Whose Right Is It Anyway? Welfare

Implications of Food Security Programs

Sanjukta Das †

November 2018

Abstract: The debate over whether food security legislation should follow a targeted

approach or aim for universalization persists due to lack of empirical evidence on

relative welfare impacts on the poor. Utilizing a natural experiment in South India, I

analyze this question in the context of the world’s largest food security program, the

Indian Public Distribution System. A more universal approach to food security reduces

the average household’s probability of becoming poor by 12% over a strictly targeted

approach. The driving force behind the higher welfare benefits under a universal

system is lower errors of wrongfully excluding genuinely deserving households.

JEL: H23, H53, H75, I38, J22

Keywords: food security, targeting, universalism, redistribution, vulnerability to

poverty, Public Distribution System, PDS, India, geographic regression discontinu-

ity

†Associate Fellow, National Council of Applied Economic Research (NCAER), Parisila Bhawan, 11 IPEstate, New Delhi-110002, India. Email: sdas@ncaer.org. I am grateful to Laura V. Zimmermann, SantanuChatterjee, David R. Agrawal, Arthur A. Snow, Josh Kinsler, William D. Lastrapes, Julio Garin and IanSchmutte for their guidance. I thank World Bank and NCAER for providing me with data. I also thankSandhya Garg and Girish N. Bahal for helpful discussions.

1 Introduction

Poverty is a fundamental cause of food insecurity, which in turn makes the poor unproduc-

tive, so that they remain poor.1 This circularity implies that the two primary sustainable

development goals, ‘Zero Hunger’ and ‘No Poverty’, are interdependent. Hence, it may be

possible to move closer to both these goals, with one right kind of strategic welfare interven-

tion. As the poor spend a large part of their budgets on food,2 it then becomes necessary

to question whether by subsidizing their food and generating additional income for them (a

food security intervention), their living standards can also be improved (poverty reduction).

In practice, however, it is often unclear whether food security programs work as well as other

anti-poverty programs, and how they should be implemented to be most effective.

The biggest obstacle policymakers face when implementing income transfers is deciding who

should be eligible for the program. Under a universal program everyone can claim the benefits

of the program, whereas in a targeted program the benefits are delivered selectively to those

in need. Universalism implies treating food as a “merit good”3 which should be available to

everyone as a right or obligation. Further, if treated as a right, then subsidized food should

be the default option, unless people choose to opt out. That is, it should work through an

automatic process of self-selection.4 At the other end, targeting is based on the premise

that as government funds are limited, it is necessary to prioritize allocation of resources.

However, identification of the poor, for the purpose of targeting benefits, is difficult and

1This is the nutrition based poverty trap, which is based on the relationship between wage and nutritionon one hand, and nutrition and productivity on the other (Banerjee and Duflo 2011).

2The average poor household in my sample spends close to 60% of its budget on food.3The concept of a merit good, introduced in economics by Richard Musgrave (1957, 1959), is a commodity

which is judged that an individual or society should have on the basis of some concept of need, rather thanability and willingness to pay. Examples include the provision of food stamps to support nutrition, thedelivery of health services to improve quality of life and reduce morbidity, subsidized housing and compulsoryeducation. (Wikipedia)

4As Khera(2009) notes,“the right to food is about ensuring freedom from hunger, malnutrition and otherdeprivations associated with the lack of food...The right to food has to be a universal right. If it is notuniversal, exclusion errors are inevitable, no matter how sound and well implemented the targeting criteriamight be”.

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expensive, especially in developing countries. Universal inclusion, on the other hand, is easy

to administer and has been found more cost effective, despite a popular belief to the contrary.5

However, there is little empirical evidence available regarding relative redistribution effects of

targeted versus universal food security programs. Hence, the debate over the question of the

ideal design of a food security program persists. The current emphasis on targeting around

the world also does not take into account the experience from many advanced countries,

which suggests that universal systems may have underappreciated long term effects.6

Should food security legislation follow a targeted approach or aim for universalization? I

address this question in this paper through an analysis of India’s Public Distribution System

(PDS). India spends significant resources on its core safety net programs7 - over 2% of GDP

in recent years (World Bank 2011). The PDS, which has been around since the 1940s, is a

government distribution network that incorporates a food subsidy and absorbs substantial

public resources at approximately 1% of GDP (see Table 1). Currently, the generosity of the

coverage and benefits extended under the PDS in India differs from state to state. In South

India,8 TN and AP run a nearly universal PDS while in the neighboring states it is strictly

targeted (see Table 2). The long border between South-eastern (TN and AP) and South-

western (KL, KA, MH) parts of the country provide an ideal quasi-experimental setting for a

geographic regression discontinuity (RD) design for evaluation of differing forms of the PDS,

in the spirit of Dell (2010). Specifically, I examine the impacts of a more generous PDS on

vulnerability to poverty of the population in South India.9 This can then also shed light on

5Dutrey (2007) notes that perfecting targeting (lowering both leakage and undercoverage) requires com-plex systems at often dramatically increased costs compared to universal systems. More on this in Section2.2.

6The experience of many European countries, for instance, suggests that universal provisioning of socialservices served as an important instrument for development and nation building. Many European countries,for example, introduced flat-rate pensions at a comparatively early stage of welfare state development, whenthese countries had the same per capita incomes that Latin American countries had in the 1980s and 1990s(Mkandawire 2005)

7PDS, Mahatma Gandhi National Rural Employment Guarantee scheme (MGNREGA), Indira AwaasYojana (IAY), and Indira Gandhi National Old Age Pension Scheme (IGNOAPS) among others

8Tamil Nadu (TN), Andhra Pradesh (AP), Kerala (KL), Karnataka (KA) and Maharashtra (MH)9Vulnerability to poverty is the ex-ante risk that a household will, if currently non-poor, fall below the

poverty line, or if currently poor, will remain in poverty (Chaudhuri et al 2002). See section 4 for more

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the broader question of the impacts of universal versus targeted food programs on poverty

alleviation.

My empirical results indicate that a household’s ability to mitigate risk and cope with shocks

is enhanced with a more universal PDS. A more generous PDS induces households to allo-

cate their labor supply towards higher paying jobs, thereby reducing variability in income

which makes them less vulnerable to poverty. Households use this additional income to make

various types of risk averse investments, such as increasing their asset base or investing in

property and livestock, all of which further protect them in contingencies and make them

less vulnerable to poverty. A more generous PDS reduces overall household vulnerability to

poverty by around 12% for the full sample. The impacts are larger, at 54%, for the most

marginalized group in my sample (SC /ST). The driving force behind the higher welfare

benefits under a universal system is lower errors of wrongfully excluding genuinely deserving

households. A more generous PDS also has significantly positive impacts on child malnour-

ishment in general. These results indicate, that not only are food security measures effective

at tackling both malnutrition and poverty, but a more universal approach is more effective,

at least in the context of developing countries like India.

My paper extends the general literature on food security programs and also the debate over

universal and targeted programs. Evidence on food security programs elsewhere, such as

Food For Education in Bangladesh, indicates that they have performed well, even while

adopting different targeting methods (Bishop et al 1996, Ahmed et al 2001, Meng and Ryan

2007, Alderman et al 2010, Hoynes et al 2009). However, these studies are usually in field

survey settings where external validity may be low and scaling up differences not accounted

for. The PDS is a very large program affecting millions of people in the context of a large

developing country, and hence the results in this paper have greater external validity. Also,

the empirical literature on the debate over universal and targeted programs has mostly

details.

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concentrated on the effectiveness of different types of targeting10 rather than on a direct

comparison of the two types of systems (Coady et al 2004, Alatas et al 2012, Bigman et

al 2000, Benfield 2007). Despite the centrality of targeting in these studies, we know little

about the relative effects of targeted and universal welfare policy for poverty. The results

in this paper are validated by two previous studies - Brady et al (2012) and Niehaus et al

(2013). Brady et al (2012) look at the impacts of targeted versus universal social policy

on single-mother poverty with a multilevel analysis across 18 affluent Western democracies

and also find that universal social policy is much more effective than targeted social policy.

However, they conduct their analysis by constructing an index of welfare generosity on a

standardized scale based on public expenditures on various social programs as a percentage

of GDP. In contrast, this paper looks at the actual eligibility criteria of a particular program

to classify states as universal or targeted. This is closer to the heart of the debate since even

programs with very strict rules for eligibility (and hence would be termed as ‘targeted’) could

have very high levels of welfare expenditure. Niehaus et al (2013) assess the welfare gains

from status quo targeting as opposed to universal eligibility with respect to the PDS. They

reach conclusions similar to this paper that targeting would be optimal if enforcement were

perfect, but given widespread rule violations by implementing agents, universal eligibility is

better in practice. However, their results are based on only one Indian state, whereas my

analysis incorporates five large Indian states11 and hence is more robust.

My paper also contributes to the active literature on the PDS in India. Whereas evaluations

of other programs in India, such as the MGNREGA, show that it is not ineffective in altering

the situation of the poor despite low take up (Zimmermann 2017), the PDS has not received

similarly positive reviews from economists.12 Yet it is the most far reaching welfare program

10for instance, community based versus proxy means tested11accounting for 30% of the Indian population as of 2011-12, versus 5% of the population in Niehaus et

al (2013)12Extensive research on the subject has shown (Planning Commission 2005, Jha and Ramaswami 2010,

Khera 2011b, among others), that the PDS is plagued by numerous issues in implementation, with leakagesand inaccurate identification of poor households. Other studies have revealed that nearly 44 to 60% or moreof the needy population are wrongly excluded in the relatively backward states of Bihar, Madhya Pradesh,

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operational in India currently, in terms of coverage as well as public expenditure.13 Existing

empirical analyses on the PDS have mainly focused on consumption and nutritional gains

and the evidence is mixed.14 In contrast, this paper looks at both poverty and food security

impacts of the PDS and hence provides a more complete picture. This is also the first paper

to use geographic RD to study the PDS to estimate causal effect of PDS generosity. The

geographic RD in this paper gives estimates that have greater internal validity as statistical

theory shows that a correctly implemented and analyzed regression discontinuity design

gives unbiased effect estimates at the discontinuity. Thus, this paper adds value to our

understanding of the PDS and like Khera (2011a),15 paints a more positive picture of the

PDS than most existing papers do.

2 Background

2.1 Institutional Details

The Public Distribution System (PDS) in India is a large scale producer price support-

cum-consumer subsidy program that evolved in the wake of extensive food shortages and

fluctuating high food prices in the 1940s. By the 1980s its reach widened considerably.

Operating together with the free market, it provides rice, wheat, edible oils, sugar and

Rajasthan and Uttar Pradesh (Khera 2008, Swaminathan 2008). A few studies have estimated the impact ofthe PDS on poor households, in terms of reductions in the incidence and severity of poverty (Radhakrishnaet al 1997, Tritah 2003), but have been unable to detect substantial effects.

13As Khera (2011a) notes,for BPL households in many states, the implicit subsidy from PDS food grainsalone is roughly equivalent to a week’s MGNREGA wages (without having to work) every month, andhence, the PDS has the potential to improve the living standards of many more. However, it is a historicalinstitution in the country and changes were made to its functioning in an adhoc manner, which makesempirical assessments of its role difficult.

14For instance, while Kochar (2005) and Kaushal and Muchomba (2015) find negligible impact on nutri-tional status of households of the PDS, Krishnamurthy (2014), Kishore and Chakrabarti (2015) and Rahman(2016) find that the PDS has led to significant improvements in household nutritional intake and diet quality.Bhalotra (2002) and Tarozzi (2005) study the impact on child health and give contradictory results.

15Khera (2011a) gives a more positive outlook on the PDS. She finds that respondents in an exhaustivenine-state PDS survey received 84-88% of their full entitlement and attributes this revival to a move towardsuniversalism.

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kerosene at subsidized prices through fair price shops (FPS) in rural and urban areas across

the country. The subsidy implied by the program has grown significantly over the years

(Table 1), from Rs. 25 billion in 1990-91 to about Rs. 724 billion in 2011-12, an increase of

over 28 times in 21 years.

