Markets, Subsidies, and Corruption: Evidence from India

Siddharth Hari ⇤

New York University

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Abstract

Corruption often prevents well-intentioned government-run programs from having desired ef-

fects. In this paper I study how the timing of corruption reduces access to a large scale food

subsidy program following adverse economic shocks. The Public Distribution System (PDS) in

India provides subsidized grains to eligible households through a network of fair price shops which

operate in parallel with the private food grain market. Shocks to the price of grain on the market

alter the incentives of agents along the PDS supply chain to divert grains meant to be sold under

the subsidy program into the private market. I provide evidence for increased corruption in the

program after adverse rainfall shocks in a district. These shocks reduce rice production, and in

the absence of well-integrated markets, push up its price locally, thereby increasing incentives for

corruption. Using data from a large household survey, I analyze consumption patterns and find

that households are able to buy less rice through the PDS and buy more from the market, follow-

ing adverse shocks. Using a unique administrative data set I show that official disbursements of

rice made to a district under the PDS do not change in response to these local shocks. Since these

adverse shocks also lower wages and earnings, the timing of corruption hampers the program’s

ability to provide social insurance, resulting in significant welfare consequences.

⇤Department of Economics, New York University, 19 West 4th Street, 6th Floor, New York, NY 10012. Email:sh2599@nyu.edu. I am extremely grateful to Debraj Ray for constant guidance and support throughout the course ofthis project. I would like to thank Hunt Allcott, Rajeev Dehejia and Raquel Fernandez for several helpful discussions.I would also like to thank Ashna Arora, Rakesh Banerjee, Laurent Cavenaile, Ritam Chaurey, Kunjal Desai, OeindrilaDube, William Easterly, Morgan Hardy, Clément Imbert, Reetika Khera, Dilip Mookherjee, Martin Rotemberg, andparticipants at the India-China Workshop at the Paris School of Economics, the Applied Micro Reading Group andDevelopment Workshop at New York University for useful comments. All remaining errors are my own.

1

1 Introduction

Social assistance programs form an integral part of developmental policy, and gov-

ernments across the world devote considerable resources to them.1 How well these

programs function depends crucially on how well they are designed and implemented.

Corruption is a major impediment to successful service delivery, with a growing litera-

ture documenting the prevalence and levels of corruption in a wide range of government

run programs(Olken and Pande (2012)). Most existing studies on corruption focus on

one of two measures - the average rate of corruption, which measures the proportion

of each dollar spent on a program that fails to reach intended beneficiaries, or the

marginal rate of corruption, which measures how much of each additional dollar spent

on a program fails to reach intended beneficiaries.

If corruption varies over time or across states of the world, then the average rate of

corruption will either overstate or understate the true welfare loss. In this paper, I focus

on a relatively under-studied aspect of corruption - its timing. I find that corruption

in India’s flagship food subsidy program, the Public Distribution System (or PDS) is

higher in times of agricultural distress, restricting the ability of the program to provide

social insurance. The PDS is a massive food subsidy program, costing the Indian

government more than 1% of GDP each year. Eligible households are provided food

grains up to a certain quota each month, at heavily subsidized prices sold through a

network of fair price shops - stores set up exclusively for this program.2 The size of

the subsidy results in a large gap between the price of grain on the market and its

price at fair price shops, creating an incentive for officials implementing the program

to divert grains away from the system into the black market. I study how this illegal1See World Bank, 20152Many other countries, such as Mexico, Egypt and Sri Lanka run similar food subsidy programs

2

diversion changes in response to exogenous rainfall-induced shocks to the market price

of grain.This correlation between local economic conditions and corruption implies that

estimates of welfare loss based on average rates of corruption would underestimate the

true welfare loss to households.

Agriculture in India is still largely rainfall dependent, and rice in particular is a crop

which requires large amounts of water. I first establish that a deficit of rainfall in a

district, lowers the local production of rice. While an aggregate shock to production

across the country will affect prices, such local shocks should not - provided markets

are well integrated. However, this is hardly true of India. Significant internal barriers

to trade (Atkin (2013) ; Jacoby (2013); Wadhwa (2001)) and high transportation costs

result in far from perfectly integrated markets. I find that in this context, a reduction

in rice production in a district increases its price locally. Prices under the PDS, on the

other hand, are set by state governments for the whole state and do not respond to

district level shocks.3

An increase in the market price increases the incentives of agents along the supply

chain to divert rice away from the PDS into the black market. At the same time,

adverse rainfall shocks lower wages and earnings in the local economy - both through

the direct effect of lowering agricultural productivity, and through spillover effects on

the non-agricultural sector (Santangelo (2016)). Thus, the structure of the PDS gives

rise to corruption incentives precisely when households need the program the most.

I first develop a model of household behavior to predict how consumption patterns

should react to shocks in the presence and absence of corruption in the program.

Households have preferences over three goods - rice bought through the PDS, rice

bought from the free market, and other goods. I assume that purchases made through3These prices themselves maybe revised by state governments from time to time, but these changes are not correlated

with district level rainfall shocks.

3

the PDS require the household to incur a fixed utility cost, because of the long waiting

lines at ration shops as well as due to the social stigma associated with being seen

purchasing through the PDS. As a result, eligible households whose income exceeds a

certain threshold choose not to buy rice from the PDS. Adverse shocks which simul-

taneously push up the market price of rice, and lower wages and earnings, shift the

entire income distribution to the left. In the absence of corruption, therefore, more

households consume rice through the PDS after bad rainfall shocks.

As described above, however, the significant price differential between the PDS and

the market provides a natural incentive for agents along the supply chain to illegally

divert rice. The PDS store owner chooses the optimal amount to divert in each period

taking into account the probability of being caught. I assume this probability is an

increasing function of the amount diverted. The resulting optimal diversion amount

is an increasing function of the market price. What implications does this have for

household consumption? Following adverse shocks, we should observe less rice being

consumed through the PDS overall. The effect on the number of households who are

able to purchase a positive amount of rice through the PDS would depend on how the

store owner chooses to ration. The store owner could sell an amount less than the

mandated quota to all households who who demand rice, in which case the number of

households who purchase through the PDS would go up after bad shocks. On the other

hand, the store owner may provide some households their entire quota, and sell nothing

to other households. decreasing the number of households who access the PDS.

The model thus yields testable implications for both the existence and nature of corrup-

tion. In my empirical analysis, I find that households are indeed able to purchase less

rice through the PDS after adverse rainfall shocks. Furthermore, there is an increase

in the number of households which are completely rationed out of the system, and the

4

corresponding welfare loss to these households is large. I also find that members of

traditionally disadvantaged communities such as schedule castes and schedule tribes4

are disproportionately adversely affected as a consequence of this responsiveness in

corruption.

It is possible that rainfall shocks might affect a household’s consumption patterns

through other channels. One leading candidate is a change in the allocation made

under the PDS. If, perversely, state governments provide less food under the PDS

to districts affected by negative rainfall shocks, then we would observe declines in

consumption following poor rainfall. Reduced access to the PDS in such a scenario

would be due to poor management, rather than outright corruption. In order to see

if this is indeed the case, I collected a unique administrative data set on district level

allocations made under the program in four states. I find that allocations to a district

are largely formula-based and do not change in response to local rainfall shocks. What

this means, therefore, is that districts receive the same amount of rice through the PDS

regardless of local weather conditions. However, households are able to buy less of it

in bad times because of increased diversion.

