 Kerala Malappuram
49 Madhya Pradesh Bhind
50 Madhya Pradesh Morena
51 Madhya Pradesh Sheopur
52 Madhya Pradesh Tikamgarh
53 Madhya Pradesh Datia
54 Madhya Pradesh Agar Malwa
55 Madhya Pradesh Panna
56 Madhya Pradesh Shivpuri
57 Maharashtra Parbhani
58 Maharashtra Hingoli
59 Maharashtra Buldana
60 Maharashtra Bid
61 Manipur Tamenglong
62 Meghalaya North Garo Hills
63 Mizoram Lawngtlai
64 Nagaland Mon
65 Odisha Kendrapara
66 Odisha Ganjam
67 Odisha Bargarh
68 Odisha Mayurbhanj
69 Odisha Kendujhar
70 Odisha Bhadrak
71 Odisha Nayagarh
72 Odisha Debagarh
73 Odisha Jajapur
74 Odisha Baleshwar
75 Punjab Pathankot
76 Punjab Gurdaspur
77 Rajasthan Dausa
78 Rajasthan Bikaner
79 Rajasthan Churu
80 Rajasthan Nagaur
81 Rajasthan Jalor
82 Sikkim North District
83 Tamil Nadu Ariyalur
84 Tamil Nadu Dharmapuri
85 Telangana Nalgonda
86 Telangana Mahbubnagar
87 Telangana Medak
88 Tripura Sepahijala
89 Uttar Pradesh Hapur
90 Uttar Pradesh Kushinagar
91 Uttar Pradesh Auraiya
92 Uttar Pradesh Moradabad
93 Uttar Pradesh Muzafarnagar
94 Uttar Pradesh Sambhal
95 Uttar Pradesh Deoria
96 Uttar Pradesh Shamli
97 Uttarakhand Chamoli
98 Uttarakhand Bageshwar
99 Uttar Pradesh Baghpat
100 Uttar Pradesh Azamgarh
101 Uttar Pradesh Budaun
102 Uttar Pradesh Sant Kabir Nagar
103 Uttar Pradesh Etawah
104 Odisha Cuttack
105 Odisha Sundargarh
106 Odisha Jagatsinghapur
107 Odisha Anugul
108 Odisha Puri
109 Chhattisgarh Koriya
110 Chhattisgarh Surguja
111 Chhattisgarh Raigarh
112 Chhattisgarh Kabeerdham
Table A.5: Control Group for DiD approach for Health and Nutrition Sector
APPENDIX63 S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
S.no State District
1 Andhra Pradesh Chittoor
2 Andhra Pradesh Sri Potti Sriramulu Nellore
3 Andhra Pradesh Kurnool
4 Arunachal Pradesh East Kameng
5 Assam Kokrajhar
6 Assam Karimganj
7 Assam Bongaigaon
8 Assam Tinsukia
9 Assam Dima Hasao
10 Assam Sonitpur
11 Assam Nalbari
12 Bihar Purba Champaran
13 Bihar Darbhanga
14 Bihar Siwan
15 Bihar Madhubani
16 Bihar Saharsa
17 Bihar Madhepura
18 Bihar Jehanabad
19 Bihar Supaul
20 Bihar Gopalganj
21 Bihar Munger
22 Bihar Kaimur (Bhabua)
23 Bihar Pashchim Champaran
24 Bihar Bhagalpur
25 Chhattisgarh Balrampur
26 Chhattisgarh Baloda Bazar
27 Chhattisgarh Bemetara
28 Chhattisgarh Surajpur
29 Chhattisgarh Balod
30 Chhattisgarh Mungeli
31 Chhattisgarh Jashpur
32 Chhattisgarh Gariyaband
33 Chhattisgarh Bilaspur
34 Chhattisgarh Janjgir - Champa
35 Gujarat Mahisagar
36 Gujarat Dawarka Devbhoomi
37 Haryana Jind
38 Himachal Pradesh Lahul & Spiti
39 Jammu & Kashmir Srinagar
40 Jammu & Kashmir Punch
41 Jharkhand Dhanbad
42 Jharkhand Jamtara
43 Jharkhand Kodarma
44 Jharkhand Saraikela-Kharsawan
45 Jharkhand Deoghar
46 Karnataka Chikkaballapura
47 Karnataka Bidar
48 Kerala Malappuram
49 Madhya Pradesh Bhind
50 Madhya Pradesh Morena
