General Equilibrium Effects of (Improving) PublicEmployment Programs: Experimental Evidence from India∗

Karthik Muralidharan†

UC San DiegoPaul Niehaus‡

UC San DiegoSandip Sukhtankar§

University of Virginia

April 21, 2017

Abstract

Public employment programs play a large role in many developing countries’ anti-povertystrategies, but their impact on poverty reduction will depend on both direct program effectsas well as indirect effects through changes induced in market wages and employment. Weestimate both of these effects, exploiting a large-scale experiment that randomized the roll-outof a substantial improvement in the implementation of India’s public employment scheme. Wefind that this reform increased the overall earnings of low-income households by 13.3%, andreduced an income-based measure of poverty by 18.1% despite no increase in fiscal outlays onthe program. Income gains were overwhelmingly driven by higher private-sector earnings (90%)as opposed to program earnings (10%). Conservatively estimated, private sector low-skilledwages increased 6.1%, while days without paid work fell 7.1%. Migration, land utilizationand prices were unaffected. Our results highlight the importance of accounting for generalequilibrium effects in evaluating social programs, and also illustrate the feasibility of usinglarge-scale experiments to study them.

JEL codes: D50, D73, H53, J38, J43, O18

Keywords: public programs, general equilibrium effects, rural labor markets, NREGA, employ-ment guarantee, India

∗We thank Abhijit Banerjee, Gordon Dahl, Taryn Dinkelman, Gordon Hanson, Supreet Kaur, Aprajit Mahajan,Edward Miguel, and several seminar participants for comments and suggestions. We are grateful to officials ofthe Government of Andhra Pradesh, including Reddy Subrahmanyam, Koppula Raju, Shamsher Singh Rawat,Raghunandan Rao, G Vijaya Laxmi, AVV Prasad, Kuberan Selvaraj, Sanju, Kalyan Rao, and Madhavi Rani; aswell as Gulzar Natarajan for their continuous support of the Andhra Pradesh Smartcard Study. We are also gratefulto officials of the Unique Identification Authority of India (UIDAI) including Nandan Nilekani, Ram Sevak Sharma,and R Srikar for their support. We thank Tata Consultancy Services (TCS) and Ravi Marri, Ramanna, and ShubraDixit for their help in providing us with administrative data. This paper would not have been possible withoutthe continuous efforts and inputs of the J-PAL/IPA project team including Kshitij Batra, Prathap Kasina, PialiMukhopadhyay, Michael Kaiser, Frances Lu, Raghu Kishore Nekanti, Matt Pecenco, Surili Sheth, and PratibhaShrestha. Finally, we thank the Omidyar Network – especially Jayant Sinha, CV Madhukar, Surya Mantha, andSonny Bardhan – for the financial support that made this study possible.†UC San Diego, JPAL, NBER, and BREAD. kamurali@ucsd.edu.‡UC San Diego, JPAL, NBER, and BREAD. pniehaus@ucsd.edu.§University of Virginia, JPAL, and BREAD. sandip.sukhtankar@virginia.edu.

1 Introduction

Public works programs, in which the government provides daily-wage jobs to those who

seek them, are among the most common anti-poverty programs in developing countries.

The economic rationale for such programs (over direct income support for the poor) include

self-targeting through work requirements, public asset creation, and establishing a more

effective wage floor by having the government be an employer of last resort.1 An important

contemporary variant is the National Rural Employment Guarantee Scheme (NREGS) in

India. Launched in 2006, the scheme is the world’s largest workfare program, with 600

million rural residents eligible to participate and a fiscal allocation of 0.8% of India’s GDP.

A program of this scale and ambition raises several fundamental questions for research

and policy. First, how effective is it in reducing poverty? In particular, while direct income

support programs would typically reduce poverty, the general equilibrium effects of public-

works programs on the larger rural economy could amplify or attenuate the direct gains in

wage income for the poor.2 Second, what is the relative contribution of direct income gains

from the program and indirect income changes outside the program? Third, what are the

impacts on broader labor market outcomes such as wages, employment, and migration?

Given the importance of NREGS, a growing literature has tried to answer the questions

above, but the evidence to date has been hampered by two limitations. The first is identifi-

cation, with the results being quite sensitive to the methods used and studies often reaching

opposite conclusions depending on the identification strategy used (see Sukhtankar (forth-

coming) and section 2.1.2 below). Second, implementation of the scheme has proven so varied

that interpreting the existing evidence is difficult because heterogeneity in results could also

reflect variation in the extent of effective NREGS implemented in practice (that is not easy

to measure precisely). For example, the most-cited study of wage impacts finds them only

in states coded broadly as having implemented the program well (Imbert and Papp, 2015).

In this paper we aim to provide credible estimates of the anti-poverty impact of public

works programs by combining exogenous experimental variation, a demonstrable first-stage

impact on program implementation, and units of randomization that are large enough to

capture general equilibrium effects. Specifically, we worked with the Government of the

Indian state of Andhra Pradesh,3 to randomize the order in which 157 sub-districts (with an

1Work-fare programs may also be politically more palatable to tax-payers than unconditional doles. Suchprograms have a long history, with recorded instances from at least the 18th century in India, the publicworks conducted in the US by the Work Projects Administration during the Depression-era in the 1930s,and more modern “Food-for-Work” programs across Sub-Saharan Africa and Asia.

2A practical way of differentiating partial and general equilibrium effects (which we follow) is to definepartial equilibrium effects as those estimated at constant prices, and general equilibrium effects as thosewhich incorporate the effects of interventions on market prices.

3The original state of AP (with a population of 85 million) was divided into two states on June 2, 2014.

1

average population of 62,500 each) introduced a new technology (biometric Smartcards) for

making payments in NREGS. In prior work, we show that the new technology significantly

improved the performance of NREGS on several key dimensions: it reduced leakage or

diversion of funds, reduced delays between working and getting paid, reduced the time

required to collect payments, and increased access to work, without changing the fiscal

outlays on the program (Muralidharan et al., 2016). Thus, the Smartcard intervention

brought NREGS implementation in AP closer to what its architects intended (Khera, 2011).4

Evaluating the impact of improving NREGS implementation (as we do here) is not the

same as evaluating the impact of rolling out the program itself. Yet, given well-documented

implementation challenges in NREGS including poor access to work, high rates of leakage,

and long delays in receiving payments (Mehrotra, 2008; Imbert and Papp, 2011; Khera,

2011; Niehaus and Sukhtankar, 2013b), a significant improvement in implementation quality

is likely to result in a meaningful increase in a measure of effective NREGS.5 Further, since

significant improvements in program performance were achieved without increasing the fiscal

outlay on NREGS, our results on poverty impacts are more likely to reflect the structure of

NREGS rather than simply reflecting additional fiscal transfers to treated areas.

We report six main sets of results. First, we find large increases in household income

in areas where Smartcards were rolled out. We find this result both when using our own

survey data as well as when using data from the Socio-Economic and Caste Census (SECC), a

household census conducted by the national government independently of our activities. The

SECC collected coarse data by income categories of the highest earner in the household; we

find that the Smartcards intervention made it 4.8% (3.9 percentage point reduction on a base

proportion of 81.2%) more likely that this earner moves out of the lowest income category.

Using our survey data on income, we find a 13.3% (Rs. 9,594) increase in household income

in treated areas, which corresponds to a 18.1% reduction in an income-based measure of

poverty (a 5.2 percentage point reduction on a base poverty rate of 28.7%).

Second, we find that the vast majority of income gains are attributable to indirect market

effects rather than direct increases in NREGS income from the improved program imple-

mentation. For NREGS beneficiaries, increases in program income accounted for only 10%

Since this division took place after our study, we use the term AP to refer to the original undivided state.4Smartcards were also used to make payments for rural social security pensions and reduced leakage here

as well, but these are unlikely to have affected broader labor markets (see Section 2.2).5One natural interpretation is to consider the randomized roll-out of the Smartcards intervention to be

an instrument for an endogenous variable that we may call “effective NREGS.” However, given the manydimensions on which NREGS implementation quality can vary (ease of access to work, availability of workon demand, payment delays and inconvenience, and leakage) it is difficult to construct a single-dimensionalsummary statistic of effective NREGS that can be instrumented for. Our estimates are therefore bestinterpreted as the reduced form impact of improving NREGS implementation on multiple dimensions.

2

of the increases in total income, with the remaining 90% attributable to increases in private

sector earnings. Thus, the general equilibrium impacts of NREGS through the open market

appear to be a much more important driver of its impact on poverty reduction than the

direct income provided by the program.

Third, we find that improving the performance of NREGS led to a significant increase in

private market wages. Market wages for NREGS workers rose by 6.1% in treated areas, with

a similar 5.7% increase in reported reservation wages, suggesting that an improved NREGS

likely improved workers’ bargaining power by improving their outside option. We find no

evidence of corresponding changes in consumer goods prices, implying that the earnings and

wage effects we find are real and not purely nominal gains.

Fourth, we find little evidence of distortionary effects on factor allocation. Despite higher

wages in treated areas, we find a significant 7.1% reduction in the number of days idle or

without paid work, with (insignificant) increases in the number of days of both NREGS and

private sector employment. We find no impacts on migration or on measures of land use.

Fifth, we find evidence that households used the increased income to purchase productive

assets. We find no increase in consumption expenditure, but find an 8.3% increase in the

fraction of households reporting land ownership. Using the 2012 livestock census conducted

by the Govt. of India (also collected independent of our study) we find a significant increase

in the total livestock in treated mandals. Households in treated mandals also had higher

outstanding loans suggesting reduced credit constraints for acquiring productive assets.

Finally, we find evidence that the labor market effects of treatment “spill over” into ge-

ographically proximate markets. We estimate spillovers by exploiting the fact that the

randomization design generated variation in both a mandal’s own treatment status and that

of it’s neighbors. Spillover effects are consistent in sign with the direct effects of treatment,

implying that the latter likely underestimate the “total treatment effect” a mandal would

experience if all mandals were treated. We develop methods to estimate this total effect,

and obtain estimates that are significant and substantially larger than our more conservative,

unadjusted estimates, with multiples ranging from 1.5x to 5x depending on the outcome.

An improved NREGS is likely to affect wages, employment, and income through several

channels including increased labor market competition, direct productivity effects of NREGS

asset creation, indirect productivity effects through improved credit access and purchase of

productive assets, local aggregate demand externalities from more money in the hands of

the poor, and changes in investment behavior in response to better wage insurance. In

addition to taking place simultaneously during our 2-year study period, these channels are

likely to interact with each other, which makes “isolating” any one mechanism an implausible

exercise. Thus, our experiment lets us credibly estimate the composite “general equilbrium”

3

effect of an improvement in effective NREGS on a broad set of outcomes (income, poverty,

wages, employment), but is not designed to isolate specific mechanisms of impact.6

Our first contribution is to the growing literature on the impact of public works pro-

grams on rural labor markets and economies (Imbert and Papp, 2015; Beegle et al., 2015;

Sukhtankar, forthcoming). We present experimentally-identified estimates of improving the

implementation of NREGS with units of randomization large enough to capture general

equilibrium effects and find that doing so led to a significant increase in incomes for the poor

and a reduction in poverty, even without spending additional funds on the program. We

also find that the majority of the impact on income and poverty was due to indirect market

effects of a better implemented NREGS rather than direct increases in NREGS income.

Second, these results highlight the importance of accounting for general equilibrium effects

in program evaluation (Acemoglu, 2010). Ignoring these effects (say by randomizing access to

the program at an individual worker level) would have led to a substantial underestimate of

the impact of improving NREGS implementation on poverty reduction. Even analyzing our

own data while maintaining the assumption of stable unit treatment values (i.e. no spillovers

across mandals) would arguably lead us to substantially understate market impacts. On an

optimistic note, however, our study demonstrates the feasibility of conducting randomized

experiments with units of randomization that are large enough, and specifications nuanced

enough, to capture such general equilibrium effects (Cunha et al., 2013; Muralidharan and

Niehaus, 2016).

Third, our results contribute to the general literature on rural labor markets in developing

countries (Rosenzweig, 1978; Jayachandran, 2006), as well as the more specific literature

on the impacts of minimum wages in developing countries (e.g. Dinkelman and Ranchhod

(2012)). Commentators on NREGS have argued that it could not possibly have led to mean-

ingful impacts on rural wages and poverty because the days worked on NREGS constitute

only a small share (under 4%) of total rural employment (Bhalla, 2013). Our results suggest

that this argument is incomplete, as much larger shares of the workforce have jobcards (38%)

and actively participate (23%) in the program, and for these workers the very existence of

a well-implemented public works programs can raise market wages as it increases their bar-

gaining power over wages by providing a more credible outside option (Dreze and Sen, 1991;

Basu et al., 2009).

Fourth, our results highlight the importance of implementation quality as a first-order

consideration in program effectiveness and in the interpretation of program evaluations (es-

6We discuss the extent to which our data are consistent with several individual mechanisms in Section 4.Overall, we do not find evidence for any individual channel other than labor market competition, but we alsocannot rule out the possibility that each channel did contribute to the overall effects on wages, employment,and income.

4

pecially in developing countries). Strikingly, our estimates of the private market wage im-

pacts of improving NREGS implementation are of a similar magnitude as reported by the

most credible estimates to date of the impact of rolling out NREGS itself (Imbert and Papp,

2015). More generally, programs are not just an “intervention”, but an intervention and an

implementation protocol and investing in improved implementation may often yield greater

improvements in the effective presence of a program than increased fiscal outlays on the

program itself.7

Finally, we contribute to the literature on political economy of development. Jayachandran

(2006) shows that landlords typically benefit at the cost of workers from the wage volatility

induced by productivity shocks and may be hurt by programs like NREGS that provide

wage insurance to the rural poor. Consistent with this, Anderson et al. (2015) have argued,

that “a primary reason... for landlords to control governance is to thwart implementation

of centrally mandated initiatives that would raise wages at the village level.” Our results

showing that improving NREGS implementation substantially raised market wages suggest

that landlords may have been made worse off by the reform, and may partly explain the

widely documented resistance by landlords to NREGS (Khera, 2011).

The rest of the paper is organized as follows. Section 2 describes the context, including

NREGS and prior research, and the Smartcard intervention. Section 3 describes the research

design, data, and estimation. Section 4 presents our main results on income, wages, and

employment, and discusses mechanisms. Section 5 examines spillover effects, while Section

6 concludes.

2 Context and intervention

2.1 National Rural Employment Guarantee Scheme

The NREGS is the world’s largest public employment program, making any household living

in rural India (i.e. 11% of the world’s population) eligible for guaranteed paid employment of

up to 100 days. It is one of the country’s flagship social protection programs, and the Indian

government spends roughly 8% of its budget (∼ 0.75% of GDP) on it. The program has broad

coverage; 65.7% of rural households in Andhra Pradesh have at least one jobcard, which

is the primary enrollment document for the program. Workers can theoretically demand

7For instance Niehaus and Sukhtankar (2013b) show that an increase in the official NREGS wage andcorresponding increase in fiscal outlays had no impact on the actual wages received by workers. This presentsa striking contrast with our results in this paper finding significant increases in wages despite no increase infiscal outlay. In a similar vein, Muralidharan et al. (2014) show that reducing teacher absence by increasingmonitoring would be ten times more cost-effective at reducing effective student-teacher ratios (net of teacherabsence) in Indian public schools than the default policy of hiring more teachers.

