MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

MULTIDIMENSIONAL
POVERTY IN INDIA SINCE
2005-06
A DISCUSSION PAPER

Abstract
The National Multidimensional Poverty Index by NITI Aayog uses the internationally acclaimed
Alkire Foster methodology with a difference that National MPI covers 12 indicators while global
MPI covers 10 indicators. This analysis attempts to study the decline in poverty rates and numberof multidimensionally poor people across various time periods. The recent report on National
MPI was based on National Family Health Surveys 4 (2015-16) and 5 (2019-21). Owing to lack ofdata for the years between 2005-06 and 2015-16 and after 2019-21 concerning the incidence ofpoverty levels, headcount poverty ratios for 2013-14 and 2022-23 have been estimated basedon compound growth rate of the reduction in the incidence of poverty levels between 2005-06
and 2015-16 and 2015-16 and 2019-21 respectively. Multidimensional poverty in India was foundto decline from 29.17% in 2013-14 to 11.28% in 2022-23 with about 24.82 crore people escaping
poverty during this period. At the States’ level, Uttar Pradesh topped the list with 5.94 crore
people escaping poverty followed by Bihar at 3.77 crore and Madhya Pradesh at 2.30 crore. All 12
indicators of MPI have shown remarkable improvement during this period.

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MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Introduction
Traditionally, poverty has been measured by assessing monetary resources available for anindividual or household. The monetary metric generally used were household income orconsumption expenditure. However, this approach of measuring poverty has been criticized for not
capturing the multiple deprivations which may be faced by individuals in their lives. Development
not only constitutes economic progress, but also the process of expanding people’s capabilities
to convert resources into valuable outcomes.
The conventional measures of monetary poverty also suffer from several other limitations like:
1. Income, while necessary, is not a sufficient condition to ensure the overall well-being ofindividuals, as, income in some cases is spent on items which do not involve the welfare of thehousehold.
2. Due to unavailability of income estimates, monetary poverty measures often rely on consumption
expenditure. This may not accurately represent the household’s income, especially whenborrowed funds contribute to consumption. In such cases expenditure may surpass or fall shortof the actual income depending on borrowing and lending/ saving behavior of household.
3. Household welfare depends not only on its own spending but also on state expenditures forpeople’s well-being. Monetary poverty measurements overlook the effect of state interventions
like subsidized food grains, shelter, sanitation, free education and health, and nutritional
support.
Thus, the Multidimensional Poverty Index (MPI) is considered as a more direct and comprehensive
measure of people’s deprivation and poverty. MPI captures and reveals the outcome of economic
growth and development, income and its distribution and various development initiatives of theState.  Besides, it has been globally realised that non-monetary measures complement monetarymeasures to capture diverse dimensions of poverty.

Multidimensional Poverty Index
The global Multidimensional Poverty Index (MPI) is based on the of Alkire and Foster (AF)methodology that identifies people as poor or not poor based on a dual-cutoff counting method.
It follows the universally acknowledged metric designed to assess acute poverty, providing acomplementary perspective to conventional monetary-based measures of poverty assessment.
Global MPI uses ten indicators covering three areas namely health, education and standard ofliving. Health dimension includes Nutrition and Child & Adolescent Mortality indicators, education
dimension includes Years of Schooling and School Attendance indicators and standard of living
dimension includes 6 household specific indicators namely, housing, household assets, type of
Cooking Fuel, access to Sanitation, Drinking water and Electricity.
The MPI assesses poverty at the individual level. If a person is deprived in a third or more of ten(weighted) indicators, the global MPI identifies them as ‘MPI poor’.
The MPI is computed by multiplying the incidence of multidimensional poverty (H) and theintensity of poverty (A), denoted as MPI = H x A. Incidence (H) shows the percentage of people
who are multidimensionally poor, and Intensity (A) shows the percentage of weighted deprivations
the average multidimensionally poor person suffers from. The MPI value for a given population,
therefore, is the share of weighted deprivations faced by multidimensionally poor individuals
divided by the total population. Hence the MPI is known as the adjusted headcount ratio. MPI
includes both the share of people in multidimensional poverty and the extent to which they are
deprived.