51 Madhya Pradesh Sheopur
52 Madhya Pradesh Tikamgarh
53 Madhya Pradesh Datia
54 Madhya Pradesh Agar Malwa
55 Madhya Pradesh Panna
56 Madhya Pradesh Shivpuri
57 Maharashtra Parbhani
58 Maharashtra Hingoli
59 Maharashtra Buldana
60 Maharashtra Bid
61 Manipur Tamenglong
62 Meghalaya North Garo Hills
63 Mizoram Lawngtlai
64 Nagaland Mon
65 Odisha Kendrapara
66 Odisha Ganjam
67 Odisha Bargarh
68 Odisha Mayurbhanj
69 Odisha Kendujhar
70 Odisha Bhadrak
71 Odisha Nayagarh
72 Odisha Debagarh
73 Odisha Jajapur
74 Odisha Baleshwar
75 Punjab Pathankot
76 Punjab Gurdaspur
77 Rajasthan Dausa
78 Rajasthan Bikaner
79 Rajasthan Churu
80 Rajasthan Nagaur
81 Rajasthan Jalor
82 Sikkim North District
83 Tamil Nadu Ariyalur
84 Tamil Nadu Dharmapuri
85 Telangana Nalgonda
86 Telangana Mahbubnagar
87 Telangana Medak
88 Tripura Sepahijala
89 Uttar Pradesh Hapur
90 Uttar Pradesh Kushinagar
91 Uttar Pradesh Auraiya
92 Uttar Pradesh Moradabad
93 Uttar Pradesh Muzafarnagar
94 Uttar Pradesh Sambhal
95 Uttar Pradesh Deoria
96 Uttar Pradesh Shamli
97 Uttarakhand Chamoli
98 Uttarakhand Bageshwar
99 Uttar Pradesh Baghpat
100 Uttar Pradesh Azamgarh
101 Uttar Pradesh Budaun
102 Uttar Pradesh Sant Kabir Nagar
103 Uttar Pradesh Etawah
104 Odisha Cuttack
105 Odisha Sundargarh
106 Odisha Jagatsinghapur
107 Odisha Anugul
108 Odisha Puri
109 Chhattisgarh Koriya
110 Chhattisgarh Surguja
111 Chhattisgarh Raigarh
112 Chhattisgarh Kabeerdham
ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL64 65
S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
Table A.6: Comparison of means of treatment and
control group for H&N Sector
Indicator AD (Treatment) Control
  2018 2018
PMJJBY enrolments per 1 Lakh population 1790.36 1646.82
PMSBY enrolments per 1 Lakh population 6815.16 6686.75
APY beneﬁciaries  per 1 Lakh population 591.1 588.3
% of Account seeded with Aadhaar 77.07 75.75
PMJDY Accounts opened per lakh of population 31100.5 28371.56
Table A.7: Comparison of means of treatment and
control group for FI Sector
Indicator AD (Treatment) Control
  2018 2018
Percentage of Pregnant Women receiving four or   66.86 65.46
more antenatal care check-ups against total ANC
registrations
Percentage of ANC registered within the ﬁrst trimester  67.46 61.67
against total ANC registrations
Percentage of Pregnant women having severe  41.2 29.6
anaemia treated against PW having severe anaemia
tested cases
Sex Ratio at birth ((Female Live Births/ Male   35.6 26.4
Live Births) *1000)
Percentage of institutional deliveries out of total 87.2 88.88
estimated deliveries
Percentage of home deliveries attended by an SBA   96.09 94.02
(Skilled Birth Attendance) trained health worker out
of total home deliveries
Percentage of new-borns breastfed within  11.47 11.77
one hour of birth