I conduct two sets of placebo tests to provide further evidence of the mechanisms

outlined above. First, I look at other commodities sold through the PDS - kerosene

and sugar - whose prices are unaffected by rainfall shocks. I find that the availability

of these products at the PDS stores does not change with changes in rainfall. Second,

I look at districts that are not major producers of rice, but still consume it. In these

places, since local supply is low to begin with, adverse rainfall shocks have a limited

impact on the local market price of rice, and therefore on corruption incentives. In

line with my hypothesis, I find that in these districts consumption from the PDS does4Scheduled Castes (SCs) and Scheduled Tribes (STs) are official designations given to various historically disadvan-

taged socio-economics groups in India

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not react to negative shocks. These results support the hypothesis of changes in prices

driving corruption incentives.

I also use my model to conduct welfare analyses by calculating the welfare gains to

households under two scenarios - first, from eliminating corruption in the program

completely and second, by keeping the level of corruption constant, but eliminating its

responsiveness to shocks. The welfare gains from eliminating corruption are significant,

but I also find that the welfare cost of the responsiveness of corruption for the worst-

affected households is nearly one third of the total welfare cost due to corruption

This paper makes two main contributions. First, it highlights an important channel

through which corruption may impact welfare - its timing, which may significantly

hamper the ability of a social protection program to provide social insurance. Reducing

average rates of corruption in social programs is likely to result in significant welfare

gains to program participants. Here I show that in certain settings, the average levels

of corruption might understate the true welfare loss arising due to corruption.

Second, in contexts where social programs run in parallel to the market, there could be

important spillover effects from one to the other. A large literature on program evalu-

ation documents the impact of social programs on market outcomes (Imbert and Papp

(2015); Cunha et al. (2014)).5 This paper focuses on the reverse relationship - shocks

in the market may have affect the functioning of social programs. Since social protec-

tion programs involve large government expenditures and are often targeted towards

the most vulnerable sections of society, it is very important to pay close attention to

factors which may affect their performance.

This paper is related to three strands of literature. First, it contributes to the growing5For instance, Imbert and Papp find that the introduction of a large scale public works program in India, pushed up

wages in the labor market.

6

literature on corruption in developing countries. Several studies have documented the

prevalence of corruption in a large range of government programs (Olken (2007);Olken

(2006);Reinikka and Svensson (2004) ), and provided evidence of its responsiveness to

incentives ((Niehaus and Sukhtankar (2010); Ferraz and Finan (2011)). One common

approach used in the literature to estimate corruption is to compare two separate mea-

sures of the same variable - one from official records and another from independent

estimates. For example, Olken, 2006 studies corruption in a food subsidy program

in Indonesia, by comparing administrative data on rice disbursements made to vari-

ous villages with consumption survey data on how much was received by households.

Reinikka and Svensson, 2004 see what proportion of central government grants made

to public schools in Uganda actually reach the intended beneficiaries by comparing

data from central government records to data from a survey of school administrators.

I follow a similar approach in this paper to study the responsiveness of corruption to

shocks. I show that the official disbursements of rice under the PDS do not react to

rainfall shocks, whereas household consumption falls.

Second, this paper is related to the literature which analyzes the impact of weather

related shocks in agrarian settings. There is a large body of work which documents the

impact of rainfall and temperature shocks on a range of outcomes, such as wages and

the labor market (Kaur (2012); Jayachandran (2006)), cross-sector linkages (Santangelo

(2016); Colmer (2016)), conflict (Fetzer (2014)), human capital investments (Shah and

Steinberg (Forthcoming)). I add to this literature by showing how weather shocks can

impact the functioning of social protection programs.

Finally, this paper contributes to the literature on the functioning and impact of the

PDS. Previous studies have analyzed and found this program to have had limited suc-

cess in providing direct nutritional support (Kochar (2005); Tarozzi (2005)). However,

7

Kaul (2014) finds that the program frees up resources, allowing households to increase

overall food intake, and Gadenne (2014) finds that the ration shop system is welfare im-

proving relative to linear taxes or subsidies for most of the goods sold through the PDS.

One reason for its limited success is bad implementation, with high levels of corrup-

tion on average (Khera (2011a); Jha and Ramaswami (2011)). Nagavarapu and Sekhri

(Forthcoming) find that monitoring via caste networks can play an important role in

service delivery under the PDS. I focus on one of the most important determinants of

corruption in the program, the open market price.

The rest of this paper is organized as follows. Section 2 provides some background to

the Public Distribution System. Section 3 develops a model of household behavior.

Section 4 describes the various data sets used in the analysis. Section 5 outlines

the empirical strategy. Results are in section 6, and section 7 analyzes the welfare

implications. Section 8 concludes.

2 Background

2.1 The Public Distribution System

The Indian government intervenes heavily in food markets. Each year it announces a

minimum support price (MSP) for rice and wheat, i.e. the price at which it commits

to purchase from any producer. This price policy was put in place in order to ensure

remunerative prices to farmers. Part of the grain so procured is added to the central

government’s buffer stock of food grain, which serves as a buffer against to aggregate

production shocks. However, the main purpose of this procurement is to provide food

grain at discounted prices to the poor under the PDS. It traces its origins to World War

II, when the British government in India introduced rationing of food grains in certain

8

cities. It has undergone significant expansions post-independence and now provides

subsidized food to large sections of the Indian population.

The grains are procured from farmers by the Food Corporation of India (FCI) and

other government agencies at the pre-announced MSP. Each state is then allocated a

certain quantity of grain each month, in proportion to the number of eligible households

residing in the state. From there on, state governments take over the distribution of

these grains to households via a network of Fair Price Shops (or ration shops). These

government licensed stores were set up exclusively to sell commodities under the PDS.

Depending on the state, these maybe operated by the government, by cooperatives or

by private agents. There are roughly 500,000 ration shops across India.

Prior to 1997, the PDS had universal coverage. In 1997, however, the government

decided to make it a targeted program. Households which were classified as being

Below Poverty Line (BPL) were eligible for the subsidy, while Above Poverty Line

(APL) households were not. To carry out this classification, the government conducted

a BPL Census in 1997. Households were assigned a score, based on a set of observable

characteristics such as land holdings, the structure of the house etc. Households which

received a score below a certain cutoff were deemed to be BPL. Similar censuses have

taken place roughly every 5 years since then. In 2011, 41% of rural households, and

27% of urban households had a BPL card.6

State governments have a major say in the functioning of the PDS, and some states,

(such as Tamil Nadu) continue to run a universal PDS. Some other states have ex-

panded coverage by issuing additional BPL cards and paying for the additional subsidy.