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MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Constructing India’s Multidimensional Poverty Index
In its pursuit of addressing the intricacies of poverty, both NITI Aayog and its predecessor, thePlanning Commission, have systematically generated poverty estimates over time. Furthermore,
NITI Aayog has been entrusted with the task of formulating an indigenized index for measuringmulti dimensional poverty estimates as part of the Global Indices for Reform and Action (GIRG)
initiative.
The national MPI adopts the dual-cutoff approach of the AF methodology, consistent with themethodology used in the global MPI report. The national MPI retains all the ten indicators fromthe global MPI and incorporates two additional indicators, Maternal Health and Bank Accounts, inline with India’s national priorities.
India, comprising 36 States & Union Territories, and more than 700 administrative districts, requiresa refined policy tool for data-driven decision-making and the formulation of sectoral policies
at the sub-national level. In contrast to conventional monetary poverty assessments based on
consumption surveys, the national Multidimensional Poverty Index (MPI) delves into deprivations
up to the district level, providing a more nuanced perspective. This granular approach proves
essential in India’s diverse context, enabling targeted interventions to address acute poverty anduphold the principle of inclusivity, ensuring that “no one is left behind.” It also offers a methodfor determining overlapping deprivations in health, education, and standard of living, directly
influencing individuals’ quality of life and overall well-being.

Estimates of National Multidimensional Poverty
Multidimensional poverty for India for the years 2005-06, 2015-16, and 2019-21, was estimated using
data from the corresponding National Family Health Survey (NFHS) rounds (3 to 5). The estimates
are computed with due consideration to the harmonization of indicators across the aforementioned
survey rounds. The results are presented in Table 1 which shows the levels of incidence and intensity ofpoverty as well as MPI values across 2005-06, 2015-16, and 2019-21. More than half of India’s population(55%) was multidimensionally poor in year 2005-06 and the intensity of poverty was also as high as
55 per cent. The proportion of multi dimensionally poor persons in total population has fallen from55.34% in 2005-06 to 24.85% in 2015-16 and to 14.96% in 2019-21.
Table 1: India’s Headcount Ratio, Intensity and MPI
NFHS Year

Headcount Ratio (H)%Intensity (A)%MPI = H*A

2019-21

14.96

44.39

0.066

2015-16

24.85

47.14

0.117

2005-06#

55.34

54.96

0.304

#Based on estimations provided by Oxford Policy and Human Development Initiative (OPHI)

These estimates show that on one hand India has significantly reduced the proportion of multidimensional
poor people by 40.38 percentage points in roughly 15 years after 2005-06 while on the other hand
the Intensity of Poverty, which measures the average deprivation score among the multidimensionallypoor, reduced by 10.57 percentage points in the span of 15 years from 54.96% in in 2005-06 to 47.14%
in 2015-16 and further to 44.39% in 2019-21. This implies that extent of deprivation among the deprived
population is falling. As a result, MPI value, which subsumes headcount ratio and extent of deprivation,
witnessed reduction (improvement) from 0.304 to 0.117 in about 10 years after 2005-06. MPI reached
0.066 in the next 4.5 years to 2019-21.
It is interesting to find out whether the pace of decline in headcount poverty ratio, its severity andextent of multidimensional poverty (MPI) accelerated or decelerated over time. These changes canbe ascertained from the annual rate of decline in the three types of estimates presented in Table 2.

