Malnutrition and poverty in India: Does the use of public distribution system matter?

Abstract

Background
Large scale public investment in Public Distribution System (PDS) have aimed to reduce poverty and malnutrition in India. The PDS is the largest ever welfare programme which provides subsidised food grain to the poor households. This study attempt to examine the extent of stunting and underweight among the children from poor and non-poor households by use of public distribution system (PDS) in India.

Methods
Data from the National Family and Health Survey-4 (NFHS-4), was used for the analysis. A composite variable based on asset deprivation and possession of welfare card provided under PDS (BPL card), was computed for all households and categorised into four mutually exclusive groups, namely real poor, excluded poor, privileged non-poor and non-poor. Real poor are those economically poor and have a welfare card, excluded poor are those economically poor and do not have welfare card, privileged poor are those economically non-poor but have welfare card, and non-poor are those who are not economically poor and do not have welfare card. Estimates of stunting and underweight were provided by these four categories. Descriptive statistics and logistic regression were used for the analysis.

Results
About half of the children from each real poor and excluded poor, two-fifths among privileged non-poor and less than one-third among non-poor households were stunted in India. Controlling for socio-economic and demographic covariates, the adjusted odds ratio of being stunted among real poor was 1.42 [95% CI: 1.38, 1.46], 1.43 [95% CI: 1.39, 1.47], among excluded poor and 1.15 [95% CI: 1.12, 1.18], among privileged non-poor. The pattern was similar for underweight and held true in most of the states of India.

Conclusions
Undernutrition among children from poor households those excluded from PDS is highest, and it warrants inclusion in PDS. Improving the quality of food grains and widening food basket in PDS is recommended for reduction in level of malnutrition in India.

Full text

R E S E A R C H A R T I C L E Open Access
Malnutrition and poverty in India: does
the use of public distribution system
matter?
Basant Kumar Panda
1*
, Sanjay K. Mohanty
2
, Itishree Nayak
1
, Vishal Dev Shastri
3
and S. V. Subramanian
4,5
Abstract
Background: Large scale public investment in Public Distribution System (PDS) have aimed to reduce poverty and
malnutrition in India. The PDS is the largest ever welfare programme which provides subsidised food grain to the
poor households. This study attempt to examine the extent of stunting and underweight among the children from
poor and non-poor households by use of public distribution system (PDS) in India.
Methods: Data from the National Family and Health Survey-4 (NFHS-4), was used for the analysis. A composite
variable based on asset deprivation and possession of welfare card provided under PDS (BPL card), was computed
for all households and categorised into four mutually exclusive groups, namely real poor, excluded poor, privileged
non-poor and non-poor. Real poor are those economically poor and have a welfare card, excluded poor are those
economically poor and do not have welfare card, privileged poor are those economically non-poor but have
welfare card, and non-poor are those who are not economically poor and do not have welfare card. Estimates of
stunting and underweight were provided by these four categories. Descriptive statistics and logistic regression were
used for the analysis.
Results: About half of the children from each real poor and excluded poor, two-fifths among privileged non-poor
and less than one-third among non-poor households were stunted in India. Controlling for socio-economic and
demographic covariates, the adjusted odds ratio of being stunted among real poor was 1.42 [95% CI: 1.38, 1.46],
1.43 [95% CI: 1.39, 1.47], among excluded poor and 1.15 [95% CI: 1.12, 1.18], among privileged non-poor. The
pattern was similar for underweight and held true in most of the states of India.
Conclusions: Undernutrition among children from poor households those excluded from PDS is highest, and it
warrants inclusion in PDS. Improving the quality of food grains and widening food basket in PDS is recommended
for reduction in level of malnutrition in India.
Keywords: Poor, Underweight, Stunting, BPL, PDS, India, Welfare card
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* Correspondence: basantpanda99@gmail.com
1
International Institute for Population Sciences, Mumbai, India
Full list of author information is available at the end of the article
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Background
Poverty is a proximate determinant of malnutrition that
mediates through inadequate dietary intake, lack of med-
ical care, lack of access to sanitation and hygiene and
poor environment [1–5]. Reduction of poverty and mal-
nutrition through various welfare measures has been the
central focus of developing countries. While the national
and local government has been implementing many wel-
fare schemes in the key domain of livelihood, health, nu-
trition, education for reduction of poverty, the effect of
these schemes varied by type of services, outcome vari-
able and country-specific [6–9]. Malnutrition among
children is positively associated with poverty and has
many adverse short and long term consequences; ill-
health, cognitive impairment, childhood mortality in
short term and the likelihood of developing the non-
communicable diseases in the long term [10–15]. Nutri-
tion sensitive interventions for children are increasingly
emphasized in the poverty reduction programme of de-
veloping countries. Public policies which are successful
in reducing poverty has intrinsically targeted multifa-
ceted approach in reduction of malnutrition [16–20].
Despite these, the global progress in reduction of money
metric poverty and malnutrition is slow and largely un-
even [21,22]. An estimated 736 million of people in de-
veloping countries are living below poverty line (1.90 US
Dollar in Purchasing Power Parity income per day) and
151 million under-five children are stunted in 2018 [23,
24].
A large number of studies across and within the coun-
tries suggests the strong and significant association of
malnutrition with economic factors. While a limited
number of studies use income as an economic measures,
many studies used consumption expenditure and asset-
based index (henceforth refer as wealth index) in
explaining malnutrition [25–29]. Cross-country studies
found that the gross domestic products (GDP) per capita
is negatively associated with malnutrition [30,31]. Stud-
ies also link the absolute income with malnutrition due
to unavailability of direct income data in many lower-
middle-income countries [31]. Households consumption
expenditure, especially food expenditure, is positively as-
sociated with the reduction of malnutrition among chil-
dren and adults [32–34].
A growing number of studies also link malnutrition
with asset-based measures using data from demographic
health surveys (DHSs). These studies evident that, des-
pite the declination of inequality across the socioeco-
nomic groups, the level of malnutrition is still higher for
the poor and disadvantaged and holds true across the
countries [22,28,29,35]. Other than economic factors,
many non-economic factors such as social identity,
household environment, water and sanitation are consid-
ered as significant predictors of child malnutrition at
household level [3,35–37]. The maternal characteristics
such as maternal education, anthropometry, mothers’
hygienic practice and individual characteristics such as
child’s sex, birth order, immunisation, and birth weight
are factors associated with malnutrition [25,36,38–42].
Poverty estimation and identification of beneficiaries
for the welfare programme in India are carried out inde-
pendently. Poverty estimation at the state level is carried
out by the Government of India using consumption ex-
penditure data collected by the National Sample Survey
(NSS) on a regular interval. But the households are iden-
tified as poor using an independent survey carried out
by the state government with central guidelines. The
poor households are given a Below Poverty Line (BPL)
card that entitled the households to benefits from vari-
ous welfare schemes of central and state government.