5

employment at any time, and the government is obligated to provide it or pay unemployment

benefits (though these are rare in practice).

Work done on the program involves manual labor compensated at statutory piece rates.

The physical nature of the work is meant to induce self-targeting. NREGS projects are

proposed by village governance bodies (Gram Panchayat) and approved by mandal (sub-

district) offices. These projects typically involve public infrastructure improvement such as

irrigation or water conservation works, minor road construction, and clearance of land for

agricultural use.

The NREGS suffers from a number of known implementation issues including rationing,

leakage, and problems with the payment process. Although the program is meant to be

demand driven, rationing is common, and work mainly takes place in the slack labor demand

season (Dutta et al., 2012; Muralidharan et al., 2016). Corruption is also common, with theft

from the labor budget taking the form of over-invoicing the government for work not done or

paying worker less than statutory wage rates for completed work (Niehaus and Sukhtankar,

2013a,b). The payment process itself is slow and unreliable, with the norm being payment

delays of over a month, uncertainty over payment dates, and lost wages as a result of time-

consuming collection procedures (Muralidharan et al., 2016; Pai, 2013).

2.1.1 Potential aggregate impacts of NREGS

In theory, employment guarantee schemes such as the NREGS are expected to affect equilib-

rium in private labor markets (Dreze and Sen, 1991; Murgai and Ravallion, 2005). A truly

guaranteed public-sector job puts upward pressure on private sector wages by improving

workers’ outside options. As Dutta et al. (2012) puts it,

“...by linking the wage rate for such work to the statutory minimum wage rate, and

guaranteeing work at that wage rate, [an employment guarantee] is essentially a

means of enforcing that minimum wage rate on all casual work, including that not

covered by the scheme. Indeed, the existence of such a program can radically alter

the bargaining power of poor men and women in the labor market... by increasing

the reservation wage...”

Depending on the structure of labor markets, increased wages may crowd out private

sector employment, perhaps reducing efficiency.

In addition to this competitive effect, NREGS could affect the rural economy through the

channels of public infrastructure, aggregate demand, and the relaxation of credit constraints.

The public goods that NREGS projects create - such as irrigation canals and roads - could

increase productivity, possibly mitigating negative impacts on efficiency. Given the size

6

of the program, it could also have effects through increased aggregate demand as workers’

disposable income increases, given the presence of agglomeration economies or barriers to

trade (for internal barriers to trade in India see Atkin (2013)). Finally, increased income

may relax credit constraints and thereby increase output.

Given the implementation issues discussed in the previous section, it is unclear whether

any of these effects are actually witnessed in practice. For example, Niehaus and Sukhtankar

(2013b) point out that because of corruption by officials who steal worker’s wages, NREGS

does not serve as an enforcement mechanism for minimum wages in the private sector, but

rather functions as a price-taker.

2.1.2 Prior evidence on NREGS impact

The impact of the NREGS on labor markets, poverty, and the rural economy has been hotly

debated since inception.8 Supporters claim it has transformed the rural countryside by

increasing incomes and wages; creating useful rural infrastructure such as roads and canals;

and reduced distress migration (Khera, 2011). Detractors claim that funding is simply

captured by middlemen and wasted; that it couldn’t possibly affect the rural economy since

it is a small part of rural employment (“how can small tail wag a very very large dog?”); or

that even if it increases rural wages and reduces poverty, that this is at the cost of crowding

out more efficient private employment, in rural areas and cities (Bhalla, 2013). The debate is

still politically salient, as the current national government has been accused of attempting to

let the program slide into irrelevance by defunding it,9 while a group of prominent academics

signed a letter asking it to not do so.

The problem with this heated debate is that the debate-to-evidence ratio is high, as

credibly identifying causal impacts of the program is difficult. There was no evaluation

prior to the program’s launch or built in to program rollout, while the selection of districts

for initial deployment was politicized (Gupta, 2006; Chowdhury, 2014). The vast majority

of empirical work estimating impacts of NREGS thus uses one of two empirical strategies,

both relying on the fact that there was a phase-in period for program implementation: it

was implemented first in a group of 200 districts starting in February 2006, followed by a

second group of 130 in April 2007, while the remaining rural districts entered the program in

April 2008. This allows for a difference-in-differences or regression discontinuity approach,

by comparing districts in Phase I with those in Phase II and III (or those in I and II with

III, etc).

8See Sukhtankar (forthcoming) for a review of the literature on the impacts of NREGS.9See, for example, the following article on the most recent apparent attempt: http://thewire.in/

75795/mnrega-centre-funds-whatsapp/, accessed November 3, 2016.

7

These approaches are non-experimental, and as such rely on strong assumptions in order

to identify causal impacts of NREGS. While this problem is well understood - and some of

the studies may well satisfy the necessary assumptions - there is another less appreciated

problem with these types of comparisons. Given that NREGS suffered from a number of well-

documented “teething problems” - for example, two years after the start major issues with

basic awareness, payment delivery, and monitoring were still to be worked out (Mehrotra,

2008) - any estimated impacts are less likely to be informative about steady state effects.

Possibly the most consistent evidence comes from estimated impacts on labor markets,

with three papers (Imbert and Papp, 2015; Berg et al., 2012; Azam, 2012) using a similar

difference-in-differences approach but different datasets estimating that the NREGS rollout

may have raised rural unskilled wages by as much as 4-5%. Yet these papers disagree

on the timing of the effects; while Imbert and Papp (2015) suggest that wage effects are

concentrated in the slack labor season, Berg et al. (2012) find that they are driven by

peak labor seasons. Meanwhile Zimmermann (2015) finds no average effects on wages using

a regression discontinuity approach. Effects on potential crowd-out are also inconclusive:

Imbert and Papp (2015) find 1.5% decrease in private employment concentrated in the slack

season, while Zimmermann (2015) finds a 3.5% reduction in private employment for men,

year-round.

The estimated effects on other outcomes present an even more conflicting picture. Given

the potential for labor market effects to spill over into schooling, a number of papers have

examined educational outcomes. Mani et al. (2014) find that educational outcomes improved

as a result of NREGS; Shah and Steinberg (2015) find that they worsened; while Islam

and Sivasankaran (2015) find mixed effects. Given that the program was targeted towards

underdeveloped areas suffering from civil violence related to the leftist Naxalite or Maoist

insurgency, some papers have examined effects on violence. While Khanna and Zimmerman

(2014) find that such violence increased, Dasgupta et al. (2015) find the opposite.

While no doubt there is variation across these papers in the samples used, as well as in

the risk of bias, the conflicting results do underscore the difficulty of relying exclusively on

non-experimental analysis. Yet experimental estimates have also been difficult to obtain, in

part since capturing general equilibrium effects would require randomization at large scales.

2.2 Smartcards

In an attempt to address problems with implementation, the Government of Andhra Pradesh

(GoAP) introduced a new payments technology based on electronic transfers of benefits

to beneficiary bank accounts and biometric authentication of beneficiaries prior to benefit

8

withdrawal. This technology - which we collectively refer to as “Smartcards” - had two

major components. First, it changed the last-mile payments provider from the post office to

a private Technology Service Provider / Customer Service Provider, along with the flow of

payments to these providers. Second, it changed the authentication technology from paper

documents and ink stamps to a Smartcard and digital biometric check.

The intervention had two major goals. First, it aimed to reduce leakage from the NREGS

labor budget in the form of under-payment and over-reporting. Second, it targeted improve-

ments in the payment experience, in particular delays in NREGS wage payments. More

details on the functioning of the intervention and the changes that it introduced are avail-

able in Muralidharan et al. (2016); for this paper what is relevant is that the intervention

dramatically improved the implementation of NREGS, which we describe briefly in section

2.3.

Note that the Smartcards intervention affected both NREGS as well as the Social Security

Pension (SSP) program. In this paper, we focus on effects coming from improvements to

NREGS, as it is unlikely that improvements to SSP affected the labor market or the broader

rural economy for two reasons. First, the scale and scope of SSP is fairly narrow: only 7%

of rural households are eligible, as it is restricted to those who are Below the Poverty Line

(BPL) and either widowed, disabled, elderly, or had a (selected) displaced occupation. It is

meant to complement NREGS for those unable to work, and the most prominent benefit level

of Rs. 200 per month is small (about $3, or less than two days earnings for a manual laborer).

Second, the improvements from the introduction of Smartcards were less pronounced than

those in NREGS: there were no significant improvements in the payments process, while

reductions in leakage only amounted to Rs. 12 per household.10

2.3 Effects on program performance

In Muralidharan et al. (2016), we show that Smartcards significantly improved the func-

tioning of NREGS in AP on multiple dimensions. Two years after the intervention began

in treatment mandals, the NREGS payments process got faster (29%), less time-consuming

10Of course, SSP households could be affected by changes in wages and employment, and many SSPhouseholds are also NREGS jobcard holders and work on NREGS. Indeed, prior versions of this paper pooledNREGS and SSP samples for analysis in order to take advantage of additional sample size. While resultswith pooled samples are almost exactly the same as the results that we present here, pooling the samplescreates a complicated weighting issue. Samples for NREGS and SSP surveys were drawn independently,with the NREGS sample designed to be representative of all NREGS jobcard-holders while over-weightingrecent workers, while the SSP sample was drawn to be representative of all SSP beneficiaries. It is not clearwhat the pooled sample is representative of. Since NREGS jobcard holders represent 65.7% of the ruralpopulation, while SSP beneficiaries who are not NREGS jobcard holders comprise only 2.2% of the ruralpopulation, it is easiest to simply ignore the SSP sample rather than present all our results with robustnesschecks for multiple weighting schemes. These results are available on request.

9

(20%), and more predictable (39%). Additionally, households earned more through working

on NREGS (24%), and there was a substantial 12.7 percentage point reduction (∼ 41%) in

leakage. Moreover, access did not suffer: both perceived access and actual participation in

the program increased (17%). We find little evidence of heterogenous impacts, as treatment

distributions first order stochastically dominate control distributions for all outcomes on

which there was a significant mean impact. Reflecting this, users were strongly in favor of

Smartcards, with 90% of households preferring it to the status quo, and only 3% opposed.

The improvements in implementation reflect intent-to-treat (ITT) estimates, which is

important since implementation was far from complete. Logistical problems were to be

expected in an intervention of this scale, and two years after implementation the proportion

of payments in treated areas made using Smartcards had plateaud to 50%. It is important to

note that these estimates do not reflect “teething” problems of Smartcards, since Smartcards

had been implemented in other districts in AP for four years prior to their introduction in

our study districts. The estimates reflect steady state, medium run impacts that are net of

management, political economy, and other challenges.

A result that is important to the interpretation of general equilibrium effects of Smartcards

is that there was no increase in NREGS expenditure by the government. Thus unlike the

introduction of NREGS itself, no new money flowed into treatment areas. Any increases in

earnings were due to a reduction in leakage corresponding to a redistribution from corrupt

officials to workers. This is the main significant difference if one wishes to compare the effect

of Smartcards to an idealized effect of “NREGS itself,” although one could potentially use

the mean level of program earnings in the control group as an indicator of what the effect

of the program itself may have been on this dimension.

Other mechanisms via which the reform affected rural economies are similar to those

that one might expect from the introduction of a well-implemented NREGS. First, the

improvement in payments logistics such as timeliness of payments and the increase in earnings

on NREGS made the program a more viable outside option to private sector employment,

and thus led to an increase in competitive pressure in the labor market. Since there was

additional participation in NREGS, there was also an increase in the amount of rural work

done and rural public goods created. The increases in NREGS earning could also have

relaxed credit constraints on participants.

10

3 Research design

3.1 Randomization

We summarize the randomization design here, and refer the reader to Muralidharan et

al. (2016) for further details. The experiment was conducted in eight districts11 with a

combined rural population of around 19 million in the erstwhile state of Andhra Pradesh

(now split into two states: Andhra Pradesh and Telangana). As part of a Memorandum of

Understanding with JPAL-South Asia, GoAP agreed to randomize the order in which the

Smartcard system was rolled out across mandals (sub-districts). We randomly assigned 296

mandals - with average population of approximately 62,500 - to treatment (112), control (45),

and a “buffer” group (139); Figure 1 shows a map showing the geographical spread and size

of these units. We created the buffer group to ensure that we could conduct endline surveys

before deployment began in control mandals, and restricted survey work to treatment and

control mandals. We stratified randomization by district and by a principal component of

mandal socio-economic characteristics.

We examine balance in Tables A.1 and A.2. The former (reproduced from Muralidharan et

al. (2016)) simply shows balance on variables used as part of stratification, as well as broader

mandal characteristics from the census. Treatment and control mandals are well balanced,

with two out of 22 variables significant. The latter shows balance on the outcomes that are

our primary interest in this paper, as well as key socio-economic household characteristics

from our baseline survey (see below). Here, four out of 34 variables are significantly different

at the 10% level at least, which is slightly more than one might expect. Where feasible, we

also test for sensitivity of the results to chance imbalances by controlling for village level

baseline mean values of the outcomes.

3.2 Data

3.2.1 Socio-Economic and Caste Census

Our primary data source is the Socio-Economic and Caste Census (SECC), an independent

nation-wide census for which surveys in Andhra Pradesh were conducted during 2012, our

endline year. The primary goal of the SECC was to enable governments to rank households

by socio-economic status in order to determine which were “Below the Poverty Line” (BPL)

11The 8 study districts are similar to AP’s remaining 13 non-urban districts on major socioeconomicindicators, including proportion rural, scheduled caste, literate, and agricultural laborers; and represent allthree historically distinct socio-cultural regions. See the online appendix to Muralidharan et al. (2016) fordetails.

11

and thereby eligible for various benefits. A secondary (and controversial) goal was to capture

data on caste, which the regular decennial census does not collect. The survey collected data

on income categories for the household member with the highest income (less than Rs. 5000,

between Rs. 5000-10,000, and greater than Rs. 10,000), the main source of this income,

household landholdings (including amount of irrigated and non-irrigated land) and caste,

and the highest education level completed for each member of the household.

The SECC was conducted using the layout maps and lists of houses prepared during the

conduct of the 2011 Census. Enumerators were assigned to cover the households listed in

each block, and were also instructed to attempt to interview homeless populations. The

total number of households in our SECC sample, including treatment and control mandals,

is slightly more than 1.8 million.

3.2.2 Original survey data

We complement the broad coverage of the SECC data with original and much more detailed

surveys of a smaller sample of households. Specifically, we conducted surveys of a represen-

tative sample of NREGS jobcard holders and SSP beneficiaries during August to October of

2010 (baseline) and 2012 (endline). Surveys covered both respondents’ participation in and

experience with these programs, and also their earnings, expenditure, assets and liabilities

more generally. Within earnings, we asked detailed questions about household members’ la-

bor market participation, wages, reservation wages, and earnings during the month of June

(the period of peak NREGS participation in Andhra Pradesh).

Full details of the sampling procedure used are in Muralidharan et al. (2016). In brief,

we drew a sample of NREGS jobcard holders that over-weighted those who had recently

participated in the program according to official records. In Andhra Pradesh, 65.7% of rural

households have a jobcard according to our calculations based on the National Sample Survey

Round 68 in 2011-12. We sampled a panel of villages and repeated cross-sections of the full

concurrent NREGS sampling frame. The sample included 880 villages, with 6 households

in each village. This yielded us 5,278 households at endline, of which we have survey data

on 4,943 households; of the remaining, 200 were ghost households, while we were unable to

survey or confirm existence of 135 (corresponding numbers for baseline are 5,244, 4,646, 68

and 530 respectively).

