DP2018-20

Does the Old Age Pension Scheme
Improve Household Welfare?
Evidence from India
Vidhya UNNIKRISHNAN
Katsushi S. IMAI

Revised May 11, 2020

Does the old age pension scheme improve household welfare? Evidence from India

Vidhya Unnikrishnan* and Katsushi S. Imai†€*Global Development Institute, The University of Manchester, UK† Department of Economics, The University of Manchester, UK€ RIEB, Kobe University, Japan
This Draft: 9th May 2020

Abstract
As India’s population has been ageing due to declines in fertility and mortality rates, morepolicy emphasis has been placed on reducing poverty among the elderly. The present study
aims to examine the effect of Indira Gandhi National Old Age Pension Scheme (IGNOAPS)
on household welfare indicators, such as consumption expenditure, income, assets, andpoverty. Using the household longitudinal data based on the India Human Development
Survey in 2004-5 and 2011-12, we have applied the Propensity Score Matching to build acounterfactual group and have used the propensity-score weighted fixed effects model toeliminate time-invariant unobservable household characteristics. The results show that the
IGNOAPS participation increased consumption expenditure, food and non-food
expenditure and assets, while reducing the household labour supply. The sub-sample
estimations show that these effects are observed only when recipients are women.
However, the results of Difference-in-Difference model imply that the poverty-reducing
effect deteriorated in 2011-12 after the government made several changes to programme
specifications in 2007. This was likely because some households which accessed
IGNOAPS after 2007 reduced the labour supply significantly while keeping the level ofconsumption.

Keywords: Pension, Ageing, IGNOAPS, Poverty, Household Welfare, India
JEL codes: C23, I38, H75
Acknowledgements: The authors would like to acknowledge valuable feedback from Kunal Sen,
Raghav Gaiha, David Fielding, Subhasish Dey, and participants in internal seminar at the Global
Development Institute, anti-poverty transfers research group seminar at the University of Manchester,
13th Annual Conference on Economic Growth and Development Economics and Planning Unit atIndian Statistical Institute, and the First Italian Workshop of Econometrics and Empirical Economics(IWEEE 2018): Panel Data Models and Applications. The authors appreciate valuable comments from
two anonymous referees. However, the usual disclaimers apply.
*Corresponding Author: Vidhya Unnikrishnan, Global Development Institute, University ofManchester, Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK; Email:vidhya.unnikrishnan@manchester.ac.uk

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Does the old-age pension scheme improve household welfare? Evidence from India
1. Introduction
As India’s population has been ageing due to the decline in fertility and mortality rates, thefocus of poverty alleviation programs has been gradually shifted to social assistance to theelderly which provides them with a safety net (Pal and Palacios, 2011). The data from theWorld Bank indicate that social assistance program accounts for 1.5% of India’s GDP in 2016,
while in Brazil and South Africa, the government expenditure on social assistance programs
accounts for 1.4% and 3.3% of GDP in 2015.1 The Employees’ Provident Fund Organizationand the Employees’ State Insurance Corporation offer major social security plans and pensions
to the workers in the formal sector (Uppal and Sarma, 2007). However, the formal pension
schemes do not reach most of the informal workers which account for 92% of total employment
in India (ILO, 2016). Also, the informal sector is usually associated with subsistence economic
activities at low wages, which prevents workers in the informal sector - who are often poor from accumulating wealth over the life cycle. The lack of an institutionalised social security
arrangement for the poor has highlighted the role played by Indira Gandhi National Old Age
Pension Scheme (IGNOAPS) – a national social assistance program targeted on the elderly
members of poor households in India. The primary objective of the present study is to evaluate
the impact of the program on household welfare and poverty using the household longitudinal
data based on the India Human Development Survey (IHDS) in 2004-5 and 2011-12. We
propose to examine the impact of IGNOAPS on household welfare indicators capturing both
direct and indirect effects, ranging from consumption expenditure, food and non-food
expenditure (all on a monthly and per-capita basis) and household assets, to the number of
persons working in the household and the household income excluding the IGNOAPS income.
Our estimates on the programme impact on household consumption, the labour market
participation and household income would shed some light on the mechanism whereby a2

recipient household reduced poverty. The secondary objective of this study is to examine
whether these effects improved or weakened, after the government made a few change to theprogramme specification in 2007, such as the eligibility criteria.
Research shows that two-thirds of the elderly in India live in villages, nearly half of whomare poor (Lena et al., 2009). The census in 1991 shows that about half of the elderly females
were either widowed, divorced, or separated, and they were likely to be vulnerable (Rajan,
2001). There has also been an increase in the economic compulsion to work for the elderly in
India (Singh and Das, 2015). Although the option of continuing to work helps the elderly tolead an active life, the elderly engage in the informal sector and earn lower pay than workers
in their prime age in developing countries (Kidd and Whitehouse, 2009).The rapid ageing
population growth in India2 makes it essential to understand the impact of existing policy levers,
as these policies are likely to be expanded in the future with the increase in the elderly
population.
There are three major contributions the present study aims to make. First, on the technicalside, we use a longitudinal dataset based on IHDS in 2004-5 and 2011-12 and apply thehousehold fixed effects (FE) model to it to estimate the impact of IGNOAPS on household
welfare indicators. In this model, participating and non-participating households are matchedby Propensity Score Matching (PSM) so that they are comparable with each other. Furthermore,
the regression is weighted by Propensity Score (PS) to take account of the probability ofparticipating in the household to reflect its differential incentive to join the scheme. While theprevious study on IGNOAPS has only used cross-section data (Garroway, 2013; Kaushal,2014), our study provides more robust estimates based on the improved methodology and data.
Second, we estimate the impact of IGNOAPS on a wide range of household outcome variables,such as household consumption, income, assets and household labour supply to identify how
households behaved differently depending on whether they participated in the programme. The
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results of various sub-sample estimations compare the programme effects by gender,
household’s political connection and caste group. Third, we have examined whether the change
in the programme specification in 2007, such as the change in the eligibility criteria,
strengthened or weakened the welfare effects based on the Difference-in-Difference (DID)
model applied to the panel data. This is, to our knowledge, the first study to examine whether
and how IGNOAPS reduced household poverty drawing upon the panel data based on thenationally-representative household survey in the Indian context.
The rest of the paper is organised as follows. The next section will review the relevant
literature. We will explain our data and empirical strategies in detail in Section 3 while
presenting and discussing the econometric results in Section 4. Concluding observations will
be offered in Section 5.

2.

Literature review

Social assistance programs in the form of cash transfers have become one of the most common
policy options for poverty alleviation in developing countries given the widely known successof conditional cash transfer programs in Latin America, notably in Mexico and Brazil since thelate 1990s, and their replications in other developing countries, including India (Randive et al.,2013). Cash transfer programs can be both conditional and unconditional. Conditional cashtransfers provide payments as long as beneficiaries commit to attaining the program objectives;
unconditional cash transfer offers cash transfers without any conditions attached to transfer. It
will work if the recipients are aware of the choices that need to be made with the money(Narayanan, 2011). The advantage is that it involves fewer compliance issues.
IGNOAPS is as an unconditional cash transfer program targeted to the elderly. Due to the
non-restrictive nature of the program, unconditional cash transfers can potentially result inchanges in various outcome variables.The impact of South Africa’s old-age pension scheme is4

a widely touted example of unconditional cash transfer. Case and Deaton (1998) studied there-distributive effects of South Africa’s old-age pension and found that both pension and nonpension income had the same positive effect on food expenditures. The arrival of social pension
can also change the living arrangements of the recipient households. Old age pension received
by an elderly grandmother in South Africa promotes interdependence, as grandmothers act as
caretakers for grandchildren, encouraging labour market mobility of younger mothers(Edmonds et al., 2005). Duflo (2003) confirmed a positive effect of old-age pension on thehealth outcomes of girls in the household.
On the impact of UCTs on labour supply Abel (2019) found that pension reduced thehousehold labour supply as it increases household income and pushes up reservation wages.
Posel et al. (2006) showed that pension income for old women enables working-age women to
migrate and work away from the household. Haushofer and Shapiro (2016) found that in Kenya
cash transfers provided every month improved household food security, while a lump-sum
payment increased household expenditure and enhanced the beneficiaries’ psychological wellbeing. On evaluating the impact of South African Old Age Pension, Case and Menendez (2007)
confirmed that the presence of a pensioner reduces household food insecurity among adults
and children and co-residing with a female pensioner encouraged enrolment of girls in school.
The theory of change illustrated by Bastagli et al. (2016) highlights various responses generated
by the cash transfer program. The most immediate response of recipients to a cash transfer
programme is to increase their general household consumption expenditure, food expenditure,and spending on other necessities. The arrival of additional non-labour income also may easecredit constraints and help forming household assets. Cash transfer programs increasehousehold consumption, which can shorten the time spent on work (the income effect), thereby
reducing the household labour supply. Drawing upon the literature on evaluations of social

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protection policies and the theory of change reviewed above, we have selected various outcome
variables to estimate both direct and indirect effects of IGNOAPS.
There have been few studies in the literature that have evaluated the impact of the pension
programme in the Indian context despite its scale and significance in governmental policies tosupport poor households with elderly members. An exception is Kaushal’s (2014) work that
estimated the effects of IGNOAPS on the elderly well-being, living arrangements, employment,
and expenditure pattern based on the National Sample Survey (NSS) data, but the author used
only a proxy measure on education to identify program recipients because NSS did not collectany data on IGNOAPS recipients. To overcome this drawback, we have used the IHDS panel
dataset which has precise details on program beneficiaries. We can thus identify the treatment
status regarding IGNOAPS participation far more accurately than Kaushal (2014) who used
only a proxy measure. Furthermore, we will use a PS weighted FE model for the panel data
where PSM matches beneficiaries and non-beneficiaries to remove any bias due to sample
selection and the effects of time-invariant unobservable characteristics. This methodology
takes account of different eligibility criteria before and after 2007 and across different states.
This is a significant development from Kaushal (2014) based on the cross-sectional data.