The first BPL survey in India was carried out in 1992,
and four rounds of the BPL surveys have been carried
out since then (Latest in 2011–12) (http://pib.nic.in/
newsite-/PrintRelease.aspx?relid=72217). The criteria
adopted in each of the rounds to identify the beneficiar-
ies were not uniform. The BPL survey conducted in
1992 used the household income limit of Rs 11,000 an-
nually in classifying poor. The 1997 round of survey
used the exclusion and inclusion criterion. In 2002 BPL
survey, a set of 13 socio-economic variables were used
to compute the wellbeing of the households and identi-
fying the poor. Recent BPL survey adopted three-step
methods recommended by Saxena committee report
[43]. The BPL beneficiaries are categorised using a 0–10
scoring method based on a weighted score of a set of
variable [43,44]. Studies suggest that a section of the
poorer households are excluded from the BPL card [45].
Such exclusion has been attributed to errors of inclusion
and exclusion criteria, political influence and corruption
[44–46].
The BPL households have long been accorded priority
in the welfare programme of central, state and local
government in India. Initially, the BPL beneficiaries were
provided free/ highly subsidized ration through the Public
Distribution System (PDS). However, it has now been
extended to a wider domain of goods and services;
housing, cooking fuel, health insurance, employment,
sanitation, old age pension etc. The benefits are rolled
through a number of welfare schemes such as Pradhan
Mantri Awas Yojana, Swachh Bharat Mission, Janani Sur-
aksha Yojana (JSY), Ayushman Bharat Yojana, PM Ujj-
wala scheme etc. For example, under the Swachh Bharat
Mission, the Government of India provided 9.2 crore toilet
to the poor households. Recently, the Govt. of India an-
nounced the Ayushman Bharat Scheme, a health insur-
ance scheme for protecting poorest 40% of the households
with coverage up to five lakh rupees for hospitalization
(https://www.india.gov.in/spotlight/ayushman-bharat-
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national-health-protection-mission). We termed the BPL
card as a welfare card that accounts not only free/subsi-
dized ration but also other benefits. Thus, availing the wel-
fare card entitled a poor household to multiple benefits
that has a direct and indirect bearing on reduction of mal-
nutrition in India.
The provisioning of subsidized ration through welfare
card aims to ensure food availability to the poor house-
holds. We conceptualise the paper with the assumption
that, the welfare card entitled a household to subsidized
ration, other free/subsidized goods and services that
have a direct association on the reduction of malnutri-
tion of children in households. However, if a household
is excluded from the welfare programme (not having a
welfare card), they are more likely to be vulnerable.
Though studies have identified the factors affecting mal-
nutrition and poverty, there has been no study on the
association of poverty, distribution of welfare card and
the nutritional outcome. This paper aims to examine the
association of asset deprivation, welfare card with stunt-
ing and underweight among children in India. Thus,
children belonging to poor households but did not
get benefit from welfare schemes are likely to remain at
a high level of malnutrition.
Methods
Data
The unit data from fourth round of National Family
Health Survey (NFHS 4) was used for the analysis. The
NFHS 4 is a nationwide cross-sectional household sur-
vey conducted with a representative sample of 601,509
households throughout the country during 2015–16.
The survey was conducted by the International Institute
for Population Sciences (IIPS) with stewardship of Min-
istry of Health and Family Welfare (MoHFW), Govt. of
India (GOI), and technical support from ICF inter-
national. The primary objective of the survey was to col-
lect comprehensive information on socio-economic,
demographic, health, and nutritional status of mothers
and children in the households. The detailed sampling
methods, design of the study and findings are available
in the national report [47].
The unit of the analysis for this study is the children
below 5 year age. Out of the 601,509 households, 164,
664 households have at least one children in the selected
age-group. A total of 225,002 children under 5 years of
age who have the information on height and weight
form the final sample of the analysis.
Outcome variables
Stunting and underweight among children below 5 year
are two dependent variables used in the analysis. Stunt-
ing is a chronic measure of malnutrition, while under-
weight indicates both chronic as well as an acute form
of undernourishment. In NFHS, the anthropometric in-
formation such as height, weight and age in month of
the child were recorded. This information was used to
calculated the height-for-age Z-scores (HAZ), weight-
for-age Z-scores (WAZ), weight-for-height Z-scores
(WHZ) using the standardized age and sex-specific
growth reference developed by the World Health Organ-
isation (WHO). These z scores are ranged between -6SD
to +6SD. If the Z score, falls above or below the limit, it
was dropped. If the height for age Z score falls between
-6SD to -2SD, the child is classified as stunted. Similarly,
if the weight for age Z score (WAZ) falls between the
-6SD to -2SD, the child is classified as underweight.
Independent variables
The main predictor variable of the analysis, is a compos-
ite variable based on the poverty level of the household
and possession of welfare card. The NFHS like other
DHSs, does not collect data on income or consumption
expenditure of the household. To present the economic
status of the households, the asset-based wealth index
was largely being used in these surveys. The wealth
index factor score (WIFS) was computed based on a set
of 31 variables in the domain of consumer durables,
basic household amenities, housing quality and land
ownership of the households using the principal compo-
nent analysis (PCA). These WIFS are categorised into
five mutually exclusive categories named poorest,
poorer, middle, richer and richest. These categories are
widely used in the literature to estimates the different
economic group and to understand economic inequality
across and within countries. The national wealth index
in NFHS does not account for interstate variations in
wealth possession as well as rural-urban differences
within states, which lead to biased outcomes. In our
study, we had recomputed the WIFS using the principal
component analysis (PCA) separately for all the states by
place of residence. The wealth index factor score was di-
vided into the 100 percentile for each state and place of
residence. The estimates of monetary poverty were avail-
able from the Rangarajan Committee report for each
state and by place of residence. The detailed estimate of
the poverty for each state and place of residence is pro-
vided in Appendix 1. The variable of asset deprived
(similar to poverty) is derived under the assumption that
the distribution in WIFS and the distribution of con-
sumption poverty are similar across the household. For
example, in Maharashtra, 23% households in rural areas
and 17% households in urban areas are consumption
poor based on the Rangarajan Committee report. So, the
bottom 23% of distribution of wealth index in rural areas
and 17% of distribution of wealth index in urban areas
are labelled as asset poor in the state. The household
poverty, along with possession of welfare card, was used
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to compute a composite variable of poverty and bene-
ficiaries of welfare schemes. All households are classi-
fied into four groups, and their classification is given
below.
Real poor
Households categorised as poor based on asset
deprivation and having welfare card.