3.2.3 District Statistical Handbook data

We use District Statistical Handbooks (DSH) published by the Andhra Pradesh Directorate

of Economics and Statistics, a branch of the Central Ministry of Agriculture and Farmers

12

Welfare, to obtain additional data on land use and irrigation – including details by season –

and on employment in industry. DSH are published every year and are not to be confused

with the District Census Handbooks which contains district tables from the Census of India.

Land coverage data presented in the DSH is officially provided by the Office of the Surveyor

General of India. Ideally, land coverage data is obtained from so-called village papers pre-

pared by village accountants. These village papers contain information on land cover that

varies (area sown or fallow) while forest and mountainous areas is recorded centrally. For

cases in which no village papers are maintained, “ad-hoc estimates of classification of area

are derived to complete the coverage.”12

3.2.4 National Sample Survey data

We use unit cost data from Round 68 (2011-2012) of the National Sample Survey (NSS)

published by the Ministry of Statistics and Programme Implementation. The NSS contains

detailed household × item-level data on a sample that is representative at the state and

sector level (rural and urban). The goal of the 68th Round was to obtain estimates on

key household consumer expenditure, employment, and unemployment indicators for States

and UTs. The data provides a comprehensive picture of household-level consumption and

expenditure for over 300 goods and services in categories including food, fuel, clothing, rent

and other fees or services over mixed reference periods varying from a week to a year.

3.2.5 Census data

We use spatial data from the 2001 Indian Census which contains comprehensive geographic

information at the village-level. For each census village, the data contains both a point and

a polygon of geographic coordinates (latitude, longitude) to define the precise location and

borders of the village.

3.3 Estimation strategy

We begin by reporting simple comparisons of outcomes in treatment and control mandals

(i.e. intent-to-treat estimates). Our base regression specification includes district fixed

effects and the first principal component of a vector of mandal characteristics used to stratify

randomization (PCmd), with standard errors clustered at the mandal level:

Yimd = α + βTreatedmd + δDistrictd + λPCmd + εimd (1)

12Information from http://eands.dacnet.nic.in/, accessed March 22, 2016.

13

where Yimd is an outcome for household or individual i in mandal m and district d, and

Treatedmd is an indicator for a treatment group mandal. In some cases we use non-linear

analogues to this model to handle categorical data (e.g. probit, ordered probit). When using

our survey data, we also report specifications that include the baseline GP-level mean of the

dependent variable, Y0

pmd, when available in order to increase precision and assess sensitivity

to any randomization imbalances:

Yipmd = α + βTreatedmd + γY0

pmd + δDistrictd + λPCmd + εipmd (2)

where p indexes panchayats or GPs. Note that we easily reject γ = 1 in all cases and

therefore do not report difference-in-differences estimates.

If outcomes for a given unit (household, GP, etc.) depend only on that unit’s own treat-

ment status, then Equation 1 identifies a well-defined treatment effect. If Smartcards do have

general equilibrium effects, however, then these effects need not be confined to the treated

units. Upward pressure on wages in treated mandals, for example, might affect wages in

nearby areas of control mandals. In the presence of such spillovers, (1) underestimates the

“total” effect of treatment, conceptualized as the difference between average outcomes when

all units are treated and that when no units are treated. Estimating this “total” effect

is complex enough that we defer it to Section 5, and simply note for now that our initial

estimates are likely to be conservative.

Regressions using SECC data are unweighted. Regressions with survey samples are weighted

by inverse sampling probabilities to be representative of the universe of jobcard-holders.

When using survey data on financial outcomes we trim the top 0.5% of observations in both

treatment and control groups to remove outliers, but our results are robust to including

them.

4 Results

4.1 Effects on earnings and poverty

Figure 2 compares the distributions of SECC income categories in treatment and control

mandals. Note that these are raw data without district fixed effects conditioned out, and

so could theoretically reflect cross-district differences in treatment probability as well as

treatment effects. With that caveat in mind, the treatment distribution evidently first-order

stochastically dominates the control, with 4.0 percentage points fewer households in the

lowest category (less than Rs. 5,000), 2.7 percentage points more households in the middle

category (Rs. 5,000 to 10,000), and 1.4 percentage points more in the highest category

14

(greater than Rs. 10,000). Overall, these estimates imply that Smartcards moved 53,626

households out of the lowest income category and into a higher one.

Table 1a reports statistical tests, conditioning on district effects to obtain experimental

estimates of impact. Given the categorical nature of our outcome measure, we report logistic

regressions for each category individually (showing marginal effects) and also an ordered

logistic regression for the combined categories. We find that treatment significantly increased

the log-odds ratio of being in a higher income category, with results strongly significant at

the 1% level. As a sanity check we also confirm that these estimates are unaltered when

we control for demographic characteristics (age of household head, caste, literacy). This

suggests that the Smartcards randomization was indeed orthogonal to other determinants of

earnings.

The SECC income measures let us test for income effects in the entire population of

interest, but have two limitations when it comes to estimating magnitudes. First, much

information is lost through discretization: the 4.0% reduction in the share of households in

the lowest category which we observe, for example, could reflect a small earnings increase for

households that were just below the Rs. 5,000 threshold, or a large impact for households

that were further away from it. Second, because the SECC only captures the earnings of

the top income earner in each household, it is possible that it over- or under-states effects

on overall household earnings.

For a better sense of magnitudes we therefore turn to our survey data. Columns 1 and 2 of

Table 1b reports estimated impacts on overall annual household income, with and without

controls for the mean income in the same village at baseline. In both specifications we

estimate that that treatment increased income by over Rs. 9500. This is a large effect,

equal to 13.3% of the control group mean or 19.6% of the national expenditure-based rural

poverty line for a family of 5 in 2011-12, which was Rs. 48,960 (Government of India, 2013).

Of course, expenditure- and income-based poverty lines may differ and this comparison is

illustrative only. But if these lines were taken as equivalent, we see a 5.2 percentage point

or 18.1% reduction in poverty: this is evident in Figure 3 which plots the empirical CDF of

household earnings for treatment and control groups.

Our survey data are also useful for examining distributional impacts. Estimated earnings

impacts are weakly positive throughout the distribution, but visibly larger at the higher end,

with 55% of the total earnings increase accruing to households above the 80th percentile

(corresponding to annual household income of Rs. 103,000). Of course, a household earning

this much remains poor by any absolute standard; this number is twice the expenditure-based

poverty line, but corresponds to less than $4/day per capita (in PPP exchange rates).

15

4.2 Direct versus indirect effects on earnings

Mechanically, the effects on earnings and poverty we find above must work through some

combination of increases in households’ earnings from the NREGS program itself and in-

creases in their non-program (i.e. private sector) earnings. To examine this decomposition

we use our survey data, which includes measures of six income categories: NREGS, agricul-

tural labor income, other physical labor income, income from own farm, income from own

business, and miscellaneous income (which includes all remaining sources, including salaried

income). In the control group, the average household earns roughly 1/3 of its income from

wage labor, primarily in agriculture; 1/3 from self-employment activities, also primarily in

agriculture; and the remaining 1/3 from salaried employment and public programs, with the

latter making up a relatively small share.

Columns 3-9 of Table 1b report treatment effects on various income categories separately.

Strikingly, effects on NREGS earnings are only a small proportion of overall income gains,

accounting for only 10.4% of the overall increase.13 Instead the primary driver of increased

earnings is an increase in paid labor, both in in the agricultural and non-agricultural sectors.

Effects on own farm earnings are positive but insignificant.

The distribution of impacts is also consistent with private-sector earnings being the main

driver. Table A.5 examines how treatment effects vary with a household’s ability to perform

manual labor, proxied by the fraction of the household that is eligible for the SSP program

(which is reserved for the elderly, widows, and disabled people). Impacts on earnings – and

especially labor earnings – are concentrated in households not headed by widows and with

few members eligible for SSP.

4.3 Effects on private labor markets

We next unpack impacts on private sector earnings, examining how wages and labor quan-

tities were affected.

To examine wage effects we use our survey data, as the SECC does not include wage

information. We define the dependent variable as the average daily wage rate earned on

private-sector work reported by respondents who did any private-sector work. We report

results for the full sample of workers and also check that results are robust to restricting the

sample to adults aged 18-65. (We report additional robustness checks in Section 4.6 below).

13The observant reader may note that the (marginally) insignificant effect on program earnings hereappears to contrast with the significant effect in Muralidharan et al. (2016). The two estimates are for twodistinct measures of NREGS earnings: the measure in Muralidharan et al. (2016) comes from questions abouta specific 6-week study period shortly before surveys were conducted and asked to the worker themselves,while the measure here is from an aggregate, annual recall question posed to the head of the household, andis thus much less precise. The point estimates are nevertheless economically similar.

16

We estimate a significant increase of Rs. 7.9 in private sector wages (Table 2, Column 2).

This is a large effect, equal to 6.2% of the control group mean. In fact, it is slightly larger

than the highest estimates of the wage impacts of the rollout of the NREGS itself reported

by Imbert and Papp (2015).

We then examine how labor market participation was affected by this large wage increase.

We classify days spent during the month of June by adults (ages 18-65) into three categories:

time spent working on the NREGS, time spent working on the private sector, including self-

employment, and time spent idle / on leisure.

We find a significant decrease of 1.2 days per month in days spent idle, equal to 7% of the

control group mean (Table 3, Columns 5 & 6). This time appears to have been reallocated

across both NREGS work and private sector work, which increase by roughly 0.9 days and

0.5 days per month, respectively (though these changes are not individually significant)

(Columns 1-4). In Figure 4 we plot the full distribution of private sector days worked for

treatment and control mandals separately; this shows that the increase in private-sector work

is spread fairly evenly through the distribution. Moreover, it is not the case that private

sector was too low to drop further; 51% of our sample reported doing at least some private

sector work in June (50% in control and 52% in treatment), and there is no evidence of a

reduction in the right tail of the distribution.14

The fact that private sector employment does not fall with higher wages is intriguing,

suggesting some degree of monopsony power in labor markets. A long tradition of research

on rural labor markets has argued that large land-owners hold substantial market power. Our

estimate is imprecise, however, and a 95% confidence interval for the wage elasticity of labor

demand is (−0.44, 0.8) which includes negative values, consistent with perfect competition.

In particular, it includes the estimate of −0.31 reported by Imbert and Papp (2015). In a

statistical sense we thus rule out only competitive markets with a wage elasticity of labor

demand larger than (minus) 0.44.

Given that NREGS peaks in the late spring and early summer, one natural question is

whether the wage effects we observe in June are seasonal, or persist throughout the year.

While our household survey data on individual labor spells cover only the month of June,

we also have data from village leaders on “going rates” throughout the year. With one

observation per village-month power is limited, but these data do suggest persistent wage

differences between treatment and control mandals (Figure A.1). These could imply that

14The pattern of impacts we find on labor supply is at least potentially consistent with results in Imbertand Papp (2015). They estimate a 1-for-1 reduction in “private sector employment” as NREGS employmentincreases, but their measure of private sector employment (based on NSS data) does not distinguish domesticwork and self-employment from wage employment for others. Of course, they also study impacts on a differentpopulation.

17

the NREGS still provides an meaningful outside option even during peak labor demand

season, or alternatively could reflect nominal wage rigidity Kaur (2015) and/or labor tying

(Bardhan, 1983; Mukherjee and Ray, 1995).15

Finally, we examine impacts on labor allocation through migration. Our survey asked

two questions about migration for each family member: whether or not they spent any days

working outside of the village in the last year, and if so how many such days. Table 4

reports effects on each measure. We estimate a small and statistically insignificant increase

in migration on both the extensive and intensive margins, inconsistent with the prevailing

view that the NREGS reduces migration to cities. As our migration questions may fail to

capture permanent migration, we also examine impacts on household size and again find no

significant difference.16

4.4 Effects on consumer goods prices

One potential caveat to the earnings results above is that they show impacts on nominal,

and not real, earnings. Given that Smartcards affected local factor (i.e. labor) prices, they

could also have affected the prices of local final goods, and thus the overall price level facing

consumers, if the markets we study are not sufficiently well-integrated into larger product

markets.

To test for impacts on consumer goods prices we use data from the 68th round of the Na-

tional Sample Survey. The survey collected data expenditure and number of units purchased

for a wide variety of goods; we define unit costs as the ratio of these thing. We restrict the

analysis to goods that have precise measures of unit quantities (e.g. kilogram or liter) and

drop goods that likely vary a great deal in quality (e.g. clothes and shoes). We then test

for price impacts in two ways. First, we define a price index Pvd equal to the the price of

purchasing the mean bundle of goods in the control group, evaluated at local village prices,

following Deaton and Tarozzi (2000):

Pvd =n∑

c=1

qcdpcv (3)

Here qcd is the estimated average number of units of commodity c in panchayats in control

areas of district d, and pcv is the median unit cost of commodity c in village v. Conceptually,

15Note that given this persistence, our point estimates for wage, labor and earnings effects are internallyconsistent: the 11.3% increase in private sector earnings is almost exactly equal to the sum of the 6.2%increase in wages and (insignificant) 5% increase in employment.

16In estimating poverty and labor market effects we also examined interactions between treatment andvarious individual and village characteristics. Generally speaking we find few significant predictors of het-erogeneity, and therefore omit these results (available on request).

18

treatment effects on this quantity can be thought of as analogous to the “compensating

variation” that would be necessary to enable households to continue purchasing their old

bundle of goods at the (potentially) new prices.

The set of goods for which non-zero quantities is purchased varies widely across house-

holds and, to a lesser extent, across villages. To ensure that we are picking up effects on

prices (rather than compositional effects on the basket of goods purchased), we initially re-

strict attention to goods purchased at least once in every village in our sample. The major

drawback of this approach is that it excludes roughly 40% of the expenditure in our sample.

We therefore also present a complementary set of results in which we calculate (3) using

all available data. In addition, we report results using (the log of) unit cost defined at

the household-commodity level as the dependent variable and including all available data.

While these later specifications potentially blur together effects on prices with effects on the

composition of expenditure, they do not drop any information.

Regardless of method, we find little evidence of impacts on price levels (Table 5). The

point estimates are small and insignificant, and when we use the full information available

are also precise enough to rule out effects as large as those we found earlier for wages. These

results suggest that the treated areas are sufficiently well-integrated into product markets

that higher local wages did not affect prices, and can thus be interpreted as real gains for

workers (and real losses for employers).17

4.5 Channels of impact

Our experiment was not designed to tease apart the mechanisms through which improving

the NREGS might affect local markets and earnings. Nonetheless, in this section we assess

what the data can tell us about those mechanisms.

We begin with the hypothesis that a (better-run) employment guarantee puts competitive

pressure on labor markets, driving up wages and earnings. Theoretical models emphasize

this mechanism (Ravallion, 1987; Basu et al., 2009), and it has motivated earlier work on

NREGS wage impacts (e.g. Imbert and Papp (2015)). A key differentiating implication of

this hypothesis is that wages rise not because labor demand increases (as would be the case

if labor become more productive, or aggregate demand rose) but rather because workers

demand higher wages (i.e. the labor demand curve shifts up).