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MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Headcount ratio of poverty declined at the rate of 7.7 per cent between 2005-06 and 2015-16. The
pace of decline became much faster in the next phase i.e. between 2015-16 and 2019-20.
Severity of deprivation declined at a slightly lower rate between NFHS year 2015-16 and 2019-21
compared to the previous decade. However, the product of the two i.e. MPI, showed 31 per cent higher
rate of reduction after 2015-16 compared to 2005-06 to 2015-16. These results show a significantly
faster reduction in deprivation after 2015-16 as compared to the previous decade.
Table 2: Compounded annual rate of change (%) in Headcount Ratio, Intensity and MPI
NFHS Year

Headcount Ratio (H)Intensity (A)MPI = H*A

2015-16 to 2019-21-10.66-1.33-11.95

2005-06 to 2015-16

-7.69

-1.52

-9.11

Note: Assuming 10 years between 2005-06 and 2015-16 and 4.5 years between 2015-16 and 2019-21

The progress in MPI demonstrates India’s dedication to achieving the target 1.2 of Sustainable
Development Goal to reduce the proportion of men, women and children in all ages living in poverty
in all its dimensions at least to half by 2030.
The recently released report ‘National Multidimensional poverty: A Progress Review -2023’ by NITI
Aayog, based on the data of NFHS 4 and 5, shows that 13.5 crore people have escaped poverty
between 2015-16 and 2019-21. Poverty in rural India fell from 32.59% to 19.28% while in urban areas it
declined from 8.65% to 5.27%. Thus, rural population witnessed a larger decline in poverty as comparedto urban population.
1.

Significant reductions (improvement) were observed in all 12 indicators over the two time periods:
Notably, deprivations in sanitation decreased by 21.8 percentage points, and deprivations incooking fuel decreased by 14.6 percentage points between 2015-16 and 2019-21.

2. The overall decline in the Multidimensional Poverty Index (MPI) value can be predominantly
attributed to advances in nutrition, years of schooling, sanitation, and cooking fuel, although there
remains room for further enhancements.
3. The fastest reduction in the proportion of multidimensional poor was observed in the States of
Uttar Pradesh, Bihar, Madhya Pradesh, Odisha, and Rajasthan.
4. Bihar, the state with the highest MPI value in NFHS-4 (2015-16), experienced the largest decline in
the headcount ratio. Following closely, Madhya Pradesh and Uttar Pradesh demonstrated the next
largest reductions in the headcount ratio. In terms of the number of MPI-poor individuals, Uttar
Pradesh topped the list with 3.43 crore people escaping multidimensional poverty over the past
five years, followed by Bihar (2.25 crore) and Madhya Pradesh (1.36 crore).

Computing MPI for 2013-14 and 2022-23
A better understanding of the impact of various initiatives launched during the previous decade on
poverty and deprivation can be obtained by comparing the estimates of poverty and deprivationin year 2013-14 with year 2022-23 even though the actual estimates for 2015-16 and 201921 clearly point to acceleration in rate of reduction in MPI after 2015-16 compared to 2005-06 to
2015-16. This requires interpolation of estimates for the year 2013-14 and extrapolation for the year2022-23. Thus, 2013-14 figure was computed from the 2005-06 estimate using annual compound
rate of 7.69% reduction (Table 2). Similarly, 2022-23 estimate was projected by applying compound
rate of reduction achieved during 2015-16 to 2019-21. This method involves a lower percentage points
reduction in poverty each year which is consistent with experience of the real world. This assumption
entails a trajectory characterized by the same rate of decline rather than using a linear trend.
Based on the interpolation of the MPI estimates between 2005-06 and 2015-16 the headcount ratio forthe year 2013-14 comes to 29.17%. Similarly, the proportion of multidimensional poverty for the year2022-23 is projected to be 11.28% based on the trend rate of 10.66% decline per year between 2015-16

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MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

and 2019-21. With this, India is much ahead of the target of reducing poverty in all its dimensions byhalf, by the year 2030.

11.28%

2022-23 (Projected)

during the last
9 years

29.17%

Poverty
Headcount
Ratio

24.82 crore
individuals estimatedto have escaped
multidimensional
poverty during last
9 years

However, it is important to note that the reduction in poverty may differ, as various factors andexternalities can influence the pace and trajectory of poverty alleviation efforts. The pace of
reductionis generally
India likely
achieved faster when poverty levels are higher than when poverty levels are lower.
The method used for extrapolation, based on rate of decline rather than percentage point decline,
takes care of this to an extent. Besides, since part of the NFHS-5 data were collected before theAll fully reflect the impact of Covid on the
pandemic, the estimates presented in this paper may not(reducing
multishow
signiﬁcant signs of improvement
economy or the implications of subsequent government
interventions.