Indira Gandhi National Old Age Pension Program
The Central Government of India introduced the National Old Age Pension Scheme in 1995.
The program eligibility criteria used in the initial phase was that the beneficiary has to be 65
years of age or older, and a destitute with no regular source of income or support from family.
The central government provided 75 Indian National Rupees (INR) to eligible beneficiaries. In
2003, National Old Age Pension was transferred into State plan. This offered the State
Government the flexibility with the implementation process, but the Central Government
continued assisting the programme. In 2007, the scheme was renamed and relaunched as Indira
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Gandhi National Old Age Pension Scheme (IGNOAPS). 3 The eligibility criterion changed withsomeone being destitute to belonging to a Below Poverty Line, also known as BPL household.
The Central Government also increased the benefits from INR 75 to INR 200. The second
round of change happened in 2011 when the Central Government reduced the age eligibility
threshold for the program from 65 years of age to 60 years.4
Overall, the program underwent several rounds of iterations, which include a reduction in
the age threshold for the program participants, an increase in the transfer amount, and theprogram covered Below Poverty Line households. State Government in India has reacted tothese changes differently. Kaushal (2014) has provided a rich discussion on the varied response
which the State Governments provided regarding the reduction in age threshold and theincrease in transfer amount. The author highlighted that in 2007, 12 States did not make any
contribution in addition to the Central Government’s assistance, and there are unevencontributions made by other States towards IGNOAPS. Even when the pension eligibility age
for the program was 65 years, there were variations in eligibility age followed the States; withsome States using the threshold of 60 years and some following 65 years for male and 60 years
for female. Rajasthan adopted the lowest age eligibility criterion. The targeted beneficiaries inthe state include males who were 58 years old and females who are 55 years old. The
unevenness in the State contribution towards IGNOAPS persisted even in 2011when someStates did not assist, and some States topped up four times more than the Central Government’s
input (see Appendix 1). From the IHDS data, we infer that the average annual transfer amountreceived from IGNOAPS was INR 1818 in 2004-5, and this increased to INR 4105 in 201112. The IHDS data show that between the two rounds, the total number of recipients in the
program grew from 1520 to 4477 and the number of poor recipients from 915 to 1723.
With the increase in the elderly population in India, there has been a rapid expansion of thisprogramme. In 2002-03 IGNOAPS covered 6.7 million beneficiaries, and by 2012-13, the7number of beneficiaries covered under the scheme has more than tripled to 22 million.5 The
expenditure allocation on IGNOAPS has increased by 1.4 times between 2007-08 (INR 29
billion) and 2011-12 (INR 42 billion).6

3.

Empirical framework

Data
We have used the panel data based on IHDS, which collected the information at both household
and individual levels. The survey has covered a wide range of topics that include social capital,
labour, education, health, and expenditure incurred. IHDS currently consists of two rounds.
IHDS-1 is a nationally representative survey conducted in 2004-5. IHDS-2 was conducted in
2011-12 to re-interview 40,018 households (including split households) and add further 2,134
households. For our research, we have used the panel dataset at the household level. The
household panel data have 40,018 households interviewed in both periods (Desai, Vanneman
and National Council of Applied Economic Research, 2005 and 2012). Appendix 2 reports
definition of the variables used in this research.
Table 1 shows descriptive statistics of the variables that we use in our regression analyses.
This provides a contrast between beneficiaries and non-beneficiaries at household levels overoutcome variables, (ii) explanatory variables used for probit models (for PSM) and (iii) thosefor FE or DID models. Table 1 is based on the data matched by PSM and weighted by theestimated PS. The second panel of Table 1 shows that beneficiaries and non-beneficiaries arereasonably balanced in the explanatory variables for probit models as the means of most of the
explanatory variables are similar, although there are a few variables (e.g. Antyodaya card)
where t-test rejects the null hypothesis of equality due to the large sample size. The averageannual amount of IGNOAPS increased from INR 2242 to INR 2548.7 Most of the outcome
variables (e.g. MPCE (food), assets and household income excluding the IGNOAPS payment
8

in the first round) on average higher for beneficiaries than for non-beneficiaries. In the next
section we will examine more formally whether the programme actually improved the outcome
variables.
[Table 1 to be inserted around here]
Empirical Strategies
There are three empirical challenges that we face, as we estimate the effects of IGNOAPS on
household welfare. The first challenge is that, unlike an experimental framework, such as
Randomised Control Trials, we lack a valid counterfactual group. As a household can selfselect itself into IGNOAPS, the estimates on the effect of the programme may be biased without
addressing the sample self-selection bias. The second constraint is to eliminate time-invariant
unobservable characteristics in our model. The third challenge arises from the change of the
programme specification, in particular the eligibility criteria, between 2004-5 and 2011-12. An
incentive to participate in the programme changed between the two rounds in response to thespecification change. We will estimate the household-level incentive to participate in the
programme to take into account any change in incentives for participation that was caused bychanges in the programme specification.
We will address these three challenges by two-step procedures. First, we will estimate thePS, or the probability of a household participating in the programme, by the probit model
applied to each round of the panel. PS reflects differential incentives of the household
participating in the programme not only across households but also over time. Hence, this willaddress the third challenge, that is, the change in the specification of the programme in thepanel setting. We use the PS to match the beneficiary households and non-beneficiaryhouseholds to make them comparable. The PSM method eliminates households which are notcomparable with each other. For instance, if non-beneficiaries include wealthy households withlittle incentive to participate in IGNOAPS, the impact of the programme of improving welfare
9might be underestimated. The use of a matched panel in all our estimations ensures robust
estimates.
In the second step, we will apply the fixed-effect (FE) model to take account of theunobservable factors at household levels, which are assumed to be unchanged over time (thesecond challenge). Here we weight the regression based on a PS. Using a PS as a weight will
not only minimise a potential bias in our estimates of the effect of IGNOAPS on outcome
variables that may arise from the sample selection into the programme (the first challenge) but
also take into account any changes in household incentives to participate in the scheme due tothe programme specification change in 2007 (the third challenge). The method of matchingand employing the FE model answers the question on the impact of a household gaining accessto the program in any one of the rounds on the consumption outcomes, labour supply decisions
and assets held. As the PS reflects how likely each household would participates in the
programme given the household characteristics, including age profiles, our methodology will
also address differential incentives in response to different eligibility criteria across states.
As an extension, to estimate the impact of the program specification changes which took
place in 2007, we will use the DID method in the matched data. The DID model is used toestimate the impact of the policy changes that took place in 2007 and compares the outcome
variables between the program recipients and non-recipients. In subsequent paragraphs, we will
discuss the methodologies outlined above in more details.

First Step: PSM
The PSM method (first proposed by Rosenbaum and Rubin, 1984) matches the treated groupof households with the control group based on observable characteristics. We match units ofobservations (households) based on their propensity to participate in IGNOAPS. We carriedout matching based on the binary variable on participation status rather than the amount of10social pension that participants received to avoid complication. For the first round, we used theentire sample of households, including those which dropped out in 2011-12, in order to address
the attrition problem. We construct a PS based on the covariates that determine participation in
the program, and also simultaneously affect outcomes (Caliendo and Kopeinig, 2008). The
covariates in the probit model includes ‘BPL card’, ‘Antyodaya card’, ‘the presence of anelderly member in the household if the household lives in a rural area’ and ‘attends public
meetings’, ‘highest education in the household’ and ‘if the household belongs to a scheduled
caste or a scheduled tribe’.
There were two changes the central government made in the eligibility criteria between thetwo rounds. The first is that the eligibility criterion changed from someone being destitute tobelonging to a BPL household. However, there was actually no formal identification
mechanism used to identify a destitute and so we cannot use the criterion on destitute in the
2004-5 analysis. The age eligibility criterion also changed in 2011 as the central government
lowered the age threshold from 65 years to 60 years. Despite a few changes in the eligibility
criteria between the two waves, we use broadly same variables in estimating the probit model.8
The result from the first stage probit model in Appendix 3 shows that a household having a
BPL card has a higher probability of receiving the treatment. It is noted that the BPL cardholding is determined by 13 parameters, one of which is the operational landholding in 2002.
As such, the BPL card holding status captures a poverty status in multi-dimensions, including
asset status of a household, which would justify our use of current household asset index as
one of the outcome variables. Similarly, households having an Antyodaya card also have ahigher probability of receiving treatment. A household with an elderly member has a higher
probability of receiving the treatment, and if the household head belongs to a sociallydisadvantaged caste, then the probability of receiving it increases. Residing in the rural area11