Excluded poor
Households categorised as poor based on asset
deprivation and do not have welfare card.
Privileged non-poor
Households categorised as asset non-deprived and have
welfare card.
Non-poor
Households categorised as asset non-deprived and not
having welfare card.
A set of individual maternal and household level fac-
tors are used as independent variables based on the lit-
erature review. The individual factors include age (in
months), sex, birth order and size of birth of the child.
The size of the birth was categorised as very small, small,
average and large based on the mother’s response. Simi-
larly, the maternal factors include mother’s education
(no education, primary, secondary, higher) and the body
mass index (BMI). The BMI of mothers is calculated
from the height and weight of the mother and cate-
gorised as thin (BMI < 18.5), normal (18.5 < BMI < 25),
overweight (BMI > =25). Apart from these the household
level factors include caste (SC, ST, OBC and Other), reli-
gion (Hindu, Muslim and Other), and place of residence
(Rural and Urban) were included as predictor variables
in the analysis.
Statistical analysis
The logistic regression analysis was used to estimate the
association of the composite variable of poverty and wel-
fare card on stunting and underweight at national as
well as state level. The logistic regression model is one
of the widely used statistical techniques for the binary
dependent variable which provide the valid and reliable
regression coefficient adjusting for the study design and
confounder. Stunting and underweight were the binary
variables used as outcome variables in the study. The
general equation of the logistic model is provided below.
Iogit Pi
ðÞ¼αþβ1poverty wci
ðÞþβ2agei
ðÞ
þβ3sexi
ðÞþβ4bordi
ðÞþβ5bsizei
ðÞ
þβ6edui
ðÞþβ7bmii
ðÞþβ8ðcasteiÞ
þβ9reli
ðÞþβ10 pori
ðÞþei
where αis the intercept, poverty_wc refers to the com-
posite variable of the poverty and availability of the wel-
fare card in the household. Age, sex, bord, bsize refers to
age, sex, birth order and birth size of the child, respect-
ively. Similarly, edu refers to mothers education while
bmi is the categorised body mass index of the mother.
Moreover, caste refers to caste of household, rel refers
to the religion of the household, por includes place of
residence.
The results of the logistic regression was presented as
the adjusted odds ratio (AOR), as it is controlled for all
other confounding variables. The AOR is here defined as
the ratio of the odds of being stunted in the reference
category to the odds of being stunted in the predictor
category controlled for the other covariates. The Intra-
class correlation coefficient (ICC) and the variance infla-
tion factor (VIF) was calculated to understand the
multicollinearity among the independent variables. The
Pearson chi-square, as well as F-adjusted test statistic
was used to test the goodness of fit of the model. The
whole analysis was carried out using the STATA 15 soft-
ware [48].
Results
Table 1presents the sociodemographic characteristics of
the households (who had at least one child under 5 year
age) by the composite variable based on the asset
deprivation and availability of welfare card in India. The
mean age of the head of household by type of asset
deprivation and welfare card is similar. The mean num-
ber of children in households are also similar across the
four groups. A large proportion of real and excluded
poor lives in rural areas. Out of the total households,
57% of real poor and excluded poor households have at
least one stunted children while 43% among privileged
non-poor households and 36% among the non-poor
households. Similarly, the higher proportion of real poor
and excluded poor households have at least one under-
weight children.
The state-specific variation in the percentage of house-
holds (who had at least one eligible child) by the com-
posite variable based on the asset deprivation and
availability of welfare card in India was presented in
Table 2. At the national level, an estimated 15% of the
households is classified as real poor, 16% as excluded
poor, 23% as privileged non-poor and 46% as non-poor.
The distribution of households by type of poverty varies
enormously across the states of India. For example, in
Punjab, 1% of households are categorised as real poor
while it was 30% in Bihar. Similarly, 1% of households in
Telangana are categorised as excluded poor while it was
37% in Manipur. One of the interesting patterns was ob-
served in terms of privileged non-poor in India. The pri-
vileged non-poor avail the welfare schemes that are not
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meant for them. The pattern shows two south Indian
states Andhra Pradesh and Telangana which have
more than 70% of the non-poor household had wel-
fare card. Apart from that Kerala, Karnataka and
Chhattisgarh possess a higher share of household with
a non-poor category having welfare cards. The non-
poor who did not have the welfare card is highest in
Punjab and Tamil Nadu.
The state-specific estimates of stunting and under-
weight among the children below 5 year age by the com-
posite variable based on the asset deprivation and
availability of welfare card of households were presented
in Table 3. The states are arranged in the ascending
order of stunting among the excluded poor household.
This pattern provides a clear distinction in estimated
stunting and underweight among the poor and rich
households. About half of the children from real poor
and excluded poor households were stunted compared
to 37% children from privileged non-poor households
and 35% children from the non-poor household. The
pattern was similar with respect to underweight. An esti-
mated 48% of children from real poor household, 47%
from an excluded poor household, 35% from privileged
non-poor household and 29% from the non-poor house-
hold were underweight. The pattern shows the vulner-
ability of both real poor and excluded poor household in
terms of stunting and underweight. Interstate variation
of the prevalence of stunting and underweight showed a
mixed result. In 16 states, the extent of stunting among
children of excluded poor households was higher as
compared to real poor households. The higher gap
among these groups were found in states of Himachal
Pradesh followed by Tripura. In 12 states, the extent of
stunting among real poor households was found higher
as compared to excluded poor household. These in-
cludes the states like Telangana, Chhattisgarh and
Andhra Pradesh, which had a higher prevalence of wel-
fare cards. In 14 states, the extent of underweight chil-
dren among real poor households was higher compared
to the excluded poor household. Similarly, the preva-
lence of stunting and underweight were lower in the pri-
vileged non-poor as compared to real poor and excluded
poor households.