SDG
Target 1.2

12 indicators

dimensional poverty

ThebyWorlddefines the international poverty line at US $2.15 (in 2017 purchasing power
at leastBankhalf) muchparity[PPP]terms)a day to measure monetary poverty. The latest World Bank data indicatesahead of 2030
that the poverty headcount ratio at US $2.15 a day (2017 PPP) decreased from 18.73% in 2015 to
11.9% in 2021 in India (Poverty and Inequality Platform, World Bank). With the ongoing rate of
decline in India’s Multidimensional Poverty, the country is expected to reach single digit level ofpoverty by the year 2024-25.
Graph 1: Headcount Ratio (Proportion of population who are multidimensionally poor)

INDIA: HEADCOUNT RATIO% OF POPULATION WHO ARE MULTIDIMENSIONALLY POOR

2019-2114.96%

Steep decline in

2013-14 (Projected)

Consequently, the estimated absolute change in the headcount ratio over the 9-year period from2013-14 to 2022-23 is 17.89 percentage points, which is equivalent to an approximately 24.82 crore
individuals escaping multidimensional poverty.

60%

50%

55.34%

40%30%
29.17%
24.85%

20%

14.96%

10%

11.28%

0%

2005-06

2013-14 (PROJECTED)

2015-162019-212022-23 (PROJECTED)

5MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Multidimensional Poverty at State Level during the last 9 years

29.17%

Poverty
HeadcountRatio

2022-23 (Projected)

2019-2124.82 crore

The fastest reduction in the number of multidimensional poor was observed in the States of
Uttar Pradesh followed by Bihar, Madhya Pradesh, and Rajasthan. Uttar Pradesh registered theindividuals estimatedlargest decline in the number of poor with 5.94 crore people escaping multidimensional poverty
to have escaped
during the last nine years followed by Bihar at 3.77 crore and Madhya Pradesh at 2.30 crore. It ismultidimensional
also observed that the States with higher poverty incidence have witnessed higher reduction inpoverty during last
during the lastheadcount ratio poverty over the years, thus indicatingthat inter-state multidimensional povertyyears
9 years across various States have declined over9thedifferentials
years. The correlation between incidenceof poverty during 2013-14 and percentage point reduction in poverty by 2022-23 was 0.939.

11.28%

14.96%

Steep decline in

2013-14 (Projected)

Similar to the national level projections, estimates of multidimensional poverty for the years at
sub-national levels are presented in Graphs provided below. Estimate of projected headcountratios for the years 2013-14 and 2022-23 along with the number of people who escaped poverty
in various States/UTs is provided in Table 1 in the Annexure.

India likely achieved

SDG
Target 1.2(reducing multidimensional poverty
by at least half) muchahead of 2030

6

12 indicators

Allshow signiﬁcant signs of improvement

MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Graph 2: State-wise: Headcount Ratio