The government redesigned the PDS to form the Targeted Public Distribution System

(TPDS) in 1997 due to poor assessments of the PDS and growing fiscal deficits. The new

system distinguished between households that fall ‘below the poverty line’ (BPL) and those

who are ‘above the poverty line’ (APL), and reduced subsidies to the latter, while increasing

those to the former. Based on the Planning Commission’s recommendation, a BPL list was

drawn up in each state based on parameters like size of land holding, assets owned, e.t.c.

However, many states in India rejected the Planning Commission’s norms and expanded the

PDS well beyond the BPL list. TN and AP were two such states in South India. Whereas

the PDS in TN remained universal, AP expanded their system to a quasi-universal approach

in 2008. The rest of the 3 states follow strict targeting (KL, KA & MH). Food grains are

allocated to states for distribution through the PDS by the central government as per the

proportion of BPL families fixed by the Planning Commission. This would mean that a state

which wishes to follow a more generous system would need additional means of procuring

extra grains. One possible reason the Southeastern states (TN & AP) could afford to follow

a more generous system could be that the deltaic tracts of the rivers Krishna, Kaveri and

Godavari (lying in the Southeast) are major rice producing regions in South India and are

characterised by a relative abundance of rice as compared to the control states (KL, KA &

MH). Hence, for districts in control states far away from the boundary it will be expensive

for the state government to buy rice locally, whereas for districts within treatment states it

will be much cheaper. Thus, we can hypothesize, that state governments in treatment states

optimally introduced a more generous PDS than control states because the average cost of

procuring additional rice is much lower.

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The PDS is a Centrally Sponsored Scheme (CSS), which means that it is implemented

by state governments but is largely funded by the central government with a defined state

government share. It is mandatory for FPS to maintain display boards with the BPL list and

containing information about entitlement, availability of foodgrain and issue price. Table

2 shows the key characteristics of the PDS in South India, in terms of the eligibility and

entitlements of BPL ration cards16. Ration cards are issued based on the total members in

a family and has to be transferred from one state to another, through a written application

and proof of address, if the family relocates. While the entitlements are well publicized

through government orders and newspaper articles, except in TN, there is little transparency

regarding the eligibility criteria. The functioning of the FPS are monitored through periodic

inspections by government officials regarding whether commodities are being distributed

correctly to all beneficiaries as per their entitlement, whether the commodities are being

sold at the specified rate and so on. While state governments are supposed to review the

BPL list every year for the purpose of deletion of ineligible families and inclusion of eligible

families, this rarely occurs in practice (Wadhwa 2009-2011). For instance, prior to the Socio-

Economic Caste Census of 2011, a BPL census was last held in 2002 in the country.

For categorizing universal and targeted PDS states I employ the definitions in Vanneman &

Dubey (2011). They identify ‘middle class’ families as those with incomes above half and

below twice the all-India median, between Rs. 6,809 and Rs. 27,235 and ‘affluent’ families

as those with incomes more than twice the Indian median, an average equivalence income

of Rs. 54,451 annually. The middle class they identify would still be relatively poor, while

the ‘affluent’ class would be a more recognizable middle class with comfortable existence in

the Indian context. Hence, I term the system in a state to be ‘more generous’ if the income

exclusion cutoff is above Rs. 27,235 per annum. The rationale for using this income cutoff is

16A Ration Card is a document issued under an order or authority of the State Government, as per thePublic Distribution System, for the purchase of essential commodities from fair price shops. They are animportant subsistence tool for the poor as they provide proof of identity and a connection with governmentdatabases. (Wikipedia)

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the fact that India’s national poverty line is too low to be acceptable in middle-income and

richer countries.17 The ‘middle class’ line in Vanneman & Dubey (2011) is also closer to the

international poverty line of $1.25 a day in 2011. Table 2, Row 5 gives the income cutoffs for

eligibility of BPL ration cards in South India. In TN and AP, this cutoff is well above Rs.

27,235 per annum, while in the other 3 states (KL, KA and MH), it is well below. Hence, I

define TN and AP as having a more ‘generous’ form of PDS (as a proxy for a universal food

security program), while the other 3 are said to run a less generous PDS (as a proxy for a

targeted food security program).

To reasonably rule out that any negligible impacts on the poor are due a dysfunctional

PDS, it is necessary to concentrate on an area with a well-functioning PDS, so that it can be

assumed that the subsidies are reaching the intended beneficiaries mostly. Thus, to determine

the states where the PDS is working well, I focus on trends in diversion of food grains, as

this has been one of the areas of major concerns for the PDS. Khera (2011b) notes that while

grains could be lost in transportation or due to poor storage, e.t.c., the general consensus

has been that the grains are sold illegally on the open market. She finds that states can

be divided into those with a functioning, reforming and languishing PDS, in terms of per

capita purchase of food grains and proportion of grains diverted. The states I include in my

analysis are all ‘functioning’ states, where diversion of food grains from the PDS is lower

than the all-India average and not a major concern (see Table 2).

2.2 Lessons Learned from Targeting

The shift away from universalistic policies towards targeted welfare programs began in the

1980s with the rise of the neoliberal ideological shift in industrialized countries, which gave17As Choudhury (2015) says, even compared to less developed regions, India’s poverty line is too low. For

instance, the three poverty lines in South Africa - food, middle and upper - are all higher than that of India.The food poverty line in Indian rupees was Rs. 1,841 per capita per month in 2010, middle poverty line wasat Rs. 2,445 and upper poverty line was at Rs. 3,484. Per capita poverty line of a rural adult Rwandian inIndian terms comes out to be Rs. 892 per month, slightly more than Rs. 816 for a person in rural India,even though food is cheaper in Rwanda.

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more importance to individual responsibility and promoted a limited role for the state (Mkan-

dawire 2005). Perhaps the most serious of the criticisms levelled against universalism is that

it is not redistributive unlike a targeted program which is said to generate a pro-poor dis-

tribution of social services in society. However, as Korpi and Palme (1998) argue, though

targeted programs may be more redistributive per unit of money spent, other factors are

likely to make universal programs more redistributive. This can be illustrated by means of

a simple example (an extension on the work of Rothstein 2001), as shown in Tables 3 and 4.

Though targeting assumes that it is possible to precisely identify the poor on the basis of fac-

tors like land and income, a targeted welfare program tends to suffer from two types of errors

in general due to imperfect measurement. Type I errors, or errors of wrongfully excluding

genuinely deserving households from a program, and Type II errors, or errors of inclusion

of privileged households ineligible for the program. Excluding privileged households implies

significant risk of exclusion for poor households in a targeted program. When these errors

are factored in, a targeted program may not be as redistributive as desirable. As can be seen

from Tables 3 and 4, under a universal program, only 40% of the taxes raised are distributed

to the poor, compared to a targeted program which results in 60-100% redistribution to the

poor. When targeting is absolutely precise (Table 4 column 5), the reduction in inequality

is greater under a targeted program than a universal program. However, its accuracy is

unlikely to be perfect. When factoring in a leakage to non-poor households of 40%18, the

resulting reduction in inequality is less than that under a universal system (Table 4 Column

7).

Another point not often highlighted in these debates is that targeting also suffers from the

problem of substantial administrative and transaction costs, which may overshadow the

cost of subsidies under a universal system. Accurately identifying and maximizing coverage

among the poor, controlling leakage (while maintaining high rates of efficiency) and imple-

18This is calculated by taking an average of the first 5 rows of Column 6 of Table 5. This is taken to bean estimate of the typical leakage rate for a social welfare program in a developing country.

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menting well-developed fraud control, can turn out to be a very expensive process. While

the exact differences in costs between targeted and universal provision is not clear, it has

been argued that targeting often signifies a substantially higher cost.19

In a cross-country study of nine countries, Cornia and Stewart (1993) note that the poorer

a society, the more serious will be errors of omission or undercoverage relative to the costs

of leakage. Table 5 provides support for this intuition. It lists 8 countries with different

targeted welfare programs, in ascending order of GDP per capita (that is, poor to rich).

As we can see from columns 3, the benefits share of the poorest quintile in total subsidies

under a program rises steadily as a country gets richer. Column 6 indicates that these also

translate into lower exclusion rates for richer countries. That is, the severity of undercoverage

(or type I errors) of the poor in Zambia in sub saharan Africa (a less developed country)

is much more than in Brazil (a developing country). Further, in developed countries, like

Chile and the United States, the problem is more one of leakage (Type II error), rather than

undercoverage. Thus, we can say, in developing countries, it is more likely that probability

of Type I error will be high due to corruption and other inefficiencies, hence a universal

approach makes more sense. Whereas in developed nations probability of Type II error is

likely to be high, such that a targeted program makes more sense.

In the Indian context, Dreze and Sen (2013) note that India’s experience with targeting

has been far from encouraging, while many public policies based on the principles of uni-

versalism with ‘self-selection’ (like mid day school meals and MGNREGA) have performed

comparatively well. They contend that the problems of exclusion and divisiveness inher-

ent in targeted programs are exacerbated by the fact that India’s official poverty lines are

too low. Also, studies show that there might be important spillover benefits of including

more households under a social welfare program (that is, higher type II errors), especially if19In low-income countries, total administrative costs for targeted programs comes to about 30% of total

costs, compared to 15% for running a universal program. Comparisons between universal and targetedprograms in the United Kingdom indicated administrative costs of 3.5% for universal programs and about10% for means-tested targeted programs. Studies from the United States found similar results (2.5% universalversus 13% targeted) - Dutrey (2007)

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non-participation is the result of a ‘stigma’ or disutility associated with participation. For

instance, Kochar (2005) finds that take-up rates by BPL households increase with the level

of benefits provided to the non-poor under the PDS. Greater public awareness regarding

consumer rights is another example of such a spillover effect, as another reason for the poor

not using the PDS could be lack of awareness regarding their entitlements. For instance,

Mooij (1994) examines the PDS in Kerala prior to the introduction of targeting and finds

that public awareness coupled with grass roots organisations to channel consumer complaints

provided an effective check and control on the functioning of the PDS, and the state had a

successful system in place. Complaints by card holders are also usually taken more seriously

by officials if there are greater numbers. This goes back to the argument of Korpi and Palme

(1998), that encompassing institutions pool the risks and resources of all citizens and thus

create converging definitions of interest which targeting might fail to achieve.

3 Data

The analysis uses the India Human Development Survey 2012 (IHDS II) data, which was col-

lected between January 2011 and March 2013. It is a nationally representative, multi-topic

survey of 42,152 households in 384 districts, 1420 villages and 1042 urban neighborhoods

across India. Two one-hour interviews in each household covered health, education, em-

ployment, economic status, marriage, fertility, gender relations, and social capital. Children

aged 8-11 completed short reading, writing and arithmetic tests. These data are mostly

re-interviews of households interviewed for IHDS-I in 2004-05. The sample for IHDS-I was

drawn using stratified random sampling and contains 13,900 rural households who were in-

terviewed in 1993-94 in a previous survey by the National Council of Applied Economic

Research (NCAER) and 28,428 new households. IHDS II re-interviewed 83% of the original

households as well as split households residing within the village and an additional sample

of 2134 households. Villages and urban blocks (comprising 150-200 households) formed the

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primary sampling unit (PSU) from which the households were selected. I also use the IHDS-I

data in one section to calculate graduation rates out of poverty. IHDS was jointly organized

by researchers from the University of Maryland and NCAER.

I restrict my sample to the five South Indian states, TN, AP, KR, KA and MH which

generates a sample of 12,929 households. I use the National Sample Survey Organization

(NSSO) - Consumer Expenditure Survey (CES) data from 2011 as a robustness check for

my main results. I also use Census 2011 data for generating community characteristics

for estimating vulnerability. These were aggregated to district level from village level data

released under the Census, and then merged with the main data. I use the 2011-12 state

wise poverty lines based on MRP consumption published by the Reserve Bank of India, as

they are the most recent estimates available. Lastly, I use the 2011 Consumer Price Index

(base year 2010) and 2014 Inflation Rates (base year 2012) from Ministry of Statistics and

Program Implementation, India.