Table 4presents the estimates of stunting and under-
weight by poverty level and sociodemographic character-
istics in India. Both stunting and underweight among
the children from the real poor and excluded poor
households were higher than that of non-poor house-
holds. The levels of stunting and underweight were
higher among children of higher birth-order, lower birth
size, the mother with lower education level and lower
anthropometry. For example, among the excluded poor
households, 54% of children of mothers with no educa-
tion were stunted compared to 36% among mothers with
12 or more year of schooling. Similarly, among real poor
households, 54% of children among mother with no edu-
cation were stunted compared to 34% among mothers
with 12 or more year of schooling. A similar pattern was
also observed for the other categories. But the gap in es-
timates of stunting and underweight among the well off
and worst off varies across the categories and among the
variables. For example, for the mothers education, the
gap among the mothers who had no education and high-
est education is higher among the privileged non-poor
households (26%) followed by non-poor households
(25%), real poor households (20%) and excluded poor
households (18%). A similar pattern was also observed for
the level of underweight. The gap among the mothers
who had no education and highest education among the
privileged non-poor and non-poor households was 22%
while it is 21% among excluded poor households and 10%
among real poor households. This pattern also remains
Table 1 Sociodemographic profile of households by welfare card and asset poor in India, 2015–16
Variables Real Poor (Asset
deprived and have
welfare card)
Excluded Poor (Asset
deprived and do not have
welfare card
Privileged Non-Poor (Asset
non-deprived and have
welfare card
Non-Poor (Asset non-
deprived and do not have
welfare card
Total
Mean age of head of
household
43.35 38.64 46.42 45.63 44.34
Mean household size 6.20 5.60 6.51 6.18 6.15
Mean number of children
in household
1.56 1.59 1.49 1.44 1.50
Percentage of
households living in
urban areas
6.32 10.50 28.20 42.84 28.73
Percentage of
households had a
stunted child
56.88 56.95 42.86 35.89 44.06
Percentage of
households had an
underweight child
54.76 53.46 40.11 33.28 41.35
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consistent for all other variables such as birth order, size at
birth, mothers BMI category and caste of the households.
To find the exposition of malnutrition through poverty
status across the household in India and states, multi-
variate logistic regression method was used. Table 5pre-
sents the adjusted odds ratio (AOR) (with 95%
confidence interval) of the child being stunted/under-
weight by type of poverty of households controlling for
all other sociodemographic correlates. Our results sug-
gest that poverty level of the household is a major deter-
minant of stunting and underweight among the under-
five children. The risk for stunting in India was highest
among the children who were belonging to the excluded
poor household with an adjusted odds ratio (AOR) 1.43
[95% CI: 1.39, 1.47] than that of the non-poor house-
hold. Similarly, the AOR of a child being stunted from
the real poor households was found to be 1.42 [95% CI
1.38,1.46] and privileged non-poor households was found
to be 1.15 [95% CI: 1.12, 1.18] as compared to non-poor
households. Compared to the children from a non-poor
household, the children from real poor households had an
AOR of 1.46 (95% CI 1.42, 1.50), the children from ex-
cluded poor households had an AOR of 1.37 (95% CI 1.33,
1.40), the children from privileged non-poor households
had an AOR of 1.15 (95% CI 1.12,1.18) to be underweight.
The children from a mother who had higher education
were less likely to be stunted as compared to those with
mothers having no schooling (AOR = 0.47, 95% CI: 0.45,
Table 2 Percent distribution of households by welfare card and asset poor in states of India, 2015–16
States Real Poor (Asset
deprived and have
welfare card)
Excluded Poor (Asset
deprived and do not have
welfare card
Privileged Non-Poor (Asset
non-deprived and have
welfare card
Non-Poor (Asset non-
deprived and do not have
welfare card
Total
Households
Telangana 8.68 0.90 71.91 18.51 1504
Andhra Pradesh 9.36 1.46 74.27 14.91 1901
Karnataka 14.20 3.66 56.22 25.92 4672
Kerala 4.22 3.66 26.21 65.91 1855
Chhattisgarh 40.66 5.37 37.07 16.90 6016
Jammu & Kashmir 9.57 7.00 28.40 55.02 5404
Punjab 0.90 7.44 10.49 81.16 3667
Himachal Pradesh 3.72 7.45 19.56 69.27 1935
Uttarakhand 5.87 8.16 22.34 63.63 3728
Nagaland 2.09 8.96 20.08 68.88 2721
Haryana 3.81 9.73 17.88 68.57 4883
Maharashtra 8.38 12.09 20.02 59.52 5882
Gujarat 17.27 14.66 18.97 49.11 4777
Bihar 29.61 15.07 27.44 27.87 14,612
Rajasthan 7.77 15.30 13.79 63.15 10,367
Tamil Nadu 3.47 15.83 7.85 72.85 5372
Mizoram 15.44 16.34 11.10 57.13 3276
West Bengal 15.14 16.73 20.04 48.09 3967
Tripura 10.22 17.55 21.40 50.83 1041
Madhya Pradesh 25.60 19.41 20.69 34.30 15,054
Arunachal Pradesh 22.72 21.35 22.17 33.76 3131
Jharkhand 23.95 21.58 22.02 32.45 7669
Odisha 21.53 24.01 17.22 37.23 8001
Meghalaya 6.98 26.18 12.12 54.72 2744
Assam 21.05 26.77 18.96 33.23 7335
Uttar Pradesh 11.13 27.49 12.19 49.18 24,981
Manipur 9.66 37.29 10.03 43.02 4110
India 15.00 16.13 23.30 45.57 164,664
*The estimates for states and UTs with sample size less than 1000 are not shown in the above table but included for the India estimates
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0.48). A similar finding was also observed for the under-
weight among the children in India. Apart from the
mother’s education, mothers BMI status, caste, religion
and place of residence of household was also significantly
determined the stunting and underweight among the chil-
dren in India. Apart from these characteristics, the birth
size of the children significantly contributes to stunting
and underweight among the children in India. Large or
average-sized babies were less likely to be stunted and
underweight as compared to the children who were very
small at birth (AOR = 0.60, 95% CI: 0.57,0.64). Moreover,
the female child and the children from urban areas were