We are able to test this prediction using information on reservation wages that we elicited

in our survey. Specifically, we asked respondents if in the month of June they would have

17We also find little evidence of impacts on household expenditure (Table 7), which one would expect ifeither (i) households interpreted their earnings increase as permanent, or (ii) local prices increased. Thatsaid, confidence intervals around these estimates are wide.

19

been “willing to work for someone else for a daily wage of Rs. X,” where X started at Rs.

20 (15% of average wage) and increased in Rs. 5 increments until the respondent agreed.

Among respondents who worked, 98% reported reservation wages below or equal to the wages

they actually earned, suggesting that they correctly understood the question (Table A.3).

We find that treatment did in fact significantly increase workers’ reservation wages, by

approximately Rs. 5.5, or 5.7% of the control group mean (Table 2, Columns 5-8). This

implies that having a better alternative to wage labor must be at least part of the explanation

for higher private-sector earnings. It does not necessarily imply that the better alternative is

NREGS employment, of course, but (as we discuss below) we do not see evidence of higher

returns to other activities we measure, including self-employment in either farm or non-farm

activities. Moreover, the fact that estimated effects on reservation wages and actual wages

are nearly identical suggests that the labor market competition effect is strong enough to

explain the entire wage effect.18

The most obvious alternative hypothesis is that an improved NREGS created additional

assets – roads, irrigation facilities, soil conservation structures, etc. – which increased pro-

ductivity. We find no evidence of such an effect, however: the number and composition of

projects implemented did not change in treatment areas relative to control (Table 6).

Another a priori plausible explanation is that the reform increased cash flow into treated

areas, stimulating local economic activity and driving up wages and earnings (Krugman,

1991). In practice, however, we find no effect of the intervention on the amount of money

disbursed by the NREGS in administrative data (see Muralidharan et al. (2016)), and no

significant increase in any measure of household expenditure in our survey data (Table 7). We

do see some evidence of improved credit access, with higher borrowing in treated areas (Table

8), but as we will see below cannot connect this with evidence of higher factor utilization or

earnings.

Among various other potential mechanisms – including productivity shocks, aggregate

demand shocks, and expansionary credit access, among others – one common differentiating

feature is that each should increase the utilization of some factor(s) of production in addition

to labor. For example, if an improved NREGS leads to the creation of roads that raise revenue

productivity in a village, then it would directly increase demand for both labor and land,

and might thus increase cultivation of marginal land. We therefore look to see whether the

reform affected utilization of any factors other than labor.

We find little impact for the factors we are able to observe. For land, Table 9 shows no

significant effect on the amount of land under cultivation (% area sown or % area fallow) or

18With the caveat that economically meaningful differences remain statistically possible; a 90% confidenceinterval for this difference in the combined sample is Rs. (-2.78, 7.44).

20

on the total area irrigated.19 The implied confidence intervals let us rule out effects larger

than 4 percentage points in all cases.

A similar argument can be made regarding patterns of earnings: if the reform directly

increased revenue productivity then we might expect to see impacts on both employment and

own-account earnings. For example, better market access would increase the profitability

of both large plantations and small owner-farmed plots; better credit access would allow

beneficiaries to acquire more capital and increase their own-account earnings. Yet we see

no significant impacts on earnings from self-employment, with gains concentrated in labor

income categories (Table 1b).

Overall, we find strong evidence consistent with the labor market competition hypothesis,

and little support for alternative interpretations. Yet each data point has its own caveats,

and so we do not view them as collectively dispositive. We leave it to the judicious reader

to decide his or her own degree of certainty.

4.6 Robustness & other concerns

In this section we describe the main robustness checks of our results on income and wages.

The estimated income effects in Table 1b are robust to a number of checks. Since the SECC

data are categorical, we have used logit and ordered logit models for estimation. The results

are robust to using probits or linear probability models instead (results available on request).

Our survey data on income contains possible outliers. As a robustness check, we exclude

observations at the top 0.5% in treatment and control. This does not change the results:

the estimates are slightly smaller but remain significant at the 1% level (Table A.4).

Our wage results are also robust to alternative choices of sample. We top-censor wages

to account for outliers. Although noisier, results including these observations are similar

(Table A.6a). The main results include data on anyone in the household who reports wages.

Restricting the sample to only those of working age (18-65) again does not qualitatively

affect results (Table A.6b). Next, dropping the small number of observations who report

wages but zero actual employment again does not matter (Table A.6c).

Given that we observe wage realizations only for those who work, a potential concern is

that the effects we estimate are driven by changes in who reports work (or wages) and not

by changes in the distribution of wage offers in the market. We test for such selection effects

as follows. First, we confirm that essentially all respondents (99%) who reported working

also reported the wages they earned, and that non-response is the same across treatment

19In earlier drafts we presented evidence that the intervention increased vegetative cover in the month ofMay as measured based on satellite imagery using the Enhanced Vegetation Index (EVI). When we examinedyear-round impacts on EVI, however, we do not find a robust pattern. Results available upon request.

21

and control. (First row of Table A.3). Second, we check that the probability of reporting

any work is not significantly different between treatment and control groups ( Table A.3).

Third, we check composition and find that treatment did not affect composition of those

reporting in Table A.7. Finally, as we saw above treatment also increased reservation wages,

which we observe for nearly the entire sample (89%) of working-age adults.

5 Spatial spillovers

Given results thus far, it seems plausible that implementing Smartcards in one mandal may

have affected outcomes in other neighboring mandals – through labor market competition,

for example. We turn now to testing for such effects and to estimating “total” treatment

effects that account for them.

As with any such spatial problem, outcomes in each GP could in principle be an arbitrary

function of the treatment status of all the other GPs, and no feasible experiment could

identify these functions nonparametrically. We therefore model spillovers as a simple function

of the fraction of neighboring GPs treated within various radii R. Specifically, we define NRp

the fraction of GPs within a radius R of panchayat p which were assigned to treatment.

Figure 5 illustrates the construction of this measure.

One might hope that the random assignment of mandals to treatment and control arms

ensures that the neighborhood measure NRp is also “as good as” randomly assigned – but

this is not the case. To see this, consider constructing the measure for GPs within a treated

mandal: on average, GPs closer to the center of mandal will have higher values of NRp

(because more of their neighbors are from the same mandal), while those closer to the border

will have lower values (because more of their neighbors are from neighboring mandals). The

opposite pattern will hold in control mandals. Thus, we cannot interpret a coefficient on NRp

as solely a measure of spillover effects unless we are willing to make the (strong) assumption

that the direct effects of treatment are unrelated to location.

To address this issue, we construct a second measure NRp defined as the fraction of treated

GPs within a given radius R and within a different mandal. This has the advantage of

being exogenous conditional on own treatment status (with the one disadvantage that it

is not defined for all GPs at smaller values of R). We use this measure both to test for

the existence of spillovers (where we are interested in testing rather than point estimation)

and as an instrument for estimating the effects of NRp , which we view as the “structural”

parameter.

We construct our measures of neighborhood treatment intensity at radii of 10, 15, 20, 25,

and 30 kilometers. Figure A.2 plots smoothed kernel density estimates by treatment status

22

for several of these measures to illustrate that there is meaningful overlap in the distributions

for control and treatment group. Tables A.8 and A.9 report tests showing that our outcomes

of interest are balanced with respect to these measures at baseline.

5.1 Testing for spillovers

To test for the existence of spillovers, we estimate

Yipmd = α + βNRp + δDistrictd + λPCmd + εimd (4)

separately for the treatment and control groups. This approach allows for the possibility

that neighborhood effects differ depending on one’s own treatment status. We also estimate

a variant that pools both treatment and control groups (and adds an indicator for own

treatment status), which imposes equality of the slope coefficient β across groups. In either

case we interpret rejection of the null β = 0 as evidence of spillover effects.

We find fairly consistent evidence of spillover effects on market wages, consistent in sign

with the direct effects we estimated above (Table 10, Columns 1-5). The effects are strongest

for households in control mandals, where we estimate a significant relationship at all radii

greater than 10km; for those in treatment mandals the estimates are smaller and significant

for three out of five radii, but uniformly positive. For days spent on unpaid work or idle,

the estimated effects are all negative and economically meaningful, as above, and significant

except at smaller radii when we split the sample (Table 10, Columns 6-10). In particular,

when we pool the samples we estimates significant spillover effects at all radii (except at R

= 10 for days spent on unpaid work or idle).

Note that sample sizes increase as we increase the neighborhood radius R, since at larger

values of R a larger share of panchayats have at least one neighbor within distance R and

in another mandal; we use between 90% and 100% of the available data depending on

specification. Effect sizes and t-statistics are for the most part larger, however, for larger

values of R, suggesting that excluding sample is not biasing us towards rejecting the null.20

5.2 Estimating total treatment effects

To estimate the total effect of treatment, we wish to estimate

Yipmd = α + βTTm + βNNRp + βTNTm ·NR

p + δDistrictd + λPCmd + εimd (5)

20Alternatively, we can construct tests using 100% of the data if we are willing to make the (strong)assumption that NR

p is exogenous. We obtain directionally similar results when doing so (Table A.10), butthe estimates are less precise.

23

In the context of this model, we define the total effect of treatment as

β = βT + βN + βTN (6)

Intuitively, this is the difference in expected outcomes between a unit in a treated mandal

with 100% of its neighborhood treated, and that for a unit in a control mandal with 0% of

its neighborhood treated. Notice that this is an partially extrapolative exercise, as much of

our sample is not in either of these conditions. Nevertheless, we are interested in estimating

it since it is this “total” effect one would ideally use for determining policy.

Since NRp is potentially endogenous, we instrument for it in (5) using NR

p (and for it’s

interaction with t using the corresponding interaction). Not surprisingly, we estimate a strong

first-stage relationship between the two, with a minimum F -statistic across specifications

reported here of F = 115. For completeness we also report OLS estimates of (5) in Table

A.11; these are qualitatively similar to the results we present here, and mostly significant,

but less precise.

After adjusting for spillovers, we estimate effects on all of our main outcomes – wages,

labor, and earnings – which are (i) significant for most or all values of R, (ii) consistent in sign

with those we estimate in simple binary treatment specifications, and (iii) meaningful larger.

Table 11 summarizes these results; the estimate of interest is the total treatment effect. We

begin in panel (a) with wage outcomes. Depending on choice of R, the estimated effect on

realized wages is 14-20% of the control mean, and uniformly significant. The estimated effect

on reservation wages is 8-10% of the control mean, significant except for large values of R.

All told, these estimates suggest large general equilibrium effects of Smartcards on labor

markets.

In panel (b) we examine impacts on labor allocation. As above, we estimate that treat-

ment significantly increased days of work done in the private sector (for all R > 10km), and

significantly reduced the number of days spend idle or doing unpaid work. As one would

expect, the effects are again larger than more conservative estimates based on binary T/C

classification. Finally, in panel (c) we document the same pattern for total earnings: the esti-

mated total treatment is significant (except at R = 30km) and larger than the conservative,

unadjusted estimate.

5.3 Implications

Evidence on the extent of spatial spillovers helps to characterize the degree of spatial inte-

gration in rural labor markets in India. This is useful for interpreting past work on these

markets, as much of this research has been constrained by data availability to treat districts

24

as if they were separate labor markets (e.g. Jayachandran (2006), Imbert and Papp (2015),

Kaur (2015)). While this is understood to be an approximation, little is known about how

reasonable it is. Our data seem reasonably consistent with it: the average rural district in

AP had an area of approximately 10,000 square kilometers (equivalent to an idealized 100km

× 100km square), while we find evidence of spillovers at radii of up to 20-30km.21 If any-

thing, we suspect that treating districts as unitary markets under-estimates the importance

of within-district market segmentation.

6 Conclusion

This paper examines the impact of a major reform to a large public works program - the

National Rural Employment Guarantee Scheme - in India. Such large programs often have

general equilibrium impacts, which are difficult to capture non-experimentally or through

experiments where the scale of the randomized unit is small. We take advantage of an un-

usually large-scale intervention that introduced biometric “Smartcards” to make payments

to beneficiaries of the NREGS. In previous work we find that Smartcards significantly im-

proved the implementation of NREGS. Here we examine the corresponding effects of this

improvement on beneficiaries livelihoods and rural labor markets.

We find large increases in income, using not only our representative survey data but also

an independent and concurrent census conducted by the government. We also find that

the indirect effects of the reform are an order of magnitude larger than the direct effect on

NREGS earnings. These indirect effects appear to be driven by effects on private sector

labor markets, particularly increased wages. They also “spill over” into neighboring villages,

further increasing wages and earnings. Finally, we do not find evidence of factor allocation

distortions related to NREGS.

While we estimate the effects of improving NREGS implementation, one might also won-

der how our estimates compare to those from a hypothetical comparison between a “well-

implemented NREGS” and “no NREGS.” Our conjecture is that the effects would be broadly

comparable, but with larger income effects. The Smartcards reform increased the labor-

market appeal of the NREGS and increased participation in its projects, but did not in-

crease the flow of funds into treated areas. In contrast, the NREGS per se clearly represents

a significant transfer of funds from urban to rural areas.

21For context, note that 20km is a 4 hour walk at the average human walking speed of 5km/hour, androughly 7 times the width of an average village (2.9km). NREGS rules, meanwhile, stipulate that employmentshould be provided within 5km of the beneficiary’s home. While individual workers need not travel 20km forthese spillover effects to be observed at that distance, it is in fact plausible that they might do so, as mostuse bicycles for transport. Nearly 80% of households in our sample own at least one bicycle.

25

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28

Table 1: Income

(a) SECC data

Lowest bracket Middle bracket Highest bracketIncome bracket

3 levels

(1) (2) (3) (4) (5) (6) (7) (8)

Treatment -.041∗∗∗ -.039∗∗∗ .026∗∗ .025∗∗ .014∗∗ .012∗∗ -.041∗∗∗ -.000088∗∗∗

(.014) (.014) (.011) (.011) (.0065) (.0061) (.014) (.000018)District FE Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared 0.01 0.03 0.01 0.02 0.02 0.04 0.01 0.02Control Mean .83 .83 .13 .13 .038 .038N of cases 1.8M 1.8M 1.8M 1.8M 1.8M 1.8M 1.8M 1.8MEstimator Logit Logit Logit Logit Logit Logit Ordered logit Ordered logitControl Variables No Yes No Yes No Yes No Yes

(b) Survey data

Total income NREGA Ag. labor Other labor Farm Business Misc

(1) (2) (3) (4) (5) (6) (7) (8)

Treatment 10308∗∗ 9594∗∗ 905 3675∗∗ 4471∗∗∗ 1738 -773 293(4638) (4642) (589) (1485) (1585) (2704) (1359) (2437)

BL GP Mean .059(.075)

District FE Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .03 .03 .02 .04 .03 .01 .01 .01Control Mean 71935 71935 4743 14784 9315 21708 6620 14765N. of cases 4932 4898 4931 4932 4932 4932 4932 4932

This table shows treatment effects on various measures of household income. Panel a) uses data from the Socioeconomic andCaste Census (SECC), which reports income categories of the highest earner in the household (HH): the “Lowest bracket”corresponds to earning < Rs. 5000/month, “Middle bracket” to earning between Rs. 5000 & 10000/month, and“Highestbracket” to earning > Rs. 10000/month. The table reports marginal effects, or changes in the predicted probability ofbeing in the respective income bracket (columns 1-6) resulting from a change in a binary treatment indicator from 0 to1. In columns 7-8, we show the marginal effects on the predicted probability of being in the lowest income category. Forcolumns where there “Control Variables” is Yes, the following control variables are included: the age of the head of HH, anindicator for whether the head of HH is illiterate, indicator for whether a HH belongs to Scheduled Castes/Tribes. Panel b)shows treatment effects on types of income using annualized HH data from the endline HH survey for the NREGS sample.“BL GP Mean” is the GP mean of the dependent variable at baseline. “Total Income” is total annualized HH income (inRs.). “NREGS” is the earnings from NREGS. “Ag labor” captures income from agricultural work for someone else, while“Other labor” is income from physical labor for someone else. “Farm” combines income from a HHs’ own land and animalhusbandry, while“Business” captures income from self-employment or a HH’s own business. “Other” is the sum of HHincome not captured by the other categories. Note that the income categories were not as precisely measured at baselineso we cannot include the respective lag of the dependent variable. All regressions include the first principal component of avector of mandal characteristics used to stratify randomization as a control variable. Standard errors clustered at mandallevel are in parentheses. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

29

Table 2: Wages

Wage realizations (Rs.) Reservation wage (Rs.)