Despite these expansions, there remain significant deficiencies in the targeting of the

program, with significant errors of inclusion and exclusion (Niehaus et al. (2013)). Jha6Author’s calculations based on the 68th round of the NSS. See below for a description of the data

9

and Ramaswami (2011) estimate that roughly 70% of poor households in India still

don’t have access to these BPL cards. At the same time, a large portion of beneficia-

ries are non-poor households. Figure 2 shows the rates of BPL card ownership across

consumption deciles in 2011-12. 40% of households in the bottom decile do not have a

BPL card, while 20% in the 7th decile do.7

Eligible households are provided cereals (rice, wheat or both, depending on the state),

as well as sugar and kerosene8, up to a certain quota. However, this quota is often less

than the desired consumption, and even eligible households supplement this amount

with grain from the market. In my sample, more than 80% of BPL households bought

a positive amount of rice from the market. The value of the subsidy, though different

across states, is substantial. For example, in 2011, the price of rice under the PDS was

on average 33% of its price on the market.

2.2 Corruption in the PDS

The PDS suffers from systematic corruption in the form of diversion of grains into the

black market.9 Khera (2011a) estimates corruption levels to be as high as 43%, with

significant heterogeneity across states. These estimates, arrived at by comparing offi-

cial allocations with reported consumptions, should be thought of as an upper bound,

since they do not account for losses during transportation and storage. The structure

of the program lends itself quite naturally to corruption. First, there is the natural

incentive to divert grains into the black market given the magnitude of the price differ-

ence. Second, the fair price shop owners themselves are paid very low commissions for7Figure 3 compares the characteristics of BPL and non-BPL households, and as can be seen BPL households are

more likely belong to traditionally disadvantaged castes (SC/STs), more likely to live in rural areas, and less likely tobe literate

8In most states many non-BPL households are also eligible for the subsidy on kerosene and sugar9See Planning Commission, 2005

10

selling under the program, magnifying the incentive effect. Third, there is very limited

monitoring of the ration shops to curb the diversion of food grains.

Corruption could take place on the intensive and extensive margins, and there is anec-

dotal evidence for both (Khera (2011b)). Shop owners often sell less than the full

quota to households, and either fake their signatures, or coerce them into signing re-

ceipts for the full quota amount. This is particularly true in areas where literacy levels

are low, and households have very little bargaining power vis-a-vis the shop owner.

Alternatively, shop owners sell the full quota to some customers, and nothing to oth-

ers, claiming that they did not receive enough grain from the government. Unlike

under-selling to households, these fake “stock-outs” have the added advantage of be-

ing unverifiable (Nagavarapu and Sekhri (Forthcoming)). Corruption could be taking

place anywhere along the supply chain, and agents other than fair price shop owners

might be involved. Data restrictions do not allow me to separately identify the level at

which this corruption is taking place. However, my focus is on highlighting the perverse

cyclicality in corruption, which has welfare consequences for households, irrespective

of level at which it takes place.

3 Model

In this section I develop a model to analyze the responses in the local economy to

adverse shocks, and to study how this affects a household’s access to the PDS.

3.1 Household Preferences

Households have preferences over 3 commodities - rice from the PDS (XG

), rice from the

market (XM

) and other goods (Z). Their preferences are represented by the following

11

utility function:

U(XG

, XM

, Z) =

✓⇣X

G

+XM

� c̄⌘↵

⇣Z⌘1�↵

◆1��

1� �� 1F

G

(1)

where W represent the agent’s income. FG

is a fixed utility cost the household has

to incur if it purchases a positive amount from the PDS store. This function is a

reduced form way of capturing the costs of visiting the PDS store - both in terms of

the inconvenience of waiting in long lines, as well as the social stigma associated with

purchasing rice through the PDS. c̄ is the subsistence level of rice consumption, ↵ is

the Cobb-Douglas share parameter and � is the coefficient of risk aversion.

The agents face 2 constraints. The first is the budget constraint (given the subsidy,

PG

⌧ PM

and the price of Z is normalized to 1):

PG

XG

+ PM

XM

+ Z = W (2)

and the PDS quota constraint:

XG

6 X̄ (3)

where X̄ denotes the state specific quota.

Given the structure of the utility function, there are 2 threshold wage levels, w⇤1 and

w⇤2, such that the demand functions for PDS and market rice are given by10,11:10See appendix for details11I assume FG < ↵Log(PM

PG ) which ensures that not all households purchase exclusively through the market.

12

XG

=

8>>>>><

>>>>>:

↵W+(1�↵)c̄PG

PGif W < PGX̄

↵

X̄ if PGX̄

↵

< W < w⇤2

0 if W > w⇤2

XM

=

8>>>>><

>>>>>:

0 if W < w⇤1

↵W�(1�↵)PM (X̄�c̄)�↵PGX̄

PMif w⇤

1 < W < w⇤2

↵W+(1�↵)c̄PM

PMif W > w⇤

2

Figure 1 shows how consumption patterns change with income levels.12The income

distribution is divided into 4 zones. The first are household which are extremely poor,

and choose not to consume their PDS quota entirely. Next are households for whom

the PDS quota binds, but they don’t consume rice through the market. The third

zone consists of households which exhaust their PDS quota, and consume a positive

amount from the market. And finally, households whose income is above w⇤2 choose

not to purchase rice through the PDS, and only buy from the market.

How does a household’s consumption react to adverse shocks? I model an adverse

rainfall as a shock which lowers total factor productivity in agriculture, which has two

effects. One, it lowers local production, and in the presence of imperfectly integrated

markets, push up the local price of rice. Second, it lowers labor demand, and conse-

quently reduces wages and earnings. How a household’s consumption reacts to these

shocks will depend crucially on how access to the PDS responds to these changes.12In figure 1, there is no mass to the left of w⇤

1 . This is because in the data only a tiny fraction of households are sopoor as to not be able to afford their PDS quota. The qualitative predictions of the model are unchanged if I allow fora positive mass to the left of w⇤

1

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3.2 Benchmark Model - No Corruption

Consider a scenario where there was no corruption in the PDS, and eligible households

could access the PDS whenever they desired. As described above, an adverse rainfall

shock has two effects. One, it lowers wages, shifting the income distribution to the

left. For given levels of w⇤1 and w⇤

2 more households are to the left of each threshold.

Second, adverse shocks push up PM

, which increases the threshold levels w⇤1 and w⇤

2.13

Therefore, more households start purchasing through the PDS and consequently, the

aggregate (and average14) amount of rice purchased through the PDS goes up. Con-

versely, fewer households rely exclusively on the market for rice consumption, and the

amount bought from the market falls.

3.3 The Model with Corruption

What if the there was corruption in the PDS in the form of illegal diversion of grain?

In this section, I first model the incentives of the PDS store owner, and examine its

implications for household consumption behavior.

3.3.1 The PDS Store owner’s Problem

The PDS store owner faces a choice between selling to eligible households, and diverting

grains into the black market. The profits from selling under the program are assumed to

be zero, whereas the profit from diverting grains into the black market is just the price

difference multiplied by the amount diverted. If there were no costs associated with

diverting grains, the store owner would divert the entire amount each period. I assume

that if caught, the store owner faces a large penalty (this may include fines, losing his13See appendix for details.14This average is taken over all BPL households

14

license or even jail time). The probability of being caught is an increasing function of

the amount diverted. Therefore, the store owner chooses the level of diversion, D to

solve the following problem:

Max⇧(D) = (PM

� P

G

)D � q(D)J

where J denotes the fine. q(D) is assumed to be an increasing, convex function. The

optimal diversion would be such that:

q0(D) =PM

� PG

J

Since q(D) is a convex function, we can see that the incentives for diversion increase

when PM

increases.