Table 3 Percentage of children stunted and underweight by welfare card and asset poverty in states of India, 2015–16
States Percentage of children stunted Percentage of children underweight
Real Poor
(Asset
deprived
and have
welfare
card)
Excluded Poor
(Asset deprived
and do not
have welfare
card
Privileged Non-
Poor (Asset
non-deprived
and have wel-
fare card
Non-Poor
(Asset non-
deprived and
do not have
welfare card
Real Poor
(Asset
deprived
and have
welfare
card)
Excluded Poor
(Asset deprived
and do not
have welfare
card
Privileged Non-
Poor (Asset
non-deprived
and have wel-
fare card
Non-Poor
(Asset non-
deprived and
do not have
welfare card
Kerala 24.90 24.25 23.26 18.25 22.32 25.54 19.84 14.08
Telangana 46.34 25.56 28.00 19.66 47.70 37.22 28.48 18.65
Chhattisgarh 43.30 32.74 36.54 29.11 45.34 41.66 34.36 28.44
Andhra
Pradesh
42.36 33.88 31.38 27.79 42.35 50.65 31.65 27.99
Arunachal
Pradesh
34.68 34.71 25.22 25.16 26.34 27.10 14.64 13.34
Sikkim 41.49 34.97 30.44 24.77 23.46 12.06 15.60 12.05
Tamil Nadu 34.71 35.46 30.56 24.85 35.00 31.64 23.34 22.15
Manipur 32.85 35.67 27.43 22.40 18.10 16.99 13.98 10.20
Tripura 32.01 37.99 19.87 19.33 29.71 34.71 25.33 18.80
Jammu and
Kashmir
37.19 39.30 30.04 23.48 26.15 28.07 16.25 14.02
Nagaland 31.02 39.82 28.96 27.12 24.07 25.01 16.56 15.73
West Bengal 39.84 40.04 33.32 27.97 39.68 45.33 28.29 26.36
Punjab 42.93 40.47 24.37 24.63 30.11 35.50 23.00 20.15
Mizoram 36.94 41.15 27.48 21.83 16.39 18.85 13.97 8.31
Odisha 44.28 43.56 32.17 23.11 45.75 43.87 31.30 23.09
Uttarakhand 42.33 44.01 35.95 31.28 36.05 38.24 30.08 23.57
Himachal
Pradesh
35.89 45.02 29.11 23.29 27.68 29.86 24.95 19.94
Maharashtra 44.75 45.09 36.89 29.70 51.32 49.64 35.36 31.31
Gujarat 51.23 46.56 40.48 29.96 51.95 49.09 40.65 31.31
Assam 42.07 46.74 31.37 26.94 35.19 40.43 23.12 21.74
Madhya
Pradesh
48.17 47.94 39.91 35.29 50.89 48.61 40.91 35.03
Rajasthan 52.53 48.58 38.71 35.59 55.34 47.80 37.22 31.51
Karnataka 48.33 48.63 36.23 28.76 47.84 47.34 35.05 27.62
Haryana 50.38 49.48 37.02 30.40 41.11 38.57 33.80 26.68
Meghalaya 49.51 50.87 47.96 38.85 35.75 35.20 29.78 25.22
Jharkhand 52.84 53.20 45.25 36.35 56.34 55.24 46.50 38.72
Bihar 57.36 56.27 46.83 37.34 50.92 52.00 42.35 34.89
Uttar
Pradesh
55.40 57.16 44.63 39.12 47.05 48.63 37.29 34.08
India 49.79 49.65 37.16 31.51 47.73 46.51 34.54 29.00
*Estimates for states with sample size less than 1000 are not shown the above table but included for the India estimates
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Table 4 Percentage of children stunted and underweight by type of poverty and background characteristics in India, 2015–16
Socio
demographic
variables
Percentage of children stunted Percentage of children underweight Total
Number
of
children)
Real Poor
(Asset
deprived
and have
welfare
card)
Excluded
Poor (Asset
deprived and
do not have
welfare card
Privileged
Non-Poor
(Asset non-
deprived and
have welfare
card
Non-Poor
(Asset non-
deprived and
do not have
welfare card
Real Poor
(Asset
deprived
and have
welfare
card)
Excluded
Poor (Asset
deprived and
do not have
welfare card
Privileged
Non-Poor
(Asset non-
deprived and
have
welfare card
Non-Poor
(Asset non-
deprived and
do not have
welfare card
Age in Month
< 12 27.30 27.28 20.03 18.91 37.67 36.20 25.40 23.31 41,871
12–24 53.91 54.42 42.12 36.04 50.18 47.24 34.24 27.56 48,967
> 25 55.31 54.41 41.09 33.61 49.99 49.22 37.71 31.26 1,34,164
Birth Order
1 46.43 45.80 33.24 27.75 45.70 44.31 31.63 25.89 83,046
2–3 48.95 49.01 38.45 32.74 46.63 46.09 35.20 30.03 1,06,012
4+ 55.32 55.46 46.97 43.64 52.21 49.94 43.13 38.93 35,944
Size at Birth
Very small 64.04 60.24 50.16 46.39 64.28 59.68 51.66 47.71 6178
Small 56.20 54.05 43.29 38.02 55.62 53.87 42.93 37.49 20,046
Average and
above
48.48 48.74 36.24 30.47 46.17 45.15 33.29 27.65 1,98,778
Sex
Female 49.22 49.16 35.97 31.05 47.11 46.43 33.31 28.71 1,16,360
Male 50.33 50.12 38.28 31.93 48.33 46.59 35.68 29.26 1,08,642
Mother Education
No
Education
54.28 54.37 48.30 45.15 50.91 50.66 44.03 40.68 68,978
Primary 47.24 48.59 41.58 40.50 46.18 45.07 38.46 35.82 32,835
Secondary 42.15 42.06 33.58 29.60 42.04 40.44 31.49 27.97 1,02,191
Higher 34.01 36.62 22.69 20.08 40.26 29.60 21.85 17.92 20,998
Place of Residence
Urban 47.56 46.12 33.10 28.33 42.00 44.79 31.56 26.73 53,483
Rural 49.93 50.08 38.72 33.78 48.12 46.72 35.68 30.61 1,71,519
Caste
Scheduled
Caste
51.94 50.96 40.08 35.65 48.68 47.21 36.95 31.89 42,540
Scheduled
Tribe
48.79 48.72 39.88 35.32 51.44 50.14 38.60 35.94 44,440
OBC 50.42 50.22 37.89 32.70 46.77 45.80 35.40 29.99 88,803
Others 45.87 47.12 31.57 26.47 41.79 44.58 29.04 24.90 39,399
Religion
Hindu 50.11 49.48 37.22 31.04 48.62 46.83 34.91 28.98 1,63,089
Muslim 49.15 51.70 37.82 35.28 43.28 45.89 33.22 30.90 35,241
Others 42.92 45.08 33.19 26.36 40.58 42.74 32.84 23.68 26,672
BMI
Underweight 52.96 54.16 43.29 38.30 56.21 56.14 44.36 40.40 53,286
Normal 48.49 48.04 36.67 32.13 43.99 42.58 33.17 28.99 1,39,618
Overweight 41.59 41.77 29.24 24.00 31.71 33.64 24.13 19.30 31,530
Panda et al. BMC Nutrition (2020) 6:41 Page 8 of 14
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also less likely to be stunted and underweight as compared
to their respective counterpart. Table 6presents the AOR
(with 95% confidence interval) of the child being
stunted/underweight by type of poverty of households
controlling for all other sociodemographic correlates
among the states of India. These results are robust
with the pattern obtained at the national level. The
results clearly indicate that there is a higher likeli-
hood of stunting as well as underweight across the
real poor and excluded poor households than that of
the non-poor households.
Discussion
Poverty eradication and reduction of malnutrition have
been the central feature of public policy in India since
independence. The national, state and local government
designed several welfare programmes to reduce the ex-
tent of poverty and malnutrition (http://pib.nic.in/news-
ite/PrintRelease.aspx?-relid=113725). While there has
been a secular decline in money metric poverty (from
45% in 1993 to 22% in 2011–12), the reduction of mal-
nutrition remained slow and insignificant [22,47,49].
