metadata: authors: 0: author: Bahal,Girish
count: India
docna: 0: docna: A Tale of Two Programs: Assessing Treatment
            and Control in NREGA Studies
docty: Policy Research Working Paper
subtopic: Labor Policies,Agricultural Growth and Rural Development,Rural Settlements,Labor Standards,Child Labor Law,Rural Labor Markets,Labor Markets,Child Labor,Food Security
teratopic: Communities and Human Settlements,Law and Development,Agriculture,Social Protections and Labor,Rural Development
abstracts: cdata!: This paper revisits impact evaluation
            studies on the largest public workfare in the world, NREGA.
            In an environment where randomization is not feasible, I
            show why an impact evaluation exercise on NREGA should
            acknowledge the existence of an older program, SGRY. Using
            novel district-level expenditure data on SGRY, this article
            shows how ignoring the older program is likely to
            underestimate the general equilibrium impact of the
            employment policy on various relevant socio-economic
            outcomes. In most cases, ignoring SGRY underestimates
            NREGA’s impact by 30–40 percent.
geo_reg_key: 517198,517212,517191
repnme: repnme: A Tale of Two Programs: Assessing Treatment
            and Control in NREGA Studies
display_title: A Tale of Two Programs: Assessing
            Treatment and Control in NREGA Studies
listing_relative_url: /research/2021/11/33579104/tale-two-programs-assessing-treatment-control-nrega-studies
topicv3: Child Labor m1326042 1859,Agriculture and Food Security m1326205 435,Labor Markets m1326488 855,Rural Development m1504505 2667
publishtoextweb_dt: 2021-11-03T00:00:00Z
dois: 10.1596/1813-9450-9835
historic_topic: Communities and Human Settlements,Law and Development,Agriculture,Social Protections and Labor,Rural Development
origu: DEC-Development Policy (DECDP)
no_of_pages: 30
model_extractions: Policy Research Working Paper 9835

# A Tale of Two Programs

A Tale of Two Programs: Assessing Treatment and Control in NREGA Studies _Girish Bahal_ World Bank Economic Review _accepted_ Paper November 2021
Policy Research Working Paper 9835

# **Abstract**

This paper revisits impact evaluation studies on the largest public workfare in the world, NREGA. In an environment where randomization is not feasible, I show why an impact evaluation exercise on NREGA should acknowledge the existence of an older program, SGRY. Using novel district-level expenditure data on SGRY, this article shows how ignoring the older program is likely to underestimate the general equilibrium impact of the employment policy on various relevant socio-economic outcomes. In most cases, ignoring SGRY underestimates NREGA’s impact by 30–40 percent.

This paper has been accepted for publication in _The World Bank Economic Review_ . It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at girish.bahal@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **A Tale of Two Programs: Assessing Treatment and Control in NREGA Studies**

## **Girish Bahal**

#### **JEL classification:** H53, I38, J38

**Keywords:** Impact evaluation, Treatment-Control, Public workfares, SGRY, NREGA

### **1. Introduction**

Often, similar older policy initiatives precede “new” anti-poverty programs implemented by governments across the world. For example, Brazil’s flagship anti-poverty program Bolsa Escola, a conditional cash transfer program, was replaced with Bolsa Familia in 2003. In 2011, Bolsa Familia was extended to Brasil Sem Mis ́eria (Brazil without poverty). In Mexico, Prospera was preceded by Oportunidades, which in turn was built on Progresa. Similarly, when India introduced the largest public works program in the world in 2006 – the National Rural Employment Guarantee Act (henceforth NREGA) – it replaced an older program – Sampoorna Grameen Rozgar Yojana (henceforth SGRY). Such government interventions naturally raise important questions for research and policy. In an environment where randomization is not feasible, quasi-experimental pretest-posttest designs often identify the impact of a change in economic policy. For example, a difference-in-differences model identifies the policy impact by comparing the average change over time in the outcome variable in the treatment and control groups.