4 Vulnerability to poverty

I refrain from using the usual measures of poverty and concentrate on a relatively broader

and more dynamic measure of deprivation - vulnerability to poverty, which incorporates

the destitution of individuals from future shocks. Vulnerability is the prospect that an

individual will fall below some norm or benchmark of welfare at a given time in the future,

where the time period and welfare measure are sufficiently general. It is an ex-ante measure

of a household’s well-being (unlike poverty, which is an ex-post measure).20 The literature

proposes three alternative approaches to assessing vulnerability (Hoddinott and Quisumbing

2003): vulnerability as expected poverty (VEP), vulnerability as low expected utility (VEU)20Poverty and vulnerability, though correlated, are conceptually different as depending on whether a

household has secure income sources, a household above the poverty line may be vulnerable, or one just belowthe poverty line may not be vulnerable. The presence of risk or uncertainty about the future distinguishesthe two concepts. A household faces multiple sources of risk which makes its future uncertain, like weathershocks, health shocks, e.t.c. Without this risk, the two concepts would be identical.

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and vulnerability as uninsured exposure to risk (VER). VEP and VEU approaches measure

vulnerability at the individual level as a probability and summing up over all households

gives a measure of aggregate vulnerability. VER measures do not construct probabilities,

but rather assess ex post whether observed shocks generate welfare losses at the aggregate

level. VEU approaches are problematic because the unit of measurement is units of utility,

which many policymakers might not be familiar with and may be difficult to understand.

The appropriate measure for this paper would be an ex-ante individual measure of poverty

that is easy to understand. Also, a measure of vulnerability that takes account of poverty

levels seems preferable. Hence, following the VEP approach in Chaudhuri et al. (2002),

I define vulnerability as “the ex-ante risk that a household will, if currently non-poor, fall

below the poverty line, or if currently poor, will remain in poverty.”

Formally, the vulnerability level of a household h at time t is defined as the probability that

the household will find itself consumption poor at time t+1:

vht = Pr(ch,t+1 ≤ z)

where ch,t+1 is the household’s per-capita consumption level at time t+1 and z is the ap-

propriate consumption poverty line. The procedure for estimating household vulnerability

follows from Chaudhuri et al. (2002).

The stochastic process generating the consumption of a household h is given by:

lnch = Xhβ + eh

where, ch is per capita consumption expenditure, Xh represents selected observed household

and community level characteristics, β is a vector of parameters, and eh is a mean-zero

disturbance term that captures idiosyncratic factors (shocks) that contribute to different per

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capita consumption levels for households that are otherwise observationally equivalent. Xh

includes:

A: A set of variables indicating household characteristics, such as (i) Family size and squared

(ii) Dependency ratio - child (0-14), teen (15-20) and old (60+) (iii) Proportion of adults

(21-60) (iv) Age of head and squared (v) Proportion of adults illiterate (vi) Proportion of

adults with primary education (upto grade 5) (vii) Proportion of adults with secondary

education (grades 6 to 8) (viii) Proportion of adults with some higher education (grades

9 to 12) (ix) Proportion of adults with some tertiary education (greater than 12 and upto

15)) (x) Dummy for male head, whether head is married, single or divorced, self-employed

with some assistance, self-employed with no assistance, salaried worker (xi) Dummy for

whether farming is main livelihood (xii) total land owned, per capita land area availability

and squared.

B: A set of variables indicating community characteristics (at district level), such as (i)

Proportion of villages with schools (ii) Proportion of villages with pucca roads (iii) Proportion

of villages with transport facilities, like buses, railways, etc (iv) Proportion of villages with

health facilities, like primary health centres, community health centres, etc (v) Proportion

of villages with improved water sources (vi) Proportion of villages with proper sanitation

facilities (vii) Proportion of villages with banking facilities (viii) Proportion of villages with

markets (ix) Proportion of villages with electricity (x) Proportion of area irrigated (xi)

Proportion of minority population (xii) Population density (xiii) Dummy for whether district

is a boundary or coastal district.

The variance of eh (and hence of ln ch) is allowed to depend upon observable household

characteristics in some parametric way. The estimates (σ2e,h) are generated assuming the

following functional form:

σ2e,h = Xhθ

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β and θ are estimated using a three-step feasible generalized least squares (FGLS) procedure.

Using the estimates β and θ that are obtained, expected log consumption and the variance

of log consumption can be directly estimated as follows:

E[lnch|Xh] = Xhβ

V [lnch|Xh] = σ2e,h = Xhθ

for each household h. By assuming that consumption is log-normally distributed (i.e., that

ln ch is normally distributed), these estimates can then be used to form an estimate of the

probability that a household with the characteristics, Xh, will be poor, i.e, of the household’s

vulnerability level.

vh = Pr(lnch < lnz|Xh) = Φ(lnz −Xhβ√

Xhθ)

An advantage of this vulnerability measure is that it can be estimated with cross-sectional

data and the possibility of poverty traps and other non-linear dynamics are implicitly built

in. However, it assumes that the distribution of consumption across households, given the

household characteristics at time t, represents time-series variation of household consump-

tion. Thus, a large sample is required for this measure in which some households experience

positive shocks and others negative shocks. It also assumes economic stability and does not

incorporate the possibility of aggregate shocks.

5 Geographic Regression Discontinuity Design

The Geographic Regression Discontinuity (RD) design leads to identification of the local

treatment effect at the boundary of treated and control areas, like a standard RD design

15

with multiple forcing variables (Keele & Titiunik, 2015). I exploit a geographic RD design

that is based on four borders (KR & TN, KA & TN, KA & AP, MH & AP) between the

adjacent South Indian states (see Figure 1). The idea is to interpret the distance to the

closest state border as an assignment variable that decides about the more generous versus

less generous PDS ‘treatment’. In implementing the geographic RD design, I use a ‘naive’

measure of distance, that is, the shortest or perpendicular distance to the nearest border of

the opposite group from the district centroid, for computational ease. Note that the type of

distance measure does not influence the geographic RD results, because in a local region of

the border they would be highly correlated (Agrawal 2015). Districts with a negative distance

to the border are located on the more generous PDS side from various state borders. The

treatment effects are then estimated by pooling the data around the common boundary.

The use of distance can be justified as follows. Rice yield per hectare is a monotonically

decreasing function of the distance from the deltas on the Southeastern side. Conversely, we

would also expect the average cost of procuring rice to increase monotonically with distance

from the deltas. However, the boundaries between states is random (that is, independent

of rice production) in the sense that they were determined by The States Reorganisation

Act, 1956, which organized the provinces under British India along linguistic lines.21 This

would mean that we can think of the distance measure intuitively as an instrument for PDS

generosity, with the border acting as a line of ‘random cutoff’, so that it would be simply

luck which determines whether a household ended up getting the more generous PDS.

Figures 2 and 3 show the discontinuous jump at the boundary in two proxies for generosity

that can be used - the proportion of BPL ration card holders22 and the average PDS subsidy

21When India gained independence in 1947, the grouping of the states was done more on the basis ofhistorical and political principles, involving the reduction of princely states from 571 to 27. Languagewas decided as the basis on which India’s states were to be reorganised due to popular demand and alsobecause linguistic regions were geographically contiguous and this made them easily governable. The StatesReorganization Act was passed by parliament in November 1956. It provided for fourteen states and sixcentrally administered territories. The state of Bombay was further split into two states, viz, Maharashtraand Gujarat, in 1960. (Wikipedia)

22Includes Antyodaya Anna Yojana (AAY) households as well as these were formed out of BPL households.

16

received by the household in the district. The second, the PDS subsidy the household

is getting, is a more direct measure of generosity, since it captures the actual assistance

extended by the PDS. The first, whether a household has a BPL ration card, is a more

indirect measure of generosity. But it captures both the ease with which a poor household

can get subsidized food in a state and also the entitlements (so both the intensive and

extensive margins), and hence I use the ration card information. As the jump in the average

PDS subsidy is being driven by take up rather than differences in entitlements (that is, value

of the ration card), we can say that the Compound Treatment Irrelevance assumption (Keele

& Titiunik 2015) holds here and identification is possible. That is, I can assume that there

is no separate subsidy effect on outcomes, so that the generosity of PDS impacts can be

exactly reduced to the BPL ration card treatment. Figure 2(a) shows that, on average, the

proportion of BPL ration card holders increase by roughly 0.4 at the boundary. Relative to

the level at the less generous side, this translates approximately to an 80% increase. Since

the jump is not from 0 to 1, the appropriate method would be a fuzzy RD.

The assumptions for a geographic RD framework to be valid are analogous to those in a

regular RD model. First, given that treatment assignment in a spatial RD design is non-

random, households must not sort conditional on generosity of PDS. This is unlikely here,

since obtaining a BPL ration card and availing subsidized rations in a state requires one to

be a resident of the state, along with other eligibility criteria (Table 2). Also, as can be seen

from Table 6, the mean years a household has resided in a place is over 70 in all states and

the expansion of the PDS in India began around 2008 in AP, while TN was always universal.

Checking the sample for migration statistics, this assumption seems valid. About 1% of the

sample migrated from another state or country (177 households). Out of this, about 18%

(32 households) have been in place for less than 15 years (that is migrated after 1997, when

targeting was introduced). Out of this, about 34% (11 households) migrated to AP or TN

(the more generous states). Out of this, about 45% (5 households) are affluent.23 This is

23As per the definition of Vanneman & Dubey (2011), those with incomes more than twice the median

17

0.12% of the sample of households in the generous states. So a strategic move due to a

difference in PDS generosity is not likely to be a concern.

Second, in a standard RD design, beyond the treatment status variable, no other relevant

variable should change discontinuously at the boundary. However, in a geographic RD design,

we are often confronted with the problem of compound treatments, where extraneous factors

also change discontinuously at the border such that isolating the effect of the treatment of

interest becomes impossible. To assess the validity of the identifying assumptions, Figure

4 and 5 provides graphical evidence on placebo tests that explore possible discontinuities

in key observable household and village characteristics which could be driving the results.

The graphs indicated that my conjecture on the similarity of South Indian states is correct

since these variables appear to be quite continuous at the border. Of particular importance

is figure 4(c), which shows that the assistance households receive from other governmental

programs, NGOs and other donors do not change discontinuously at the boundary. This is

also corroborated by figure 5(i) at the village level. This alleviates concerns regarding other

policies determined at the state level confounding the impact of the generosity of the PDS.

This does not rule out that there are differences on some unobservables between the states,

but this is likely to be minimal since I have isolated the southern states which are comparable

on geographic, demographic and development indicators (see Table 6). These states have

similarly high incomes per capita and net state domestic product per capita, infrastructure,

advantageous location in terms of access to sea, primary language spoken belong to the same

language family (Dravidian), and so on. Another possibility of compound treatments arises

when two or more geographic boundaries coincide, such as a county and an electoral district.

In India, however, parliamentary and district borders overlap but do not coincide exactly,

and parliamentary borders do not cross state borders. Further, since I am looking at state

borders, this is not a concern here since moving from a district located at the border of one

state into a district on the other side of the border in a different state, would still give us thein the sample.

18

effect of the state. Thus, we can be reasonably confident that the identifying assumptions

are fulfilled.

6 Empirical Specification

In estimating treatment effects at the cutoff, the preferred way in the literature is to run local

linear or polynomial regressions on a restricted sample of observations close to the cutoff.24

The main concerns with estimating the model is to choose the size of the bandwidth within

which to restrict the model to find an optimal balance between precision and accuracy.

Using a larger bandwidth yields more precise estimates, since more data points are used in

the regression. However, the linear specification is less likely to be accurate, which can lead

to bias when estimating the treatment effect. A solution is to try more flexible specifications

by adding higher order polynomials in the running variable.

The two main types of RD design considered in the literature are the sharp and fuzzy RD

designs (Lee & Lemieux 2010). With a sharp design, the treatment variable xi depends in

a deterministic way on some observable variable zi, such that xi = f(zi), where zi takes

on a continuum of values and there is a known point of discontinuity in the function, z0.