3.3.2 Implications for Household Consumption

With illegal diversion going up in response to price increases, the aggregate (and av-

erage) amount of rice consumed through the PDS must fall after adverse shocks. The

effect of increased diversion on the number of people who can access the program is am-

biguous. If the store owner simply reduces the amount sold to each household (but sells

to all households who show up), then the number of beneficiaries would increase. This

is because, as argued above, more people demand PDS rice after adverse shocks. On

the other hand, if the PDS store owner rations on the extensive margin by completely

denying some households access to the program, then the number of beneficiaries will

fall.

To summarize, the model predicts that in response to bad shocks:

15

• In the absence of corruption: The number of households purchasing through the

PDS goes up, and the average amount of rice purchased through the PDS should

increase.

• In the presence of corruption: The average amount bought from the PDS falls.

If corruption is taking place only on the intensive margin, then the number of

households who are able to buy some amount through the PDS would go up.

On the other hand, if corruption were taking place on the extensive margin, the

number of households who are able to buy rice through the PDS would fall.

Below, I test these predictions empirically.

4 Data

To carry out the empirical analysis, I combine data from several sources, which are

described below

4.1 Agricultural Production and Prices

For information on agricultural production, I use a data set provided by the Directorate

of Economics and Statistics, Ministry of Agriculture. For each crop grown in a district,

this data set contains information on the total sown area and output. Using this data

set, I construct a district level panel for rice production covering the period 1999-2010.

Data on agricultural prices comes from the ICRISAT district level data base, which

have been compiled primarily from reports published by the Directorate of Economics

and Statistics, Govt. of India. This database has information on farm harvest prices

for 14 major crops. These are the prices received by the farmer at the first point of

16

sale. For most districts, this data set contains information on the price of paddy (and

not rice). I use this to construct an unbalanced panel of rice prices at the district level,

for the period 1999-2009.

4.2 Wage Data

Data on wages and employment comes from the Employment Unemployment Module

of the National Sample Survey. I use five rounds of this data - the 61st, 62nd, 64th,

66th and 68th, covering the period 2004-2012. This data set contains detailed infor-

mation both on the time individuals spent on various activities in the week prior to

the survey (such as self employment, salaried employment, casual labor, unpaid activ-

ities, unemployment etc.), and their earnings from each of them. I use this data set

to construct two measure of wages. The first is wages for casual labor, which can be

thought of as the price in the spot market for labor. To do this, I restrict attention to

the set of individuals who report having worked as casual laborers in the week prior to

the survey. Their wage rate is defined as their earnings from casual labor divided by

the number of days they were involved in casual labor.

Rainfall shocks may also increase the amount of time individuals spend unemployed. I

therefore also construct a measure of “expected wages” at the district level, to account

for increased unemployment. To do this, I restrict attention to individuals who reported

either working as casual laborers, or reported looking for but not finding employment.

I then aggregate these to the district level, to create a measure of expected wages,

which is the weighted average of casual labor wage rate and 0. The weights are the

probability of finding employment, and being unemployed respectively.

Another source of data on wages comes from the ICRISAT district level data base

described above. I use this data set to construct a district level panel of wages for the

17

period 1999-2009.

4.3 Consumption Data

My main source of data on consumption comes from the Consumption Expenditure

Module of National Sample Survey (NSS). These are large scale households surveys

conducted regularly in India. I use 7 rounds of data, covering the time period 2004

to 2012. These include 4 “thick” rounds of data (55th, 61st, 66th and 68th), and 3

“thin” rounds (62nd, 63rd and 64th).15 The NSS surveys contain detailed information

on consumption and expenditure of households on various durable and non-durable

goods. For food items, these surveys include information on whether the commodity

was purchased or home produced. Crucially for my analysis, for items sold under the

PDS, the survey includes information on how much each household purchased from the

market and from the Fair Price Shop. I use unit values from this survey as a measure

of retail prices.16 Further, to minimize measurement errors, I average unit values at

the village level. I use data on monthly household expenditure from the survey as a

proxy for income.

One drawback of the NSS consumption module, however, is that only in two rounds

(the 61st and the 68th) does it contain information on whether a household owns a BPL

card. Without this information, it would be impossible to say whether a household did

not buy from the PDS because it was not eligible, or because it was denied access. I

report results by restricting the sample to only BPL households for the 2 rounds where

this information is available. I also report results using all households from the seven

rounds.15“Thick Rounds” refer to larger scale surveys, conducted approximately once every 5 years. “Thin Rounds” are

conducted at a greater frequency, but have a sample size of roughly 30-40% of the thick rounds.16Strictly speaking, unit values are not the same as prices, since they can also be affected by quality choices. As

described above, I use an additional source of price data - farm gate prices.

18

In my main specification, I focus on districts which are major producers of rice. Using

the data on agricultural production, for each district I calculate the average proportion

of land that is allotted to rice production. I then define a district to be a major rice

producer, if rice is grown on at least 5% of its agricultural land. These districts are

shown in figure 4. In all my analyses I use sampling weights provided in the survey.

4.4 Allocation Data

As described above, the central government procures food grains, and allocates them

to the state government, in proportion to the number of eligible households residing

there. The state governments are then responsible for ensuring these grains reach the

ration shops located all over the state. Typically, the state government stores the

grains received from the central government in warehouses located all over the state.

Dealers and ration shop owners are then responsible for picking up grain from these

warehouses and transporting them to the ration shop.

I collected a unique administrative data set on district level allocations made under

the PDS, for 4 states - Andhra Pradesh, Chhattisgarh, Kerala, and Tamil Nadu -

covering the period 2007-2013. This data set contains information on how much rice

was actually sent out to to fair price shops in each district, each month. This allows

me to test how the allocation made to a district reacts to rainfall shocks.

4.5 Rainfall Data

Data on rainfall comes from the Terrestrial Precipitation: 1900-2014 Gridded Monthly

Time Series, constructed by the Center for Climatic Research, University of Delaware.

This data set provides monthly rainfall data on a 0.5� ⇥ 0.5� grid. To get rainfall

19

at the district level, I construct a weighted average, taking all the grid points within

200 kms of the district centroid, the weights being the inverse of the distance to the

district center. As is standard in the literature on the rainfall shocks in India, I focus

on monsoon rainfall. In particular, I consider average rainfall in the first 3 months of

the monsoon, May, June and July to construct my variables.

In defining rainfall shocks, I follow Kaur (2012) and Jayachandran (2006) - a district

is said to have a negative rainfall shock, if rainfall is below the 20th percentile of

the (district specific) empirical distribution.17 Similarly, a positive shock is defined as

rainfall above the 80th percentile. While my focus is on the impact of negative rainfall

shocks, in all my regressions, I estimate the impact of negative and positive shocks

separately, since they may have asymmetric effects. For example, a negative rainfall

shock is unambiguously bad for agricultural production. A positive rainfall shock can

generally be though of as improving productivity and yields, but excessive rainfall may

cause severe flooding which could destroy crops.