This article highlights an important aspect of impact evaluation studies on NREGA. While estimating the general equilibrium impact of the program, it is crucial to control for a similar older workfare that was exclusively present in the “control” districts that received NREGA late. This article shows how ignoring the continued existence of the SGRY program has first-order effects on estimating the employment policy’s real impact. I show how ignoring SGRY overestimates the gap in employment provision (or the treatment dosage) between the treatment and control districts and underestimates the effect on various relevant socio-economic outcomes studied in the literature.<sup>1</sup> Specifically, I analyze the general equilibrium impact of NREGA on i) agricultural wages, ii) casual earnings, iii) casual employment in the public and private labor market (for men and women), and iv) human capital outcomes in terms of children’s school enrollments and child labor. For all outcomes, the impact of NREGA is more pronounced when we control for SGRY. The extent of underestimation of program Girish Bahal is Assistant Professor at the University of Western Australia, Perth, Australia; his email address is girish.bahal@gmail.com. The author is very grateful to the editor in charge of this article, Eric Edmonds, and three anonymous referees for thoughtful and constructive comments. The author thanks Toke Aidt, Tiago Cavalcanti, Giancarlo Corsetti, Douglas Gollin, Rema Hanna, Cl ́ement Imbert, Sriya Iyer, Michael Jetter, Kaivan Munshi, Anand Shrivastava, Laura Zimmermann, and seminar participants at Cambridge University, Delhi School of Economics, Indian Statistical Institute Delhi and International Association for Applied Econometrics for helpful comments and suggestions. The author also thanks Thiemo Fetzer for sharing TRMM rainfall data and Manisha Shah and Bryce Millett Steinberg for sharing data and codes for their study on NREGA’s impact on human capital outcomes. Finally, the author gratefully acknowledges the Ministry of Rural Development, Government of India, for help in data collection. A supplementary online appendix is available with this article at The World Bank Economic Review website. 1 See Sukhtankar (2017) for a thorough review of the literature on NREGA.

1
2 impact without SGRY is expectedly heterogeneous across outcomes. However, in most cases, the program impact without controlling for SGRY is underestimated between 30-40%.

I use a novel dataset of annual program expenditure for every district in the country for SGRY and NREGA programs. The expenditure data for SGRY is from its implementation in 2001 until its last operational year in 2007. For NREGA, the expenditure data is between 2006 to 2010. To the best of my knowledge, this is the most detailed and disaggregated expenditure data used in any comparable analysis on public works in a developing country. The data reveal three salient findings. First, the two programs never coexisted in a district at any point in time. Next, NREGA was implemented in phases, covering 200, 330, and all the districts of the country by 2006, 2007, and 2008, respectively. The second finding shows that during the staggered roll-out of NREGA, SGRY continued to be present exclusively in the “non-NREGA” or “control” districts during 2006 and 2007. Third, the availability of public employment under the SGRY was not inconsequentially small relative to NREGA. Approximately 14 percent of the districts covered under NREGA during 2006 and 2007 spent less than the average per capita program expenditure incurred under SGRY in the control districts during both these years. These findings blur the distinction between treatment and control districts where NREGA substituted and not supplemented the older program.

I start by estimating the general equilibrium impact of large public workfares on short-term wages for comparable manual labor work in the private sector. The question itself is of policy relevance since wage income from casual labor is a principal source of income for the poor in a developing country like India (Banerjee and Duflo 2007). A consensus among the recent literature on NREGA is that the program did lead to modest increases in private wages (Azam 2012; Berg et al. 2018; Imbert and Papp 2015; Muralidharan et al. 2017; Zimmermann 2012). Notably, the income gain due to higher private wages is not limited to program beneficiaries but also non-beneficiaries. For example, Muralidharan et al. (2017) note that the general equilibrium effect of NREGA is “a much more important driver of poverty reduction than the direct income provided by the program ”.<sup>2</sup> First, I estimate the impact of the employment policy on agricultural wages. Since agricultural wage data is available at annual frequency, I control SGRY by redefining “treatment” as spending in a district under public workfares. Analogous to the study on Minimum Legal Drinking Age (MLDA) laws in the US by Angrist and Pischke (2014), I use program expenditure as a measure of “exposure to the policy” and use the within and across district variation in program expenditure to estimate the effect of public workfares on private wages. A limitation of this approach is that program spending may correlate with local labor market conditions, which can influence wage rates in the private sector. I partially address this issue by controlling for district and year fixed effects and other relevant time-varying district controls. I also control for ‘potentially endogenous’ changes in program expenditure at a district-year level that represent under (or over) utilization of funds made available by the government.<sup>3</sup> The critical point is the extent of underestimation in the results when we ignore SGRY. Controlling for expenditure under SGRY, the results indicate public workfares to have increased agricultural wages between 0 _._ 4 _−_ 1 percent per annum. However, if one assumes no expenditure to have occurred under the older program, this underestimates NREGA’s impact on agricultural wages by 43%, making it insignificantly different from zero. This null result without SGRY is consistent with the findings reported by Berg et al. (2018) who also use AWI data.<sup>4</sup> This supports the hypothesis that ignoring the older program leads to an overestimation of the “dosage” of treatment by public workfares, which downward biases the results.