and attending public meeting increases the probability of receiving IGNOAPS. The variablereflects upon the awareness of the scheme, which increases the likelihood to receive
IGNOAPS. However, higher years of education reduce the probability of receiving IGNOAPS.
Education is a predictor for better-earning outcomes, and this reduces the dependence on thewelfare program.
We then use kernel matching where we apply weighted averages of all households in thecontrol group to build the counterfactual group to pair treatment with control units. We have
performed a test to ensure that the covariates used in the construction of PS are balanced inboth treated and control groups such that these groups are comparable.9 The graphs on common
support regions (Figures 1 and 2) and the distribution of the propensity score (Figures 3 and 4)
in the treatment and matched control group are shown below. As shown in Figures 1 and 2, theobservations outside the region of common support (those with the propensity score above 0.2
in 2004-5 and above 0.45 in 2011-12) have been dropped as no comparable control households
exist. Figures 3 and 4 show that the treatment and control units have a similar distribution in
the area with the propensity score above 0.05.
[Figures 1, 2, 3 and 4 to be inserted around here]
We repeated the same procedure separately for the two rounds and dropped the households
outside the region of common support (1,698 households in the first round and 11 in the second
round). After dropping them, we have 38,320 households in 2004-5 and 40,007 households in
2011-12 rounds. As a result, we lost 4.24% of households in the 2004-5 rounds, and 0.03% of
the households in 2011-12. We then constructed a balanced panel dataset with households thatare present in the common support in both rounds of the survey. The balanced panel dataset
consists of 38,309 households.
Second Step: FE model with PS weighting
12Angrist and Pischke (2008) suggest that if the panel data are used for impact evaluations theeffects of confounding unobservable time-invariant characteristics on the outcome variable andprogram participation can be eliminated by using FE regression estimation technique. The
advantage of using a FE regression method is that it wipes out the effect of time-invariant
unobservable household characteristics that would affect outcome variables. Therefore, weestimate the effect of treatment on the outcome variables using a FE model.
Even if the exogeneity condition is satisfied for the IGNOAPS participation, a bias in theestimate may remain if participants and non-participants are not comparable where, forinstance, rich households without any incentive to participate in the scheme are included in thecontrol group. To address this limitation, we have combined the FE model with the matching
method to ensure that treatment and control groups are comparable. 10 Furthermore, we put
differential weights for the treatment and the control using the PS. The methodologicalframework of combining PSM with fixed effects was also used by Imai and Azam (2012) tostudy the impact of microfinance on poverty in Bangladesh. The method of matching with FE
is also used by Kim et al. (2008) to understand the effects of precarious employment on selfreported health in South Korea.

The FE Model with PSM and with PS weighting11
The fixed effects equation that we estimate is:
𝑌ℎ𝑡 = 𝛽0 + 𝛽1 𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 + 𝑋ℎ𝑡 𝛽2 + 𝜇ℎ + 𝑒ℎ𝑡(1)

The outcome variables (𝑌ℎ𝑡 ) estimated here is log monthly real per capita expenditure (MPCE)at the household levels for the hth household in round t (2004-5 and 2011-12). Alternatively,
we have also used for a dependent variable, 𝑌ℎ𝑡 , log monthly real per capita food expenditure(MPCE (food)), log monthly real per capita non-food expenditure (MPCE (non-food)), the log
13

of the asset index, log number of household members working or log household income
excluding IGNOAPS. We also use poverty based on per capita household expenditure as 𝑌ℎ𝑡 .12
Weareprimarilyinterested

in

the

parameter

estimate 𝛽1 , forthevariable

𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 (a binary variable that takes the value one if the household receives any
money from IGNOAPS and zero otherwise). As we have noted, while the eligibility criteriachanged between the two rounds, the estimation will be weighted by the PS, which reflects adifferential incentive into the programme across households over the years. 13 Explanatory
variables in the probit model in the first step include the eligibility criteria for the programmeand, despite the limitations14, our methodology would be one of the few to address the thirdchallenge, that is, the change in the programme specification in 2007. Here 𝛽̂1 indicates theaverage effect of receiving IGNOAPS on each outcome variable.
It is noted that IHDS has constructed monthly per capita expenditure using a broad set ofvariables that reflect the purchasing power of the household. The food and non-food categoriesof spending represent only a subset of the total monthly per capita expenditure. On average,
the basket of food items included in the food expenditure represents 41% of the total spendingin 2004-5 and 39.6% of the share in total expenditure in 2011-12. The list of non-food items
included in the analysis on average constitutes 23.3% of the aggregate expenditure in 2004-5
and 25.8% in 2011-12. We have controlled for the effects of other time-varying covariates (𝑋ℎ𝑡 )in the regression model. 𝜇ℎ represents the time-invariant unobserved household
heterogeneities. 𝑒ℎ𝑡 is the error term at the household level. Although IGNOAPS is provided
at the individual level, the outcome variable we use is defined at the household level mainly
because the survey data do not have information on assets held, consumption, food, non-food
expenditure incurred at individual levels. We also implicitly assume the unitary household
model in defining poverty drawing upon a large body of empirical literature on poverty studies
in developing countries where a household is assumed to have a common set of preferences14while recognizing the importance of taking into account the individual welfare in defining
poverty (Alderman et al., 1995). The unit of analysis is key to specifying the fixed effects
imposed, and this is the reason that we used household fixed effects.
A vector of the control variables (𝑋ℎ𝑡 ) include the number of children, the number of elderly
male, the number of elderly female, household compositions or the size excluding children andelderly members, the highest level of education in the household, place of residence, women’s
access to mass media, the household head is a Hindu, caste, and other welfare programs
received by the household. In selecting these control variables, we have followed the empiricalliterature on the impact evaluations based on non-experimental household data we havereviewed in Section 2 (e.g. Pal and Palacios, 2011) while adjusting the specification given thedata availability and the study context. The regression is weighted by the estimated propensity
score following Hirano and Imbens (2001), Hirano et al. (2003), and Michalopoulos et al.
(2004). Under the unconfoundedness assumption for PSM that the treatment is independent ofoutcome conditional on pre-treatment variables, we assign the sample weight of 1 for all thehouseholds in the treatment group and p/(1-p) in which p is the estimated Propensity Score (PS)for households in the control group to derive the (conditional) average treatment effect on
treated(ATT) (Hirano and Imbens, 2001).The PS weighted FE model applied to the matchedpanel is designed to minimize sample selection bias arising from the differential propensity toparticipate in IGNOAPS in the panel data setting (Linden and Adams, 2010). As we have noted
earlier, our model will take into account the fact that the programme specification or theeligibility criteria changed between 2004-5 and 2011-12. This is because in our model nonparticipating households in each round are assigned the weight, or the probability of being
included in the regression, reflecting how likely they could have participated in the programme
which is a function of the eligibility criteria in each round. This is, in our view, one of themethodologies which can provide robust estimates of the programme impact given the data
15

constraints where (i) there was no baseline without interventions and (ii) the eligibility criteriachanged over time.15

An extension: the DID model to evaluate the effect of changes of the programme in 2007
In light of the major changes of the programme specification in 2007, we examine whether thehouseholds which received the programme in 2011-2012 had a better welfare outcome thannon-beneficiaries. We apply a version of the DID model to identify the effect of programme
specification changes in 2007. Here the PS-weighted FE model is used for the matched panel
only for a sub-set of the panel in which households did not participate in the programme in
2004-5. This model identifies the effect of the change in participation status by comparing thesample households which remained non-participants and those which started to participate only
after 2007. Unlike the standard DID settings, we do not have baseline data without any
participants as there were already both participating and non-participating households in 20045. To reduce any sample selection bias as well as attrition bias, we have weighted in the
regression by PS so that the regression reflects the probability of the household participatingin the programme in 2004-5 and 2011-12 as different households had different probabilities ofparticipation. So, while this is not a perfect solution to eliminate sample selection bias
completely as PS is subject to the specification and the results of the probit model, the PSweighted DID model would provide a robust estimate given the data constraints. 16 We estimate
the following model:
𝑌ℎ𝑡 = 𝛼0 + 𝛼1 𝑃𝑜𝑠𝑡 2007 + 𝛼2 𝑛𝑒𝑤 𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡 + 𝛼3 𝑋ℎ𝑡 + 𝜇ℎ + 𝑒ℎ𝑡(2)where Yht denotes the outcomes variable, Post 2007is a dummy variable for the year 20112012, and 𝑛𝑒𝑤 𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡 stands for a dummy variable on whether a household
participated in IGNOAPS in 2011-12, not 2004-5. This variable identifies the effect of newparticipation on 𝑌ℎ𝑡 in comparison with the households which remain non-participants and are
16

comparable with new participants in terms of the probability of participating in IGNOAPS. 17
̂𝛼2 thus provides a DID estimate indicating the effect of the programme change on outcome
variables and ̂𝛼2 is shown to be consistent in a two-wave panel data model with unit and time
fixed effect (Heckman and Robb, 1985; Galiani et al., 2005). Xht denotes a vector of control
variables. 𝜇ℎ is the household level fixed effects.

4.

Econometric Results

In this section, we will present and discuss the main econometric results based on Equations(1)-(2) in the last section. We will focus mainly on key findings on whether and why IGNOAPS
reduced or did not reduce, household consumption based on the results on various outcome
variables.