With a fuzzy design, xi is a random variable given zi, where the conditional probability

f(zi) = E[xi|zi = z] = Pr[xi = 1|zi = z] has a discontinuity at point z0. Unlike the

sharp design, in the fuzzy design, there are additional unobserved variables that determine

treatment and treatment assignment is not a deterministic function of zi. The common

feature in the two designs is that the the probability of receiving treatment, Pr[xi = 1|zi],

viewed as a function of zi, has a discontinuity at z0 (Hahn et al 2001). In other words, in a

sharp design, the probability of treatment jumps from 0 to 1 when zi crosses the threshold

z0, whereas a fuzzy design allows for a smaller jump in the probability of treatment. Since24Estimation of RD designs have been increasingly viewed as a nonparametric estimation problem, starting

with Hahn et al (2001), since misspecification of the functional form can induce large bias in RD estimatesof treatment effects.

19

the probability of receiving a BPL ration card jumps by less than 1 at the threshold, in this

case, the appropriate model would be a fuzzy RD design.

When the identifying assumptions hold, estimating the treatment effect in the fuzzy RD

design is analogous to the ‘Wald’ formulation in an instrumental variables setting using two-

stage least-squares (Berger et al. 2015). Specifically, the fuzzy RD design can be described

by the following 2 equations:

bplij = λg0 + δgDj +

k∑k=1

λgkdist

kj +

k∑k=1

ζgk(Dj × distkj ) + ϵgij (1)

yij = λy0 + δyDj +

k∑k=1

λykdist

kj +

k∑k=1

ζyk (Dij × distkj ) + ϵyij (2)

where the subscripts refer to household i in district j, y is an outcome variable of in-

terest, bpl is an indicator variable for whether a household has a BPL ration card, D is a

dummy variable indicating whether a district is located on the more generous PDS side of

the boundary, dist is the distance to the nearest border. Here, we are instrumenting bpl

with D and (2) is just the reduced form equation. Both equations include trends in distance

(up to polynomial degree k), which will capture any unobserved factors that vary with the

distance and potentially influence outcomes. We can then obtain the Wald estimator for the

local average effect of generosity of PDS on outcome y as:

βRD =δy

δg

The literature provides alternative methods for bandwidth selection,including plug-in rules

and cross-validation procedures. These typically yield a ‘large’ bandwidth and lead to data-

driven confidence intervals that may be biased (Calonico et al. 2014). Hence, I implement two

of the new theory-based, more robust confidence interval estimators proposed by Calonico

et al. (2017) - Mean Square Error (MSE) optimal bandwidth selector and Coverage Error

20

Rate (CER) optimal bandwidth selector.

7 Results

7.1 Main Results

Tables 8 to 13 and Figures 6 to 7 give the results for the impact of the generosity of the PDS

on the full sample and 2 sub groups which can be identified as relatively weaker sections of

the society. The sub groups are - Scheduled Castes(SC)/Scheduled Tribes (ST) and Other

Backward Class (OBC).25 From Table 7, we can see that SC / ST are the more vulnerable

group of the two and they have significantly lower wealth. In all tables, one observation is a

household.

Table 8 gives the impacts of generosity of PDS for the 3 samples and demonstrates that a

more generous PDS has a relatively large impact on household vulnerability to poverty: the

typical estimate is negative and relatively large in magnitude and statistically significant.

Each row presents the impact of generosity of PDS on vulnerability for a different functional

form of the running variable. I concentrate on summarizing the results for polynomial of

degree two and MSE optimal bandwidth selector as per Calonico et al.(2017).26 Column 1

looks at the estimates for the full sample and the coefficient in row 2 indicates that being

in a state with more universal approach to PDS reduces the average household’s probability

of becoming poor within the next year by approximately 12% and the effect is statistically

significant at the 1% level. Column 2, row 2 reveals that a more generous PDS leads to a

reduction of household vulnerability of the SC /ST group by 54% and the effect is statistically

significant at the 1% level. Finally, Column 3, row 2 indicates that being in a state with

25These are official designations given by the Government Of India to various groups of historically, sociallyand educationally disadvantaged people in India who are the primary beneficiaries of many reservationpolicies under the Constitution in education, scholarship, jobs, and so on.

26as these are more widely used.

21

more universal approach to PDS reduces the average OBC household’s vulnerability by

approximately 9% and the effect is statistically significant at the 1% level.

Table 9 gives an occupational decomposition of the impacts of the generosity of PDS on

vulnerability for the full sample. I consider four separate groups by occupation, all of which

have mean vulnerability greater than average for the full sample. In the table, these occu-

pations are listed in order of mean vulnerability, high to low. The impact of the generosity

of PDS ranges from 18 - 30% across groups, but there is no clear pattern visible. In all cases

the impacts are larger than the average impact on the full sample. Comparing agricultural

wage laborers and construction workers, we find that the impact on the latter is larger by

almost 4%, even though this group has the lower vulnerability of the two. Comparing these

two groups, what stands out is the extent of urbanization. Agricultural wage laborers are

mostly concentrated in rural areas, whereas a large proportion of the second group is living

in urban areas (a difference of over 30% in extent of urbanization). One explanation is the

commonly held belief that the PDS in India has a strong urban bias (for instance, it could be

that rural areas have much fewer fair price shops than urban areas). However, from Howes &

Jha (1992), we see that for the South Indian states, the average urban dweller does not seem

to gain more than the average rural dweller from the PDS. Then, the second explanation

could be that urban areas provide access to fundamental infrastructure facilities and other

opportunities, that complement the effects of the food subsidies.

I test the above hypothesis by creating an infrastructure index by scoring distance to different

infrastructure facilities across 8 domains.27 The threshold between high and low infrastruc-

ture is taken as the median score in the sample. Table 10 gives the impacts of the generosity

of PDS on vulnerability, below and above the infrastructure threshold. For all 3 samples -

full sample, SC/ST and OBC - the impacts are much larger with high infrastructure. The27The domains are - health (primary health centre, hospital, e.t.c.), education (schools, colleges, e.t.c.),

finance (bank branch), communications and media (phone booth, post office, e.t.c.), safety (police station),commercial (market, shops), transport (roads, railways, buses) and general (town, district HQ, e.t.c.). Dis-tance between 0 to 2 kms is assigned a score of 2 and distance between 2 to 20 kms is assigned a score of1.

22

largest difference is seen in the case of the SC/ST group. For SC/ST, a more generous PDS

with high infrastructure leads to a reduction of household vulnerability by around 76% and

the effect is statistically significant at the 1% level. With low infrastructure, the impact is

only around 18% and the effect is statistically significant at the 10% level. This can be taken

as a serious indication of complementarities between food and infrastructure.

Table 11 decomposes the impacts of the generosity of PDS on vulnerability by poverty

thresholds.28 I consider the impacts on thresholds around two poverty lines - the national

poverty line of Rs. 12,000 per annum and an approximate international poverty line ($1.25

a day) of Rs. 27,235 per annum.29 The first two columns of the table (Threshold 1 and 2)

gives us the lower and upper income bands around the international poverty line, while the

last two columns (Threshold 3 and 4) gives us the lower and upper income bands around

the national poverty line. Four things stand out. Firstly, the impacts across all but one

thresholds are larger for SC/ST/OBC, which is consistent with the findings of Table 8.

Even for Threshold 1 the impacts are very similar across the two groups. Secondly, if we

order the thresholds according to income distribution (3,4,1,2), i.e., low to high, we find the

impacts go from larger to smaller for SC/ST/OBC. Thirdly, for the full sample the largest

impact is on the Threshold 1, the middle income group. Thus, a more generous PDS seems

to be having the largest impacts on the “middle poor”. Finally, the impacts are least for the

highest income group, Threshold 2, for both samples. Hence, this mitigates concerns that

the rich are reaping the benefits of a more generous system.

For the sake of completeness, I also look at the impacts on actual poverty and food security

measures like malnutrition and diet quality. Table 12 gives the impacts on poverty for the 3

samples. The typical estimate is statistically insignificant. This brings out the importance

of a broader and more dynamic class of measures for destitution, like vulnerability, to gauge

28SC/ST/OBC have been combined here as one group due to insufficient data points.29The distribution of income per capita in my sample is left skewed, and households are concentrated

closer to the national poverty line. Thus, the income bands I consider are larger around the internationalpoverty line (± 10,000) and smaller around the national poverty line (± 5,000).

23

welfare impacts. Table 13 gives the impacts on moderate and severe malnutrition30 and diet

quality.31 I find significant impacts for the full sample and OBC category for malnutrition.

A more generous PDS reduces moderate malnutrition by 0.083 percentage points for the full

sample and 0.090 percentage points for OBC and these effects are statistically significant at

the 10% level. This translates into a reduction of 22% for the full sample (as mean children

under 5 in a household in the sample is 0.376) and 25% for OBC (as mean children under

5 in a household in the sample is 0.362). For severe malnutrition, the impacts are 29% and

48% for the full sample and OBC respectively. A more generous PDS also improves diet

quality by 0.051 to 0.188 percentage points and the effects are statistically significant at the

10% level.

One point of concern regarding the results of Table 13 is that there is no significant impact

on child malnourishment of SC/ST households, the most vulnerable of the 3 samples. This

is despite the fact that we see the largest impacts on diet quality of these households. On

further investigation, I find that the incidence of malnutrition is 15% higher for SC/ST

households as compared to the rest of the sample and this is significant at the 1% level.

Additionally, the total better quality food consumption is significantly lower for this group.

Hence, even though the savings generated through the PDS aids in improving diet quality

to some extent, the nutrient intake is possibly not enough to bring SC/ST households, with

a longer history of poverty and malnutrition, up to the nutrition level of other households.

Thus, we can infer that the impact on malnutrition is working mainly through the improved

30Specifically, I look for the prevalence of moderately and severely malnourished children in the house-hold by assessing the nutritional status of children under 5 through anthropometric measurements. Calorieadequacy is consistent with malnutrition, hence anthropometric measures are preferable to a calorie measureof nutrition. The commonly used anthropometric indices are weight-for-height (acute malnutrition or wast-ing), height-for-age (chronic malnutrition or stunting), weight-for-age (any protein-energy malnutrition orunderweight), and finally, the body mass index (acute protein-energy malnutrition). I calculate Z-scores forchildren under 5 in my sample, and use a Z-score cut-off point of < -2 SD to classify moderate and < -3 toclassify severe undernutrition, as per World Health Organization (WHO) guidelines. An indicator variablefor the presence of any moderately / severely stunted, wasted or underweight child under 5 in a householdis created.

31Diet quality is the proportion of food consumption expenditure on vegetables, pulses, milk, eggs, meatand fruits.

24

diet quality channel and PDS grains themselves do not have sufficient nutritious value.

In summary, these results show that the impacts of a more generous PDS on poverty re-

duction is significantly larger than a less generous system, with the largest impacts being

on more vulnerable groups in areas with better infrastructure facilities. This indicates that

lack of access to basic infrastructure facilities might hinder the effects of food subsidies, and

swamp the impacts of the PDS in other areas of the country. For instance, where there are

no clinics or hospitals available, or where lack of roads or bridges makes them inaccessible,

people cannot access the medical services that they require to be healthy and productive,

even if they have access to adequate food. This indicates that while it is important to ensure

that leakages in the PDS are curtailed in other parts of India, the development of funda-

mental infrastructure facilities, that complement the functions of food, is equally important.

These results also corroborate my initial hypothesis, that is, in a developing country like

India, a more universal approach to food security is more welfare enhancing.

7.2 Mechanisms

The above results show empirical evidence regarding what effect the generosity of PDS has on

vulnerability to poverty. Here, I will explore how the generosity of PDS affects vulnerability,

that is, the potential channels through which the effects operate. Tables 14 to 16 and Figures

8 to 10 give the relevant results.