(1) (2) (3) (4)

Treatment 6.6∗ 7.8∗∗ 4.9∗ 5.5∗

(3.6) (3.6) (2.9) (2.8)

BL GP Mean .15∗∗∗ .12∗∗∗

(.053) (.043)

District FE Yes Yes Yes Yes

Adj R-squared .07 .07 .05 .05Control Mean 128 128 97 97N. of cases 7304 7090 12905 12791

This table shows treatment effects on wage outcomes from the private labor market in June using survey data. The outcome

“Wage realizations” in columns 1-2 is the average daily wage (in Rs.) an individual received while working for someone else

in June 2012. The outcome “Reservation wage” in columns 3-4 is an individual’s reservation wage (in Rs.) at which he or

she would have been willing to work for someone else in June 2012. The outcome is based on an a question in which the

surveyor asked the respondent whether he or she would be willing to work for Rs. 20 and increased this amount in increments

of Rs. 5 until the respondent answered affirmatively. Observations in the top .5% percentile of the respective wage outcome

in treatment and control are excluded in all regressions. “BL GP Mean” is the Gram Panchayat mean of the dependent

variable at baseline (May 31 to July 4, 2010). All regressions include the first principal component of a vector of mandal

characteristics used to stratify randomization as control variable. Standard errors clustered at mandal level in parentheses.

Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

30

Table 3: Employment

Days workedon NREGS

Days workedprivate sector

Days unpaid/idle

(1) (2) (3) (4) (5) (6)

Treatment .95 .88 .44 .53 -1.2∗∗ -1.2∗∗

(.66) (.64) (.57) (.56) (.59) (.59)

BL GP Mean .14∗∗∗ .22∗∗∗ .16∗∗∗

(.043) (.068) (.052)

District FE Yes Yes Yes Yes Yes Yes

Adj R-squared .09 .10 .01 .02 .06 .07Control Mean 8.2 8.2 7.9 7.9 17 17N. of cases 10504 10431 14514 14429 14163 14078

This table analyzes different labor supply outcomes for June using survey data. “Days worked on NREGS” is the number of

days an individual worked on NREGS during June 2012. “Days worked private sector” is the number of days an individual

worked for somebody else in June 2012. Finally, “Days unpaid/idle” is the sum of days an individual did unpaid work or

stayed idle in June 2012. “BL GP Mean” is the Gram Panchayat mean of the dependent variable at baseline. All regressions

include the first principal component of a vector of mandal characteristics used to stratify randomization as a control variable.

Standard errors clustered at mandal level are in parentheses. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05,∗∗∗p < 0.01. Note that observation count varies between columns due to differences in non-response rates between their

corresponding survey questions. An test of non-response rates by treatment status is shown in Table A.3.

31

Table 4: Migration

Did migrate? Days migrated Hhd sizeMigration common

in May?

(1) (2) (3) (4) (5) (6) (7) (8)

Treatment .024 .022 1.1 .75 .059 .054 .047 .049(.017) (.018) (4.9) (5.1) (.1) (.1) (.055) (.038)

BL GP Mean .093 .3 .044(.09) (.19) (.048)

Migration previously common .54∗∗∗

(.044)

District FE Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .03 .03 .01 .02 .02 .02 .12 .45Control Mean .075 .075 16 16 4.3 4.3 .21 .21N. of cases 4907 4873 4943 4909 4943 4909 809 808Level Hhd Hhd Hhd Hhd Hhd Hhd GP GP

This table illustrates treatment effects on various measures of migration using survey data as well as from a separately

conducted village survey. In columns 1 and 2, the outcome is an indicator for whether any household member stayed away

from home for the purpose of work during the last year. Last year refers to the respective time period from the point of

the endline survey (May 28 to July 15, 2012). In columns 3 and 4, the outcome is sum of all days any household member

stayed away from home for work, while in columns 5 and 6 the number of household members is the dependent variable.

“BL GP Mean” is the Gram Panchayat mean of the dependent variable at baseline. In columns 7-8, the outcome is an

indicator for whether it was common for workers to migrate out of the village in search of work during the month of May

ever since the implementation of NREGS. “Migration common prior to NREGS” is an indicator for whether the same type

of migration during the same time was common prior to the start of NREGS. Note that “prior to NREGS” does not refer to

the Smartcards intervention but rather to the rollout of the entire employment guarantee scheme. All regressions include the

first principal component of a vector of mandal characteristics used to stratify randomization as a control variable. Standard

errors clustered at mandal level are in parentheses. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

32

Table 5: Price levels

Log of Price Index Log of Individual Prices

(1) (2) (3)

Treatment .0039 .0047 -.014(.065) (.024) (.012)

District FE Yes Yes Yes

Item FE No No Yes

Adjusted R-squared 0.98 1.00 0.95N. of cases 60 60 18242Level Village Village Item x HouseholdDropped Goods? Yes No

In this table, we use NSS data on household consumption and prices to test for impacts on price levels for a subset of our

study sample. Columns 1 and 2 show analysis of a village-level price index constructed from NSS data. The outcome variable

is the log of the price index. Column 3 shows analysis on observed commodity prices at the household level. The outcome

is the log of prices. Standard errors clustered at mandal level are in parentheses. Statistical significance is denoted as:∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

33

Tab

le6:

Impac

tson

NR

EG

Spro

ject

counts

&ty

pes

#of

dis

tinct

pro

ject

s#

day

ssp

ent

wor

kin

gon

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Tot

alC

onst

r.Ir

rig.

Lan

ddev

.R

oads

Tot

alC

onst

r.Ir

rig.

Lan

ddev

.R

oads

Tre

atm

ent

-1.2

.11

.099

-1.2

∗61

-10

25-1

1916

1(2

.9)

(.44

)(.

31)

(2.7

)(.

12)

(441

)(1

03)

(247

)(4

35)

(112

)

BL

GP

Mea

n.6

1∗∗∗

.23∗

∗∗.0

67∗∗

∗1.

3∗∗∗

.099

∗∗∗

.4∗∗

∗.0

68∗

.23∗

∗∗.3

6∗∗∗

.11

(.07

5)(.

089)

(.02

1)(.

23)

(.02

6)(.

039)

(.03

9)(.

055)

(.06

3)(.

07)

Dis

tric

tF

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

Adj.

R-s

quar

ed.2

4.1

7.1

1.2

.13

.35

.3.4

7.2

4.1

1C

ontr

olM

ean

322.

81.

816

.51

6,53

949

21,

770

2,60

632

9N

.of

case

s2,

837

2,83

72,

837

2,83

72,

837

2,89

92,

837

2,83

72,

837

2,83

7

Th

ista

ble

anal

yze

sw

het

her

trea

tmen

tim

pac

ted

the

crea

tion

of

pro

du

ctiv

ity-e

nh

an

cin

gass

ets

thro

ugh

the

typ

eof

NR

EG

Sp

roje

cts

imp

lem

ente

dat

the

GP

-lev

el

usi

ng

NR

EG

Sm

ust

erro

lld

ata.

Th

eou

tcom

esin

colu

mn

s1-5

are

cou

nts

of

un

iqu

ep

roje

cts

inG

Ps

as

iden

tifi

edby

thei

rpro

ject

iden

tifi

cati

on

nu

mb

ers

inth

eN

RE

GS

mu

ster

roll

dat

a.T

he

rele

vant

per

iod

isth

een

dli

ne

stud

yp

erio

d(M

ay28

toJu

ly15,

2012).

Th

eca

tegori

esin

colu

mn

s2-5

(an

dals

oin

6-1

0)

are

base

don

manu

al

mat

chin

gof

pro

ject

titl

esto

any

ofth

efo

llow

ing

cate

gori

es:

con

stru

ctio

n,

irri

gati

on

,la

nd

dev

elop

men

t,ro

ad

s,p

lanta

tion

work

,d

esil

tin

gan

doth

erp

roje

cts

(wit

hth

e

latt

erth

ree

omit

ted

from

the

tab

le).

Inco

lum

ns

6-10

,th

eou

tcom

eva

riab

leis

the

sum

of

day

sw

ork

edw

ith

ina

GP

inth

ere

spec

tive

cate

gory

.T

he

“B

LG

PM

ean

”

isco

nst

ruct

edin

the

sam

ew

ayw

ith

the

refe

ren

ceb

ein

gth

eb

ase

lin

est

ud

yp

erio

d(M

ay31

toJu

ly4,

2010).

All

regre

ssio

ns

incl

ud

eth

efi

rst

pri

nci

pal

com

pon

ent

ofa

vect

orof

man

dal

char

acte

rist

ics

use

dto

stra

tify

ran

dom

izati

on

as

aco

ntr

ol

vari

ab

le.

Sta

nd

ard

erro

rscl

ust

ered

at

man

dal

leve

lare

inp

are

nth

eses

.S

tati

stic

al

sign

ifica

nce

isd

enot

edas

:∗ p

<0.

10,∗∗p<

0.0

5,∗∗∗ p

<0.

01.

34

Table 7: Expenditure

Short-term Expenditure Longer-term expenditureMonthly Per

Capita Expenditure

(1) (2) (3) (4) (5)

Treatment -108 -428 -24 -646 6653(1029) (1033) (3239) (3227) (12579)

BL GP Mean .051∗∗ -.003(.02) (.006)

District FE Yes Yes Yes Yes Yes

Adj R-squared .01 .02 .01 .01 .03Control Mean 18915 18915 38878 38878 189430N. of cases 4943 4909 4943 4909 479Recall period 1 month 1 month 1 year 1 year 1 monthSurvey NREGA NREGA NREGA NREGA NSS

This table analyzes different categories of household expenditure using data from our household survey and the NSS. The

outcome in columns 1 & 2 “Short-term expenditure” is the sum of spending on items such as produce, other food items,

beverages, fuel, entertainment, personal care items or rent measured from our survey data. The time frame for this category

is one month, which is also the time period that was referred to in the survey. The outcome in columns 3 & 4 “Longer-term

expenditure” comprises medical and social (e.g. weddings, funerals) expenses as well as tuition fees and durable goods. In the

survey, people were asked to indicate their spending on these items during the last year. “BL GP Mean” is the Gram Panchayat

mean of the dependent variable at baseline. The outcome in column 5 “Monthly Per Capita Expenditure” is measures at the

household-level in the NSS survey. Note that the households from the NSS data are not the same as our sample households.

All regressions from our survey data include the first principal component of a vector of mandal characteristics used to stratify

randomization as a control variable. Standard errors clustered at mandal level are in parentheses. Statistical significance is

denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

35

Table 8: Savings, assets and loans

Total savings (Rs.) Total loans (Rs.) Owns land (%)

(1) (2) (3) (4) (5) (6)

Treatment 1064 1120 11210∗∗ 11077∗∗ .056∗∗ .049∗∗

(859) (877) (4741) (4801) (.024) (.024)

BL GP Mean .027 .038 .21∗∗∗

(.071) (.039) (.042)

District FE Yes Yes Yes Yes Yes Yes

Adj R-squared .00 .00 .01 .01 .01 .03Control Mean 2966 2966 68108 68108 .59 .59N. of cases 4916 4882 4943 4909 4921 4887

This table analyzes household assets using survey data. “Total savings (Rs.)” is defined as the total amount of a household’s

current cash savings (in Rs), including money kept in bank accounts or Self-Help Groups. “Total loans (Rs.)” is the total

principal of the household’s five largest active loans (in Rs). “Owns land (%)” is an indicator for whether a household

reports to own any land. “BL GP Mean” is the Gram Panchayat mean of the dependent variable at baseline. All regressions

include the first principal component of a vector of mandal characteristics used to stratify randomization as a control variable.

Standard errors clustered at mandal level are in parentheses. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05,∗∗∗p < 0.01.

36

Tab

le9:

Lan

duti

liza

tion

and

irri

gati

on

Irri

gate

dla

nd

(ac.

)T

otal

land

(ac.

)T

otal

fallow

s(%

)N

on-a

gri.

use

(%)

Net

area

sow

n(%

)N

etar

eair

riga

ted

(%)

(1)

(2)

(3)

(4)

(5)

(6)

Tre

atm

ent

-5.4

-6.7

-.74

-.83

1.1

.001

8(5

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37

Table 10: Testing for existence of spatial spillovers

(a) Control

Wage realizations (Rs.) Days unpaid/idle

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

% GPs treated within R km 7.8 17∗∗ 22∗∗ 21∗∗ 24∗∗ -.76 -1.5 -1.7 -3.4∗ -5.4∗∗

(6.6) (6.9) (8.6) (8.6) (11) (1.2) (1.4) (1.8) (2) (2.2)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .06 .05 .05 .05 .05 .08 .07 .07 .07 .07Treatment Mean 128 128 128 128 128 17 17 17 17 17N. of cases 1850 2057 2063 2063 2063 3701 4076 4095 4095 4095% of sample 90 100 100 100 100 90 100 100 100 100

(b) Treatment

Wage realizations (Rs.) Days unpaid/idle

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

% GPs treated within R km 12∗∗∗ 11∗ 14∗ 14 15 -1.1 -2.3∗∗ -3.1∗∗ -3.5∗∗ -4.3∗∗

(4.4) (5.9) (7.5) (10) (12) (.81) (1.1) (1.3) (1.5) (1.8)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .08 .08 .08 .08 .08 .07 .07 .07 .07 .07Control Mean 134 134 134 134 134 16 16 16 16 16N. of cases 4710 4992 5129 5182 5206 9021 9613 9882 9969 10000% of sample 90 96 99 100 100 90 96 99 100 100

(c) Pooled

Wage realizations (Rs.) Days unpaid/idle

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

% GPs treated within R km 9.9∗∗∗ 12∗∗ 13∗∗ 13∗ 16∗ -1.1 -2∗∗ -2.5∗∗ -3.3∗∗ -4.2∗∗∗

(3.6) (4.8) (6.2) (7.8) (9.5) (.72) (.91) (1.1) (1.3) (1.5)Treatment 8.5∗∗ 8.3∗∗ 8∗∗ 7.4∗∗ 7.3∗∗ -1.5∗∗ -1.5∗∗ -1.5∗∗ -1.5∗∗∗ -1.5∗∗∗

(3.5) (3.5) (3.5) (3.6) (3.6) (.61) (.58) (.58) (.58) (.57)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .07 .07 .07 .07 .07 .07 .07 .07 .07 .07Control Mean 128 128 128 128 128 17 17 17 17 17N. of cases 6560 7049 7192 7245 7269 12722 13689 13977 14064 14095% of sample 90 97 99 100 100 90 97 99 100 100

This table show the impact of NRp on treatment effects at radius 10, 15, 20, 25, and 30 kilometers on private wages and days

worked using survey datas. All analysis was conducted separately for (a) control and (b) treatment, and then for (c) the

pooled sample. In each panel, the outcome “Wage realizations” in columns 1-5 is the average daily wage (in Rs.) an individual

received while working for someone else in June 2012 (endline). The outcome “Days worked private sector” in columns 6-10

is the number of days an individual worked for somebody else in June 2012 (endline). The “% GPs treated within R km”

is NRp , or the ratio of the number of GPs in treatment mandals within a given radius R km over the total GPs within wave

1, 2 or 3 mandals. Note that wave 2 mandals are included in the denominator, and that same-mandal GPs are excluded in

both the denominator and numerator. The entire GP sample used in randomization are included. Standard errors clustered

at mandal level are in parentheses. All regressions include the first principal component of a vector of mandal characteristics

used to stratify randomization as a control variable. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

Note that the variation in observation counts is due to the construction of the spatial exposure. Observations with no value

for NRp at a given radius R will not be included for the respective regression.