Child malnutrition is continued to be a major public
health challenge, affecting around two-fifths of the chil-
dren in India. The level of stunting among children
under 5 year of age had declined from 48% in 2005–06
to 38% in 2015–16, and that of underweight children
had declined from 46% in 2005–06 to 34% in 2015–16
[47]. The Government of India implemented various
welfare policies which have direct and indirect bearing
on the reduction of malnutrition [19,20,38,50–52]. As
a programme for reduction of poverty, hunger and food
insecurity, the central government issues welfare card to
poor households to avail subsidised food through the
Public Distribution System (PDS) and other benefit ran-
ging from health insurance, free/subsidised housing and
sanitation, cooking fuel, old age pension, electricity and
direct cash transfer etc. But some of the disadvantaged/
poor households did not get the benefit of this safety net
programme, which have a direct and indirect impact on
food security. In this context, this is the first-ever study
that attempts to link the poverty, welfare card and mal-
nutrition in India. The salient findings of the study are
as follows.
First, we found large variation in asset deprivation and
possession of welfare card in states of India. At the na-
tional level, two-fifths of the household have the welfare
card while around one-third of the household is asset
deprived. Among the households who had at least one
children under 5 year age, 15% were asset deprived and
had a welfare card, 16% were asset deprived and did not
have a welfare card, 23% were asset non-deprived and
had a welfare card and 46% were not asset deprived and
did not have a welfare card. The state variation in the
distribution of asset deprivation and welfare card is
large. Though economically poor states of Uttar Pradesh,
Bihar, Chhattisgarh, Madhya Pradesh and Jharkhand had
a higher proportion of asset deprived and had welfare
card, the exclusion of welfare card among asset poor was
also large in these states. For example, in states of Uttar
Pradesh, around 27% of households were asset deprived
and did not have the welfare card while it was 15% in
Bihar and 21% in Jharkhand. In a similar line, the states
Andhra Pradesh, Telangana and Karnataka, the largest
share of the non-poor household had welfare card.
This pattern possesses the inference that there are
irregularities in the distribution of welfare card across
the households. This indicates that the welfare
schemes meant for the poor and needy people are
not reaching to them.
Second, descriptive analyses suggest the pattern of stunt-
ing and underweight by a composite variable of asset
deprivation and provision of welfare card is mixed. At the
national level, half of the children from real poor and
excluded poor households, while 37% of children from pri-
vileged non-poor households and 32% of children from
Table 4 Percentage of children stunted and underweight by type of poverty and background characteristics in India, 2015–16
(Continued)
Socio
demographic
variables
Percentage of children stunted Percentage of children underweight Total
Number
of
children)
Real Poor
(Asset
deprived
and have
welfare
card)
Excluded
Poor (Asset
deprived and
do not have
welfare card
Privileged
Non-Poor
(Asset non-
deprived and
have welfare
card
Non-Poor
(Asset non-
deprived and
do not have
welfare card
Real Poor
(Asset
deprived
and have
welfare
card)
Excluded
Poor (Asset
deprived and
do not have
welfare card
Privileged
Non-Poor
(Asset non-
deprived and
have
welfare card
Non-Poor
(Asset non-
deprived and
do not have
welfare card
Number of children in Household
1 47.82 47.82 34.46 28.73 47.46 45.80 33.06 26.99 113,566
2 51.70 51.31 38.90 34.37 48.06 47.19 35.27 31.27 86,872
3 or more 50.81 50.88 42.61 36.55 47.69 46.84 38.15 31.94 24,564
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Table 5 Odds ratio and 95% CI of socio-demographic correlates of stunting and underweight among underfive children in India
2015–16
Sociodemographic background characteristics Stunting Underweight
Classified Poor
Non Poor®
Real Poor 1.42***(1.38,1.46) 1.46***(1.42,1.50)
Excluded Poor 1.43***(1.39,1.47) 1.37***(1.33,1.4)
Privileged Non-Poor 1.15***(1.12,1.18) 1.15***(1.12,1.18)
Age of the child (in months)
<12®
12—25 2.73***(2.65,2.81) 1.40***(1.36,1.45)
> 25 2.66***(2.59,2.73) 1.64***(1.60,1.69)
Sex of the child
Male®
Female 0.91***(0.89,0.92) 0.93***(0.92,0.95)
Birth Order
1®
2—3 1.10***(1.08,1.12) 1.10***(1.08,1.13)
4+ 1.31***(1.27,1.35) 1.23***(1.19,1.26)
Size at Birth
Average and above®
Small 0.78***(0.74,0.83) 0.75***(0.71,0.80)
Very small 0.60***(0.57,0.64) 0.52***(0.49,0.55)
Mother’s Education
No Education®
Primary 0.88***(0.85,0.90) 0.86***(0.84,0.89)
Secondary 0.68***(0.66,0.70) 0.69***(0.67,0.70)
Higher 0.47***(0.45,0.48) 0.47***(0.45,0.49)
Place of Residence
Urban®
Rural 1.08***(1.06,1.11) 0.99 (0.97,1.02)
Caste
Scheduled Caste®
Scheduled Tribe 0.87***(0.85,0.90) 0.95***(0.92,0.98)
OBC 0.90***(0.88,0.92) 0.93***(0.91,0.95)
Other 0.72***(0.70,0.74) 0.73***(0.71,0.76)
Religion
Hindu®
Muslim 1.11***(1.08,1.14) 0.98 (0.95,1.01)
Other 0.82***(0.79,0.85) 0.54***(0.52,0.56)
BMI of Mother
Underweight®
Normal 0.79***(0.78,0.81) 0.60***(0.59,0.61)
Overweight 0.59***(0.57,0.61) 0.38***(0.37,0.39)
Panda et al. BMC Nutrition (2020) 6:41 Page 10 of 14
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Table 5 Odds ratio and 95% CI of socio-demographic correlates of stunting and underweight among underfive children in India
2015–16 (Continued)
Sociodemographic background characteristics Stunting Underweight
Number of children in Household
1®
2 1.15***(1.13,1.18) 1.05***(1.03,1.07)
3 or more 1.23***(1.20,1.27) 1.05***(1.02,1.09)
® Reference category.