From Table 14 we see that PDS subsidy only marginally impacts consumption (less than

0.5%), and the impacts are not statistically significant. Hence we can infer that the gains

of the food subsidy is being transferred elsewhere. This could be either towards paying off

outstanding debt or in some form of saving or investment. For those households repaying

loans (54% of sample), the average debt size in the data is Rs. 10,444 and the average

monthly rate is 2.6%, which implies a monthly payment of around Rs. 272.32 The average32assuming the household only pays the interest accruing on the loan each month

25

PDS subsidy in the generous states is Rs. 401 (about $7) per household per month.33

Hence, at a minimum, households must be saving / investing around Rs. 129 (about $2) in

the generous states per month. However, 46% of the sample is not repaying loans, and hence

would be saving / investing almost the entire amount of the subsidy. Column 2 of Table 14

gives the impact of the generosity of the PDS on monetary savings.34 A more generous PDS

increases savings of the average household in the full sample and OBC category by 0.8% and

the effects are significant at the 1% level. For SC/ST, the impact is an increase of around

3.2% and also significant at 1% level. These translate into an increase of Rs. 0.45 ($0.01)

and Rs. 2.33 ($0.04) respectively as mean savings per capita is around $1 for both groups.

Hence there is still around $1.96 - $6.99 of the PDS subsidy to account for.

From the data, I find significantly positive impacts on certain types of asset accumulation.

With only 58.7% households with access to formal banking services as of 2011 (RBI Report

2013) the poor in India have to resort to creative ways of saving their spare cash, like keeping

money with relatives or neighbours. Though all 5 states have a moderate to high degree of

financial inclusion (51% of households have bank accounts in the sample), it is possible that

most bank accounts lie dormant as has been seen in previous research (Dupas and Robinson

2013). Columns 3 - 5 of Table 14 looks at impacts of the generosity of PDS on three different

modes of saving typically used by the poor in India, and finds significant impacts on all three.

The variable ‘Wealth’ is a wealth index calculated using data on a household’s ownership

of selected assets (such as television and cooler, materials used for housing construction,

types of water access, sanitation facilities, e.t.c) by the method of Principal Component

Analysis.35 The variable ‘Investment’ is an indicator variable for whether the household

has bought property or expanded property. The variable ‘Livestock’ gives the number of

331$ = Rs. 54.409, monthly average exchange rate for March 2013, end of the survey period34log of income minus consumption per capita35Filmer and Pritchett (2001) made the use of Principal Component Analysis (PCA) for estimating wealth

levels using asset indicators popular. The wealth index assigns households scores on a continuous scale ofrelative wealth. I base my procedure for construction of the wealth index on the method followed by theDemographic and Health (DHS) surveys.

26

livestock animals (draft animals, goats, poultry, e.t.c.) owned by the household.

The impacts on the full sample are given in row 1 of Table 14. Column 3 indicates that

households in the states with more generous PDS increase their wealth holding by 1.2%,

and this is significant at the 5% level. The coefficient in Column 4 indicates that being in

a state with more generous PDS increases the average household’s probability of making

investments by 0.21 percentage points. This translates into an increase of 136% since mean

investment is 0.155, and the effect is statistically significant at the 10% level. Finally, the

coefficient in Column 5 indicates that households in states with more generous PDS increase

their livestock holding by 3.787 animals, and this is significant at the 1% level. The impacts

on OBC are very similar to that of the full sample. The impacts on SC/ST households are

given in row 2 of Table 14. Column 3 indicates that SC/ST households in the states with

more generous PDS increase their wealth holding by around 4%, and this is significant at

the 10% level. The impact on investment for SC/ST is insignificant. Lastly, the coefficient

in Column 5 indicates that SC/ST households in states with more generous PDS increase

their livestock holding by 6.398 animals, and this is significant at the 1% level.

As Banerjee and Duflo (2011) observe, decisions to save require a certain amount of self con-

trol from rich and poor alike. However, while the rich have a variety of tools at their disposal,

like banks and financial advisors, to aid them in the process, the poor have to do a much

harder job from their limited resources. Hence, investing in relatively illiquid assets, like

livestock, durable goods, property, e.t.c., makes it easier for the poor to forgo “temptation”

spending (like alcohol, cigarettes, tea, snacks) and force them not to be “myopic” about the

future. All of the above are also relatively risk free forms of investment. This makes sense

since poor people are very likely to be risk-averse, since losing money could mean starving

as they have limited access to sources of credit. Thus, a more generous PDS seems to be

aiding in the process of resource allocation towards relatively low-risk investments, which in

turn is protecting the poor from shocks in income, and hence reducing their vulnerability to

27

poverty.

One of the criticisms levelled against welfare programs, no matter how broadly targeted, is

that of “perverse incentives” created by changes in the behaviour of households attempting to

become beneficiaries of welfare policies, especially through disincentive effects on the labour

supply of the poor. Households may avoid activities that improve their living standards in

order to remain eligible for public support. To test for this under a more generous regime of

the PDS, I look at the impact of the generosity of the PDS on labor supply, casual, contractual

and permanent employment and MGNREGA employment. The variable ‘Labor Supply’ is

the average number of hours in a day worked by a household member across all jobs. The

variable ‘Casual’ is the average number of hours in a day worked by a household member

in daily/piecework casual jobs as the primary occupation. The variable ‘Contractual’ is the

average number of hours in a day worked by a household member in short term contract

jobs as the primary occupation. The variable ‘Permanent’ is the average number of hours

in a day worked by a household member in regular/permanent/long term contract jobs as

the primary occupation. The variable ‘MGNREGA’ is the average number of hours in a day

worked by a household member in MGNREGA jobs.

Table 15 gives the coefficients of interest. Row 1 gives the estimates for the full sample. The

coefficient in Column 1 indicates that a more generous PDS induces households to supply

1.110 hours of a day more of work across all jobs, and this is significant at the 10% level.

At the same time, a more generous PDS induces households to supply 3.387 hours of a day

more of work in casual employment and 0.342 hours of a day less of work in short term

contractual employment, and these are significant at the 1% level (Columns 2 and 3). There

is no significant impact on the hours worked in permanent jobs. Looking at the daily wage

rates for casual and contractual jobs in the data, I find that these are Rs. 268 ($4.93) and Rs.

118 ($2.17) respectively. The typical individual in a household in the sample works 109 days

a year for 7.03 hours a day. Hence, taking up less contractual employment and allocating

28

more time to a casual job implies a gain of $1.20 per day. Over a year, this implies a net

gain in income of $130. Additionally, Column 5 indicates that a more generous PDS induces

households to supply 4.664 hours of a day more of work in MGNREGA jobs, and this is

significant at the 1% level. The daily wage rate for MGNREGA jobs is Rs. 107 ($1.97) in

the data. The typical individual in a household in the sample works 59 days a year for 6.81

hours a day in MGNREGA jobs. Hence, taking up more time MGNREGA jobs implies a

gain of $1.35 per day. Over a year, this implies a net gain in income of $79. Hence, the total

gain in income over a year under a more generous system for the full sample is $209.

Row 2 gives the estimates for SC/ST and the impacts are much larger here. The coefficient

in Column 1 indicates that a more generous PDS induces SC/ST households to supply 2.926

hours of a day more of work across all jobs, and this is significant at the 10% level. At

the same time, a more generous PDS induces SC/ST households to supply 10.046 hours of

a day more of work in casual employment and 1.535 hours of a day less of work in short

term contractual employment, and these are significant at the 1% level (Columns 2 and 3).

As before, there is no significant impact on the hours worked in permanent jobs. The daily

wage rates for casual and contractual jobs for SC/ST in the data are Rs. 271 ($4.98) and

Rs. 98 ($1.80) respectively. The typical individual in a SC/ST household in the sample

works 117 days a year for 7.39 hours a day. Hence, taking up less contractual employment

and allocating more time to a casual job implies a gain of $3.66 per day. Over a year, this

implies a net gain in income for SC/ST of $428. Additionally, Column 5 indicates that a

more generous PDS induces SC/ST households to supply 16.184 hours of a day more of

work in MGNREGA jobs, and this is significant at the 1% level. The daily wage rate for

MGNREGA jobs for SC/ST is Rs. 104 ($1.91) in the data. The typical individual in a

SC/ST household in the sample works 62 days a year for 6.79 hours a day in MGNREGA

jobs. Hence, taking up more time MGNREGA jobs implies a gain of $4.56 per day. Over a

year, this implies a net gain in income of $282. Hence, the total gain in income over a year

under a more generous system for SC/ST is $710.

29

For non-MGNREGA work, the impacts for OBC are only significant for casual labor. A

more generous PDS induces OBC households to supply 1.211 hours of a day more of work in

casual employment (significant at the 10% level), without any significant reduction in other

jobs. The daily wage rate for casual jobs for OBC in the data is Rs. 252 ($4.63). The typical

individual in an OBC household in the sample works 107 days a year for 6.98 hours a day.

Hence, taking up more casual jobs implies a gain of $0.80 per day. Over a year, this implies

a net gain in income for OBC of $86. Additionally, Column 5 indicates that a more generous

PDS induces OBC households to supply 2.734 hours of a day more of work in MGNREGA

jobs, and this is significant at the 5% level. The daily wage rate for MGNREGA jobs for

OBC is Rs. 108 ($1.98) in the data. The typical individual in an OBC household in the

sample works 58 days a year for 6.75 hours a day in MGNREGA jobs. Hence, taking up

more time MGNREGA jobs implies a gain of $0.80 per day. Over a year, this implies a net

gain in income of $47. Hence, the total gain in income over a year under a more generous

system for OBC is $133.

Thus, a more generous PDS is making it possible for households to allocate their time more

efficiently towards jobs where they can get higher returns. When households do not have to

worry about food security, they have the luxury of choosing a job. With greater food security,

households become more proactive in terms of work, which generates a more steady source

of income and make them less vulnerable to poverty. There could also be an indirect effect

of this time allocation channel. Households would be likely to invest this extra income in

further low risk asset accumulation, such that the working of the resource allocation channel

would be enhanced.

Lastly, I check how much the generosity of the PDS insures households against external

shocks. I test the resilience of households under the generous system compared to the less

generous system in the presence of individual36 and aggregate37 shocks. Table 16 gives36Household level shocks, including any large losses incurred from major illness / accidents, drought,

flood, fire, loss of job, marriage, crop failure, death and other.37Village level shocks, including at least 2 exposures to natural disasters like earthquake, flood, tsunami,

30

the coefficients of interest. In all cases, households in the generous system are much less

vulnerable even after facing multiple shocks than households in the less generous system. In

fact, the differences between the two systems is more stark in the presence of shocks. Hence,

the PDS acts like an adequate safety net.

To test whether a more generous PDS also provides a pathway out of poverty through the

resource allocation channel, I look at the graduation out of poverty rates between 2004-05

and 2011-12. I construct two graduation rates. One is the proportion of households who were

poor in 2004-05 but are no longer poor in 2011-12. A second is the proportion of households

who were poor in 2004-05 but comfortably well-off in 2011-12.38 Table 2 gives the two rates.

As can be seen, the graduation rates in the universal states are much higher than that in

the targeted states. Of particular interest are the rates in AP, where the expansion in the

PDS happened in 2008, in between the two time periods. Hence, the rates for AP give a

rough idea about the movement out of poverty due to the transition of the PDS regime.

Approximately 81% of poor households transitioned out of poverty in AP during this time,

out of which 26% transitioned into a comfortable existence. The comparable average rates

in the targeted states, 38% and 9% respectively, are much lower. A more generous PDS,

thus, acts like a cargo net as well.

7.3 Sources

In this section I will explore why the generosity of PDS affects vulnerability, that is, the

potential sources through which poverty is impacted.

The impact on vulnerability in my main result section can be decomposed into higher benefits

at the intensive and extensive margins. That is, the sources are both better/more reliable

cyclone, hailstorm, and drought and epidemic over a period of 6 years.38Consumption above Rs. 27,235 is taken as an indication of comfortably well-off as per the discussion

in the Background section. Numerators of both rates only include households covered under the PDS as of2011-12.

31

benefits under the universal system and giving more people access to the program. Since

entitlements across states are similar, ‘better/more reliable benefits’ would translate into

higher take up of rations under a universal system presumably due to less stigma associated

with the program. Thus, total improvement of universal over targeted system equals the

average of (a) Higher utilization: people under income cutoff covered under both systems

but better off under universal system AND (b) Lower Type I errors: people under income

cutoff who get excluded under a targeted system, but get the benefits under a universal

system AND (c) Higher eligibility: people above income cutoff who wouldn’t be eligible

for a BPL card under the targeted system but get the benefits under a universal system.