38

Table 11: Estimating total treatment effects including spillovers (IV)

(a) Wage

Wage realizations (Rs.) Reservation wage (Rs.)

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

Treatment -10 4.2 4.9 5.6 7.6 1.3 6.1 5.4 4.8 2(9.3) (9) (9.2) (9.4) (10) (6.7) (6.2) (6.3) (6.9) (7.2)

% GPs treated within R km 13 31∗ 26 21 27 9.6 12 8.4 5 -.39(16) (17) (18) (15) (17) (14) (12) (13) (12) (11)

% GPs treated within R km X Treatment 16 -13 -7.2 -4.6 -9 -2.5 -8.2 -3.9 -1.1 6.2(20) (21) (22) (21) (23) (16) (15) (16) (16) (16)

District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Total Treatment Effect 18∗∗∗ 23∗∗∗ 24∗∗∗ 22∗∗ 26∗∗ 8.5∗∗ 9.8∗∗ 9.9∗ 8.7 7.8SE (5.6) (6.6) (7.8) (9.1) (11) (4) (4.6) (5.4) (6.5) (7.4)F-stat for % GPs treated within R km 355 205 259 340 311 344 205 267 367 321F-stat for % GPs treated within R km X Treatment 224 156 165 171 115 226 165 186 187 118Control Mean 128 128 128 128 128 97 97 97 97 97N. of cases 6560 7049 7192 7245 7269 11614 12498 12732 12818 12852

(b) Labor

Days worked private sector Days idle/unpaid

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

Treatment .31 -.27 .037 .27 .48 .33 .79 .68 -.25 -.86(1.6) (1.6) (1.7) (1.7) (1.8) (1.6) (1.5) (1.5) (1.4) (1.5)

% GPs treated within R km 3.5 3.1 3.4 4.1 5.1 -2 -2.7 -2.5 -4 -5.8∗∗

(3.1) (2.9) (3) (3.1) (3.4) (3.3) (3) (3) (2.8) (2.8)% GPs treated within R km X Treatment -2.2 -.53 -.64 -.82 -1.2 -.98 -1.8 -2.5 -.86 .48

(3.6) (3.6) (3.8) (4) (4.3) (3.7) (3.6) (3.5) (3.4) (3.4)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Total Treatment Effect 1.6 2.3∗ 2.8∗ 3.5∗∗ 4.4∗∗ -2.7∗∗∗ -3.7∗∗∗ -4.3∗∗∗ -5.1∗∗∗ -6.2∗∗∗

SE (1.1) (1.3) (1.5) (1.7) (1.9) (1) (1.3) (1.5) (1.6) (1.8)F-stat for % GPs treated within R km 362 206 242 362 360 365 202 236 360 367F-stat for % GPs treated within R km X Treatment 247 158 146 166 136 246 156 143 168 144Control Mean 7.9 7.9 7.9 7.9 7.9 17 17 17 17 17N. of cases 13008 13995 14300 14397 14441 12722 13689 13977 14064 14095

(c) Total Income

Total Income

R = 10 R = 15 R = 20 R = 25 R = 30

Treatment 6269 18794 19742 10544 5341(11543) (11846) (12616) (11412) (11225)

% GPs treated within R km 28562 34327 34456 15459 2569(30417) (27451) (30555) (24440) (19364)

% GPs treated within R km X Treatment -16074 -33250 -31296 -4914 10254(33624) (31336) (34779) (29362) (25663)

District FE Yes Yes Yes Yes Yes

Total Treatment Effect 18757∗∗∗ 19872∗∗ 22903∗∗ 21089∗ 18164SE (7140) (8944) (11560) (11973) (12448)F-stat for % GPs treated within R km 330 216 255 361 361F-stat for % GPs treated within R km X Treatment 225 166 159 177 150Control Mean 71935 71935 71935 71935 71935N. of cases 4420 4767 4864 4892 4903

This table provides estimates for total treatment effects using comprehensive spatial exposure NRp while instrumenting for

exogenous spatial exposure NRp for (a) wage outcomes, (b) labor outcomes, and (c) total income. Each structural equation

contains a treatment indicator, NRp , and an interaction between NR

p and treatment. The NRp is the ratio of the number of

GPs in treatment mandals within a given radius R km over the total GPs within wave 1, 2 or 3 mandals. The instruments

used in the first stage are NRp and the interaction between NR

p and treatment. The total treatment effect estimate reported

in the second section of the table is the sum of the coefficients for those three variables. All regressions also include the first

principal component of a vector of mandal characteristics used to stratify randomization as a control variable. Statistical

significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

39

Andhra PradeshStudy Districts and MandalsGroup

Treatment

Wave 2 & non-study

Control

Figure 1: Study districts with treatment and control mandals

This map (reproduced from Muralidharan et al. (2016)) shows the 8 study districts - Adilabad, Anantapur, Kadapa, Kham-

mam, Kurnool, Nalgonda, Nellore, and Vizianagaram - and the assignment of mandals (sub-districts) within those districts

to one of four study conditions. Mandals were randomly assigned to one of three waves: 112 to wave 1 (treatment), 139

to wave 2, and 45 to wave 3 (control). Wave 2 was created as a buffer to maximize the time between program rollout in

treatment and control waves; our study did not collect data on these mandals. A “non-study” mandal is a mandal that

did not enter the randomization process because the Smartcards initiative had already started in those mandals (109 out of

405). Randomization was stratified by district and by a principal component of mandal characteristics including population,

literacy, proportion of Scheduled Caste and Tribe, NREGS jobcards, NREGS peak employment rate, proportion of SSP

disability recipients, and proportion of other SSP pension recipients.

40

Figure 2: Effects on income: SECC

Less than Rs. 5000

Between Rs. 5000 and Rs. 10000

More than Rs.10000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Control Treatment

The figure shows the proportions of households in each of the three income brackets in the Socioeconomic and Caste Census

(SECC) 2011 (enumeration started in late June 2011) by treatment and control households. The error bars indicate a 95%

confidence interval around each proportion. The standard error for every category is < 0.001, so these intervals are not visible

on the scale of the figure.

41

Figure 3: Annualized per capital income

0.00

0.25

0.50

0.75

1.00

0 20000 40000 60000 80000Annualized Per Capita Income (in Rps.)

Cum

ulat

ive

dens

ity

Control Treatment

This figure shows an empirical cdf of total annualized per capita income by household for treatment and control groups using

data from the endline household survey. Annualized per capita income was calculated by dividing the total annual household

income by number of household members. The vertical line indicates the annualized per capita poverty line (860 Rs. per

month or 10,320 Rs. per year).

Figure 4: Private sector work in June

0.00

0.25

0.50

0.75

1.00

0 10 20 30Days of private sector paid work

Cum

ulat

ive

dens

ity

Control Treatment

This figure shows an empirical cdf of the number of days an individual worked for someone else during June 2012, using data

from the endline household survey. The dashed lines indicate in-sample means (not weighted by sampling probabilities) in

treatment and control, respectively.

42

Figure 5: Constructing measures of exposure to spatial spillovers

(a) Control

R

p

(b) Treatment

R

p

This figure illustrates the construction of measures of spatial exposure to treatment for a given panchayat p (denoted by

the black X symbol) and radius R in a control mandal (a) and a treatment mandal (b). Dark (light) blue dots represent

treatment (control) panchayats; black lines represent mandal borders . As in the text, Nrp is the fraction of GPs within a

radius R of panchayat p which were assigned to treatment. Nrp is the fraction of GPs within a radius R of panchayat p and

within a different mandal which were assigned to treatment. In Panel (a) these measures are Nrp = 1

4 and NRp = 1

2 , while in

Panel (b) they are Nrp = 3

4 and NRp = 1

2 .

43

Table A.1: Baseline balance in administrative data

Treatment Control Difference p−value

(1) (2) (3) (4)

Official records from GoAP in 2010

% population working .53 .52 .0062 .47% male .51 .51 .00023 .82% literate .45 .45 .0043 .65% SC .19 .19 .0025 .81% ST .1 .12 -.016 .42Jobcards per capita .54 .55 -.0098 .63Pensions per capita .12 .12 .0015 .69% old age pensions .48 .49 -.012 .11% weaver pensions .0088 .011 -.0018 .63% disabled pensions .1 .1 .0012 .72% widow pensions .21 .2 .013∗∗ .039

2011 census rural totals

Population 45,580 45,758 -221 .91% population under age 6 .11 .11 -.00075 .65% agricultural laborers .23 .23 -.0049 .59% female agricultural laborers .12 .12 -.0032 .52% marginal agricultural laborers .071 .063 .0081 .14

2001 census village directory

# primary schools per village 2.9 3.2 -.28 .3% village with medical facility .67 .71 -.035 .37% villages with tap water .59 .6 -.007 .88% villages with banking facility .12 .16 -.034∗∗ .021% villages with paved road access .8 .81 -.0082 .82Avg. village size in acres 3,392 3,727 -336 .35

This table compares official data on baseline characteristics across treatment and control (T & C) mandals. Column 3 reports

the difference in T & C means, while column 4 reports the p-value on the treatment indicator from simple regressions of

the outcome with district fixed effects as the only controls. A “jobcard” is a household level official enrollment document

for the NREGS program. “SC” (“ST”) refers to Scheduled Castes (Tribes). “Old age”, “weaver”, “disabled” and “widow”

are different eligibility groups within the SSP administration. “Working” is defined as the participation in any economically

productive activity with or without compensation, wages or profit. “Main” workers are defined as those who engaged in

any economically productive work for more than 183 days in a year. “Marginal” workers are those for whom the period

they engaged in economically productive work does not exceed 182 days. The last set of variables is taken from 2001 census

village directory which records information about various facilities within a census village (the census level of observation).

“# primary schools per village” and “Avg. village size in acres” are simple mandal averages (others are simple percentages)

of the respective variable. Sampling weights are not needed since all villages within a mandal are used. Note that we did

not have this information available for the 2011 census and hence used 2001 census data. Statistical significance is denoted

as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01

44

Table A.2: Baseline balance in survey data

Treatment Control Difference p-value

(1) (2) (3) (4)

Hhd members 4.8 4.8 .022 .89BPL .98 .98 .0042 .73Scheduled caste .22 .25 -.027 .35Scheduled tribe .12 .11 .0071 .81Literacy .42 .42 .0015 .93Annual income 41,482 42,791 -1,290 .52Total annual expenditure 687,128 657,228 26,116 .37Short-term expenditure 52,946 51,086 1,574 .45Longer-term expenditure 51,947 44,390 7,162 .45Pay to work/enroll .011 .0095 .00099 .82Pay to collect .058 .036 .023 .13Ghost Hhd .012 .0096 .0019 .75Time to collect 156 169 -7.5 .62Owns land .65 .6 .058 .06∗

Total savings 5,863 5,620 3.7 1.00Accessible (in 48h) savings 800 898 -105 .68Total loans 62,065 57,878 5,176 .32Owns business .21 .16 .048 .02∗∗

Number of vehicles .11 .12 -.014 .49Average payment delay 28 23 .036 .99Payment delay deviation 11 8.8 -.52 .72Official amount 172 162 15 .45Survey amount 177 189 -10 .65Leakage -5.1 -27 25 .15NREGS availability .47 .56 -.1 .02∗∗

Hhd doing NREGS work .43 .42 .0067 .85NREGS days worked, June 8.3 8 .33 .65Private sector days worked, June 4.8 5.3 -.49 .15Days unpaid/idle, June 22 22 .29 .47Average daily wage private sector, June 96 98 -3.7 .34Daily reservation wage, June 70 76 -6.8 .03∗∗

NREGS hourly wage, June 13 14 -1.3 .13NREGS overreporting .15 .17 -.015 .55# addi. days hhd wanted NREGS work 15 16 -.8 .67

This table compares baseline characteristics across treatment and control mandals from our survey data. Columns 3 reports

the difference in treatment and control means, while columns 4 reports the p-value on the treatment indicator from a simple

regressions of the outcome with district fixed effects as the only controls. “BPL” is an indicator for households below

the poverty line. “Accessible (in 48h) savings” is the amount of savings a household could access within 48h. “NREGS

availability” is an indicator for whether a household believes that anybody in the village could get work on NREGS when

they want it. Standard errors are clustered at the mandal level. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05,∗∗∗p < 0.01.