The Results of the FE Model
Table 2 reports the results of the FE model (Equation (1)) based on the PS weighted FE model
applied to the matched panel data. As the first row of Table 2 indicates, a dummy variable onwhether a household receives IGNOAPS shows an expected sign with significant parameter
estimates in all the cases except the last two columns, poverty and income excluding the
IGNOAPS payment. The results support our hypothesis that participation in IGNOAPS
improved household welfare over time. Table 2 has confirmed that IGNOAPS participation
significantly increased household consumption expenditure through food expenditure andselected items on non-food spending. Increase in food expenditure as a result of cash transfers
is consistent with Haushofer and Shapiro’s (2016) results in Kenya. Non-food expenditure only
covers spending in non-luxuries, necessities and services (e.g. toiletries, transportation,
services on domestic servants, or out-patient medical expenses) and excludes luxuries or
unusual spending. The parameter estimate on non-food expenditure is larger than that of food17

expenditure or total expenditure (including luxuries or unusual spending, defined by IHDS),
implying that the programme participation enabled households to spend on necessary items
and services, which would improve the household welfare significantly. In terms of themagnitude, households receiving IGNOAPS increased their consumption expenditure by 5.5%
on average evaluated at the mean (from INR 864 to INR 912). Likewise, beneficiaries of
IGNOAPS augment their food expenditure by 5.8% (from INR 366 to INR 387) and increased
non-food expenditure (on necessities) by 8.7% (from INR. 231 to INR. 252). We have also
found that access to the program is likely to lead to a larger level of the household asset (e.g.
sewing machine, radio, TV, refrigerator) by 10%, implying that some households increased on
spending in assets.
As expected, the number of household members working in the labour market reduced as aresult of IGNOAPS participation. We find that the program significantly reduced the labour
supply by 10 % (from 2.15 members to 1.93). There is no significant effect on poverty. This
could be because a significant number of non-poor people participated in the scheme as we will
discuss later or consumption increase by 5.5% was not large enough for poor households tocross the poverty threshold. We find that the income excluding IGNOAPS reduced by 1.2%
though it is not statistically significant. Overall, IGNOAPS was found to be welfare improving
as it relaxes the budget constraint and enables households to spend more on food, necessities,
and assets without reducing the household income other than IGNOAPS significantly.
[Table 2 to be inserted around here]
IGNOAPS income for the elderly member increased the demand of goods and services for theentire household (income effect), and this is reflected in higher per capita expenditure, food,non-food expenditure and improvement in assets. Given that consumption is a broad indicatorof household wellbeing, an increase in consumption indicates the ability of the program to
reduce household poverty, although the effect on ‘poverty’ is not statistically significant.
18

Because the program is targeted at the poor, a priori expectation is that the program will havea substantial effect on food expenditure. However, we observe that there is a substantial impactof the program on non-food spending and household assets. With the increase in non-labour
income, a household became able to spend more on non-food expenditure, mainly on necessary
items and services. The category of non-food spending also includes outpatient medical
services. Households that receive IGNOAPS on average spend INR 112.6 (monthly/per capita)
for outpatient medical services, which constitute the largest component of expenditure incurredin the non-food category. UCT allows beneficiaries to decide on how to utilise funds. The
treatment group’s choice of investment in assets reflects the long-term poverty reduction
strategy of households (Angelucci et al., 2012).
On further disaggregating the data, we find that households had a higher allocation of thepayment of IGNOAPS towards non-food spending and household assets. Outpatient medical
payments mainly drive substantial spending on non-food expenditure. Although India has mademedical strides over the years in the reduction of mortality rates and the improvement in life
expectancy, there exists a vast rural-urban divide in the quality of medical services provided.
Also, the poor in India do not have easy access to the health insurance market, which further
increases their expenditure on the outpatient facility (WHO, 2010). Households also use
IGNOAPS for asset creation.
Social insurance and social assistance programs can affect the household labour supply
through income and substitution effects. An income effect does not create a deadweight loss,
but instead, it is the intended outcome of the program. A substitution effect creates a
deadweight loss in the economy. The income effect is observed if, with the arrival of IGNOAPS,
households decide to reduce the number of hours spent on work or withdraw from the labour
market. Unearned income can relax liquidity constraints or smooth household consumption
thereby helping households to reduce their labour supply (income effect) (Ning et al., 2016).
19

As Ning et al. show, the substitution effect occurs only if implicit taxes are imposed on workers
who continue to work after accessing the pension programme. In that case, the substitution
effect will create a deadweight loss in the economy as in the case of some contributory pension
system or social earnings test that discourages working. A deadweight loss is created here
because people work less and the economy’s output shrinks because of the implicit tax imposedon workers who work after the arrival of pension. The purpose behind the implicit tax for thegovernment is to increase revenue, but this becomes counterproductive as it discourages
individuals from working and reduces government tax revenue. However, we overrule any
substitution effects here as the program does not impose any labour inactivity condition. Theage eligibility criterion followed in IGNOAPS coincides with the retirement age, so thebeneficiaries are not expected to contribute to the labour market, and so it is surmised that theelderly members reduced their labour supply. Therefore, we can conclude that IGNOAPS
reduced the household labour supply through its income effect. Appendix 4 reports a full setof results where the control variables included in the models display the expected sign.18
IHDS-2 has details on whether households have Rashtriya Swasthya BimaYojana (RSBY– ‘a national health insurance programme’ for the poor) card. A little more than 25% of
IGNOAPS recipient households have RSBY card in 2011-12. Since we have information on
RSBY only for 2011-12 in our dataset, we have estimated the results for non-food expenses
for the 2011-12 rounds. After controlling for the effects of RSBY, we find a household’s accessto IGNOAPS reduces the non-food expenses in 2011-12 rounds. This finding corresponds toour earlier results that the medical expenditure drives substantial spending on non-foodpayment. To further understand the effects of health insurance, we have also estimated theeffects of having both IGNOAPS and RSBY on the non-food expenses. The interacted variable
is useful to answer if households having to access to both old-age pension and health insurance

20

still incur a substantial allocation on non-food expenses. We do not find any statisticallysignificant effect of the interacted term on the non-food expenses.

Extensions
In Table 3, we have carried out a few extensions using the PS-weighted FE model for thematched panel. First, we have replaced a dependent variable by a binary variable on whether
elderly members are working in a household. Our results on the effect of IGNOAPS on elderly
labour supply suggest that program participation reduces the probability of elderly members
working by 12.7%.This result indicates that IGNOAPS reduces elderly member’s economic
need to work. Second, we have also analysed the impact on the outcome variables if the pension
recipient is a male/female by replacing a dependent variable by a binary variable on whether
male members (or female members) are IGNOAPS recipients. We find that MPCE and foodexpenditure increased only when recipients are female. The increase in assets formation isslightly larger when the recipient is female compared to being male.
[Table 3 to be inserted around here]
The difference in the behavioural responses to cash transfer between male and female recipientssuggests that households are non-unitary. The standard unitary model assumes that a household
maximises a single utility set, and cash transfer to men and women will generate similar welfare
effect as they both have a common set of preferences. This has been increasingly challenged
by many studies, as research suggests that individual preferences influence household
allocations. Income transfers focused on women encourages expenditure on children, nutritious
specific goods (Bobonis, 2009; Duflo, 2003) as women tend to allocate higher shares ofspending on health and nutrients than men (Thomas, 1990). The positive effect of the foodexpenditure variable when the program recipients are females further substantiates the resultsfrom the literature.
21

The findings from the sub-sample estimations (male/female) suggest that participation in
IGNOAPS does not affect non-food expenditure contrary to the result in Table 2. When womenparticipate in the programme, the household significantly increases total consumption
expenditure by 5.9%, food expenditure by 7.3%, assets by 7.4%, and reduced the labour supply
by 3.2%. Non-pension income increased by 4.9%, but the parameter estimate on non-pension
income and labour supply is not statistically significant.19On the contrary, the household does
not respond to IGNOAPS when men receive the payment with much smaller and statisticallyinsignificant parameter estimates in all the cases, except improvement in household assets(5.1%). We can conclude that the household welfare improvement of the IGNOAPS is realized
when women participate in it. Importantly, our result suggests that women used the IGNOAPS
payment for the household food expenditure, which is likely to lead to better food security andnutritional conditions of household members. Thirdly, the sub-sample estimations forhouseholds with political connection indicate that welfare-improving effects are not observed
for these households, implying that there is no role of political connections in enhancing the
programme impact. Finally, our analysis on the sub-sample of households whose household
head belonged to the scheduled caste or scheduled tribe suggests that IGNOAPS is a significant
determinant of assets held (10.7%), and household labour supply (-8.9%). Given that ahousehold that is socially marginalised also tends to be economically marginalised, our results
implied that the programme increased assets formation among socially marginalized
households. The last column shows that there is no statistically significant effect on poverty
and household income.