*** p< 0.01, ** p< 0.05, *p < 0.10
Table 6 Odds ratios and 95% confidence interval of type of poverty on stunting and underweight among children in states of India,
2015–16
States/Union
Territory
Stunting Underweight
Real Poor Excluded Poor Privileged Non-Poor Real Poor Excluded Poor Privileged Non-Poor
Andhra Pradesh 1.16 (0.79,1.71) 0.85 (0.42,1.72) 1.00 (0.76,1.31) 1.12 (0.77,1.64) 1.44 (0.73,2.82) 0.98 (0.75,1.28)
Arunachal Pradesh 1.14 (0.92,1.41) 1.21* (0.96,1.52) 0.98 (0.80,1.22) 1.83*** (1.40,2.39) 1.94*** (1.47,2.56) 1.23 (0.94,1.61)
Assam 1.36*** (1.16,1.59) 1.51*** (1.29,1.76) 1.08 (0.92,1.26) 1.11 (0.93,1.33) 1.39*** (1.18,1.64) 0.92 (0.77,1.09)
Bihar 1.63***(1.50,1.77) 1.61***(1.46,1.77) 1.26***(1.17,1.36) 1.35***(1.24,1.46) 1.43***(1.30,1.57) 1.17***(1.09,1.26)
Chhattisgarh 1.33*** (1.12,1.57) 0.86 (0.67,1.10) 1.22** (1.05,1.42) 1.33*** (1.12,1.58) 1.05 (0.82,1.34) 1.11 (0.95,1.30)
Gujarat 1.78*** (1.51,2.12) 1.50*** (1.26,1.79) 1.42*** (1.22,1.66) 1.64*** (1.39,1.94) 1.55*** (1.3,1.84) 1.32*** (1.13,1.53)
Haryana 1.30** (1,1.69) 1.47*** (1.21,1.78) 1.19** (1.03,1.37) 1.09 (0.84,1.41) 1.21* (1.00,1.47) 1.11 (0.96,1.28)
Himachal Pradesh 1.44 (0.95,2.18) 1.92*** (1.39,2.65) 1.21 (0.95,1.54) 1.47* (0.95,2.28) 1.17 (0.82,1.67) 1.31** (1.02,1.69)
Jammu & Kashmir 1.39*** (1.13,1.72) 1.44***(1.16,1.80) 1.19** (1.01,1.41) 1.41***(1.12,1.78) 1.46***(1.14,1.87) 1.07 (0.88,1.31)
Jharkhand 1.34***(1.18,1.53) 1.36*** (1.19,1.54) 1.23*** (1.09,1.38) 1.39*** (1.22,1.58) 1.36*** (1.20,1.54) 1.20***(1.07,1.35)
Karnataka 1.48***(1.21,1.80) 1.55***(1.13,2.12) 1.10 (0.94,1.28) 1.36***(1.11,1.66) 1.72***(1.26,2.36) 1.04 (0.89,1.21)
Kerala 1.48 (0.90,2.41) 1.57*(0.95,2.58) 1.28 (0.99,1.66) 1.49 (0.90,2.47) 1.54 (0.92,2.58) 1.30 (0.99,1.70)
Madhya Pradesh 1.33***(1.21,1.45) 1.34***(1.22,1.47) 1.12** (1.03,1.21) 1.37***(1.25,1.50) 1.32***(1.20,1.44) 1.07*(0.99,1.17)
Maharashtra 1.20** (1.01,1.42) 1.23***(1.05,1.44) 1.16** (1.02,1.31) 1.47***(1.24,1.74) 1.36***(1.17,1.59) 1.16** (1.03,1.32)
Manipur 1.57*** (1.25,1.98) 1.45*** (1.23,1.71) 1.25* (0.97,1.60) 1.74*** (1.31,2.32) 1.33*** (1.07,1.65) 1.20 (0.87,1.66)
Meghalaya 1.67***(1.25,2.23) 1.47***(1.23,1.76) 1.45***(1.16,1.81) 1.79***(1.34,2.39) 1.39***(1.15,1.67) 1.26 (0.99,1.59)
Mizoram 1.58*** (1.28,1.96) 1.44*** (1.17,1.79) 1.18 (0.94,1.48) 1.56*** (1.18,2.07) 1.54*** (1.17,2.03) 1.31* (0.97,1.77)
Nagaland 0.94 (0.57,1.53) 1.16 (0.90,1.49) 1.02 (0.85,1.23) 1.25 (0.73,2.16) 1.18 (0.88,1.58) 1.03 (0.82,1.28)
Delhi 1.61 (0.84,3.09) 1.49*(0.97,2.29) 1.20 (0.82,1.76) 0.84 (0.43,1.67) 1.17 (0.75,1.82) 0.89 (0.59,1.35)
Odisha 1.63***(1.42,1.87) 1.68***(1.47,1.92) 1.32*** (1.15,1.51) 1.58*** (1.38,1.82) 1.51*** (1.32,1.73) 1.19** (1.04,1.36)
Punjab 1.37 (0.8,2.34) 1.20 (0.93,1.54) 0.92 (0.74,1.14) 1.26 (0.72,2.22) 1.25*(0.96,1.61) 1.04 (0.83,1.30)
Rajasthan 1.32***(1.15,1.52) 1.24***(1.12,1.37) 1.01 (0.91,1.12) 1.54***(1.35,1.77) 1.35***(1.22,1.50) 1.07 (0.97,1.18)
Sikkim 1.35 (0.82,2.22) 1.04 (0.64,1.69) 1.21 (0.79,1.85) 2.03** (1.12,3.69) 0.93 (0.49,1.77) 1.39 (0.8,2.40)
Tamil Nadu 1.24 (0.94,1.64) 1.35*** (1.16,1.56) 1.24** (1.01,1.52) 1.36** (1.03,1.80) 1.30***(1.12,1.51) 0.98 (0.79,1.22)
Tripura 1.52 (0.9,2.55) 1.89***(1.20,2.97) 1.01 (0.64,1.58) 1.35 (0.81,2.27) 1.50 (0.95,2.36) 1.20 (0.78,1.83)
Uttar Pradesh 1.37***(1.26,1.48) 1.45***(1.36,1.54) 1.14***(1.06,1.22) 1.21***(1.12,1.31) 1.30***(1.22,1.37) 1.06 (0.98,1.13)
Uttarakhand 1.08 (0.85,1.36) 1.32** (1.06,1.64) 1.06 (0.91,1.24) 1.4***(1.10,1.78) 1.38***(1.10,1.73) 1.21** (1.03,1.42)
West Bengal 1.26** (1.02,1.55) 1.29** (1.05,1.59) 1.19*(0.99,1.44) 1.26** (1.02,1.55) 1.54***(1.25,1.89) 1.00 (0.83,1.22)
Telangana 1.68** (1.09,2.61) 0.87 (0.31,2.46) 1.16 (0.85,1.58) 1.72** (1.12,2.65) 1.83 (0.71,4.68) 1.28 (0.94,1.74)
*Estimates are controlled for Age, sex, birth order, size at birth of the child, mothers education and BMI of the mother, number of children, place of residence, caste and
religion of the household
*** p< 0.01, ** p< .05, * p< 0.10
Non-poor children is the Reference category
Panda et al. BMC Nutrition (2020) 6:41 Page 11 of 14
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non-poor households are stunted. Similarly, 48% chil-
dren from real poor households, 47% from excluded
poor households, 35% of children from privileged
non-poor households and 29% of children from non-
poor households were underweight. The state vari-
ation of stunting and underweight were striking. The
estimates of stunting found higher in real poor house-
holds in 13 states of India while it was higher in ex-
cluded poor households in another 16 states. A
similar pattern was also observed for the level of
underweight among the children. Mostly in North-
Eastern states such as Manipur, Meghalaya, Tripura
etc. the excluded poor households had a higher share
of stunting as well underweight. This pattern was also
perceived in some high focussed states such as
Assam, Uttar Pradesh and Uttarakhand.