45

Table A.3: Non-response and response composition rates by treatment status

(a) Full sample

Treatment Control Difference p-value N

Wage realizations (Rs.) .013 .011 .0018 .59 7417Reservation wage (Rs.) .4 .39 .0072 .64 21435Days worked private sector .33 .3 .031∗∗ .038 21435Days unpaid .36 .34 .021 .11 21435Days idle .35 .33 .02 .12 21435Days unpaid/idle .34 .33 .019 .13 21435Days worked > 0 .52 .49 .028 .21 14512Avg. wage ≥ reservation wage .98 .99 -.0029 .57 7286

(b) People of working age (18-65)

Treatment Control Difference p-value N

Wage realizations (Rs.) .013 .012 .0014 .68 7101Reservation wage (Rs.) .15 .15 -.002 .92 14425Days worked private sector .085 .082 .0034 .63 14425Days unpaid .098 .097 .0016 .86 14425Days idle .088 .088 -.000085 .99 14425Days unpaid/idle .086 .087 -.00095 .89 14425Days worked > 0 .54 .52 .016 .44 13210Avg. wage ≥ reservation wage .98 .99 -.0025 .62 6973

This table analyzes response rates to key questions regarding labor market outcomes. Columns 1 & 2 show the proportion

of missing answers to the respective question in treatment and control. Column 3 reports the regression-adjusted treatment

difference between treatment and control from a linear regression with the first principal component of a vector of mandal

characteristics used to stratify randomization and district fixed effects as the only control variables. Column 4 reports the

p-value of a two-sided t-test with the null-hypothesis being that the difference (Column 3) is equal to 0. Column 5 reports

the number of individuals who ought to have answered the question. “Wage realizations” is the average daily wage (in Rs.)

an individual received while working for someone else in June 2012. “Reservation wage” is an individual’s reservation wage

(in Rs.) at which he or she would have been willing to work for someone else in June 2012. The outcome is based on an a

question in which the surveyor asked the respondent whether he or she would be willing to work for Rs. 20 and increased

this amount in increments of Rs. 5 until the respondent answered affirmatively. “Days worked private sector” is the number

of days an individual worked for somebody else in June 2012. “Days idle” and “Days unpaid” is the number of days an

individual stayed idle or did unpaid work in June 2012. “Days unpaid/idle” is the sum of the latter two variables. Note that

the base group for “Wage realizations” is the set of individuals who reported a strictly positive number of days worked for

someone else. Similarly, the base group for “Days worked > 0” is the set of individuals that reported non-missing values for

days worked for someone else. Panel b) restricts the sample to individuals of age between 18 and 65 years. Standard errors

clustered at mandal level are in parentheses. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

46

Table A.4: Income (Survey Data), with censoring

Total income NREGA Ag. labor Other labor Farm Business Misc

(1) (2) (3) (4) (5) (6) (7) (8)

Treatment 9511∗∗ 8761∗∗ 905 3675∗∗ 4471∗∗∗ 1738 -773 293(3723) (3722) (589) (1485) (1585) (2704) (1359) (2437)

BL GP Mean .025(.071)

District FE Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .04 .04 .02 .04 .03 .01 .01 .01Control Mean 69122 69122 4743 14784 9315 21708 6620 14765N. of cases 4908 4874 4931 4932 4932 4932 4932 4932

In this table, we perform a robustness check for Table 1b, which shows treatment effects on various types of income using

annualized household data from the household survey. We censor observations in the top .5% percentile of total annualized

income in treatment and control households. “BL GP Mean” is the Gram Panchayat mean of the dependent variable at

baseline. “NREGS” is the earnings from NREGS. “Ag labor” captures income from agricultural work for someone else, while

“Other labor” is income from physical labor for someone else. “Farm” combines income from a households’ own land and

animal husbandry, while“Business” captures income from self-employment or through a household’s own business. “Other”

is the sum of household income not captured by any of the other categories. Note that the income categories were not as

precisely measured at baseline which is why we cannot include the respective lag of the dependent variable “ BL GP Mean”.

All regressions include the first principal component of a vector of mandal characteristics used to stratify randomization

as a control variable. Standard errors clustered at mandal level are in parentheses. Statistical significance is denoted as:∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

47

Tab

leA

.5:

Het

erog

enei

tyin

inco

me

gain

sby

hou

sehol

dch

arac

teri

stic

s

Tot

alin

com

eSSP

Ag.

lab

orO

ther

lab

orF

arm

Busi

nes

sM

isc

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

Tre

atm

ent

1243

6∗∗

1115

2∗∗

287

402

4528

∗∗∗

4335

∗∗∗

5206

∗∗∗

5306

∗∗∗

3096

1423

-694

-128

1-2

213

08(5

776)

(498

0)(6

58)

(487

)(1

592)

(148

1)(1

610)

(176

1)(3

362)

(299

3)(1

611)

(160

5)(3

264)

(251

0)

Hhd

frac

tion

elig

ible

for

SSP

-377

08∗∗

∗24

81∗∗

-605

3∗∗∗

-682

1∗∗∗

-130

72∗∗

∗-5

679∗

∗∗-1

1624

∗∗

(735

1)(1

009)

(196

8)(2

124)

(498

3)(1

817)

(445

9)

Hhd

frac

tion

elig

ible

for

SSP

XT

reat

men

t-1

4538

3234

-461

2∗-3

977

-735

4-4

3617

75(1

0185

)(3

679)

(256

4)(3

537)

(628

1)(2

326)

(577

0)

Hea

dof

hhd

isw

idow

-178

21∗∗

∗-5

426

72-1

334

-149

95∗∗

∗-5

853∗

∗∗-3

12(6

372)

(657

)(2

645)

(222

8)(2

907)

(172

6)(3

690)

Hea

dof

hhd

isw

idow

XT

reat

men

t-8

804

3375

-481

6-6

076∗

1010

3157

-302

8(8

533)

(261

1)(3

755)

(318

1)(3

930)

(216

0)(4

363)

Dis

tric

tF

EY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

es

BL

GP

Mea

nY

esY

esN

oN

oN

oN

oN

oN

oN

oN

oN

oN

oN

oN

oA

dj

R-s

quar

ed.0

57.0

42.0

28.0

21.0

48.0

38.0

31.0

28.0

23.0

22.0

11.0

093

.01

.006

9C

ontr

olM

ean

7193

571

935

6079

6079

1478

414

784

9315

9315

2170

821

708

6620

6620

1342

813

428

N.

ofca

ses

4898

4836

4932

4870

4932

4870

4932

4870

4932

4870

4932

4870

4932

4870

Inth

ista

ble

we

anal

yze

wh

eth

era

hou

seh

old

s’s

pot

enti

alab

ilit

yto

per

form

manu

al

lab

or

aff

ects

the

ou

tcom

esof

the

inte

rven

tion

.W

eco

nsi

der

vari

ou

sty

pes

of

inco

me

usi

ng

annu

aliz

edh

ouse

hol

dd

ata

from

the

end

lin

eh

ouse

hold

surv

eyfo

rth

eN

RE

GS

sam

ple

.“B

LG

PM

ean”

isth

eG

ram

Pan

chay

at

mea

nof

the

dep

end

ent

vari

ab

le

atb

asel

ine

(du

eto

avai

lab

ilit

yon

lyin

clu

ded

inco

lum

ns

1-2

).“N

RE

GS”

are

earn

ings

from

the

NR

EG

Sp

rogra

m.

“A

g.

lab

or”

cap

ture

sin

com

efr

om

agri

cult

ura

lw

ork

for

som

eon

eel

se,

wh

ile

“Oth

erla

bor

”is

inco

me

from

physi

cal

lab

or

for

som

eon

eel

se.

“F

arm

”co

mb

ines

inco

me

from

ah

ou

seh

old

s’ow

nla

nd

an

dan

imal

hu

sban

dry

,

wh

ile“

Bu

sin

ess”

cap

ture

sin

com

efr

omse

lf-e

mp

loym

ent

or

thro

ugh

ah

ou

seh

old

’sow

nb

usi

nes

s.“O

ther

”is

the

sum

of

hou

seh

old

inco

me

not

cap

ture

dby

any

of

the

oth

erca

tego

ries

.In

pan

ela)

,“H

hd

frac

tion

elig

ible

for

SS

P”

isth

efr

act

ion

of

hou

seh

old

mem

ber

sw

ho

iden

tify

as

elig

ible

for

SS

P,

thou

gh

they

may

not

act

uall

y

rece

ive

pen

sion

.“H

hd

frac

tion

elig

ible

for

SS

Px

Tre

atm

ent”

an

d“H

ead

of

hh

dis

wid

owx

Tre

atm

ent”

are

inte

ract

ion

term

sco

nst

ruct

edby

mult

iply

ing

the

resp

ecti

ve

vari

able

wit

hth

eb

inar

ytr

eatm

ent

ind

icat

or.

Inp

anel

b),

“D

isab

led

”is

an

indic

ato

rfo

rw

het

her

this

sam

ple

dp

ensi

on

erin

this

hou

seh

old

isel

igib

lefo

rd

isab

led

per

son

sp

ensi

ons

(Rs.

500

per

mon

that

the

tim

eof

the

stu

dy).

“D

isab

led

or

OA

P”

ind

icate

sh

ou

seh

old

sin

wh

ich

the

sam

ple

dp

ensi

on

eris

elig

ible

for

dis

ab

led

per

son

s

or

old

age

pen

sion

s(a

tth

eti

me

ofth

est

ud

y,R

s.20

0fo

rp

eop

leof

age

65-7

9an

dR

s.500

for

peo

ple

ab

ove

79

per

month

).A

gain

,b

oth

thes

eva

riab

les

are

als

o

incl

ud

edas

ali

nea

rin

tera

ctio

nw

ith

the

trea

tmen

tin

dic

ato

r.N

ote

that

pen

sion

sch

eme

was

iden

tifi

edfr

om

offi

cial

rost

ers

rath

erth

an

from

the

hou

seh

old

surv

ey.

All

regr

essi

ons

incl

ud

eth

efi

rst

pri

nci

pal

com

pon

ent

ofa

vect

or

of

man

dal

chara

cter

isti

csu

sed

tost

rati

fyra

nd

om

izati

on

as

aco

ntr

ol

vari

ab

le.

Sta

nd

ard

erro

rscl

ust

ered

atm

and

alle

vel

are

inp

aren

thes

es.

Sta

tist

ical

sign

ifica

nce

isd

enote

das:∗ p

<0.

10,∗∗p<

0.05,∗∗∗ p

<0.

01.

48

Table A.6: Robustness checks for private sector wage outcomes

(a) Includes Wage Outliers

Wage realizations (Rs.) Reservation wage (Rs.)

(1) (2) (3) (4)

Treatment 5.6 6.8∗ 5 5.6∗

(4.1) (4.1) (3.3) (3.2)BL GP Mean .15∗∗∗ .091∗∗

(.054) (.039)District FE Yes Yes Yes Yes

Adj R-squared .05 .05 .03 .03Control Mean 131 131 99 99N. of cases 7326 7112 12955 12841

(b) Restricts sample to age 18-65

Wage realizations (Rs.) Reservation wage (Rs.)

(1) (2) (3) (4)

Treatment 6.6∗ 7.9∗∗ 5∗ 5.7∗

(3.7) (3.8) (3) (2.9)BL GP Mean .16∗∗∗ .1∗∗∗

(.049) (.033)District FE Yes Yes Yes Yes

Adj R-squared .07 .07 .05 .06Control Mean 129 129 98 98N. of cases 6989 6782 12227 12124

(c) Excludes respondents who did not work in June

Wage realizations (Rs.) Reservation wage (Rs.)

(1) (2) (3) (4)

Treatment 6.4∗ 7.6∗∗ 4.7 5.4∗

(3.6) (3.6) (2.9) (2.8)BL GP Mean .16∗∗∗ .1∗∗∗

(.048) (.033)District FE Yes Yes Yes Yes

Adj R-squared .07 .07 .05 .05Control Mean 128 128 97 97N. of cases 7256 7043 12859 12745

In this table, we perform robustness checks for Table 2, which shows treatment effects on wage outcomes from the private

labor market using survey data . In Panel a), we include observations in the top .5% percentile of the respective wage

outcome in treatment and control are included in all regressions. In Panel b), the sample is restricted to respondents in age

18 to 65 and exclude observations in the top .5% percentile of the respective wage outcome in treatment and control for all

regressions. In Panel c), we drop observations from survey respondents who have did not work in the month of June and

exclude observations in the top .5% percentile of the respective wage outcome in treatment and control for all regressions.

The outcome in columns 1-4 is the average daily wage (in Rs.) an individual received while working for someone else in

June 2012. In columns 5-8, the outcome is an individual’s reservation wage (in Rs.) at which he or she would have been

willing to work for someone else in June 2012. The outcome is based on an a question in which the surveyor asked the

respondent whether he or she would be willing to work for Rs. 20 and increased this amount in increments of Rs. 5 until

the respondent answered affirmatively. “BL GP Mean” is the Gram Panchayat mean of the dependent variable at baseline

(May 31 to July 4, 2010). All regressions include the first principal component of a vector of mandal characteristics used to

stratify randomization as control variable. Standard errors clustered at mandal level in parentheses. Statistical significance

is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

49

Table A.7: Predictors of differential non-response and response composition

Missing response toDays worked

> 0Wage Realizations> Res. wage

(1) (2) (3) (4) (5) (6)Wage realizations Reservation wage Days worked Days idle/unpaid

Member is female -.0051 -.0032 -.0016 .0069 -.022 .0069(.0047) (.017) (.015) (.015) (.021) (.0063)

Above median hhd income -.0047 .018 .033∗ .011 .05 -.0045(.0055) (.017) (.019) (.016) (.033) (.0094)

Hhd is ST, SC or OBC .023 .022 .031 .012 -.0042 -.011(.016) (.03) (.025) (.025) (.045) (.012)

Hhd below BPL -.012 .024 .045 .022 .091∗∗ -.0029(.012) (.033) (.031) (.029) (.043) (.0084)

Any hhd member can read .024∗∗ -.012 .018 -.0056 .013 .0069(.011) (.023) (.021) (.019) (.04) (.017)

Head of hhd is widow -.0017 .013 .012 .011 -.022 -.0071(.0069) (.028) (.024) (.021) (.035) (.014)

Carded GP .0031 .0054 .019 .0062 .034∗ -.0038(.0036) (.013) (.014) (.011) (.019) (.0056)

District FE Yes Yes Yes Yes Yes Yes

Control Mean .011 .39 .3 .33 .49 .99Avg. number of cases 7385 21349 21349 21349 14456 7255

This table analyzes interaction effects between household or GP characteristics and treatment status for individual non-

response and strictly-positive response rates in private labor market outcomes. In columns 1-4, the outcome in a binary

indicator for whether an a survey response is missing when it should not. “Wage realizations” is the average daily wage (in

Rs.) an individual received while working for someone else in June 2012. “Reservation wage” is an individual’s reservation

wage (in Rs.) at which he or she would have been willing to work for someone else in June 2012. The outcome is based

on an a question in which the surveyor asked the respondent whether he or she would be willing to work for Rs. 20 and

increased this amount in increments of Rs. 5 until the respondent answered affirmatively. “Days worked private sector” is

the number of days an individual worked for somebody else in June 2012. “Days unpaid/idle” is the number of days an

individual stayed idle or did unpaid work in June 2012. Note that every cell in the regression table reports the coefficient of

an interaction term (except “Carded GP”, see below) of the reported variable with the treatment indicator from a separate

regression that includes the raw respective variable, the treatment indicator as well as a vector of mandal characteristics

used to stratify randomization and district fixed effects as covariates. In columns 5-6, we look examine two types of response

patterns. “Days worked private sector > 0” is an indicator for whether an individual worked in the private sector in June

2012. “Wage realizations > Reservation wage” is an indicator for whether an individual’s reported average daily wage was

great than his/her reservation wage. “Above median hhd income” is an indicator for whether an individual belongs to an

household with total annualized income above the sample median. “Hhd is ST, SC or OBC” indicates household members

belonging to Scheduled Castes/Tribes or Other Backward Castes - historically discriminated against section of the population

- while “Hhd below BPL” indicates individuals from household living below the poverty line. that “CardedGP” is a simple

indicator variable (no interaction effect) for whether a household lives in a GP that has moved to Smartcard-based payment,

which usually happens once 40% of beneficiaries have been issued a card. No interaction effect is included because all carded

GPs are in treatment mandals (by experimental design). Finally, note that each column reports results from 7 different

regressions and there is no single number of observations. This table reports the average number of observations across all

regressions in a column. Standard errors clustered at mandal level are in parentheses. Statistical significance is denoted as:∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

50

Tab

leA

.8:

Bas

elin

ebal

ance

inco

mpre

hen

sive

mea

sure

ofnei

ghb

or’s

trea

tmen

t

Wag

ere

aliz

atio

ns

Res

erva

tion

wag

eD

ays

wor

ked

pri

vate

sect

orD

ays

idle

/unpai

dT

otal

inco

me

(1)

(2)

(3)

(4)

(5)

%G

Ps

trea

ted

wit

hin

10km

-.48

-3.2

-.68

.14

5133

(6.8

)(5

.1)

(.64

)(1

)(4

572)

%G

Ps

trea

ted

wit

hin

20km

1.1

-1.8

-.69

.001

736

83(9

.3)

(6.7

)(.