Second, I use data from National Sample Surveys (NSS) to estimate a baseline specification that evaluates the effect of NREGA on daily casual earnings without controlling for expenditure under SGRY. The results indicate

> 2 Similarly, Imbert and Papp (2015) find that increases in private sector wages represent “a third of the total welfare gain for the poor.”

> 3 I acknowledge the possibility of some residual omitted variable bias even in the preferred specification with all possible controls. However, using statistics on the selection of observables and unobservables (Altonji et al. 2005; Oster 2019), I show that the degree of omitted variable bias is inconsequential and unlikely to overturn the main results.

> 4 Berg et al. (2018) do find NREGA to increase wages by 4.3% in a different specification where they use “exposure” to NREGA by adding the number of months a district is under NREGA. I abstain from discussing this specification as the focus of this paper is to highlight how the contemporaneous impact of public works policy may be under-reported if we ignore SGRY. See Bahal (2016) for a theoretical and empirical exposition on the spot vs. stock effects of public works.
NREGA increased daily private earnings in the lean or “dry” agricultural season by approximately 4.9% in the districts that received NREGA early during 2006 and 2007. I find no significant increase in daily private earnings in the peak or “rainy” agricultural season. These baseline results are very similar to the results reported by Imbert and Papp (2015). Keeping the exact baseline specifications, I then control for per-capita expenditure under SGRY during 2004, 2005, and 2007. Comparing the results with and without SGRY, I find that not controlling for SGRY underestimates NREGA’s impact on daily casual earnings by approximately 34 – 40%.

Third, I study the general equilibrium effect of NREGA on casual employment in the public and private labor market. Consistent with the results reported in the literature, I find NREGA to significantly increase (decrease) casual work in the public (private) labor market during the dry season when agricultural work in the private sector is scarce. Not controlling for SGRY, I find NREGA to have increased work in public employment by approximately 1 percentage point and decreased private sector work by 1.8 percentage points in the dry season. Not controlling for expenditure under SGRY underestimates the impact on public and private employment by approximately 25% and 34%, respectively. The same pattern emerges when I estimate employment outcomes for men and women separately.

Finally, I study NREGA’s impact on human capital outcomes. Recent studies like Islam and Sivasankaran 2015; Li and Sekhri 2020 and Shah and Steinberg 2021 conclude that school enrollment numbers declined while child labor increased due to the spillover effects of the public workfare. Adukia (2020) also finds small negative effects of NREGA on educational outcomes. However, Adukia (2020) notes that the declines in education are not substantial, especially when the cost of every one-child decline in school enrollment is compared to the benefit of the number of households employed through NREGA.<sup>5</sup> Using data and replication codes from Shah and Steinberg (2021), I reassess the effect of NREGA on school enrollment and child labor after controlling for SGRY. While the change in coefficients is not nearly as significant as the change in labor market outcomes for adults reported above, the results are more pronounced with SGRY. Controlling for SGRY, NREGA’s impact on school enrollment and child labor is more negative and positive, respectively. The marginal change in children’s outcomes is expected if, consistent with Adukia (2020), NREGA has a much more pronounced effect on adult outcomes relative to outcomes for children.

Overall, this article’s findings are consistent with the existing literature and reaffirm the significant general equilibrium effects of large public workfares. The key contribution of this study is to underscore the relevance of controlling for an older program while conducting an impact evaluation of a new policy intervention. In the absence of random treatment-control designs, ignoring relevant previous policies can substantially underestimate a new policy’s impact. Using different outcome variables and data, I show how ignoring the older program may considerably underestimate the workfare’s effect on the variable of interest.

The rest of the article is structured as follows. Section 2 discusses SGRY and NREGA in detail. Section 3 discusses the data, and section 4 presents the empirical model and results. Section 5 concludes.