DID estimates of changes in the programme specifications in 200722

Finally, Table 4 reports the results of the DID model that analyses the effect of the programme
specification changes in 2007 on outcome variables. These are based on the PS-weighted FE
model applied to the matched panel to mitigate sample selection bias.
[Table 4 to be inserted around here]
Here we use the matched panel only for the households which did not receive IGNOAPS in
2004-5. We have found that new IGNOAPS participants after 2007 did not increase total
expenditure, food or non-food expenditure, or poverty, but they significantly increased assets
by 4.7% on average evaluated at the mean and reduced household labour supply by 4.8% andalso significantly reduced the household income (excluding IGNOAPS payment) by 12.9%.
Asset increase is nevertheless realised through the income effect because, once the IGNOAPS
payment is included in the household income, ‘new IGNOAPS participants’ increase it
significantly by 23.8%. The results imply that the programme impact deteriorated as
beneficiary households further reduced the labour supply after 2007 in response to the changeof the age threshold from 65 to 60 years, leading to a significant reduction in non-pension
household income. However, the new recipient households tend to spend the income fromIGNOAPS on household assets. It is surmised that after 2007 some recipients stopped working
and invested in household assets, while keeping the level of consumption.
The DID results suggest that the expansion of the program in 2007 did not lead to asignificant welfare improvement in terms of consumption expenditure, but increased assets
significantly. This could be associated with the change in the program specifications, which
would increase the cost of participation or administrative burden for the poor (e.g. more
paperwork). This is reflected in the increase in exclusion targeting error (Type 2 error) in the
program, where poor eligible people did not participate (‘undercoverage’). The exclusion
targeting error in the program has increased from 47.5% in 2004-5 to 74.2% in 2011-12. In themeantime, the extent to which non-poor ineligible people accessed the programme (‘leakages’
23

or Type 1 error) improved from 60.2% in 2004-2005 to 38.5% in 2011-12, but it remained athigh levels in the second round. Asri (2019) discusses the widespread exclusion targeting error
prevalent in the program.
Non-poor recipient households may have fewer incentives to increase food or non-foodconsumption, but more incentives to increase assets.20 Although the program expansion has
increased the cost of participation, for those households that gained access to the program, thepension payments were large enough to reduce their household labour supply. The reduction
in household income excluding the payment from IGNOAPS further corroborates that the newprogram recipient households have reduced their efforts in the labour market.21
While we examined the overall effect of IGNOAPS in Table 2, we focused only on theeffect on the new participants in the second round to analyse the effect of the specification
change of IGNOAPS in Table 4, and the results can be different. However, given that the newparticipants are a subset of the entire participants in the programme, it is evident that thewelfare-improving effect of the programme deteriorated significantly after the programme
specification change in 2007.22 Given the size of average reduction of non-pension income in
2011-12 (13.4%, INR 6611), which is much larger than the average IGNOAPS payment in2011-12 (INR 2548), there was likely to be a disincentive effect of the programme where somebeneficiary households became dependent too much on the programme after 2007. Why thisoccurred only after 2007 is a puzzle. This may be due to the psychological effect or thedifference in behavioural response of poor and non-poor households. This is beyond the scopeof the present study; future research can investigate this.

5. Conclusion
This study has evaluated the effects of IGNOAPS on a range of welfare indicators, such as
household consumption, assets, non-pension income and poverty, by using the panel data on
24

India based on the IHDS survey in 2004-5 and 2011-12. Given that households self-selectthemselves in participating the programme and the programme specification, such as theeligibility criteria, changed in 2007, we have first applied the FE model for the panel data whereparticipating and non-participating households matched by PSM to take account of differential
incentives for participating in the programme across different households and over time. This
identifies the overall effect of programme participation. We have then applied a DID model fora subset of the matched model (for the households which did not participate in 2004-5) andhave weighted by the PS to identify the effect of IGNOAPS on household welfare for those
who chose to participate after the specification of the programme in 2007.
The results of FE with PSM and PS weighting show that IGNOAPS increased household
welfare outcomes in terms of total consumption by 5.5% on average, food consumption by
5.8%, non-food consumption on necessities by 8.7%, household assets by 10.0%, while it
reduced the household labour supply by 10% and had no effect on poverty and non-pension
household income. With the arrival of an additional source of non-labour income through
IGNOAPS for the elderly member, there is an increase in demand of goods services for theentire household (income effect), and this is reflected through higher per capita expenditure,
food, non-food expenditure and improvement in assets. Presumably, while the participant
members work less in the labour market, non-pension income is kept. These results confirm
the welfare-improving effect of the programme. We have also analysed the impact on theoutcome variables if the pension recipient is a male or a female and have found that
consumption expenditure, food expenditure increased only when recipients are female. The
increment in assets formation is more substantial in households with female recipients thanthose with male recipients. This suggests different behavioral responses between men andwomen (e.g. women tend to use the pension income for the household food expenditure), which25

have been found in the previous works (Thomas, 1990; Duflo, 2003, Bobonis, 2009) and does
not support the unitary household model.
However, the welfare-improving effect deteriorated after 2007 as suggested by the DID
results with PSM, and PS weighting applied to a sub-set of beneficiary households, that is,
those who started to participate after 2007 (“new IGNOAPS participants”) and the matchednon-beneficiary households. While consumption expenditure remained unchanged regardlessof its definitions (i.e., total, food, or non-food), household assets increased by 4.7% and thelabour supply reduced by 4.8%. Notably, non-pension income reduced by 12.9% due to theparticipation. This is partly because the targeting performance of the programme worsened in
2011-12 where a significant share of non-poor households participated in the programme
despite the changes of eligibility criteria between the two rounds where the poverty criterionchanged from “being destitute” to “belonging to a BPL household” and the age thresholdchanged from 65 years to 60 years. That is, new participants in IGNOAPS that consist of asignificant number of the non-poor did not use the programme as a means of escaping frompoverty, stopped working and invested in household assets, while not changing consumption
expenditure significantly. As Asri (2019) shows, BPL card as eligibility criterion was misused
by non-poor individuals and poor older people lacking a BPL card did not access the
programme. This caused some disincentive effects among the beneficiary households, where
they reduced the labour supply and non-pension income. Our results are consistent with theexisting evidence that household income may not always increase corresponding to the transfer
amount, especially if the program reduces other possible income components (Attanasio andMesnard, 2006). The negative effect of IGNOAPS on the household labour supply implies that
other sources of income are reduced, which explains why we do not find any overall impact ofthe programme on poverty or income. It is noted that the poverty reducing effect of the cash

26

transfer program is conditional on the extent to which it affects work incentives (Skoufias and
Di Maro, 2008).
The challenge for policymakers is to improve the targeting performance of IGNOAPS toreduce poverty under the severe budgetary constraints faced by central and state governments.
Ideally, the amount of programme payment should be increased given the relatively small
effect of additional pension income on poverty, but this would further worsen the targeting
performance. The priority is for each state to review the eligibility criteria so that more poor
households access IGNOAPS to escape from poverty.
Although the findings from the paper provide key policy insights about the program, there
are some limitations here. First, the income effect is the possible mechanism through which thefindings in this paper are explained. This is justified as the program participation increases thepurchasing power of the recipients (monthly per capita expenditure) and the demand for goods
and services (food, non-food expenditure). However, to further corroborate this finding
additional evidence needs to be provided by disaggregating the food expenditure, andexamining if pension recipient households are shifting from cheaper to expensive food items.
Second, we have provided evidence on the reduction in household labour supply, but we havenot disaggregated the recipients at the individual level to see who within the household has
reduced the labour supply. Future research on this topic should explore the individual labour
supply decisions within the household. This will provide an insight into how individuals within
the household alter their labour supply decisions with the arrival of pension. Third, the subsample estimations between men and women program recipients suggest a non-unitary
household, but this requires additional empirical work highlighting different behavioural andpsychological responses to the pension programme between men and women.

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33

Table 1: Summary statistics on weighted matched panel
Variables

2004-5Mean
PensionPensionnonreceiving
receivinghouseholdhouseholds(N=(N=36,983)
1,326)

2011-12
F-statMean
PensionPensionnonreceiving
receivinghouseholdhouseholds(N=(N=34,610)
3,699)

F-statOutcome variables
MPCE [1]722.7

720.950.01

1806.45

1711.6

11.82**MPCE (food) [2]302.87

312.734.34*

733.12719.77

3.76

MPCE (non-food) [3]

178.49169.472.69

491.82472.02

3

Assets[4]

10.43

11.22

23.37***

12.92

12.76

2.61

Number of household members working

2.77

2.72

0.97

2

1.94

4.55*

Poor[5]

0.28

0.25

6.8*

0.24

0.23

0.56

Household income (excluding IGNOAPS) [6]48603.251259.2

2.0256723.849334.4

13.32***IGNOAPS amount (annually)

nil

2242

nil

nil

2548.43

nil

BPL card

0.43

0.42

0.34

-

-

Explanatory variables used in probit model to derive propensity score model-

Antyodaya card

0.06

0.05

4.95*

-

-

Household: elderly

0.95

0.95

0.08

0.92

-

0.93

1.01

Rural area & attends public meeting

0.31

0.31

0.07

0.31

0.31

0.61

Highest education in the household

6.11

6.54

9.63*

6.54

6.71

3.41

Scheduled caste or tribe

0.4

0.4

0

0.36

0.36

0.7

Women:TV

-

-

-

0.68

0.7

2.62

Household has a BPL or Antyodaya card

-

-

-

0.66

0.66

0.07

Urban area

0.21

0.13

54.38***

0.23

0.18

73.26***

Women: newspaper

0.23

0.19

12.66***

0.24

0.22

3.16

Women : TV

0.61

0.58

6.75*

Number of other welfare programs received by household [7]