73)

(1.6

)(5

992)

%G

Ps

trea

ted

wit

hin

30km

-1.2

1-.

32-.

86-3

137

(12)

(7.4

)(.

98)

(2.3

)(7

699)

Dis

tric

tF

EY

esY

esY

esY

esY

es(1

)(2

)(3

)(4

)(5

)C

ontr

olM

ean

104

785.

122

4473

2L

evel

Indiv

.In

div

.In

div

.In

div

.H

hd

Inth

ista

ble

we

anal

yze

bas

elin

eb

alan

ceof

key

outc

omes

wit

hre

spec

tto

NR p

for

GP

sin

trea

tmen

tm

an

dals

.E

ach

cell

show

sth

ere

spec

tive

coeffi

cien

tfr

om

ase

para

te

regr

essi

onw

her

eth

eou

tcom

eis

give

nby

the

colu

mn

hea

der

.“W

age

reali

zati

on

s”th

eav

erage

daily

wage

(in

Rs.

)an

ind

ivid

ual

rece

ived

wh

ile

work

ing

for

som

eon

e

else

inJu

ne

2012

.“R

eser

vati

onw

age”

isan

ind

ivid

ual

’sre

serv

ati

on

wage

(in

Rs.

)at

wh

ich

he

or

she

wou

ldh

ave

bee

nw

illi

ng

tow

ork

for

som

eon

eel

sein

Ju

ne

2012.

Th

eou

tcom

eis

bas

edon

ana

qu

esti

onin

whic

hth

esu

rvey

or

ask

edth

ere

spon

den

tw

het

her

he

or

she

wou

ldb

ew

illi

ng

tow

ork

for

Rs.

20

an

din

crea

sed

this

am

ou

nt

inin

crem

ents

ofR

s.5

unti

lth

ere

spon

den

tan

swer

edaffi

rmati

vely

.“D

ays

work

edp

riva

tese

ctor”

isth

enu

mb

erof

day

san

ind

ivid

ual

work

edfo

rso

meb

od

yel

sein

Ju

ne

2012

.“T

otal

inco

me”

isto

tal

annu

aliz

edh

ouse

hol

din

com

e,w

her

eth

eto

p.5

%of

ob

serv

ati

ons

are

sep

ara

tely

trim

med

intr

eatm

ent

an

dco

ntr

ol.

Th

e“%

GP

s

trea

ted

wit

hin

x”

isN

R p,

orth

era

tio

ofth

enu

mb

erof

GP

sin

trea

tmen

tm

an

dals

wit

hin

radiu

sx

km

over

the

tota

lG

Ps

wit

hin

wav

e1,

2or

3m

an

dals

.N

ote

that

wav

e

2m

and

als

are

incl

ud

edin

the

den

omin

ator

,an

dth

atsa

me-

man

dal

GP

sare

incl

ud

edin

both

the

den

om

inato

ran

dnu

mer

ato

r.N

ote

that

each

cell

show

sa

sep

ara

te

regr

essi

onof

the

outc

ome

wit

hth

e“%

GP

str

eate

dw

ith

inx”

an

dd

istr

ict

fixed

effec

tsas

the

on

lyco

vari

ate

s.F

inall

y,n

ote

that

each

colu

mn

rep

ort

sre

sult

sfr

om

7

diff

eren

tre

gres

sion

san

dth

ere

isn

osi

ngl

enu

mb

erof

obse

rvati

on

s.T

his

tab

lere

port

sth

eav

erage

nu

mb

erof

ob

serv

ati

on

sacr

oss

all

regre

ssio

ns

ina

colu

mn

.S

tand

ard

erro

rscl

ust

ered

atm

and

alle

vel

are

inpar

enth

eses

.S

tati

stic

al

sign

ifica

nce

isd

enote

das:∗ p

<0.

10,∗∗p<

0.05,∗∗∗ p

<0.

01.

51

Tab

leA

.9:

Bas

elin

ebal

ance

inex

per

imen

tal

mea

sure

ofnei

ghb

or’s

trea

tmen

t

Wag

ere

aliz

atio

ns

Res

erva

tion

wag

eD

ays

wor

ked

pri

vate

sect

orD

ays

idle

/unpai

dT

otal

inco

me

(1)

(2)

(3)

(4)

(5)

%G

Ps

trea

ted

wit

hin

10km

3.3

1.4

-.27

.34

-377

(4.1

)(3

.3)

(.42

)(.

53)

(287

2)%

GP

str

eate

dw

ithin

20km

22.

4-.

42.1

921

77(6

.6)

(4.8

)(.

64)

(.95

)(4

826)

%G

Ps

trea

ted

wit

hin

30km

1.3

5.6

.026

-.54

-323

2(9

.6)

(6.1

)(.

89)

(1.7

)(6

738)

Dis

tric

tF

EY

esY

esY

esY

esY

es(1

)(2

)(3

)(4

)(5

)C

ontr

olM

ean

104

785.

122

4473

2L

evel

Indiv

.In

div

.In

div

.In

div

.H

hd

Inth

ista

ble

we

anal

yze

bas

elin

eb

alan

ceof

key

outc

omes

wit

hre

spec

tto

NR p

for

GP

sin

trea

tmen

tm

an

dals

.E

ach

cell

show

sth

ere

spec

tive

coeffi

cien

tfr

om

ase

para

te

regr

essi

onw

her

eth

eou

tcom

eis

give

nby

the

colu

mn

hea

der

.“W

age

reali

zati

on

s”th

eav

erage

daily

wage

(in

Rs.

)an

ind

ivid

ual

rece

ived

wh

ile

work

ing

for

som

eon

e

else

inJu

ne

2012

.“R

eser

vati

onw

age”

isan

ind

ivid

ual

’sre

serv

ati

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52

Table A.10: Testing for existence of spatial spillovers (comprehensive neighborhoods)

(a) Control

Wage realizations (Rs.) Days unpaid/idle

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

% GPs treated within R km 14 18 24∗∗ 24∗ 26 1.1 .025 -.94 -3.1 -5.7∗

(16) (15) (11) (13) (16) (2.3) (2.6) (3) (2.9) (2.9)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .05 .05 .05 .05 .05 .07 .06 .07 .07 .07Treatment Mean 128 128 128 128 128 17 17 17 17 17N. of cases 2063 2063 2063 2063 2063 4095 4095 4095 4095 4095% of sample 100 100 100 100 100 100 100 100 100 100

(b) Treatment

Wage realizations (Rs.) Days unpaid/idle

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

% GPs treated within R km 11 15 11 8.3 6.6 -1.3 -1.8 -3∗ -3.6∗ -4.4∗∗

(8.5) (9.9) (12) (13) (14) (1.3) (1.6) (1.8) (2) (2.2)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .08 .08 .08 .08 .08 .07 .07 .07 .07 .07Control Mean 134 134 134 134 134 16 16 16 16 16N. of cases 5206 5206 5206 5206 5206 10000 10000 10000 10000 10000% of sample 100 100 100 100 100 100 100 100 100 100

(c) Pooled

Wage realizations (Rs.) Days unpaid/idle

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

% GPs treated within R km 9.5 12 10 9.1 10 -.89 -1.5 -2.5 -3.3∗ -4.4∗∗

(7.4) (8.2) (9) (9.8) (11) (1.2) (1.5) (1.6) (1.8) (1.9)Treatment 1.2 1.9 4 5 5.3 -.79 -.74 -.7 -.78 -.79

(5) (4.4) (4.2) (4) (3.8) (.95) (.82) (.72) (.64) (.6)District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj R-squared .07 .07 .07 .07 .07 .07 .07 .07 .07 .07Control Mean 128 128 128 128 128 17 17 17 17 17N. of cases 7269 7269 7269 7269 7269 14095 14095 14095 14095 14095% of sample 100 100 100 100 100 100 100 100 100 100

This table show the impact of comprehensive spatial exposure measure to treatment NRp at radius 10, 15, 20, 25, and 30

kilometers on private wages and days worked from the NREGS household survey. All analysis was conducted separately

for (a) control and (b) treatment, and then for (c) the pooled sample. In each panel, the outcome “Wage realizations” in

columns 1-5 is the average daily wage (in Rs.) an individual received while working for someone else in June 2012 (endline).

The outcome “ Days worked private sector” in columns 6-10 is the number of days an individual worked for somebody else

in June 2012 (endline). The “% GPs treated within R km” is the ratio of the number of GPs in treatment mandals within

a given radius R km over the total GPs within wave 1, 2 or 3 mandals. Note that wave 2 mandals are included in the

denominator, and that same-mandal GPs are included in both the denominator and numerator. The entire GP sample used

in randomization are included. All regressions include the first principal component of a vector of mandal characteristics

used to stratify randomization as a control variable. Standard errors clustered at mandal level are in parentheses. Statistical

significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.

53

Table A.11: Estimating total treatment effects including spillovers (OLS)

(a) Wage

Wage realizations (Rs.) Reservation wage (Rs.)

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

Treatment .69 .75 5.2 8.1 11 5.3 5.3 6.3 6.1 4.4(8.1) (8.5) (8.4) (8.6) (8.9) (5.2) (5.6) (6.4) (6.7) (7)

% GPs treated within R km 8.4 10 13 15 21 18 14 12 6.8 .59(16) (15) (15) (15) (17) (12) (14) (16) (14) (12)

% GPs treated within R km X Treatment 1.5 3.2 -3.3 -7.9 -15 -14 -8.6 -7.4 -4.4 .94(18) (18) (19) (20) (21) (14) (16) (18) (17) (17)

District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Total Treatment Effect 11∗ 14∗∗ 15∗ 15∗ 17∗ 9.5∗∗∗ 11∗∗ 11∗ 8.4 5.9SE (5.6) (6.9) (7.5) (8.5) (9.8) (3.1) (4.3) (5.6) (6.2) (6.8)Control Mean 128 128 128 128 128 97 97 97 97 97N. of cases 7269 7269 7269 7269 7269 12852 12852 12852 12852 12852

(b) Labor

Days worked private sector Days idle/unpaid

R = 10 R = 15 R = 20 R = 25 R = 30 R = 10 R = 15 R = 20 R = 25 R = 30

Treatment .26 .56 .42 .59 .82 -.087 -.16 .0037 -.52 -1(1.1) (1.3) (1.5) (1.6) (1.7) (1.2) (1.4) (1.5) (1.5) (1.5)

% GPs treated within R km .92 1.6 2.1 3 4.4 .65 -.38 -1.2 -2.8 -4.8(2.2) (2.6) (3.1) (3.1) (3.3) (2.6) (3.1) (3.4) (3.2) (3.1)

% GPs treated within R km X Treatment -.42 -1.1 -.79 -1.2 -1.8 -2.1 -1.5 -1.8 -.66 .59(2.5) (3) (3.6) (3.8) (4) (2.9) (3.4) (3.8) (3.6) (3.5)

District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Total Treatment Effect .76 1.1 1.7 2.5 3.5∗ -1.5∗ -2.1∗ -3∗∗ -4∗∗ -5.2∗∗∗

SE (.8) (1.1) (1.4) (1.6) (1.8) (.78) (1.1) (1.4) (1.6) (1.8)Control Mean 7.9 7.9 7.9 7.9 7.9 17 17 17 17 17N. of cases 14441 14441 14441 14441 14441 14095 14095 14095 14095 14095

(c) Total Income

Total Income

R = 10 R = 15 R = 20 R = 25 R = 30

Treatment 11529 13964∗ 17419∗ 12176 6428(7745) (8422) (10497) (10493) (10288)

% GPs treated within R km 27026 29317 31946 17864 5747(17527) (19081) (27257) (23996) (19520)

% GPs treated within R km X Treatment -21689 -22300 -26050 -8955 7178(19453) (21346) (30000) (27636) (24076)

District FE Yes Yes Yes Yes Yes

Total Treatment Effect 16867∗∗∗ 20981∗∗∗ 23315∗∗ 21085∗∗ 19353∗

SE (5118) (6614) (9528) (10663) (10887)Control Mean 71935 71935 71935 71935 71935N. of cases 4903 4903 4903 4903 4903

This table provides estimates for total treatment effects using the comprehensive spatial exposure measure NRp for (a) wage

outcomes, (b) labor outcomes, and (c) total income. Each specification contains a treatment indicator, NRp , and an interaction

between NRp and treatment. Recall NR

p is the ratio of the number of GPs in treatment mandals within a given radius R

km over the total GPs within wave 1, 2 or 3 mandals. The total treatment effect estimate reported in the second section

of the table is the sum of the coefficients for those three variables. “Wage realizations” is the average daily wage (in Rs.)

an individual received while working for someone else in June 2012. “Reservation Wage” is an individual’s reservation wage

(in Rs.) at which he or she would have been willing to work for someone else in June 2012 . The latter outcome is based

on an a question in which the surveyor asked the respondent whether he or she would be willing to work for Rs. 20 and

increased this amount in increments of Rs. 5 until the respondent answered affirmatively. “Days worked private sector” is the

number of days an individual worked for somebody else in June 2012. “Days unpaid/idle” is the sum of days an individual

did unpaid work or stayed idle in June 2012. “Total Income” is total annualized household income (in Rs). All regressions

also include the first principal component of a vector of mandal characteristics used to stratify randomization as a control

variable. Statistical significance is denoted as: ∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01.54

Figure A.1: Changes in wages by month and treatment status

0

20

40

60

Janu

ary

Feb

ruar

y

Mar

ch

Apr

il

May

June

July

Aug

ust

Sep

tem

ber

Oct

ober

Nov

embe

r

Dec

embe

r

Month

Wag

e

Control Treatment

This figure shows mean changes in agricultural wages between baseline and endline, by month and treatment status, weighted

by (inverse) GP sampling probability. The data, which is at the village-level, comes from surveys administered to prominent

figures in each village. Standard errors are clustered at the mandal level.

55

Figure A.2: Density of spatial measures of treatment exposure

(a) Exposure to Treatment: Including Same Mandal GPs

10km 20km 30km

0

1

2

3

0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00Fraction neighboring GPs treated

Den

sity

Control Treatment

(b) Exogenous Exposure to Treatment: Excluding Same Mandal GPs

10km 20km 30km

0.0

0.5

1.0

1.5

2.0

0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00Fraction neighboring GPs treated

Den

sity

Control Treatment

These figures show smoothed kernel density estimates of each spatial measure of exposure to treatment. The only GPs

included in these density calculations are surveyed GPs. Panel a) shows the distribution of exogenous spatial exposure to

treatment at a given distance for survey GPs. Panel b) shows the distribution of spatial exposure to treatment at a given

distance for survey GPs. The analysis was conducted at distance 10 km, 20 km, and 30 km. The spatial exposure measure is

the ratio of the number of GPs in treatment mandals within radius x km over the total GPs within wave 1, 2 or 3 mandals.

Note that wave 2 mandals are included in the denominator, and that same-mandal GPs are included in both the denominator

and numerator. The exogenous spatial exposure measure is the ratio of the number of GPs in treatment mandals within

radius x km over the total GPs within wave 1, 2 or 3 mandals. Note that wave 2 mandals are included in the denominator,

and that same-mandal GPs are excluded in both the denominator and numerator.

56