# **2. NREGA and its predecessor**

Pan-India public workfare programs did not start from SGRY and date back to the Food for Work program in 1977. We restrict our analysis to these two programs due to two key reasons. First, both SGRY and NREGA were “integrated workfare programs” with employment generation and rural infrastructure creation as their dual objectives. Before 2001, these two objectives were addressed by multiple, smaller programs. Hence, no program before 2001 was structurally similar to SGRY and NREGA. See Bahal (2020) for a detailed discussion on India’s major rural welfare programs since 1980. Second, district-level expenditure information is not available for any program before SGRY. District-level program expenditure data is publicly available only for NREGA. The expenditure data for SGRY used in this paper was specially sourced from the Ministry of Rural Development, Government of India. The sub-sections below discuss and compare SGRY and NREGA in detail.

> 5 See also Afridi et al. 2016; Das and Singh 2013; Das 2018; Mani et al. 2020.
4

# SGRY

Sampoorna Grameen Rozgar Yojana (SGRY) was launched as a nationwide program on 25 September 2001 to address rural employment and infrastructure-related issues. With the introduction of SGRY, the government discontinued previous employment programs like Jawahar Gram Samridhi Yojana (JGSY) and Employment Assurance Schemes (EAS). The motivation to implement SGRY was to integrate different programs for wage employment into one universal scheme. Initially, SGRY operated as two streams with approximately equal outlays. From 2004, SGRY worked as a single program. Like NREGA, SGRY envisaged the generation of wage employment and rural infrastructure creation through labor-intensive public projects. The central (federal) and the state governments shared the program’s overall cost in the ratio of 75:25.

# NREGA

NREGA guarantees 100 days of unskilled manual work per annum to every rural household at the state-defined minimum wage. The ministry of rural development implemented NREGA in three phases. Starting from 2006, NREGA covered the country’s 200 most economically backward districts in its first year. The criteria for judging a district’s backwardness were based on measures like agricultural productivity, past agricultural wage level, and the density of scheduled castes and scheduled tribes in a district.<sup>6</sup> In NREGA’s second phase, an additional 130 districts were covered by 2007. By the third phase in 2008, every district in the country had NREGA. Like its predecessor, NREGA aims to generate wage employment and develop rural infrastructure through public works. NREGA is a self-targeting anti-poverty policy where the government, as an employer of last resort, provides income insurance to the rural poor while creating productive public infrastructure. During the financial year 20092010, NREGA generated around 2.6 billion work-days and employed approximately 55 million households. The total expenditure under NREGA amounted to about 0.6 percent of the GDP (nearly 5 percent of the agricultural output) during the same year.

# SGRY During NREGA Implementation

This section highlights the similarities between the two programs and discusses the continued existence of SGRY during the phase-wise implementation of NREGA. As panel (a), figure 1 shows, adjusted for 2000 prices, nearly 40 billion Indian rupees were spent on SGRY at the national level in its first year in 2001. The expenditure at the national level under SGRY progressively increased to nearly 60 billion rupees by 2005. In comparison, the federal spending under NREGA in 2006 was approximately 70 billion rupees, which substantially increased to 118 billion and 178 billion in 2007 and 2008, respectively. This increase was primarily due to the scale-up of NREGA during its second and third phases.

Panel (b), figure 1 shows the annual employment generated under SGRY and NREGA at the national level in millions of person-days. As expected, the employment trend under the two programs closely matches the trend in national expenditure. Panels (a) and (b) of figure 1 highlight that although at a relatively smaller scale, a significant amount of spending and employment generation did occur under SGRY. It is important not to infer from the overlap of program expenditure in panel (a), figure 1 that both programs were simultaneously in operation in some districts during 2006 and 2007. Rather, SGRY was exclusively present only in those districts that NREGA did not cover.

Figure 2 illustrates this point more clearly by showing district level maps for southern India.<sup>7</sup> Panel (a), figure 2 shows the rollout of NREGA in its first phase in 2006. The “non-NREGA” or “late-phase” districts are in white, while the shaded districts are the phase-I districts. In contrast, panel (b), figure 2, shows the employment expenditure that occurred in SGRY or NREGA in 2006. Similarly, panels (c) and (d) of figure 2 show that even during the phase-II implementation of NREGA in 2007, SGRY was operational in the non-NREGA districts. Therefore, the district-level expenditure maps show that SGRY continued to operate in the non-NREGA districts during the phase-wise implementation of NREGA in 2006 and 2007.

> 6 This index was primarily based on district-specific and time-invariant fixed effects.