0.09

0.07

4.22*

0.2

0.09

382.99***Explanatory variables used in the Fixed Effects model and the DID model

34

Hindu

0.83

0.86

5.83*

0.83

0.85

8.68**

Household composition/size excluding elderly and children

3.24

3.19

0.77

2.58

2.53

2.94

Number of children

1.95

2.02

1.33

1.3

1.26

3.54

Number of elderly male

0.62

0.58

7.52*

0.55

0.62

68.81***

Number of elderly female

0.61

0.75

113.88***

0.63

0.71

69.61***

Notes[1] IHDS has constructed the monthly expenditure using a broad range of variables which represents the purchasing power capacity of the household. The list of items
included in the food and non-food categories are only a subset of the total expenditure incurred.
[2] Food expenditure includes spending on Rice, Wheat, Sugar, Other cereals, Cereal products, Pulses, Meat, Sweeteners, Edible oil, Eggs, Milk, Milk Products,
Vegetables, Fruits/nuts, Salt/spices and Other food.
[3] Non-food expenditure includes Paan/tobacco/intoxicants, Restaurants/Eating out, Entertainment, Telephone/Cable/Internet charges, Toiletries, Transportation,
Consumer taxes/fees, Services (domestic servants, barber, laundry, etc.) and Medical (out-patient services)[4] The assets index used here remains the same for 2004-5 and 2011-12. The assets include any vehicle, Sewing machine, Mixer/grinder, Motor vehicle, any TV,
Colour TV, Air cooler/cond, Clock/watch, Electric fan, Chair/table, Cot, Telephone, Cell phone, Refrigerator, Pressure cooker, Car, Air conditioner, Washing machine,Computer, Credit card, 2 clothes, Footwear, Piped indoor water, Separate kitchen, Flush toilet, Electricity, LPG, Pucca wall, Pucca roof and Pucca floor.
[5] We have used the poor variable as defined by IHDS. 2005. See Endnote 12 for its definition.
[6] IHDS has enquired about different sources of income. The total income variable is calculated from the income in farms, agricultural wages, non-agricultural sources,salary, business, remittances, government benefits and other sources. We have deducted the payment received from IGNOAPS from this.
[7] Other welfare programs include Widow Benefits, Disability benefits, Annapurna benefits, other income benefits, NGO benefits and Maternity benefits.
* p<0.05, ** p<0.01, *** p<0.001

35

Table 2: Effects of IGNOAPS participation on household welfare and poverty (based on FE models with PS weighting applied to thematched panel data)
Log real
householdIncomeexcluding
IGNOAPS[6]

Log real
MPCE(food)[2]

Log real
MPCE(non-food)[3]

Logassets
index
[4]

Log
number ofhouseholdmembers
working

0.0549**(0.01)

0.0575***(0.01)

0.0874**(0.03)

0.100***(0.01)-0.099***(0.01)

0.0124(0.01)-0.011(0.03)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

76,53676,51876,44876,49972,18676,56275,233

Household fixed effects
Number of Households
R-squared

Yes
38309
0.888

Yes
38308
0.886

Yes
38309
0.85

Yes
38308
0.932

Yes
37786
0.897

Yes
38309
0.843

Yes
38267
0.897

Log real
MPCE[1]

Variables

Poor
[5]

Fixed-effects model applied to the matchedpanel data with PS weighting
Householdreceiving(Yes/No)(𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )Control variables included [8]IGNOAPS

Notes[1] IHDS has constructed the monthly expenditure using a broad range of variables which represents the purchasing power capacityof the household. The list of items included in the food and non-food categories are only a subset of the total expenditure incurred.
[2] Food expenditure includes spending on Rice, Wheat, Sugar, Other cereals, Cereal products, Pulses, Meat, Sweeteners, Edible oil,Eggs, Milk, Milk Products, Vegetables, Fruits/nuts, Salt/spices and Other food.
[3] Non-food expenditure includes Paan/tobacco/intoxicants, Restaurants/Eating out, Entertainment, Telephone/Cable/Internet
charges, Toiletries, Transportation, Consumer taxes/fees, Services (domestic servants, barber, laundry, etc.) and Medical (out-patient
services)[4] The assets index used here remains the same for 2004-5 and 2011-12. The assets include any vehicle, Sewing machine,
Mixer/grinder, Motor vehicle, any TV, Colour TV, Air cooler/cond, Clock/watch, Electric fan, Chair/table, Cot, Telephone, Cell phone,36Refrigerator, Pressure cooker, Car, Air conditioner, Washing machine, Computer, Credit card, 2 clothes, Footwear, Piped indoor water,
Separate kitchen, Flush toilet, Electricity, LPG, Pucca wall, Pucca roof and Pucca floor.
[5] We have used the poor variable as defined by IHDS. 2005. See Endnote 12 for its definition.
[6] IHDS has enquired about different sources of income. The total income variable is calculated from the income in farms, agricultural
wages, non-agricultural sources, salary, business, remittances, government benefits and other sources. We have deducted thepayment received from IGNOAPS from this.
[8]The control variables included here are household size excl.children and elderly, number of children, number of elderly male,number of elderly female, the highest level of education in the household, urban area, women: newspaper, women: TV, household
head being a Hindu, caste, and other welfare programs received by the household.
[9] Robust standard errors in parentheses, * p<0.05, ** p<0.01, *** p<0.001
Source: Authors’ estimations based on IHDS data.

37

Table 3: The results of extensions and sub-sample estimations (based on the FE model using matchedpanel with PS weighting)Log real
MPCE

Log real
MPCE(food)

Log real
MPCE(non-food)Logassetsindex

Lognumber ofhouseholdmembers
working

0.0165(0.02)

0.0307(0.02)

0.0179(0.04)

0.0507*(0.02)-0.0326(0.02)

0.0122(0.02)

0.0339(0.05)

74401
38154
0.89
Yes
Yes

74382
38153
0.892
Yes
Yes

74314
38150
0.859
Yes
Yes

74363
38150
0.935
Yes
Yes

70294
37576
0.899
Yes
Yes

74425
38156
0.850
Yes
Yes

73195
38076
0.898
Yes
Yes

Household with female receiving
IGNOAPS(Yes/No)(𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑓𝑒𝑚𝑎𝑙𝑒 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )0.0593*(0.02)

0.0726**(0.02)

0.017(0.04)

0.0743***(0.02)-0.0315(0.02)

0.0332(0.02)

0.0489(0.04)

Observations
Number of Households
R-squared
Control variables
Household fixed effects

74,534
38215
0.889
Yes
Yes

74,518
38214
0.889
Yes
Yes

74,448
38211
0.853
Yes
Yes

74,501
38208
0.934
Yes
Yes

70,373
37647
0.901
Yes
Yes

74,559
38215
0.85
Yes
Yes

73,302
38126
0.90
Yes
Yes

Variables

Elderly
laboursupplyPoor

Log
householdincomeexcluding
IGNOAPS

Fixed-effects model applied tomatched panel with PS weighting
Household with IGNOAPS recipient(Yes/No)(𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )Observations
Number of Households
R-squared
Control variables
Household fixed effects

Household with male receiving
IGNOAPS(Yes/No)(𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑚𝑎𝑙𝑒 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )Observations
Number of Households
R-squared
Control variables
Household fixed effects-0.127***(0.01)
76,618
38309
0.848
Yes
Yes

38

Sub-sample estimation on
households with a politicalconnection:
Households with IGNOAPS recipient(Yes/No)(𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )0.006(0.642)0.0581(0.526)0.0901(0.811)

0.196(0.400)-0.373(0.539)0.0538(0.529)-0.530(1.062)

Observations
Number of Households
Control variables
R-squared
Household fixed effects

5292
5060
Yes
0.991
Yes

5293
5061
Yes
0.992
Yes

5258
5026
Yes
0.991
Yes

5290
5058
Yes
0.997
Yes

5096
4884
Yes
0.992
Yes

5293
5061
Yes
0.991
Yes

5171
4952
Yes
0.992
Yes

Sub-sample estimation on
households head belonging to
Scheduled Caste or Tribe:Households with IGNOAPS recipient(Yes/No)(𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )0.0315(0.03)

0.0498(0.02)

0.0521(0.05)

0.107***(0.02)-0.0892**(0.02)

0.0169(0.02)

0.0844(0.05)

Observations
Number of Households
R-squared
Control variables
Household fixed effects

23,675
13083
0.887
Yes
Yes

23,676
13082
0.885
Yes
Yes

23,645
13083
0.84
Yes
Yes

23615
13079
0.917
Yes
Yes

22,848
12894
0.899
Yes
Yes

23,683
13083
0.842
Yes
Yes

Robust standard errors in parentheses, * p<0.05, ** p<0.01, *** p<0.001
Source: Authors’ estimations based on IHDS data.

39

23,396
13048
0.889
Yes
Yes

Table 4: The results of the model to examine the programme change of IGNOAPS based on the FE model with PSM with PS weighting(Dependent variable: whether a household received in IGNOAPS)
Log real
MPCE

Variables

Case without the households who received IGNOAPS in 2004
Household receiving IGNOAPS (Yes/No)(𝑛𝑒𝑤𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )-0.004(0.02)

Log real
MPCE(non-food)Logassets index

Log number ofhouseholdmembers
working

Poor

Log
householdincomeexcluding
IGNOAPS-0.0129(0.01)-0.0043(0.03)

0.0474**(0.01)-0.0484*(0.02)

0.00969(0.01)-0.129**(0.04)

Log real
MPCE(food)

Program expansion (year dummy)

0.208***(0.01)

0.181***(0.00)

0.405***(0.01)

0.254***(0.00)-0.168***(0.00)

0.004(0.00)

0.265***(0.01)

Control variables
Household fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations
R-squared

75212
0.914

75195
0.918

75127
0.886

75176
0.957

70942
0.922

75238
0.872

73955
0.919

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimations based on IHDS data.

* p<0.05, ** p<0.01, *** p<0.001

40

Figure 1: Regions in common support 2004-5
Treated and matched control : 2004-05

0

.1

.2

.3

Propensity Score
Matched control

TreatedSource: Authors’ calculations based on IHDS data.

Figure 2: Regions in commonsupport 2011-12
Treated and matched control group: 2011-12

0

.1

.2
.3
Propensity Score
Matched control.4.5TreatedSource: Authors’ calculations based on IHDS data.