Statewise: Headcount Ratio
33.76

Bihar
Jharkhand

STATES

Chhattisgarh

16.37

Orissa15.68

Nagaland

15.43

Rajasthan

15.31

Arunachal Pradesh

13.76

Tripura13.11

West Bengal

11.89

Gujarat

11.66

Manipur

8.10

Maharashtra

7.81

Karnataka

7.58

Haryana

7.07

Andhra Pradesh

6.06

Telangana

5.88

Mizoram

5.30

Himachal Pradesh

4.93

Punjab

4.75
5.57

2.20

Tamil Nadu
Goa

0.84

Kerala

0.55
0.70

52.78
21.29

Lakshadweep

1.11
1.82

Puducherry

0.85
1.71

0

39.79

17.67

40.38

16.96

44.13
40.07

12.77

46.71
41.84
48.80

13.18
32.95

7.59

32.23
26.66
38.85
36.54
18.20
12.31
19.58
12.56

40.45

12.70
5.97

3.43
4.44
2.30

57.60

18.47

11.77

3.53

A&N Islands

60.67

4.76

3.52

64.15

11.88

4.80

Delhi

58.82
28.86

14.80

9.21

Chandigarh

63.84

25.16

9.78

DNH&DD
Ladakh

69.78

29.34

16.62

3.76

Jammu & Kashmir

60.78

29.90

2.60
3.82

Sikkim

69.44

32.65

24.23

9.67

Uttaranchal

69.52

36.57

19.35

Assam

62.54
37.68

20.63

Madhya Pradesh

74.04

32.54

22.93

Uttar Pradesh

78.28

42.10

27.79

Meghalaya

UTs

51.89

28.81

14.63

4.29

10

20

30

40

50

60

70

80

% of population who are multidimensionally poor

2019-212015-16

2005-06

7

MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Graph 3: State-wise Headcount Ratio (Projected)

State-wise Headcount Ratio (Projected)
26.59

Bihar

47.13

17.40

Uttar Pradesh

STATES

37.08

23.34

Jharkhand
Madhya Pradesh

15.01

Assam

14.47

Nagaland

11.76

Chhattisgarh

11.71

Tripura11.49

Odisha11.07

Rajasthan

10.77

Arunachal Pradesh

10.05

Gujarat

9.03

West Bengal

8.60

Karnataka

5.67

Maharashtra

5.48

Manipur

5.37

Haryana

5.30

Punjab

4.35

Andhra Pradesh

4.19

Himachal Pradesh

3.88

Mizoram

3.77

Telangana

3.76

Sikkim2.10

Tamil Nadu

1.43

Kerala

0.48
1.24
0.37

Goa
Delhi

2.97

Jammu & Kashmir

2.81

Chandigarh

2.62

Ladakh1.73

A&N Islands

1.63

Lakshadweep

0.84

Puducherry

0.58

0

42.59
41.57
36.97
29.82
35.42
20.94
34.28
33.86
29.11
21.53
25.98

6.92

Uttarakhand

DNH&DD

UTs

56.34

25.46

Meghalaya

20.85
16.55
18.06
20.53

10.14
12.47

6.07
7.16

5.15
6.06
5.64

10

20

30

40

% of population who are multidimensionally poor
2022-23 (Extrapolated value)
2013-14 (Interpolated value)

8

50

60

Percentage of population of India who are deprived ineach indicator
MULTIDIMENSIONAL
POVERTY IN INDIA SINCE 2005-06
74.40

Indicator-wise Performance

7.47

70.92
58.47

Percentage
ofof population
India
whoinarein each indicator
Percentage
population of Indiaof
who
are deprived
each deprived
indicator% of population deprived

70 31.52
60
50
40

Nutrition30% of Population
Deprived% of population deprived

80
37.60

57.47

70
60
50
40

74.40

33.00

19.17

0

21.27

30.93 70.92

30.93

11.4024.98 6.40
22.58
21.27
19.17
5.27

4.84
10
2.69
33.00
2.06
Child & 31.52
Maternal
Years of
Nutrition

SchoolingChild &
Adolescent
Mortality

Maternal

11.40

SchoolAttendance

43.90
21.34

Years of

24.98
HealthSchooling
22.58

10.92

6.40
5.27

Cooking Fuel

SchoolAttendance

2005-0619.17(NFHS-3) (NFHS -3)
2005-06
13.86

Cooking Fuel

58.11

Sanitation
Sanitation

21.27
2015-16(NFHS2015-16 (NFHS4) 4)

45.65 46.87

51.88
10.92

55.93
13.97

41.37

32.95

13.86

Health

32.95
55.93

58.4721.34

43.90

13.86
33.00

31.52

20
10

74.40
51.88

22.58 24.98
37.60

41.37

70.92
58.47

57.47

4.84
30
2.69
20
2.06
37.60
Adolescent 0
Mortality

45.65 46.87

43.90

80

58.1

55.93

51.88

12.16

7.32

10.16
45.65

3.27
13.97

12.16

10.16

7.32

Drinking
Water

Drinking3.2730.93
Electricity
Water
Electricity

Housing

Assets

41.