5 Empirical Strategy

I first study the impact of rainfall shocks on the production and price of rice, as well

as on wages. To do this, I estimate the following equation:

Ydst

= �0 + �1 ⇤Neg Rain Shockdst

+ �2 ⇤ Pos Rain Shockdst

+ �d

+ �t

+ "dst

where Ydst

refers to the outcome variable of interest in district d, in state s at time

t. I include two sets of fixed effects. The first are district fixed effects, which control

for time invarying district characteristics such as land quality. The second are year17The empirical distribution is the rainfall distribution in the district for the period 1960-2014

20

fixed effects, which control for shocks common to all districts in a given year. Standard

errors in all regressions are clustered at the district level.

Next, I analyze how household consumption from the PDS and the market responds to

rainfall shocks in major rice producing districts. I first use the 2 rounds of NSS data

for which information on BPL card ownership is available, restricting attention to the

set of BPL households. I also report results from regressions where I use all 7 rounds

of the NSS data, including at all households. The outcomes I consider are how much

rice a household purchases through the PDS and through the market. I also look at

the probability that a household has any access to the PDS, by looking at a dummy

variable which takes the value 1 if a positive amount of rice is purchased through the

PDS, and 0 otherwise.

As described above, state governments have a lot of control in determining the details

of the program. For instance, several states have periodically changed the price at

which rice is sold through the program, and anecdotal evidence suggests that in an

election year, state governments try and ensure better service delivery. This might

impact a household’s consumption through the PDS. To control for these and other

state level policy changes, I include state-year fixed effects, in addition to district and

season fixed effects, thereby estimating regressions of the form:

Outcomehdsrt

= �0+�1⇤NegRainShockdst

+�2⇤PosRainShockdst

+�d

+✓r

+�st

+µh

+"hdsrt

where Outcomehdsrt

is the outcome of interest for household h. µh

is a set of household

level characteristics, such as caste, and whether the household resides in a rural or

urban area. �st

are state-year fixed effects, whereas �d

and ✓r

are district and season

21

fixed effects respectively. Standard errors are again clustered at the district level.

I also use the above regressions to conduct placebo tests, by looking at the impact of

adverse shocks on the amount of kerosene and sugar bought through the PDS, as well

as the probability of purchasing a positive amount of these. I also estimate a similar

regression of rainfall shocks on prices and consumption behavior in “placebo” districts

- which consume rice, but are not major producers of it. As argued above, in these

states, rainfall shocks should have a limited impact on local prices, and therefore on

consumption choices of households.

Finally, to see how allocations made under the program respond to rainfall shocks, I

estimate the following regression:

Ydsmt

= �0 + �1 ⇤Neg Rain Shockdst

+ �2 ⇤ Pos Rain Shockdst

+ �d

+ ⇢m

+ �st

+ "dsmt

where Ydsmt

is the allocation made to district d by the government of state s, in month

m, of year t. Apart from district and state-year fixed effects, this regressions also

controls for month fixed effects. Standard errors are again clustered at the district

level.

6 Results

Table 1 shows the impact of rainfall shocks on agricultural production and yields. As

expected in a setting where agriculture is highly rainfall dependent, negative rainfall

shocks in a district reduce rice production. Column (2) of table 1 shows shows that

these local shocks to production influence local prices, consistent with the hypothesis

22

that markets are not perfectly integrated across the country.

The impact of these shocks on the labor market are shown in table 2. Column (1)

of this table reports the effect on wages for casual labor. Additionally, in column (2)

I show the effect of these adverse shocks on expected wages at the district level. As

described above, this variable takes into account the fact that after adverse shocks,

the probability of unemployment may be higher. As expected rainfall shocks exert a

strong and significant downward push on wages.

Next, I analyze consumption behavior of BPL households using the 61st and 68th

rounds of the consumption module. I start off by considering the effect of changes

in the market price of rice on consumption through the PDS, using a simple OLS in

Table 3. Consistent with the hypothesis of increased corruption incentives, a higher

market price leads to reduced consumption from the PDS. Column 1 shows that the

amount of rice bought from ration shops falls, and column 2 shows that the probability

of a positive purchase through the PDS falls as well. It should be noted that these

results are only suggestive, and should be interpreted accordingly. For example, an

unobserved shock which changes preferences in favor of market rice, might explain

both the increase in the price of rice on the market (due to a direct demand effect),

and reduced consumption through the PDS.

Tables 4, 5 and 6 contain the main results of this paper. Table 4 shows how prices

(measured using unit values) of various commodities, and household income18 react to

rainfall shocks. Rice prices go up, but there is no significant effect on the prices of

kerosene and sugar. On the other hand, household incomes fall. Table 5 shows how

consumption of PDS rice responds to rainfall shocks on the intensive margin. The first

two columns restrict attention to the year 2004-05 and 2011-12 focussing on the set18Household income refers to total household consumption expenditure in the 30 days prior to the survey.

23

of BPL households. As predicted by the increased corruption hypothesis, households

purchase less rice from the PDS after adverse shocks. This amounts to a reduction by

almost 15% from the average household’s PDS consumption. Consequently, they are

forced to rely more on the market, increasing market purchases. The last two columns

report results from 7 rounds of the NSS and looks at all households. Even here, PDS

consumption falls. As is to be expected, the magnitudes are smaller than in column

(1), since roughly 70% of households are not eligible for the PDS.

Table 6 looks at whether some households are denied access to the PDS completely,

using a linear probability model. Once again, the first 2 columns focus on the set

of BPL households from the 61st and 68th rounds of the NSS. Column (1) shows

that the probability that a household is able to buy a positive amount of rice from the

PDS goes down after negative rainfall shocks. Column 2 finds that the probability that

households eligible for the PDS buy exclusively from the market goes up. Put together,

these results are suggestive of rationing on the extensive margin. Qualitatively similar

but statistically weaker results are obtained when I use the entire sample of 7 years,

as shown in columns (3) and (4).

In table 7, I conduct a heterogeneity analysis to understand which groups lose out as a

result of this increased corruption. The first dimension I consider is that of income. I

divide the income distribution into terciles, and find that while on average the poorest

tercile consumes the most from the PDS, they are also the ones who differentially lose

out the most after bad shocks, as can be seen by the positive interaction terms between

negative shocks and the second and third income terciles.. I also study heterogeneity

along caste, and find that Schedule Castes and Schedule Tribes lose out the most after

adverse shocks.

As was shown earlier, the prices of kerosene and sugar are not responsive to rainfall

24

shocks. Table 8 examines how consumption of kerosene through the PDS responds to

these shocks both on the intensive and extensive margins. Unlike rice, consumption

of PDS kerosene does not fall after adverse shocks, and in fact is positively associated

with rainfall shocks. Table 9 shows that sugar consumption through the PDS does not

respond to rainfall.

Table 10 reports results from a placebo test, looking at regions which are not major

producers of rice, but where rice consumption is high.19 Column (1) shows that in

these districts, rainfall shocks do not affect the market price of rice. Columns (2)-(5)

show that consumption through the PDS on the intensive and extensive margin do not

responds to shocks either.