41

Figure 3: PS match in 2004-5

30
20
0

10

kdensity _pscore

40

50

Propensity score plot: 2004-05 round

0

.1

.2

.3

propensity score
IGNOAPS recipient hh

IGNOAPS non-recipient hh

Source: Authors’ calculations based on IHDS data.

Figure 4: PS match in 2011-1210
50

kdensity _pscore

15

20

Propensity score for 2011-12

0.1
IGNOAPS recipient hh.2.3
propensity score.4.5

IGNOAPS non-recipient hh

Source: Authors’ calculations based on IHDS data.

42

Appendix 1: Amount of pension, state contribution and eligibility criteria used
States/UTs

CentralAssistance under
IGNOAPSMonthly(Rs,2011)

Contribution ofState Govt. per
beneficiary per
month(Rs,2011)

1

Andhra Pradesh

200

Nil

2

Bihar

200

Nil

3

Chhattisgarh

200

4

Goa

5

Eligibility criteria used *Number of
beneficiaries(2011)

1,011,153
2,369,656

100

BPL card holders with anannual income of less thanRs. 30,000
n/a

200

800

n/a

2,734

Gujarat

200

200

n/a

279,834

6

Haryana200

Age, Income <Rs. 50000
per annum

130,306

7

Himachal Pradesh

200

300 for 70 years and
500 for above 70
years
130

Age, BPL

91,4408

Jammu andKAshmir

200

125

129,000

9Jharkhand

200

200

Age of proof (date of birth/age certificate) Income
certificate/ BPL certificate
n/a

10

Karnataka

200

200

n/a

797,862

11

Kerala

200

50

n/a

176,064

12

Madhya Pradesh

200

75

n/a

1,061,033

13

Maharashtra

200

400

Age, BPL

1,057,510

14

Orissa

200

Nil

n/a

1,193,176

15

Punjab

200

250

n/a

159,792

16

Rajasthan

200

300 for 70 years or
below and 550 forabove 70 years

527,636

17

Tamil Nadu

200

800

The lowest age eligibility
criterion 55 years forfemale and 58 years forfemaleAge, BPL

18

Uttar Pradesh

200

100

Age, for rural: BPL; forurban: BPL/AAY card
holder/name appears on
the survey list of District
Urban Development
Authority or Ministry of
Urban Development(MoUD) BPL list

3,274,780

19

Uttarakhand

200

200

Age

183,50120

West Bengal

200

200

n/a

1.679,381

NE

43533,665

676,003995,237

21

Arunachal
Pradesh200

NilAge, BPL

14,500

22

Assam

200

50

23

Manipur200

NilAge, BPL

72,51424

Meghalaya

200

50Age, BPL

48,11225

Mizoram

200

50

Age, BPL

23,747

26

Nagaland

200

100

Age, BPL

40,462

27

Sikkim

200

200

n/a

18,80628

Tripura200

200

Age, BPL criteria or thedestitute criteria

136,592

596,965

Union Territories
29

NCT Delhi

200

800

Age, Income <Rs. 60000
per annum

196,446

30

Puducherry

200

400

n/a

20,75731

A&N Islands

200

n/a

1,063

32

Chandigarh

800 up to 79 years
and 500 above 80
years
500

n/a

4,216

33

D&N Haveli

200

300

n/a

944

34

Daman & Diu

200

300

n/a

130

35

Lakshadweep

200

100

n/a

36

Total

17,505,053

Sources: http://socialjustice.nic.in/pdf/ar12eng.pdf, *Identity criteria havebeen taken from numerous sources, which includes Government
websites, https://ifrogs.org/EVENTS/PRESENTATIONS/sl_Rinku20150224_pensionworkshop.pdf, and helpageindia sources44

Appendix 2: Definition of variables
Variables

Definition

Constructed variable oravailable in IHDSBPL card

Household has a BPL
card

Available in IHDS

Antyodaya card

Household has an
Antyodaya card

Available in IHDSHousehold has a BPL or Antyodaya card

If the household has
either a BPLor Antyodaya
card

Household: elderly

If the household has anelderly member (age 60
years or more)

Rural area & attends public meeting

MPCE[1]

Household lives in a ruralarea & attends a public
meetingMonthly per capita
consumption expenditure

Constructed from above

Constructed

Constructed

Available in IHDSMPCE (food)[2]Monthly per capita foodexpenditure

Available in IHDSMPCE (non-food)[3]Monthly per capita nonfood expenditure

Available in IHDSAssets[4]Number of household
assetsAvailable in IHDS

Poor[5]

If the household isclassified poor

Available in IHDSHousehold Income excluding IGNOAPS [6]Annual Income of thehousehold after removing
transfers received fromIGNOAPS

Constructed

Number of household members working

Number of household
members working

Available in IHDSHousehold composition/size excluding
elderly and children

Number of persons in thehousehold excluding thenumber of elderly andchildren

Number of elderly male

Number of elderly male in
the household

Constructed

Constructed for the 2005
round/available in the
2011-12 rounds

Number of elderly female

Number of elderly female
in the household

Constructed for the 2005
round/available in the
2011-12 rounds

Number of children

Number of children in thehousehold

Available in the 2005
rounds/constructed for the
2011-12 round

Highest education in the household

Available in IHDS

45

Highest adult education(number of years) in thehousehold.
Urban area

If the household lives inan urban area

Available in IHDSWomen: newspaper

If the women in thehousehold have access tonewspaper

Available in IHDSWomen: TV

Scheduled caste

Scheduled tribe

If the women in thehousehold have access toTV
If the household head
belongs to Scheduled
Caste
If the household head
belongs to Scheduled
Tribe

Available in IHDS

Available in IHDS

Available in IHDSScheduled caste or tribe

If the household head
belongs to Scheduled
caste or tribe

Constructed from above

Number of other welfare programs received by thehousehold [7]Other welfare programs
received

Constructed

Hindu

If the household head
belongs to Hindu religion

IGNOAPS amountAmount received in
IGNOAPS (annually)

Constructed
Available in IHDS

[1]IHDS has constructed the monthly expenditure using a broad range of variables which represents the purchasing power capacity of thehousehold. The list of items included in the food and non-food categories are only a subset of the total expenditure incurred.
[2] Food expenditure includes spending on Rice, Wheat, Sugar, Other cereals, Cereal products, Pulses, Meat, Sweeteners, Edible oil, Eggs,
Milk, Milk products, Vegetables, Fruits/nuts, Salt/spices and Other food.
[3] Non-food expenditure includes Paan/tobacco/intoxicants, Restaurants/Eating out, Entertainment, Telephone/Cable/Internet charges,
Toiletries, Transportation, Consumer taxes/fees, Services (domestic servants, barber, laundry, etc.) and Medical (out-patient services)[4] The assets index used here remains the same for 2004-5 and 2011-12. The assets include any vehicle, Sewing machine, Mixer/grinder,
Motor vehicle, any TV, Colour TV, Air cooler/cond, Clock/watch, Electric fan, Chair/table, Cot, Telephone, Cell phone, Refrigerator, Pressure
cooker, Car, Air conditioner, Washing machine, Computer, Credit card, 2 clothes, Footwear, Piped indoor water, Separate kitchen, Flush
toilet, Electricity, LPG, Pucca wall, Pucca roof and Pucca floor.
[5] We have used the variable on household poverty as defined by IHDS. 2005. See Endnote12for its definition.
[6] IHDS has enquired about different sources of income. The total income variable is calculated from the income in farms, agricultural wages,
non-agricultural sources, salary, business, remittances, government benefits and other sources. We have deducted the payment received fromIGNOAPS from this.
[7] Other welfare programs include Widow Benefits, Disability benefits, Annapurna benefits, other income benefits, NGO benefits and Maternity
benefits.
Source: Authors’ estimations based on IHDS data.

46

Appendix 3: Results of Probit Model on whether a household received IGNOAPS in
PSM

Variables

BPL card
Antyodaya card

Household: elderly

Rural area & attends public meeting

Highest education in the household

Scheduled caste or tribe

2004-5

2011-12

Coefficients

Coefficients0.180***(0.03).
0.508***(0.07).
1.524***

1.625***(0.04).

(0.027).

0.077*

0.136***(0.03).

(0.022).

-0.030***-0.025***(0.00).

(0.002).

0.239***(0.03).

0.107***(0.022).
-0.047

Women: T.V(0.025).

Household has a BPL or Antyodaya

0.589***

card(0.021).

constant
Pseudo R2-2.83***-2.515***(0.049).

(0.037).

0.23

0.27

Number of observations
41499
42141
Notes: Robust standard errors in parenthesis; * p<0.05, ** p<0.01, *** p<0.001; Source: Authors’ calculations based on IHDS
data.