Percentage of low birth weight babies 93.58 89.22
(Less than 2500 grams)
Proportion of live babies weighed at birth 935.96 925.74
Percentage of children with Diarrhoea treated 18.2 15.1
APPENDIX S.no State District
1 Jammu & Kashmir Kupwara
2 Jammu & Kashmir Baramula
3 Himachal Pradesh Chamba
4 Punjab Moga
5 Uttarakhand Udham Singh Nagar
6 Uttarakhand Haridwar
7 Haryana Mewat
8 Rajasthan Dholpur
9 Rajasthan Karauli
10 Rajasthan Jaisalmer
11 Rajasthan Sirohi
12 Rajasthan Baran
13 Uttar Pradesh Chitrakoot
14 Uttar Pradesh Fatehpur
15 Uttar Pradesh Bahraich
16 Uttar Pradesh Shrawasti
17 Uttar Pradesh Balrampur
18 Uttar Pradesh Siddharthnagar
19 Uttar Pradesh Chandauli
20 Uttar Pradesh Sonebhadra
21 Bihar Sitamarhi
22 Bihar Araria
23 Bihar Purnia
24 Bihar Katihar
25 Bihar Muzafarpur
26 Bihar Begusarai
27 Bihar Khagaria
28 Bihar Banka
29 Bihar Sheikhpura
30 Bihar Aurangabad
31 Bihar Gaya
32 Bihar Nawada
33 Bihar Jamui
34 Sikkim West Sikkim
35 Nagaland Kiphire
36 Manipur Chandel
37 Mizoram Mamit
38 Tripura Dhalai
39 Meghalaya Ribhoi
40 Assam Goalpara
41 Assam Barpeta
42 Assam Hailakandi
43 Assam Baksa
44 Assam Darrang
45 Assam Udalguri
46 Jharkhand Garhwa
47 Jharkhand Chatra
48 Jharkhand Giridih
49 Jharkhand Godda
50 Jharkhand Sahibganj
51 Jharkhand Pakur
52 Jharkhand Bokaro
53 Jharkhand Lohardaga
54 Jharkhand Purbi Singhbhum
55 Jharkhand Palamu
56 Jharkhand Latehar
57 Jharkhand Hazaribagh
58 Jharkhand Ramgarh
59 Jharkhand Dumka
60 Jharkhand Ranchi
61 Jharkhand Khunti
62 Jharkhand Gumla
63 Jharkhand Simdega
64 Jharkhand Pashchimi Singhbhum
65 Odisha Dhenkanal
66 Odisha Gajapati
67 Odisha Kandhamal
68 Odisha Balangir
69 Odisha Kalahandi
70 Odisha Rayagada
71 Odisha Koraput
72 Odisha Malkangiri
73 Odisha Nawarangpur
74 Odisha Nuapada
75 Chhattisgarh Korba
76 Chhattisgarh Rajnandgaon
77 Chhattisgarh Mahasamund
78 Chhattisgarh Kanker
79 Chhattisgarh Narayanpur
80 Chhattisgarh Dantewada
81 Chhattisgarh Bijapur
109 Assam Dhubri
82 Madhya Pradesh Chhatarpur
83 Madhya Pradesh Damoh
84 Madhya Pradesh Barwani
85 Madhya Pradesh Rajgarh
86 Madhya Pradesh Vidisha
87 Madhya Pradesh Guna
88 Madhya Pradesh Singrauli
90 Gujarat DAHOD
91 Gujarat Narmada
92 Maharashtra Nandurbar
93 Maharashtra Washim
94 Maharashtra Gadchiroli
95 Maharashtra Osmanabad
96 Andhra Pradesh Vizianagaram
97 Andhra Pradesh Visakhapatnam
98 Andhra Pradesh Y.S.R. Kadapa
99 Karnataka Raichur
100 Karnataka Yadgir
101 Kerala Wayanad
102 Tamil Nadu Virudhunagar
103 Tamil Nadu Ramanathapuram
104 Punjab Firozpur
105 Chhattisgarh Bastar
106 Chhattisgarh Kondagaon
107 Chhattisgarh Sukma
108 Arunachal Pradesh Namsai
89 Madhya Pradesh Khandwa
110 Telangana Asifabad
111 Telangana Bhopapalli
112 Telangana Bhadradri Kothagudem
Notes
66 ASPIRATIONAL DISTRICTS PROGRAMME: AN APPRAISAL

Aspirational Districts Programme: An Appraisal