How do disbursements made under the program respond to these shocks? Table 11

reports that for the four states for which I have month-wise district level allocations

data, they do not respond to rainfall. This combined with the previous results implies

that the same amount of rice is being sent out to districts through the PDS at all times,

but households are able to consume less of it after adverse rainfall shocks.

7 Welfare Implications

What are the welfare implications of this perverse correlation between rainfall shocks?

To examine this, I combine the empirical results with the model described in section

3. As we saw in Table 6, some households are being completely rationed out of the

program as a result of adverse shocks. My welfare analysis focuses on these households,

and I compute the welfare cost relative to two counterfactuals. First, what would be

the welfare gain if corruption were constant across states of the world, holding its level

19These districts are shown in figure 5.

25

constant? Second, what is the welfare gain of completely eliminating corruption in the

program.

Let there be two states of the world - good and bad - and consider a household which

can get its full quota through the PDS in the good state, and is completely rationed

out in the bad state. The expected (indirect) utility function for this household would

be given by:

EV (W,P

G

, P

M

; X̄) = Prob

Good

V (WGood

, P

G

, P

Good

M

; X̄) + Prob

Bad

V (WBad

, P

G

, P

Bad

M

; 0)

where X̄ and 0 are the PDS quota constraints that this household faces in the good

and bad state respectively.

The first counterfactual I consider, keeps the aggregate level of rice diversion the same,

but eliminates its variance across the two states of the world. The PDS quota constraint

in both states of the world, therefore, is given by:

X̄

CF

= Prob

Good ⇤ X̄ + Prob

Bad ⇤ 0

The expected utility for this household under the counterfactual will be given by:

EV

CF (W,P

G

, P

M

; X̄CF

) = Prob

Good

V (WGood

, P

G

, P

Good

M

; X̄CF

)+Prob

Bad

V (WBad

, P

G

, P

Bad

M

; X̄CF

)

The welfare gain from eliminating the correlation between corruption and shocks will

be the difference between the counterfactual and actual expected utility. My measure

of welfare is the additional income the household would have to be given for it to be

indifferent between the two lotteries. i.e, I solve for the ICF which satisfies the following

26

equation20:

Prob

Good

V (WGood + I

CF

, P

G

, P

Good

M

; X̄) + Prob

Bad

V (WBad + I

CF

, P

G

, P

Bad

M

; 0) =

Prob

Good

V (WGood

, P

G

, P

Good

M

; X̄CF

) + Prob

Bad

V (WBad

, P

G

, P

Bad

M

; X̄CF

)

Finally, I compare the welfare cost of the variance of corruption across different states

of the world to the welfare gain from completely eliminating corruption in the program.

In the no corruption case, the PDS quota constraint will be XG

= X̄ in both the good

and bad state. This welfare cost is calculated by solving for the income transfer INC

which solves the following equation:

Prob

Good

V (WGood + I

NC

, P

G

, P

Good

M

; X̄) + Prob

Bad

V (WBad + I

NC

, P

G

, P

Bad

M

; 0) =

Prob

Good

V (WGood

, P

G

, P

Good

M

; X̄) + Prob

Bad

V (WBad

, P

G

, P

Bad

M

; X̄)

I carry out the estimation for each consumption decile separately, combining data from

the 68th round of the NSS with my estimates. I use the average market price of rice in

the NSS 68th round cross section as the price of rice in normal times, and the average

decile-specific consumption expenditure as income in normal times. As we saw in the

previous section wages fall by roughly 3% as a result of the shock, and rice price goes

up by approximately 2%. I apply these estimates to get a measure of incomes and

prices after bad shocks. I take the probabilities of the good and bad state to be 0.8

and 0.2 respectively, which follows naturally from my definition of rainfall shocks. I

use ↵ = 0.1 which is the average income share spent on rice, and � = 3, which is an20In these calculations, I assume that the household pays the fixed cost even when it gets 0 units through the PDS.

This would be the case, for example, if the household goes to the PDS store and then finds out that rice is not available.

27

estimate of risk aversion from from experimental evidence (Carlsson et al. (2003)). All

the parameters used are shown in Table 12.

The results from the welfare analysis are presented in figure 8. The welfare cost is

expressed as a proportion of a household’s per capita consumption expenditure. As is

to be expected, the welfare costs of corruption are large. However, Figure 8 also shows

that the welfare cost due to the perverse correlation between corruption and economic

conditions is also significant for the poorest households - roughly one third of the total

cost welfare cost due to corruption.

8 Conclusion

It is well established that corruption can prevent social programs from having desired

effects. This paper looks at an important aspect of corruption, which can have impor-

tant welfare consequences - its timing. Analyzing India’s public distribution system,

I find that structure of the program increases incentives for corruption after adverse

shocks. In the Indian context, with high rainfall dependence of agriculture, and ab-

sence of well integrated markets, rainfall shocks which reduce rice production also push

up its price locally. At the same time, these shocks lower wages and earnings. The

price effect increases incentives for diversion of grain meant to be sold under the PDS

into the black market. Examining their consumption behavior, I find that households,

have reduced access to the PDS, and are forced to rely more on the market precisely

when times are bad, because of this perverse correlation between corruption and local

economic conditions. The welfare costs of this perverse correlation are high, especially

for the poorest households. These results suggest that close attention should be paid

to understanding the determinants of corruption. Further, policies to combat corrup-

28

tion should be flexible and take into account the responsiveness of corruption to local

conditions.

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31

Figure 1: Household Income and Demand for Rice

αW

PGw∗

1 w∗

2 Income

XG < X̄

XM = 0

XG = X̄

XM = 0

XG = X̄

XM =αW−(1−α)PM (X̄−c̄)−αPGX̄

PM

XG = 0

XM =αW

PM

32

61%

52%47%

41%36%

32%28%

23%19%

14%

1 2 3 4 5 6 7 8 9 10

BPL CARD OWNERSHIP ACROSS CONSUMPTION DECILES

CONSUMPTION DECILES

Figure 2: Ownership of BPL Cards across consumption deciles. Author’s calculations based on the68th round of the NSS consumption module

33

32%

69%63%

20%

54%

81%

% SC/ST % Rural % Literate

CHARACTERISTICS OF BPL VS NON-BPL HOUSEHOLDS

BPLNon-BPL

Figure 3: Characteristics of BPL and Non-BPL households. Author’s calculations based on the 68thround of the NSS consumption module

34

Figure 4: Districts used in the Main Specification.

35

Figure 5: Districts used for the placebo Tests.

36

1.99

4.12 3.98 3.60 4.04 3.814.72 4.68

3.06

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

2 3 4 5 6 7 8 9 10

COEFFICIENTS OF A REGRESSION OF PDS QUANTITY ON RAINFALL PERCENTILE

Figure 6: This figure plots the coefficient of a regression of the quantity of rice bought through thePDS on rainfall deciles. Rainfall deciles are shown on the X axis, with the first decile being theommitted category. The red dots represent the coefficient values, while the lines represent 95 percentconfidence intervals.