9.66

32.95
Housing

3.69

AssetsBank

Bank Account

21.34

2019-21(NFHS- 5)
2019-21 (NFHS5)

10.92
12.16
4.84
6.40
11.40
Attempt has2.69
also been made to examine the indicator-wise performance between the three
7.32
5.27 across various domains. The broad
NFHS surveys toanalyse the reduction in deprivation
trend3.27
of
2.06
headcount ratio for the 12 indicators across three survey years using National MPI methodology
is presented
in theNutritionChild& following
Maternal chart:
Years of
SchoolCooking Fuel Sanitation
Drinking
Electricity
Housing
Adolescent

HealthSchooling

Attendance

Water

The graph Mortality
below recapitulates the progress in removing deprivation of India’s population. Within
the standard of living dimension, highest levels of deprivation in 2005-06 were found in indicators
(NFHS
-3)
2015-164) (58.11%)2019-21(NFHS5)such as Cooking Fuel (74.40%),2005-06
Sanitation(70.92%),andBank (NFHSAccount. On theotherhand, Child & Adolescent Mortality (4.84%), School Attendance (21.27%), and Drinking Water(21.34%) had the lowest levels of deprivation in 2005-06. The latest estimates show that Cooking
Fuel (43.90%) and Housing (41.37%) continue to have the highest deprivation, while indicators
like Child & Adolescent Mortality (2.06%), Electricity (3.27%), and Bank Account (3.69%) maintainthe lowest deprivation levels based on NFHS-5 (2019-21).

Government Interventions to reduce MPI
The Government of India has made significant strides in enhancing the quality of life for millions
of individuals, with a focus on the SDG 1.2 target of halving poverty in all its dimensions. Initiatives
such as Poshan Abhiyan and Anaemia Mukt Bharat have played a crucial role in addressing reachto health facilities, resulting in a substantial reduction in deprivation.
Government of India operates one of the largest food security programme in the world in theform of Targeted Public Distribution System through 5.4 lakh fair price shops.  Under National
Food Security Act (NFSA), foodgrains are provided to 81.35 crore beneficiaries covering 75%
population in rural areas and 50% population in urban areas.  Recently, Government has decided
to continue providing free food grains to NFSA beneficiaries under Pradhan Mantri Garib Kalyan
Anna Yojana for a period of five years w.e.f. 1st January, 2024.
Mission Poshan 2.0 and Saksham Anganwadi have significantly contributed to fostering a healthier
India, impacting millions of lives through key schemes like POSHAN Abhiyaan, Anganwadi Services,and the Scheme for Adolescent Girls. Poshan Vatikas or Nutri-gardens have been establishednationwide to facilitate easy and affordable access to fruits, vegetables, medicinal plants, andherbs. Through around 14 lakh Anganwadi centres, the programme is touching lives of nearly 10

9MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

crore beneficiaries which includes around 8.87 crore children under 6 years and 1.1 crore pregnant andlactating mothers. With hot cooked meals given to 93 lakh beneficiaries and take- home ration to 2.7
crore beneficiaries, the programme has immensely contributed to a healthy India.
Further, Pradhan Mantri Poshan Shakti Nirman (PM POSHAN), a flagship programme of Government ofIndia, is the largest School feeding Scheme in the world to address the twin problems of improving thenutritional status and school enrolment of children. Under the Scheme, there is provision of hot cooked
meal to children of pre-schools or Bal Vatika (before class I) in primary schools and 11.80 crore childrenof classes I to VIII studying in 11.20 lakh schools.
Another programme instrumental in improving maternal health is the Pradhan Mantri Surakshit Matritva
Abhiyan, offering assured, comprehensive, and quality antenatal care to all pregnant women universallyon the 9th of each month. The programme has conducted 3.94 crore free antenatal checkups.
The Ujjwala Yojana has provided clean cooking fuel to an impressive 31 crore individuals, distributing 10 crore
LPG connections. Beyond creating smoke-free kitchens, this initiative has protected numerous womenfrom chronic respiratory disorders. Increased LPG access has relieved women from the burdensome taskof collecting traditional fuels, enabling them to participate more actively in community life and pursue
income-generating opportunities. The substantial growth in LPG coverage has significantly improved
women’s health and made kitchens environmentally beneficial by eliminating smoke.
Initiatives like Saubhagya have improved electricity coverage for almost 100% of the population,
benefiting an additional 2.86 crore households and rendering kerosene lamps obsolete, thus improving
respiratory health and eyesight. Transformative campaigns like Swachh Bharat Mission (SBM) and Jal
Jeevan Mission (JJM) have led to a nationwide wave of improved sanitation facilities providing 14 crore
tap water connections and construction of 11.33 crore IHHL in rural areas.
The Pradhan Mantri Jan Dhan Yojana (PMJDY) has been a driving force for change, witnessing the
opening of over 50 crore bank accounts in recent years. This mammoth initiative has played a central
role in bringing a significant section of the population into the formal financial system, providing efficient
access to government schemes, savings, and credit.
The PM Awas Yojana has revolutionized living conditions in both urban and rural areas, facilitating theconstruction of over 4 crore homes for the underprivileged. This has ensured that a majority of families
now have access to safe, secure, and comfortable living spaces.

Conclusions
Over the last nearly two decades, there have been substantial improvements in quality of lives of people
with poverty levels declining sharply from more than 50% to 11.28%. India is all set to reach single digit
poverty levels during 2024. The rate of decline in multi dimensional poverty has accelerated during
the period 2013-14 to 2022-23. This is made possible by a large number of initiatives/ schemes of theGovernment targeted at improving specific deprivation aspects. About 24.82 crore people escaping
multidimensional poverty with a fall in headcount ratio by 17.89 percentage points will also help inaccelerating the achievement of Sustainable Development Goals. While the performance of States varies,
some States which were historically having high poverty have made remarkable progress in bringing
people out of poverty, thus reducing inter-state variations in multidimensional poverty. With this, thefundamental problems in accessing basic services are getting resolved fast so that the country can looktowards becoming a developed nation i.e. Viksit Bharat @2047.
--------------------------------------------------------------------------------------------------------This paper is written by Prof. Ramesh Chand, Member, NITI Aayog and Dr. Yogesh Suri, Senior
Adviser, NITI Aayog with technical inputs from United Nations Development Programme(UNDP) & Oxford Policy and Human Development Initiative (OPHI). Views expressed are
personal.

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MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

Annexure
Table 1: State wise Poverty estimates in last 9 years (From 2013-14 to 2022-23)
Headcount Ratio (%)
2013-14(Interpolated
S.No

State/UT

2005-06

2015-162019-21

using data

who escaped(Extrapolated

multidimensional

using datapovertyfrom 2015-16 &(Estimated)2019-21)(in lakh)

*

4.19

*

from 200506& 2015-16)