Third, the effect of the composite variable of asset
deprivation on stunting and underweight are strong.
Adjusting for the other covariates, the children from
the excluded poor households were 43% more likely
to be stunted in India. In comparison, it was 41% in
real poor households and 15% in the privileged non-
poor household as compared to the children from
non-poor households. Similarly, children from the ex-
cluded poor households were 37% higher likelihood
to be underweight, while it was 46% in real poor
households and 15% in the privileged non-poor
household. The pattern was mixed across the states
of India (Table 6). Among the states, Uttar Pradesh,
Bihar, Jharkhand and Madhya Pradesh had a signifi-
cantly higher likely of stunted children in excluded
poor households. This pattern also confirmed that
higher likely of the children underweight among the
real poor households in most of the states.
Fourth, apart from the poverty and welfare card,
this study also evident other socioeconomic factors
greatly influence nutrition among children in India.
Among the other factors, maternal education, anthro-
pometry, caste, religion and place of residence of
households have significantly correlated with stunting
as well as underweight among the children in India.
The observed differential in undernutrition widens the
gap in disparity among the poor and rich. The gap in
income, education and living standard affects the food
security of the households which is main reason be-
hindtheincreaseinthemalnutritioninIndiaand
other developing countries. Moreover, the children
from the higher birth order and lower birth size have
significantly associated with the stunting and under-
nutrition among children in India. These reasons are
multifaceted and intertwining affects the undernutri-
tion status of the children in India. There is some ex-
planation given based on the above finding. The
unavailability of the welfare card among the poor and
availability of welfare card among the richer house-
holds are explained as the misuse of welfare card
[44–46]. The non-availability of the welfare card
makes the household more vulnerable in terms of
food grain, employment, and social welfare scheme
and health accessibility, which have a combined im-
pact on child malnutrition. Moreover, the availability
of welfare cards in the non-poor household makes
them privileged non-poor in gaining these facilities.
The possibility of poor nutritional outcome among
households deprived in asset and having a welfare
card are two folds. First, though the PDS focus on
making provision ration, the quality of ration is poor.
Several studies have raised concern on the quality of
food grain under the Public Distribution System
(PDS) [53]. A Second plausible explanation is a higher
chance of selecting the real poor by two independent
approaches. While the asset deprivation is taken by
an independent survey which is based on the holding
of assets in a household, the BPL is distributed to the
poorest based on predefined criterion. What we ob-
served, the real poor and non-poor are two true
groups that are correctly defined. However, a section
of asset poor and not having a welfare card are an-
other concern. The poverty level among these house-
holds is not different from those who are real poor.
The lack of access to basic services, illiteracy and ex-
clusion from the welfare scheme in these households
are all multi-faceted manifestations of stunting and
underweight among the children. Though the Govern-
ment of India introduced the national food security
bill which guaranty food availability for the needy
people, it has not adequately addressed these house-
holds due to the exclusion of welfare card.
This study has the following policy implication. First,
the government should take a strong step in improving
nutritious food under the PDS. Provisioning of good
quality nutrients under PDS and widening baskets of
food grain under PDS may be help reduce malnutrition.
Second, households who are poor and excluded from
the welfare card should be given priority and included
under the welfare schemes. This will help to get univer-
sal access to food and improved nutrition and achieving
the central goals of development processes aiming re-
duction of poverty and inequality.
We outline following the limitations of this study.
First, we believe that those household have a welfare
card do use the card to avail benefits from welfare
schemes. Second, we also assume that the relative
ranking in wealth index and consumption expenditure
used to estimate poverty are similar. Third, the time
period of poverty estimates is based on 2011–12,
while the welfare card information was taken in
2015–16.
Panda et al. BMC Nutrition (2020) 6:41 Page 12 of 14
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Conclusion
We found that our estimates of the effects of poverty
and not having welfare cards show a positive impact on
child malnutrition in India. The study supports that sys-
tematic effort is needed to reduce the extent of malnu-
trition, especially among the poor. These results also
support the case for broadening access to universal food
security and comprehensive coverage welfare cards for
the needy population, which is vital in declining mater-
nal as well as child malnutrition in India.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40795-020-00369-0.
Additional file 1.
Abbreviations
NSS: National Sample Survey; BPL: Below Poverty Line; JSY: Janani Suraksha
Yojana; PDS: Public Distribution System; NFHS: National Family Health Survey
Acknowledgements
The authors express their gratitude to the reviewers and the editorial board
of the Journal for valuable comments and suggestions.
Authors’contributions
SKM and SVS conceptualized and designed the study. SKM, BKP and IN led
the data analysis, interpretation, wrote the manuscript, and jointly led the
revision. VDS contributed to the interpretation of the results and the writing.
All the authors approved the final submission of the study.
Funding
This research received no specific grant from any funding agency in the
public, commercial or not-for-profit sector.
Availability of data and materials
The unit-level data is available from the Demographic Health Survey (DHS)
data repository through https://dhsprogram.com/data/dataset/India_Stand-
ard-DHS_2015.cfm?flag=0/ and could be accessed upon a data request sub-
ject to non-profit and academic interest only. In another case, the
corresponding author of the paper may be contacted.
Ethics approval and consent to participate
No formal ethics approval was required in this particular case.
Consent for publication
Not Applicable.
Competing interests
The authors declare that they do not have any competing interests.
Author details
1
International Institute for Population Sciences, Mumbai, India.
2
Department
of fertility studies, International Institute for Population Sciences, Mumbai,
India.
3
Senior Advisor, FHI Solutions LLC, Alive & Thrive, # 503-506, 5th Floor,
Mohan Dev Building, 13 Tolstoy Marg, New Delhi 110001, India.
4
Harvard
Centre for Population and Development Studies, Harvard T.H. Chan School of
Public Health, 9 Bow Street, Cambridge, MA 02138, USA.
5
Department of
Social and Behavioural Science, Harvard T.H. Chan School of Public Health,
Boston, MA, USA.
Received: 23 January 2020 Accepted: 3 August 2020
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