37

0.11

0.23 0.250.21

0.25 0.250.29 0.30

0.26

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

2 3 4 5 6 7 8 9 10

COEFFICIENTS OF A REGRESSION OF PROBABILITY OF PDS PURCHASE ON RAINFALL PERCENTILE

Figure 7: This figure plots the coefficient of a regression of a dummy variable denoting PDS ricepurchase on rainfall deciles. Rainfall deciles are shown on the X axis, with the first decile being theommitted category. The red dots represent the coefficient values, while the lines represent 95 percentconfidence intervals

38

6%

3% 2% 2% 2% 1% 1% 1% 1% 0%

20%

14%

11%10%

9%7%

6%5%

4%2%

1 2 3 4 5 6 7 8 9 10

TRAN

SFER

AS

A %

OF

PER

CAPI

TA IN

COM

E

CONSUMPTION DECILE

WELFARE COST OF VARYING CORRUPTION VS. NO CORRUPTION

Flat corruption

No corruption

Figure 8: Consumption deciles are shown on the X axis. The bars represent the welfare costs ofcorruption and the welfare costs due to the variance in corruption across states of the world, measuredas a fraction of household per capita income

39

Table 1: Impact of Rainfall Shocks on Agricultural Production and Prices

(1) (2)Log(Rice Output) Log(Rice Price)

Negative Rainfall Shock -0.0615** 0.0224**(0.0275) (0.0114)

Positive Rainfall Shock 0.0406 -0.00113(0.0321) (0.0121)

Observations 3,486 1,717District FE Yes YesYear FE Yes YesThe unit of observation is a district-year. Data on rice output is from adata set provided by the Directorate of Economics and Statistics , Min-sitry of Agriculture, Govt. of India. Rice Price refers to Farm HarvestPrices, and comes from the ICRISAT district level data base, compiledusing various government sources. Standard errors in all regressions areclustered at the district level. Stars indicate *** p<0.01, ** p<0.05, *p<0.1

40

Table 2: Impact of Rainfall Shocks on Wages

(1) (2) (3)Log(Casual Wage) Log(Expected Wage) Log(Wage)

Negative Rainfall Shock -0.0207** -0.0459*** -0.0312**(0.00877) (0.0166) (0.0145)

Positive Rainfall Shock -0.0124 -0.0174 -0.0138(0.00829) (0.0128) (0.0118)

Observations 171,980 16,326 1,963District FE Yes Yes YesYear FE Yes Yes YesWage data in columns (1) and (2) comes from multiple rounds of the Employment Unemploy-ment Module of the NSS. Column (1) is a regression of wages for casual labor on rainfall shocks.The unit of observation is an individual. Column (2) is a regression of expected wages at thedistrict level on rainfall shocks (See main text for detailed explanation of this variable). Data forcolumn (3) comes from the ICRISAT district level data base. Standard errors in all regressionsare clustered at the district level. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

41

Table 3: Impact of Market Price on PDS Consumption

(1) (2)PDS Rice Qty Some PDS Rice

Log(Market Price) -1.974** -0.0977***(0.811) (0.0325)

Observations 42,695 42,695Dep. Variable Mean 14.33 0.76District FE Yes YesState Year FE Yes YesHousehold Controls Yes YesAll the data used in these regressions comes from the 61st and 68throunds of the Consumption Module of the NSS. The sample is restrictedto the set of BPL households. In column (1), PDS Rice Cons is theabsolute amount of rice bought by the household through the PDS. Incolumn (2), Some PDS Rice is a binary variable, taking the value 1 ifthe household bought a positive amount of rice from the PDS, and 0otherwise. The explanatory variable, Market Price refers to unit valuesfrom the NSS. Standard errors in all regressions are clustered at thedistrict level. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

42

Table 4: Impact of Rainfall Shocks on Prices and Incomes

(1) (2) (3) (4)Log(Rice Price) Log(Kerosene Price) Log(Sugar Price) Log(Income)

Negative Rainfall Shock 0.0185** 0.0376 -0.0438 -0.024*(0.00913) (0.0587) (0.0306) (0.014)

Positive Rainfall Shock -0.000642 -0.0630 0.00851 0.0132(0.00617) (0.0434) (0.0225) (0.010)

Observations 297,130 163,458 234,308 298,022District FE Yes Yes Yes YesState Year FE Yes Yes Yes YesAll data comes from 7 rounds of the consumption module of the NSS. Prices are unit values from the survey data.Income refers to total household consumption expenditure in the month prior to the survey. Standard errors in allregressions are clustered at the district level. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

43

Table 5: Rainfall and Household Consumption

(1) (2) (3) (4)PDS Rice Qty Mkt Rice Qty PDS Rice Qty Mkt Rice Qty

Negative Rainfall Shock -2.327*** 4.145*** -0.333* -0.496(0.714) (1.506) (0.172) (0.618)

Positive Rainfall Shock -0.501 -0.988 0.121 -0.617(0.563) (1.449) (0.200) (0.647)

Observations 42,815 42,815 298,022 298,022Dep. Variable Mean 14.33 23.72 5.54 28.57District FE Yes Yes Yes YesState Year FE Yes Yes Yes YesHousehold Controls Yes Yes Yes YesThe data used in columns (1) and (2) comes from the 61st and 68th rounds of the Consumption Module ofthe NSS. The sample is restricted to the set of BPL households in major rice producing districts. Data forcolumns (3) and (4) comes from 7 rounds of the NSS Consumption Module, and includes all all householdsin the major rice producing districts. In columns (1) and (3) PDS Rice Cons is the absolute amount of ricebought by the household through the PDS. In columns (2) and (4), Mkt Rice Qty refers to absolute amountof rice purchases through the market. Standard errors in all regressions are clustered at the district level.Stars indicate *** p<0.01, ** p<0.05, * p<0.1

44

Table 6: Rainfall and Probability of purchase from different sources

(1) (2) (3) (4)Some PDS Only Mkt Some PDS Only Mkt

Negative Rainfall Shock -0.143*** 0.138*** -0.00505 0.00618(0.0399) (0.0429) (0.00913) (0.00882)

Positive Rainfall Shock 0.0189 -0.0178 0.0122 -0.0162*(0.0223) (0.0220) (0.00953) (0.00932)

Observations 42,815 42,815 298,022 298,022Dep. Variable Mean 0.76 0.22 0.30 0.62District FE Yes Yes Yes YesState Year FE Yes Yes Yes YesHousehold Controls Yes Yes Yes YesThe data used in columns (1) and (2) comes from the 61st and 68th rounds of the Consumption Module ofthe NSS. The sample is restricted to the set of BPL households in major rice producing districts. Data forcolumns (3) and (4) comes from 7 rounds of the NSS Consumption Module, and includes all all householdsin the major rice producing districts. In columns (1) and (3) Some PDS Rice is a binary variable, taking thevalue 1 if the household bought a positive amount of rice from the PDS, and 0 otherwise. In columns (2)and (4) Only Mkt Rice is a binary variable, taking the value 1 if the household bought a positive amountof rice from the market and did not buy any rice through the PDS, and 0 otherwise. Standard errors in allregressions are clustered at the district level. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

45

Table 7: Heterogeneity

(1) (2)PDS Rice Qty Some PDS Rice

Negative Rainfall Shock -1.792** -0.117***(0.693) (0.0404)

SC ST 1.577*** 0.0429***(0.193) (0.00701)

SC ST*Shock -1.593*** -0.0759**(0.554) (0.0295)