The first is the impact at the intensive margin, while the latter two are the impacts at the

extensive margin.

It is not possible to decompose these effects through my main empirical specification. Hence

I use a propensity score matching technique to construct valid treatment and control groups

and estimate the average treatment effect on the treated (ATT). Matching relies on the

assumption of conditional independence of potential outcomes and treatment given observ-

ables, that is, the selection into the treatment should be driven by factors that can be

observed. I use variables that most likely affect both treatment and the outcome variable

to minimize selection bias. I first determine the probability of being selected into treatment

(that is, obtaining a BPL ration card) by a logit equation and then use this probability

(propensity score) to match households on either side of the border, filtering by the criteria

for margin.39

Table 17 gives the estimates of interest for the full sample, SC/ST and OBC. All estimates

are significant at the 1% level. Column 1 gives the impacts for the full sample. Row 1

gives the impact of a more generous PDS on vulnerability due to higher utilization and the39a) HH has a BPL ration card and consumption is less than cutoff, 27,235 b) HH on the generous side

of the border has BPL ration card OR HH on the generous side of the border does not have a BPL rationcard due to bureaucratic reasons OR HH on the less generous side of the border does not have a BPL rationcard due to reasons other than ‘not needed’ AND consumption is less than cutoff, 27,235 c) consumption isgreater than cutoff, 27,235.

32

impact is around 7%. Row 2 gives the impact of a more generous PDS on vulnerability due

to lower Type I errors and the impact is around 11%. Row 3 gives the impact of a more

generous PDS on vulnerability due to higher eligibility and the impact is around 4%. Thus,

the intensive margin effect (a) is 7%, while the extensive margin effect (b+c) is 15%. This

suggests that the bulk of the effect is coming from giving more people access to the program

rather than better/more reliable benefits under the universal system. The impacts of the

different sources on SC/ST (Column 2) are 9% and 18% respectively. The impacts of the

different sources on OBC (Column 3) are 6% and 12% respectively.

8 Robustness Checks

Firstly, I replicate my main analysis on vulnerability (Table 8) using NSSO-CES 2011 data.

The results are given in Table 18. The magnitudes of impacts are much smaller here, but

the direction of the impacts still holds up across all groups. That is, a more generous PDS

reduces household vulnerability to poverty compared to a strictly targeted system. The

impacts for the full sample and SC/ST are around 2% and 12% respectively. The impacts

on OBC are insignificant.

Secondly, I compare households living along the boundary between the more and less gener-

ous states with households living in the interiors of the states. Table 19 gives a comparison

of means across a few key households characteristics. I find that while incomes are similar

across both groups, consumption and outstanding debt is significantly lower and savings

in the form of assets held and bank balances are significantly higher for households in the

interiors. Average hours worked are also significantly higher for these households. This is

despite the fact that income transfers from government programs other than the PDS is

significantly lower in the interiors. This suggests that the impacts of a more generous PDS

we see at the border should be heightened in the interiors.

33

Thirdly, I conduct a couple of placebo border checks. I generate two borders randomly to the

right and left of the actual boundary. I assign districts to the right of the random borders

to the more generous side and those to the left to the less generous side. Then I check for

impacts on vulnerability along these borders. The results are given in Table 20. As expected,

all impacts are insignificant. This makes it less likely that the impacts of the generosity of

the PDS we see at the actual boundary is simply by chance.

Lastly, I employ a few alternative methods to test how robust the main results in my analysis

are. As a first check, I apply the parametric approach in Dell(2010), which uses a polynomial

in latitude and longitude.40 As a secondary check, I use the same polynomial, but augment

it with an Instrumental Variable approach (similar to the fuzzy GRD). As a third test, I

employ a standard difference in difference approach based on the multiple discontinuities,

in income cutoff and border. The income threshold which defines generosity (Rs. 27,235)

would create another discontinuity in the targeted states, above which ideally no household

should have a BPL ration card (while in the universal states they would). Hence, I can then

look at the impact on the households below the income threshold in the generous states

by applying a difference in difference approach. The fourth test simply augments the third

approach by including the polynomial in latitude and longitude as a control. The results are

given in Table 21. The impacts on the full sample range from 5 - 16%, which is consistent

with my main results.

9 Cost Effectiveness Analysis

One way to encourage policymakers to use the scientific evidence from program evaluations

in their decision making is to present evidence in the form of a cost-effectiveness analysis

(CEA), which measures the ratio of the costs of a program to the effects it has on one

outcome. This is different from a cost benefit analysis (CBA), which gives the ratio of40See Dell(2010) for the specification

34

costs to monetary value of effects on all outcomes. The advantage of CEA over CBA is its

simplicity, since there is no need for making judgments on monetary value of all the benefits.

It also allows the researcher to choose an objective outcome measure (Dhaliwal et al 2012).

There are 2 ways to do a CEA - a) Measure the cost for a given level of effectiveness (e.g.,cost

to increase school attendance by 1 year) and b)measure the level of effectiveness for a given

cost (e.g.,years of additional attendance induced by spending $100). Here I focus on the first

method of CEA, that is the cost to reduce the absolute number of the poor by 1 household.

The calculation is done using Borel’s law of large numbers, which implies that in the long

term, proportion of poor is equal to the average vulnerability of the group. Hence, taking

the impacts of a more generous PDS, we can calculate the number of households saved from

poverty, for the group of poor by international standards.

In my sample, 8,240 households have consumption per capita less than Rs. 27,235, that

is, are poor by international standards. A more generous PDS reduces vulnerability of this

group by 17.61%. Thus, 1451 households (17.61% of 8240) would be saved from poverty by

Borel’s law. The total food subsidy borne by the central government includes the economic

cost of the food grains and carrying cost of buffer stocks.41 The average economic cost of

rice and wheat is Rs. 18.59 per kg as of 2011-12. The carrying cost of buffer stocks as per

government norms is Rs. 0.12 million as of 2011-12. This is for a population 1.2 billion.

Hence, the carrying cost per capita would be negligible and I leave this out in my calculations.

Thus, average PDS subsidy per household per month:42 Rs. 18.59*35 = Rs. 650.65 = $11.96.

Total subsidy per month under universal coverage = K = Total sample (12,929 households)41Food grains are issued by the Food Corporation of India (FCI) to the States at a uniform Central

Issue Price (CIP) which is much less than the economic cost incurred by the Central Government by wayof procurement, storage, transport and distribution. The difference between the economic cost and the CIP,called the consumer subsidy, is borne by the Central Government through its annual non-Plan budget. Inaddition to this the FCI maintains a large buffer stocks of food grains, which entails a substantial carryingcost. The consumer subsidy and the carrying cost of the buffer stock together add up to the total foodsubsidy (Planning Commission Report).

42Assuming 7 kgs of food grains (rice, wheat and pulses) is distributed per individual per month free ofcost. The average number of members in a household is 5 in my sample. Hence, this comes to 35 kgs of foodgrains per household.

35

* $11.96. Total stream of subsidy under universal scheme in the long would equal K1−r

, where

r is the real discount rate for the US. Taking a real discount rate of 10% (as per Dhaliwal et

al 2012), this comes out to be $1,54,631. Thus, the food subsidy cost to the government to

reduce the absolute number of the poor by 1 household comes out to be = $1,54,6311451

= $118.

This figure can be taken as a minimum bound on the cost of poverty eradication through

food security.

Hari (2016) provides an estimate of the revenue foregone through tax exemptions in India as

of 2014-15. This figure stands at around 6.7% of GDP. To peg the CEA number as a propor-

tion of GDP as of 2014-15, it comes to be around 8.1%.43 This is an increase of 6.6% over the

food subsidy bill (rice and wheat) as of 2014-15. Hence, there is a possibility of mobilizing

resources through increased taxation, reduced tax concessions and better implementation in

India.

10 Discussion

There have been a number of developments in the PDS since 2011-12. Firstly, the National

Food Security Act (NFSA) was passed in 2013 with the aim to provide upto 75% of the rural

population and upto 50% of the urban population (close to 820 million people) with 5 Kgs of

food grains per person per month at subsidized prices through the PDS. Then in 2014, orders

for Aadhaar-integration44 in the PDS were issued on the premise that it would help curb

fraudulent transactions (Khera 2017). Subsequently, compulsory Aadhaar-Based Biometric

Authentication (ABBA) of ration cardholders, every time they buy their food rations, was

introduced in the PDS.

43Calculated by inflating the CEA figure to 2014 levels and multiplying by population of India as on 2014and finally, dividing by GDP as on 2014.

44Aadhaar or Unique Identification number (UID) is a 12-digit individual identification number issuedby the Unique Identification Authority of India (UIDAI) on behalf of the Government of India. It capturesthe biometric identity – ten finger prints, iris and photograph – of every resident, and serves as a proof ofidentity and address anywhere in India.

36

Khera (2017) notes that though ABBA can help eliminate identity fraud,it cannot curtail

quantity fraud or eligibility fraud. On top of that, ABBA is contributing to exclusion from

the PDS, sporadic denial of ration and higher transaction costs. Outright exclusion can

occur when families don’t have Aadhaar numbers45 and hence cannot get their rations, or

when there are failures of fingerprint authentication. Technology failures (e.g., connectivity,

server issues etc.) add to these problems. Also, those who are excluded by ABBA tend to

be the most vulnerable – widows, the elderly, manual workers, etc. Based on a household

survey in Jharkhand, Dreze et al (2017) find that the transaction failure rate due to ABBA

is around 20% in the Ranchi district. That is, around 81,000 cardholders are unable to buy

their PDS rations in Ranchi district alone. The starvation deaths reported in Jharkhand

highlight the seriousness of the problem.

In the context of the present study, a universal system automatically takes care of the main

form of PDS corruption - quantity fraud. If everyone is entitled to a fixed and sufficient

amount of food grains per month free of cost, then there is no market for unscrupulous PDS

dealers to siphon off rations. To make the system work smoothly, factors that must be in

place include: a)awareness regarding entitlements b)packaged food grains as per entitlement

c)ration cards which record transactions and d)robust grievance redressal mechanism. Hence,

there is no need for ABBA. However, for the last factor to work, infrastructure development

(like mobile and internet connectivity) is key, especially in rural and remote areas.

11 Conclusion

Using a geographic regression discontinuity design, this paper has analyzed the impacts of the

generosity of the PDS in India on household vulnerability to poverty and food security. The

results suggest that the impacts of the generosity of the PDS on poverty reduction are positive

and significant. The impacts remain strong for marginalised groups like SC/ST households45This is not uncommon. Around 70% of my sample don’t have Aadhaar cards as of 2012.

37

and vulnerable occupation households like landless wage laborers and construction workers.

Further, the impacts are larger towards the lower end of the income distribution than the

towards the top, alleviating concerns that under a universal system the rich would benefit

disproportionately. There is also some evidence that a more generous PDS is working best in

areas with better infrastructure facilities like schools, hospitals, etc. The general qualitative

pattern is robust across a wide range of empirical specifications and falsification tests. The

impacts of a more generous PDS on food security is a little more muted. Though the savings

generated from the PDS subsidy is leading to improved diet for all households, impacts on

malnourishment are insignificant for SC/ST households. This suggests that the quality of

PDS grains is less than desirable.

I also investigate the channel and source of the welfare impacts. The mechanism of trans-

mission of the effects of the generosity of the PDS on household vulnerability is as follows.

With assured food security, households are more selective about their work and allocate their

labor supply towards jobs with higher returns. This generates additional income which the

households invest in relatively risk free forms of assets. This is turn protects them in times

of crisis. Hence, overall households under the more generous system are less at risk of falling

into poverty. Finally, I find that the driving force behind these higher welfare benefits under

a universal system is lower errors of wrongfully excluding genuinely deserving households.

Thus, we can conclude that in developing countries like India, where it is more likely that

probability of errors of exclusion will be high due to corruption and other inefficiencies, a

universal approach to food security is more welfare enhancing.