6.06

No. of People
2022-23

1

Andhra Pradesh

48.80

11.77

2

Arunachal Pradesh

60.67

24.23

13.76

29.11

10.05

2.98

3

Assam

60.78

32.65

19.35

36.97

14.47

80.36

4

Bihar

78.28

51.89

33.76

56.34

26.59

377.095Chhattisgarh

69.78

29.90

16.37

35.42

11.71

71.55

6

Goa

18.20

3.76

0.84

5.15

0.37

0.75

7

Gujarat

39.79

18.47

11.66

21.53

9.03

89.41

8

Haryana

41.84%

11.88

7.07

*

5.30

*

9

Himachal Pradesh

32.23

7.59

4.93

10.14

3.88

4.67

10

Jharkhand

74.04

42.10

28.81

47.13

23.34

93.92

11

Karnataka

46.71

12.77

7.58

16.55

5.67

73.64

12

Kerala

12.31

0.70

0.55

1.24

0.48

2.72

13

Madhya Pradesh

69.44

36.57

20.63

41.57

15.01

230.0014

Maharashtra

40.07

14.80

7.81

18.06

5.48

159.07

15

Manipur

44.13

16.96

8.10

20.53

5.37

4.89

16

Meghalaya

62.54

32.54

27.79

37.08

25.46

3.89

17

Mizoram

32.95

9.78

5.30

12.47

3.77

1.08

18

Nagaland

58.82

25.16

15.43

29.82

11.76

4.03

19

Odisha

63.84

29.34

15.68

34.28

11.07

102.7820

Punjab

26.66

5.57

4.75

*

4.35

*

21

Rajasthan

64.15

28.86

15.31

33.86

10.77

187.12

22

Sikkim

38.85

3.82

2.60

6.07

2.10

0.27

23

Tamil Nadu

36.54

4.76

2.20

7.16

1.43

43.98

24

Telangana

*

13.18

5.88

*

3.76

*

25

Tripura

52.78

16.62

13.11

20.94

11.49

3.92

26

Uttar Pradesh

69.52

37.68

22.93

42.59

17.40

593.6927

Uttarakhand

40.38

17.67

9.67

20.85

6.92

16.21

28

West Bengal

57.60

21.29

11.89

25.98

8.60

172.1829

Andaman & Nicobar Islands

*

4.29

2.30

*

1.63

*

30

Chandigarh

*

5.97

3.52

*

2.62

*

31

Dadra & Nagar Haveli & Daman & Diu

*

19.58

9.21

*

6.06

*

32

Delhi

14.63

4.44

3.43

5.64

2.97

5.69

33

Jammu & Kashmir

40.45

12.56

4.80

*

2.81

*

34

Ladakh

*

12.70

3.53

*

1.73

*

35

Lakshadweep

*

1.82

1.11

*

0.84

*

36

Puducherry

*

1.71

0.85

*

0.58

*

India

55.34

24.85

14.96

29.17

11.28

2482.16

The State of Telangana was formed in 2014, splitting from the state of Andhra Pradesh. As such, the state existed as a separate sampling
unit in 2015-16 and 2019-21, but this was not the case in 2005-06.
The 2005-06 estimates for Jammu & Kashmir include both Jammu & Kashmir and Ladakh. Thus, separate estimates for Ladakh don’t exist
in 2005-06. However, separate estimates for Ladakh and Jammu & Kashmir exist for 2015-16 and 2019-21.
Union Territories: Andaman & Nicobar, Dadra & Nagar Haveli, Daman & Diu, Lakshadweep, and Puducherry were not sampled in NFHS
2005-06. In NFHS 3 (2005-06), the sampling unit that formed the state of Chandigarh was part of the two neighboring states of Punjab
and Haryana. While in 2015-16 and 2019-21, Chandigarh had separate estimates.
The estimates are based on the India and State/UTs population projections by MoHFW.

11

MULTIDIMENSIONAL POVERTY IN INDIA SINCE 2005-06

References
Alkire, S., & Foster, J. (2011). Counting and Multidimensional Poverty Measurement. Journal of
Public Economics, 95(7-8), 476-487.
Alkire, S., Kanagaratnam, U., & Suppa, N. (2019). The Global Multidimensional Poverty Index (MPI)
2019. OPHI MPI Methodological Note 47, Oxford Poverty and Human Development Initiative(OPHI), University of Oxford.
National Commission on Population, Ministry of Health & Family Welfare. (2020). Population
Projections for India and States 2011-2036. Government of India.
NITI Aayog. (2021) National Multidimensional Poverty Index: Baseline Report.
NITI Aayog. (2023) National Multidimensional Poverty Index: A Progress Review 2023.
UNDP (United Nations Development Programme). 2023. 2023 Global Multidimensional Poverty
Index (MPI): Unstacking global poverty: Data for high impact action. New York.
World Bank (2024), Poverty and Inequality Platform. pip.worldbank.org. Accessed on 2024-01-12

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