Observations 42,820 42,820District FE Yes YesState Year FE Yes YesHousehold Controls Yes YesAll the data used in these regressions comes from the 61st and 68throunds of the Consumption Module of the NSS. The sample is restrictedto the set of BPL households in major rice producing districts. In col-umn (1) PDS Rice Cons is the absolute amount of rice bought by thehousehold through the PDS. In column (2) Some PDS Rice is a binaryvariable, taking the value 1 if the household bought a positive amountof rice from the PDS, and 0 otherwise. Standard errors in all regressionsare clustered at the district level. Stars indicate *** p<0.01, ** p<0.05,* p<0.1

46

Table 8: Kerosene(1) (2) (3) (4)

PDS Kerosene Qty Some PDS Kerosene PDS Kerosene Qty Some PDS Kerosene

Negative Rainfall Shock 0.315* 0.0735* 0.0565 0.0185(0.168) (0.0389) (0.0461) (0.0129)

Positive Rainfall Shock -0.322** 0.00240 -0.144*** -0.0138(0.133) (0.0229) (0.0501) (0.0103)

Observations 42,815 42,815 298,022 298,022District FE Yes Yes Yes YesState Year FE Yes Yes Yes YesHousehold Controls Yes Yes Yes YesThe data used in columns (1) and (2) comes from the 61st and 68th rounds of the Consumption Module of the NSS. The sampleis restricted to the set of BPL households in major rice producing districts. Data for columns (3) and (4) comes from 7 roundsof the NSS Consumption Module, and includes all all households in the major rice producing districts. In columns (1) and (3)PDS Kerosene Qty is the absolute amount of kerosene bought by the household through the PDS. In columns (2) and (4) SomePDS Kerosene is a binary variable, taking the value 1 if the household bought a positive amount of kerosene from the PDS, and 0otherwiseStandard errors in all regressions are clustered at the district level. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

47

Table 9: Sugar

(1) (2) (3) (4)PDS Sugar Qty Some PDS Sugar PDS Sugar Qty Some PDS Sugar

Negative Rainfall Shock -0.0938 -0.00486 0.0278 0.00366(0.0875) (0.0541) (0.0194) (0.00988)

Positive Rainfall Shock -0.117 -0.0565 0.0235 0.0127(0.0764) (0.0446) (0.0206) (0.0116)

Observations 42,815 42,815 298,022 298,022District FE Yes Yes Yes YesState Year FE Yes Yes Yes YesHousehold Controls Yes Yes Yes YesThe data used in columns (1) and (2) comes from the 61st and 68th rounds of the Consumption Module of the NSS.The sample is restricted to the set of BPL households in major rice producing districts. Data for columns (3) and (4)comes from 7 rounds of the NSS Consumption Module, and includes all all households in the major rice producingdistricts. In columns (1) and (3) PDS Sugar Qty is the absolute amount of sugar bought by the household throughthe PDS. In columns (2) and (4) Some PDS Sugar is a binary variable, taking the value 1 if the household bought apositive amount of sugar from the PDS, and 0 otherwiseStandard errors in all regressions are clustered at the districtlevel. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

48

Table 10: Placebo by Region

(1) (2) (3) (4) (5)Rice Price PDS Rice Qty Some PDS Rice PDS Rice Qty Some PDS Rice

Negative Rainfall Shock 0.00379 0.290 0.0150 0.318 0.000235(0.0122) (0.308) (0.0174) (1.016) (0.0717)

Observations 113,871 112,573 112,573 11,618 11,618District FE Yes Yes Yes Yes YesState Year FE Yes Yes Yes Yes YesHousehold Controls Yes Yes Yes Yes YesThe data used in columns (1), (2) and (3) comes from 7 rounds of the NSS Consumption Module, and includes all all householdsin the placebo districts (see main text for definition). The data used in columns (4) and (5) comes from the 61st and 68th roundsof the Consumption Module of the NSS. The sample is restricted to the set of BPL households in the placebo districts. Standarderrors in all regressions are clustered at the district level. Stars indicate *** p<0.01, ** p<0.05, * p<0.1

49

Table 11: Rainfall Shocks and Allocations

Log(Allocation)

Negative Rainfall Shock -0.001(0.017)

Positive Rainfall Shock 0.006(0.008)

Observations 7,009

District FE YesState Year FE YesMonth FE YesAllocation refers to month-wise district level al-locations made under the PDS by state govern-ments of Andra Pradesh, Chhattisgarh, Keralaand Tamil Nadu for the period 2007-2013. Stan-dard errors in all regressions are clustered atthe district level. Stars indicate *** p<0.01, **p<0.05, * p<0.1

50

Table 12: Parameters for Welfare Analysis

Parameters State = Good State = Bad

↵ 0.1 0.1� 3 3Subsistence Consumption 15 15Probability 0.80 0.20P

M

20 20.40P

G

5 5PDS Quota 25 25

51

Appendix

In this section, I derive the cutoff wages w⇤1 and w⇤

2 for the consumer’s optimization

problem. The household maximizes:

U(XG

, XM

, Z) =

✓⇣X

G

+XM

� c̄⌘↵

⇣Z⌘1�↵

◆1��

1� �� 1F

G

subject to

PG

XG

+ PM

XM

+ Z = W

and

XG

6 X̄

First consider the household’s problem if there was no fixed utility cost associated with

accessing the PDS. In that case, the household’s demand functions will be given by

XG

=

8>>>>><

>>>>>:

↵W+(1�↵)c̄PG

PGif W < PGX̄

↵

X̄ if PGX̄

↵

< W

52

XM

=

8>>>>><

>>>>>:

0 if W < PGX̄

↵

↵W�(1�↵)PM (X̄�c̄)�↵PGX̄

PMif PGX̄

↵

< W

However, imposing a non-negativity constraint on XM

, we get the first cutoff value w⇤1,

which is the value of W which solves:

↵W � (1� ↵)PM

(X̄ � c̄)� ↵PG

X̄

PM

= 0

or

w⇤1 =

(1� ↵)PM

(X̄ � c̄) + ↵PG

X̄

PM

As is clear from this expression, w⇤1 is an increasing function of P

M

.

To derive the second cutoff wage w⇤2, re-introduce the fixed utility cost in the utility

function. The wage cutoff w⇤2 will be the wage at which the household is indifferent

between purchasing X̄ through the PDS and the rest of its rice demand through the

market, and purchasing exclusively through the market. i.e, the wage W which solves:

↵Log(↵W+↵(PM

�PG

)X̄+(1�↵)PM

c̄)+(1�↵)Log((1�↵)(W+(PM

�PG

)X̄+(1�↵)PM

c̄)�FG

=

↵Log(↵W + (1� ↵)PM

c̄) + (1� ↵)Log((1� ↵)(W + (1� ↵)PM

c̄)

Simplifying this expression, w⇤2 solves the following equation:

53

FG

= ↵Log(1 +↵(P

M

� PG

)X̄

↵W + (1� ↵)PM

c̄) + (1� ↵)Log(1 +

(PM

� PG

)X̄

w � c̄PM

)

The right hand side of this equation is increasing in PM

and decreasing in W , therefore

a rise inPM

increases the cutoff w⇤2.

54