In the Indian context, there is a need for reforms in the PDS, if it is to continue in its

present role. Beyond ensuring that no poor household is excluded from the benefits of the

PDS, possibly through universalization, there is also a need to ensure adequate nourishment

of the people. Hence, a PDS supplying pulses in addition to cereals and oil is necessary, so

that the diet of poor households is not excessively cereal dominated. It is also important to

38

have a proper grievance redressal mechanism in place, that is easily accessible to the most

vulnerable groups, to check the diversion of foodgrains from the PDS and other sources of

exploitation of beneficiaries by PDS dealers. Finally, there is a need to develop adequate

infrastructure facilities that complement the effect of the food subsidy, especially in rural

and remote areas.

At a more macroeconomic level, this paper sheds light on a couple of issues. Firstly, it

provides new evidence on the debate over targeted versus universal welfare programs. The

primary concerns regarding a more universal welfare program is whether it would be as

redistributive as a targeted program and also one of cost efficiency. This paper provides

evidence that a more universal approach is more welfare enhancing, in the context of the

world’s largest food security program. Further, the CEA shows that such a system can also

be feasible. Secondly, this paper shows that a food security program can be effective at

tackling both malnutrition and poverty. This implies that a well-implemented food security

program might work better than other anti-poverty programs. If taken seriously, these results

would justify expanding the reach of food security measures as a necessary means of income

support and social protection for the poor in India as well as other developing countries.

39

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https://doi.org/10.3886/ICPSR36151.v6.

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Table 1: Quantum of Food Subsidies Released by Government Of India

Year Food Subsidy GDP % of GDP Annual Growth (%)

1990-91 25 14,876 0.16 -

1991-92 29 15,033 0.19 16

1992-93 28 15,858 0.18 -2

1993-94 55 16,611 0.33 98

1994-95 51 17,717 0.29 -8

1995-96 54 19,059 0.28 5

1996-97 61 20,498 0.30 13

1997-98 79 21,328 0.37 30

1998-99 91 22,647 0.40 15

1999-00 94 24,564 0.38 4

2000-01 121 25,540 0.47 28

2001-02 175 26,803 0.65 45

2002-03 242 27,850 0.87 38

2003-04 252 30,063 0.84 4

2004-05 257 32,422 0.79 2

2005-06 231 35,432 0.65 -10

2006-07 238 38,715 0.62 3

2007-08 313 42,510 0.74 31

2008-09 437 44,164 0.99 40

2009-10 582 47,909 1.22 33

2010-11 629 52,824 1.19 8

2011-12 724 56,331 1.28 15

Unit: Rs. Billion. Source: Reserve Bank of India, Department of Economic Affairs, Ministry of Finance,Government of India.

45

Table 2: Generosity of the PDSCriteria 46 TN AP KL KA MH

I. Intensive Margin(Entitlements)

Max Qty per card (Rice &Wheat, kgs.) 30 30 32 23 35

PDS Price of Rice(Rs.) Free 1 1 3 6

Subsidy on Rice (usingCIP of GOI, Rs./kg.) 5.65 4.65 4.65 2.65 -0.35

Subsidy on Rice (usingretail prices, Rs./kg.) 24 21 31 19 19

II. Extensive Margin(Exclusion Norms)

Income per annum (> Rs.) NoneUrban – 75,000

21,000Urban – 17,000

15,000Rural – 65,000 Rural – 12,000

Land (> acres) None

2.5 wet

1 (excluding STs) 3

2.9 rain fed

5 dry 2.5 semi-irrigated

1.2 irrigated

Income tax payers None None None Exclude Exclude

Government Employees None Exclude ones withpermanent positions Exclude Exclude

Exclude doctors,advocates, architects andchartered accountants

Four wheelers owned NoneExclude (other thanprovided under govt.

schemes)Exclude Exclude Exclude

Residential telephoneowned None None None Exclude Exclude

III. Implementation

% with BPL ration cards(All India: 42) 96 86 29 65 29

% Diversion of PDS grain(All India : 44) 4 20 16 41 43

Implicit Income Transfer(Rs.) per HH per month

(All India: 181)436 370 237 255 105

Graduation Rate 1 (%)(All India: 34) 73 81 32 62 21

Graduation Rate 2 (%)(All India: 5) 17 26 10 14 3

46Sources: State Food and Civil Supplies portals, Wadhwa (2009-2011), Khera (2011b), Masiero 2012.Implicit Income Transfer is calculated as follows: Quantity purchased from PDS shops * Difference betweenPDS price and market price. Graduation Rate 1 is the proportion of households who were poor in 2004-05but are no longer poor in 2011-12. Graduation Rate 2 is the proportion of households who were poor in2004-05 but comfortably well-off, i.e., consumption above Rs. 27,235, in 2011-12. Numerators of both ratesonly include households covered under the PDS as of 2011-12.

46

Table 3: The redistributive effects of a universal program

Group 47 Average Income Tax (40%) Transfers Final Income(1) (2) (3) (4) (5)

A (20%) 1000 400 240 840

B (20%) 800 320 240 720

C (20%) 600 240 240 600

D (20%) 400 160 240 480

E (20%) 200 80 240 360

Inequality 5 (=1200) (=1200/5 = 240) 2.3

Table 4: The redistributive effects of a targeted program

Group 48 Average Income Tax (20%) Transfers 1 Final Income 1 Transfers 2 Final Income 2(1) (2) (3) (4) (5) (6) (7)

A (20%) 1000 200 0 800 64 864

B (20%) 800 160 0 640 64 704

C (20%) 600 120 0 480 64 544

D (20%) 400 0 240 640 144 544

E (20%) 200 0 240 440 144 344

Inequality 5 (=480) (=480/2 = 240) 1.8 2.5

47Inequality = Ratio of incomes between groups A and E. Final Income is the income after deductingtaxes and adding transfers. Source: Rothstein (2001).

48Assumes a proportional tax system of 20%, where the bottom 2 quintiles are not taxed. The target fortransfers are groups D and E. Column (6) assumes that targeting is imperfect, that is there is a leakage of40% to non-poor groups (A, B and C), and the poor (D and E) get only 60% of the transfers.

47

Table 5: Targeting Accuracy Of Poorest Quintile Across Countries

Country 49 Program Benefits share (%) GDP per capita ($) Income share (%) Exclusion Rate (%) Error Type(1) (2) (3) (4) (5) (6) (7)

Zambia Social Fund Program 25 1,310 3.81 68 Undercoverage

Sri Lanka Food Stamps 28 3,926 7.27 31 Undercoverage

Jamaica Food Stamps 31 5,138 5.28 30 Undercoverage

Colombia SHIR 34 6,056 3.35 37 Undercoverage

Brazil Bosla Escola 40 8,539 3.28 20 Undercoverage

Mexico Oportunidades 58 9,009 4.86 -21 Leakage

Chile SUF 66 13,384 4.63 -55 Leakage

United States Food Stamps 80 55,837 5.1 -391 Leakage

49Column (6) calculated on the basis of the income transfer required to bring the poorest quintile to theinternational poverty line of $1.90 a day. It is calculated as follows: (6) = [100 ∗ 1.90− (4)∗(5)

100∗3651.90 ]− (3). Source:

Coady et al (2004), Castaneda et al (2005), World Bank.

48

Table 6: Statewise Summary Statistics

Variable TN AP KL KA MH

Vulnerability 0.138 0.110 0.100 0.152 0.233

Wealth 0.666 -0.508 1.980 -0.672 -0.216

Malnutrition 0.117 0.101 0.107 0.120 0.151

Consumption per capita (Rs) 2,467 2,750 3,098 2,515 2,042

Households 1,982 2,203 1,570 3,865 3,309

Years in Place 70 76 71 81 73

Household Income (Rs. million) 0.12 0.09 0.17 0.12 0.13

Population density (persons per km2) 555 308 859 319 365

Infrastructure Index 50 0.74 0.69 0.78 0.74 0.70

Sex Ratio (females per 1000 males) 996 993 1084 973 929

Literacy Rate (%) 80 67 94 75 82

Mother Tongue Dravidian language (%) 51 98 94 99 84 90

Net Domestic Product Per Capita (Rs.) 57,093 38,556 52,808 41,492 61,276

% cropped area under rice cultivation 32 30 8 12 7

% BPL population 11 9 7 21 17

Distance (kms) 91 107 31 77 262

Table 7: Sub Groups Summary Statistics

Variable SC/ST OBC All

Vulnerability 0.189 0.149 0.157

Wealth -1.271 0.223 0.000

Malnutrition 0.133 0.114 0.120

Consumption per capita (Rs) 1,890 2,572 2,497

Households 3,288 6,490 12,929

Household Income (Rs. million) 0.10 0.12 0.13

BPL ration card holders (%) 75 63 60

50Computed by NCAER.51Includes Tamil, Telegu, Malayalam, Kannada and Marathi.

49

Table 8: Vulnerability

Specification Full Sample SC/ST OBC(1) (2) (3)

(a)Polynomial degree 1 (linear model)

τmse-0.091∗∗∗ -0.508∗∗∗ -0.059∗∗(0.025) (0.142) (0.026)

τcer-0.101∗∗∗ -0.411∗∗∗ -0.071∗∗∗(0.026) (0.110) (0.028)

(b)Polynomial degree 2 (quadratic model)

τmse-0.122∗∗∗ -0.543∗∗∗ -0.090∗∗∗(0.033) (0.189) (0.031)

τcer-0.183∗∗∗ -0.384∗∗∗ -0.189∗∗∗(0.035) (0.104) (0.030)

(c)Polynomial degree 3 (cubic model)

τmse-0.179∗∗∗ -0.479∗∗∗ -0.170∗∗∗(0.039) (0.166) (0.031)

τcer-0.278∗∗∗ -0.379∗∗∗ -0.369∗∗∗(0.059) (0.099) (0.055)

Note: Estimates using a triangular kernel. Bandwidth choices according to Calonico et al.(2017). Subscript mse denotes Mean Square Erroroptimal bandwidth selector and subscript cer denotes Coverage Error Rate optimal bandwidth selector. Robust standard errors, adjusted for

clustering by primary sampling unit, are in parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

Table 9: Vulnerability Across Occupational Groups

Occupational Group Mean τmse τcer

(1) (2) (3)

Ag wage lab (landless) 0.212 -0.263∗∗∗ -0.292∗∗∗(0.147) (0.088) (0.058)

Non ag wage lab (landless) 0.192 -0.177∗∗∗ -0.186∗∗∗(0.139) (0.045) (0.047)

Construction workers (landless) 0.187 -0.301∗∗∗ -0.268∗∗∗(0.139) (0.067) (0.073)

Automobile Drivers 0.172 -0.222∗∗∗ -0.194∗∗∗(0.137) (0.052) (0.064)

Note: Estimates using a triangular kernel and a functional specification of polynomial degree 2. Bandwidth choices according to Calonico etal.(2017). Subscript mse denotes Mean Square Error optimal bandwidth selector and subscript cer denotes Coverage Error Rate optimal

bandwidth selector. Robust standard errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,* indicates significanceat the 1%, 5%, 10%-level, respectively.