47

Appendix 4: A full set of results of PS weighted FE model for matched panel

Log real
MPCE (food)Log real
MPCE(nonfood)Log assets
index

Log
number ofhouseholdmembers
working

0.0549**(0.01)

0.0575***(0.01)

0.0874**(0.03)

0.100***(0.01)-0.0992***(0.01)

0.0124(0.01)-0.0119(0.03)-0.0540***(0.00)-0.0654***(0.00)-0.101***(0.00)

0.0169***(0.00)

0.166***(0.00)

0.0215***(0.00)

0.142***(0.00)

Number of children-0.113***(0.00)-0.0934***(0.00)-0.142***(0.00)-0.0170***(0.00)

0.0551***(0.00)

0.0486***(0.00)

0.0300***(0.00)

Number of elderly male

-0.142***(0.02)-0.139***(0.01)-0.176***(0.03)-0.0534***(0.01)

0.194***(0.01)

0.0119(0.01)

0.0728*(0.03)

Number of elderly female-0.0424*(0.01)-0.0768***(0.01)-0.0052(0.02)

0.0820***(0.01)

0.0477**(0.01)

0.0498***(0.01)

0.107***(0.02)

Highest education in thehousehold0.0176***(0.00)

0.00873***(0.00)

0.0246***(0.00)

0.0262***(0.00)

0.00105(0.00)-0.00460*(0.00)

0.0378***(0.00)

Urban area

0.239***(0.06)

0.173***(0.04)

0.471***(0.09)

0.145***(0.03)-0.033(0.05)

0.0691(0.05)

0.265*(0.11)

Women: newspaper

0.0474*(0.02)

0.0375*(0.01)

0.0995***(0.03)-0.00329(0.01)-0.0362(0.02)-0.0166(0.01)

0.0902**(0.03)

Women: TV

0.135***(0.01)

0.0880***(0.01)

0.221***(0.02)

0.283***(0.01)-0.0531**(0.01)-0.0499***(0.01)

0.174***(0.03)

Scheduled Caste or tribe-0.00919(0.04)-0.041(0.02)

0.00489(0.05)

0.00274(0.03)

0.0241(0.03)

0.0299(0.03)-0.0307(0.06)

Number of other welfare
programs received by thehousehold0.0808***(0.01)

0.0488**(0.01)

0.196***(0.03)

0.0921***(0.01)-0.0533**(0.01)-0.0366*(0.01)

0.120***(0.03)

Hindu

0.00745(0.04)

0.00557(0.042)0.019(0.07)

0.116**(0.04)-0.0385(0.04)

0.059(0.04)

0.105(0.08)

Constant

6.673***(0.05)

6.044***(0.04)

5.192***(0.08)

1.770***(0.04)

0.132**(0.04)

0.0627(0.04)

9.231***(0.09)

Observations

76536

76518

76448

76499

72186

76562

75233

F-stat

81.14

90.91

81.29

87.33

161.8

20.37

55.61

Variables
Household receiving IGNOAPS(Yes/No) (𝐼𝐺𝑁𝑂𝐴𝑃𝑆𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 )Log real
MPCE

Poor

Log ofhouseholdincomeminus
IGNOAPS

Control variables
Household size excl. children
and elderly

R-squared

0.888
0.886
0.85
0.932
0.897
0.843
0.897
Notes: Robust standard errors in parentheses; * p<0.05, ** p<0.01, *** p<0.001; Source: Authors’ estimations based on IHDS
data.

48

Endnotes
1

http://datatopics.worldbank.org/aspire/country/india

2

A report by the Ministry of Statistics points out that between 2001 and 2011 there was a

35% increase in the number of persons who are 60 years of age or above in India (GoI, 2016).
3

For the convenience, we call the programme IGNOAPS in both 2004-5 and 2011-12 in theempirical section.
4

Retrieved from the Government of India website, http://nsap.nic.in/ on National Social

Assistance Programme.
5

https://data.gov.in/catalog/expenditure-and-beneficiaries-under-nsap

6

https://data.gov.in/resources/physical-and-financial-progress-nsap-components

7

At the individual level, 10% of the IGNOAPS recipients receive the program in both time

periods, and 75% of the beneficiaries received the program only in 2011. The average annual
amount received by beneficiaries is INR 4107 in 2011, compared to INR 1818 in 2005.
It is noted that replacing the explanatory variable on age criteria in the probit model by ’65

8years or above’ will not change the results in the first and second stages. It should also be notedthat the definition of the elderly varied across states depending on gender, especially in 20045 (Kaushal, 2014). As a further robustness check, we used the lowest age eligibility criteria
which Rajasthan used (55 years for female and 58 years for male). Final results of the impact
estimates are broadly same regardless of the age thresholds in deriving PS.
9

For PSM the assumption for the balancing property is satisfied in both rounds where the tests

are carried out for 10 blocks at different distributional points defined by the estimated PS(Leuven and Sianesi, 2003). We have also carried out the balancing tests based on Rubin (2001)
and found that the covariates are strongly balanced with Rubin’s B 5.2 (or 3.2) and Rubin’s R

49

1.02 (or 1.02) for 2004-5 (2011-12) well within the rule of thumb (Rubins-B<25, Rubins-R[0.5; 2]).
10

The method of matching has been increasingly used in program evaluation mechanism (e.g.

Dabalen et al., 2008) to construct a valid counterfactual group, whilst the method of fixed
effects is also used to study the impact of social policies (Ardington, et al., 2009; Abel, 2019)to eliminate unobserved time-invariant heterogeneities.
11

The choice of the fixed effects model over the random effects model is supported by the

Hausman test results in all the cases we report in this paper.
12

Poverty is a binary variable defined by IHDS indicating whether the household’s monthly

consumption per capita is below the Tendulkar poverty line in 2011-12 rounds and the official
Planning Commission poverty line in 2004-2005.
13

An alternative to this method is an instrumental variable (IV) regression where

𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡ℎ𝑡 is estimated by an instrumental variable that reflects the household
inventive to participate in the programme, but not an outcome variable, but it is difficult to finda valid instrument. We have used ‘the number of beneficiaries receiving the program at thevillage/Urban Local Body level’ as an instrument and obtained broadly similar results.
However, a question on validity of IV remains and thus we have adopted our methodology
described in the text.
14

The limitations include that (i) we assume that the household unobservable characteristics

is fixed and (ii) unlike a (random effects) IV regression with a time-varying IV, we take into
account a differential incentive to participate in the programme in 2004-5 and 2011-12 only
indirectly by weighting the regression by the probability of non-participating households
joining the programme.
15

The PS weighted regression approach can also reduce any bias in our impact estimates arisingfrom the fact that different states may adopt different eligibility criteria, such as age (Appendix
501). Our study does not involve any explicit measurement errors in treatment, but can be justified
by Babanezhad et al. (2010) which showed that, if treatments are misclassified and themisclassification is correlated with confounders, the bias in the estimate of the PS adjusted
regression is likely to be smaller than OLS or other models as the bias of the former is
independent of the treatment-confounder association.
16

Validation of DID results requires the parallel trend assumption which stipulates that thedifference of the outcome variables are same for the treatment and control groups over time.
This requires at least three points of time of the data, including the baseline where neithergroups was treated. In our case as we do not have the pre-intervention data, it is impossible totest this assumption. However, the data context where the control and treatment households arescattered in every village across all the states in India would justify our use of DID as bothgroups are on average likely to show similar trends. Simulation data by Ryan et al. (2019)highlight that the DID estimates performed on the matched sample is not sensitive to theviolation of parallel trend assumption.
17

𝑛𝑒𝑤 𝐼𝐺𝑁𝑂𝐴𝑃𝑆 𝑟𝑒𝑐𝑖𝑝𝑖𝑒𝑛𝑡 is equivalent to the interaction of the (time-invariant) treatment

dummy variable (whether the household has ever been treated) and the time-dummy variable.
The fixed-effects (𝜇ℎ ) capture the treatment dummy.
18

To see whether the amount (annual) of money received by the household influences outcome

variable, we have replaced the primary explanatory variable in Equation (1) by the annual
amount of money received by the household. We have found the results broadly consistent with
Table 2 that a 10% increase in IGNOAPS payment at its mean leads to an increase of monthly
expenditure by 0.03%; food (non-food) per capita expenditure by 0.04% (0.05%): household
assets increase by 0.07%, the number of household members worked reduces by 0.07%. The
impacts on poverty and income are statistically non-significant. The marginal increase in the
IGNOAPS payment leads to relatively small increases in outcome variables given that (i) it is51

a marginal effect of an additional IGNOAPS payment evaluated at its mean of all the sampleand (ii) the IGNOAPS payment is relatively small than the total income or consumption.
19

Despite the average increase in consumption expenditure at mean (the second column),

poverty increased, suggesting that consumption increase is not uniform across different levelsof consumption and decreases around the poverty threshold when female members are therecipients. This may be due to the fact that a significant share of non-poor households
participated in IGNOAPS.
20

We estimated the effect of IGNOAPS on the asset outcome variable when the program isaccessed by non-poor households (above poverty line) based on the FE and DID models. The
effects on assets are positive and statistically significant in the FE model (by 6.3%) and positive
but not statistically significant in the DID model (2.9%).
21

The DID estimates for various sub-samples (as in Table 3) are broadly consistent with thosein Table 4. As a result of the programme expansion, (i) ‘elderly labour supply’ declined
significantly (by 7.9%), (ii) the labour supply is significantly reduced for households with malerecipients (6.2%), (iii) assets increased significantly for male, female and scheduled caste/ tribe
sub-samples (4.7% and 6.7%, 6.8% respectively) and (iv) household income excluding
IGNOAPS declined significantly for scheduled caste/tribe households (18%) and for thehouseholds with male recipients (13%). Political connections do not matter. The results will be
provided upon request.
22

We have estimated OLS with PS weighting for the matched (cross-sectional) data in 2004-5

and 2011-12 separately and have found statistically significant results on the household welfare
improvement only for 2004-5. However, these results should be interpreted with caution as
they are subject the bias as the unobservable household fixed effects are not taken into account.

52