50

Table 10: Vulnerability Across Infrastructure Threshold

High Infrastructure Low Infrastructure(1) (2)

Full Sample -0.294∗∗∗ -0.024(0.063) (0.024)

SC/ST -0.755∗∗∗ -0.176∗(0.174) (0.094)

OBC -0.211∗∗∗ -0.095∗∗(0.035) (0.040)

Note: Infrastructure index is a sum of scores on distance to different infrastructure facilities in 8 domains. The domains are - health (primaryhealth centre, hospital, e.t.c.), education (schools, colleges, e.t.c.), finance (bank branch), communications and media (phone booth, post office,e.t.c.), safety (police station), commercial (market, shops), transport (roads, railways, buses) and general (town, district HQ, e.t.c.). Distancebetween 0 to 2 kms is assigned a score of 2 and distance between 2 to 20 kms is assigned a score of 1. The threshold is the median score in thesample. Estimates using a triangular kernel and a functional specification of polynomial degree 2. Bandwidth choices according to Calonico etal.(2017) Mean Square Error optimal bandwidth selector. Robust standard errors, adjusted for clustering by primary sampling unit, are in

parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

Table 11: Vulnerability Across Poverty Thresholds

Threshold 1 Threshold 2 Threshold 3 Threshold 4(1) (2) (3) (4)

Full Sample -0.228∗∗∗ -0.079∗∗ -0.191∗∗ -0.188∗∗(0.060) (0.038) (0.075) (0.091)

SC/ST/OBC -0.202∗∗∗ -0.111∗∗ -0.353∗∗∗ -0.266∗∗(0.049) (0.049) (0.133) (0.104)

Note: Thresholds are as follows - Threshold 1: 17,235 < income < 27,235; Threshold 2: 27,235 < income < 37,235; Threshold 3: 7000 < income< 12,000; Threshold 4: 12,000 < income < 17,000. Estimates using a triangular kernel and a functional specification of polynomial degree 2.Bandwidth choices according to Calonico et al.(2017) Mean Square Error optimal bandwidth selector. Robust standard errors, adjusted for

clustering by primary sampling unit, are in parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

51

Table 12: Poverty

Full Sample SC/ST OBC(1) (2) (3)

τmse-0.038 -0.321 -0.123(0.081) (0.273) (0.086)

τcer-0.061 -0.061 -0.233∗∗∗(0.074) (0.148) (0.057)

Note: Estimates using a triangular kernel and a functional specification of polynomial degree 2. Bandwidth choices according to Calonico etal.(2017). Subscript mse denotes Mean Square Error optimal bandwidth selector and subscript cer denotes Coverage Error Rate optimal

bandwidth selector. Robust standard errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,* indicates significanceat the 1%, 5%, 10%-level, respectively.

Table 13: Food Secuity

Full Sample SC/ST OBC(1) (2) (3)

Moderate Malnutrition -0.083∗ -0.188 -0.090∗(0.048) (0.159) (0.054)

Severe Malnutrition -0.108∗∗ -0.002 -0.174∗∗∗(0.054) (0.165) (0.062)

Diet Quality 0.051∗ 0.188∗ 0.073∗(0.0528) (0.110) (0.041)

Note: Estimates using a triangular kernel. Bandwidth choices according to Calonico et al.(2017) Mean Square Error optimal bandwidth selector.Robust standard errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,* indicates significance at the 1%, 5%,

10%-level, respectively.

52

Table 14: Channels - Resource Allocation

Consumption Savings Wealth Investment Livestock(1) (2) (3) (4) (5)

Full Sample -0.058 0.762∗∗∗ 1.147∗∗ 0.210∗ 3.787∗∗∗(0.216) (0.232) (0.545) (0.115) (0.787)

SC/ST -0.221 3.204∗∗∗ 3.996∗ -0.245 6.398∗∗∗(0.435) (0.982) (2.139) (0.354) (2.187)

OBC 0.397 0.749∗∗∗ 1.132∗ 0.281∗∗ 3.997∗∗∗(0.276) (0.200) (0.593) (0.109) (1.106)

Note: Estimates using a triangular kernel and a functional specification of polynomial degree 2. Bandwidth choices according to Calonico etal.(2017) Mean Square Error optimal bandwidth selector. Robust standard errors, adjusted for clustering by primary sampling unit, are in

parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

Table 15: Channels - Time Allocation

Labor Supply Casual Contractual Permanent MGNREGA(1) (2) (3) (4) (5)

Full Sample 1.110∗ 3.387∗∗∗ -0.342∗∗∗ 0.204 4.664∗∗∗(0.599) (0.678) (0.105) (0.356) (1.382)

SC/ST 2.926∗ 10.046∗∗∗ -1.535∗∗∗ 1.115 16.184∗∗∗(1.598) (2.982) (0.564) (1.396) (5.134)

OBC 0.317 1.211∗ -0.142 0.308 2.734∗∗(0.595) (0.720) (0.106) (0.284) (1.257)

Note: Estimates using a triangular kernel and a functional specification of polynomial degree 2. Bandwidth choices according to Calonico etal.(2017) Mean Square Error optimal bandwidth selector. Robust standard errors, adjusted for clustering by primary sampling unit, are in

parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

Table 16: Channels - Resilience to Shocks

No shocks Individual shocks Aggregate shocks Both shocks(1) (2) (3) (4)

Full Sample -0.069∗∗ -0.137∗∗∗ -0.109∗∗∗ -0.138∗∗∗(0.034) (0.040) (0.036) (0.039)

SC/ST -0.342∗∗∗ -0.830∗ -0.503∗∗∗ -0.582∗∗∗(0.114) (0.457) (0.129) (0.189)

OBC -0.035 -0.096∗∗ -0.114∗∗∗ -0.066∗(0.040) (0.036) (0.038) (0.036)

Note: Dependent variable Vulnerability. Sample in Column 1 only includes households which have faced no shocks. Samples in Columns 2, 3 and4 include only those households which have faced the respective shocks. Estimates using a triangular kernel and a functional specification ofpolynomial degree 2. Bandwidth choices according to Calonico et al.(2017) Mean Square Error optimal bandwidth selector. Robust standard

errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

53

Table 17: Sources

Source Full Sample SC/ST OBC(1) (2) (3)

(a)Intensive Margin (Higher Utilization) -0.069 -0.083 -0.055(0.004) (0.009) (0.007)

(b) Extensive Margin 1 (Lower Type I Errors) -0.113 -0.147 -0.089(0.007) (0.015) (0.009)

(c) Extensive Margin 2 (Higher Eligibility) -0.035 -0.030 -0.028(0.004) (0.009) (0.005)

Note: Dependent variable Vulnerability. Average treatment effect on the treated (ATT) estimates using propensity score matching with caliper0.005, based on the following criteria: a) HH has a BPL ration card and consumption is less than 27,235; b) HH on the generous side of the

border has BPL ration card OR HH on the generous side of the border does not have a BPL ration card due to bureaucratic reasons OR HH onthe less generous side of the border does not have a BPL ration card due to reasons other than ‘not needed’ AND consumption is less than

27,235; c) consumption is greater than 27,235. Standard errors are in parentheses. All estimates are significant at the 1% level.

Table 18: Vulnerability (NSSO Data)

Specification Full Sample SC/ST OBC(1) (2) (3)

(a) Linear Model -0.017∗ -0.127∗∗∗ -0.001(0.010) (0.036) (0.010)

(b) Quadratic Model -0.014 -0.122∗∗∗ -0.015(0.013) (0.037) (0.011)

(c) Cubic Model -0.032∗∗∗ -0.123∗∗∗ -0.001(0.012) (0.040) (0.013)

Note: Estimates using a triangular kernel. Bandwidth choices according to Calonico et al.(2017) Mean Square Error optimal bandwidth selector.Robust standard errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,* indicates significance at the 1%, 5%,

10%-level, respectively.

54

Table 19: Means Test

Variable Interior HH Border HH Difference(1) (2) (3)

Years in Place 78.861 76.279 2.582∗∗∗(0.359) (0.383) (0.527)

Income (Rs.) 2,344 2,276 68(67.944) (53.595) (85.893)

Consumption (Rs.) 2,234 2,499 -265∗∗∗(37.362) (49.656) (62.878)

Assets 16.041 15.712 0.329∗∗∗(0.083) (0.080) (0.115)

Banking 0.531 0.427 0.103∗∗∗(0.007) (0.007) (0.010)

Outstanding Debt (Rs.) 4,178 6,414 -2,236∗∗∗(202.457) (295.701) (363.784)

Average Hours Worked 7.135 6.952 0.183∗∗∗(0.034) (0.035) (0.049)

Other Transfers (Rs.) 940 1,508 -568∗∗∗(44.485) (63.739) (78.853)

Note: Standard errors in parentheses. ***,**,* indicates significance at the 1%, 5%, 10%-level, respectively.

55

Table 20: Vulnerability (Placebo Border Tests)

Type Full Sample SC/ST OBC(1) (2) (3)

Random border on the left

τmse-0.617 -12.126 -0.188(1.022) (889.27) (0.145)

τcer-0.660 0.612 -0.251(0.773) (0.534) (0.159)

Random border on the right

τmse-0.283 -0.193 -6.709(0.528) (0.508) (39.078)

τcer1.414 -0.200 -2.484(0.981) (0.796) (3.034)

Note: Estimates using a triangular kernel and a functional specification of polynomial degree 2. Bandwidth choices according to Calonico etal.(2017). Subscript mse denotes Mean Square Error optimal bandwidth selector and subscript cer denotes Coverage Error Rate optimal

bandwidth selector. Robust standard errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,* indicates significanceat the 1%, 5%, 10%-level, respectively.

Table 21: Vulnerability (Other Methods)

Dell Dell Modified Diff-in-Diff 1 Diff-in-Diff 2(1) (2) (3) (4)

genrs -0.081∗∗∗ -0.002 -0.037∗∗∗(0.008) (0.005) (0.007)

pbpl -0.164∗∗∗

(0.015)

polydist 0.000∗∗∗ 0.000∗∗∗ 0.000∗∗∗(0.000) (0.000) (0.000)

cutoff 0.132∗∗∗ 0.121∗∗∗(0.005) (0.004)

cutoff*genrs -0.061∗∗∗ -0.046∗∗∗

(0.006) (0.006)

constant -0.175∗∗∗ -0.042 0.085∗∗∗ -0.201∗∗∗(0.047) (0.065) (0.003) (0.043)

R2 0.071 0.063 0.179 0.206Note: ‘genrs’ is a dummy variable indicating whether a district is located on the more generous PDS side of the boundary. ‘pbpl’ is the proportion

of households with a BPL ration card. ‘polydist’ is a cubic polynomial in latitude and longitude. ‘cutoff’ is a dummy variable for whetherhousehold per capita consumption is less than Rs. 27,235. In specification 2 (Dell Modified),‘pbpl’ is instrumented with ‘genrs’. Figures in bold

give the relevant impact estimates. Robust standard errors, adjusted for clustering by primary sampling unit, are in parentheses. ***,**,*indicates significance at the 1%, 5%, 10%-level, respectively.

56

Figure 1: South India

Note: The Quasi Experimental Border in South India highlighted in red. States highlighted

in green [yellow] are located on the more [less] generous PDS side of the boundary.

57

Figure 2: Discontinuity in Proportion of BPL ration card holders

Figure 3: Discontinuity in Average PDS Subsidy

Note: Districts with a negative [positive] distance are located on the more [less] generous

side of a border.

58

Figure 4: Distribution of household characteristics

(a) Income (b) Remittances (c) Other Transfers

(d) N Assets (e) Outstanding Debt (f) House Rent

(g) Consumer Taxes (h) Unskilled Workers (i) Unemployed

(j) Awareness (k) Networks (l) Memberships

Note: The figure illustrates the distribution of several key variables among households in districtswithin a 100 km distance around the boundary in the main sample. Districts with a negative[positive] distance are located on the more [less] generous side of a border.

59

Figure 5: Distribution of village characteristics

(a) Price of Land (b) Price of Food Crops (c) Price of Draft Animals

(d) Sharecropping (e) Agricultural Wage (f) Skilled Wage

(g) MGNREGA Job Cards (h) MGNREGA Sufficient Work (i) Other Public Programs

(j) Higher Caste (k) Lower Caste (l) Muslim / Christian

Note: The figure illustrates the distribution of several key variables among villages in districtswithin a 100 km distance around the boundary in the main sample. Districts with a negative[positive] distance are located on the more [less] generous side of a border.

60

Figure 6: Vulnerability

(a) Full Sample (b) SC/ST (c) OBC

Figure 7: Vulnerability Across Occupational Groups

(a) Ag wage lab (landless) (b) Non ag wage lab (landless) (c) Construction Workers (land-less)

Figure 8: Resource Allocation Channel (Full Sample)

(a) Savings (b) Wealth (c) Livestock

Note: Districts with a negative [positive] distance are located on the more [less] generous side of aborder.

61

Figure 9: Time Allocation Channel (Full Sample)

(a) Labor Supply (b) Casual Jobs

Figure 10: Resilience to Shocks Channel (Full Sample)

(a) HH Level Shocks (b) Village Level Shocks

Note: Districts with a negative [positive] distance are located on the more [less] generous side of aborder.

62