> 7 Maps for the whole country haven’t been displayed due to restrictions from the Cartography Unit at the World Bank. See the working paper version for the country maps.
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# **3. Data**

## Program Expenditure Data

Data on program expenditure is collected from the Ministry of Rural Development (MoRD). Although the information on NREGA is publicly available, data on SGRY was specially sourced from MoRD and Datanet (India).<sup>8</sup> MoRD reports district-wise annual physical and financial statements for both the programs. Physical statements provide information on the number of public works completed and employment generated, while financial statements give statistics on the availability of funds and actual expenditure. I use data on “actual expenditure” when referring to program expenditure in the empirical models below. Data on SGRY is from its implementation in 2001 to 2007, its last operational year. Data on NREGA is from 2006 to 2010. In total, I use ten years (20012010) of district-level data on employment expenditure. The data used in this article is the most disaggregated and detailed data on program expenditure used in any comparable analysis on public workfares.

## Agricultural Wage Data

I use data on agricultural wages from the Agricultural Wages in India (AWI) series published by the Ministry of Agriculture. The AWI data has been extensively used for time series analysis on agricultural wages in India (see for example, Ravallion et al. 1993, Ozler<sup> ̈</sup> et al. 1996, and Berg et al. 2018). The AWI district-level wage data provides monthly information on daily wage rates. I construct a measure of the agricultural wage rate from 2001 to 2010 by taking the average daily wage rates for men and women for agricultural activities like plowing, sowing, reaping, and weeding.

_Matching AWI wage data with expenditure data:_ The AWI data, unlike the program expenditure data, is not reported for all districts. Data for nearly 40 percent of the districts are available for less than six out of ten years. To improve the signal-to-noise ratio of agricultural wages, I convert the monthly AWI data to annual frequency by taking 12-month averages in the Indian fiscal year format to match the program expenditure data. I further improve the quality of the wage data by focusing on a balanced panel of 134 districts from 12 major Indian states for ten years. For the analysis of agricultural wages, I have a total of 1340 observations.

Since I lose districts with incomplete wage data, a possible objection can be the correlation of a district’s backwardness with the unavailability of wage data. If this is true, then restricting the analysis to 134 districts should result in the proportion of early phase NREGA districts being substantially lower in this sub-sample than the aggregate sample. Encouragingly, this is not the case. The percentage of phase-I and phase-II districts in the full sample – approximately 37 percent and 58 percent, respectively – closely matches the sub-sample proportion of 42 percent and 63 percent, respectively.

Both the wage and expenditure data are deflated to 2001 prices using the labor bureau’s consumer price index for rural laborers (CPI-RL). Unless otherwise mentioned, all variables in the empirical analysis are in real, percapita terms.

## National Sample Survey Data

_Casual earnings and employment outcomes_ : An alternative data used in the literature is from the nationally representative employment and unemployment surveys carried out by the National Sample Survey Organization (NSSO). The nationally representative rounds with a large sample size usually happen every three to five years. This study also employs NSSO data to compare the impact of NREGA on daily casual earnings with and without controlling for SGRY. Since both programs apply only to persons living in rural areas, I restrict the analysis to persons in rural areas and persons aged between 18 and 60. The sample includes 440 districts within the 17 largest states of India. I use data from Jul 2004 to Jun 2005 (round 61) to form the pre-NREGA period. For the post-NREGA period, I use data from Jul 2007 to Jun 2008 (round 64). Consistent with the methodology used in Imbert and Papp (2015), all statistics and estimates computed using the NSS data are adjusted using sampling weights.

> 8 To check the reliability of the SGRY district-level data, I aggregate district-level expenditure estimates to state-level. Apart from minor differences, the state-level figures match with the official state-level estimates published in MoRD reports.
8 For earnings, the key outcome variable in the NSSO data is an individual measure of daily casual earnings. The NSSO surveys distinguish whether the waged work is “salaried”, which is long-term and often involves a formal contract, or “casual” that is temporary and informal. The survey records total earnings from casual labor over the past seven days for individuals working in casual labor. I compute casual earnings for each individual as the average earnings per day worked in casual labor. I then use a monthly, state-level price index for agricultural laborers from the Indian Labour Bureau to construct real earnings. For employment outcomes, I compute for each person the percentage of days in the past seven days spent in mutually exclusive activities: private sector work and public works. Private sector work includes waged work, self-employment, and domestic work. _Child labor and human capital outcomes_ : Consistent with Shah and Steinberg (2021), I use the NSS Rounds 61 (2004-2005), 64 (2007-2008), and 66 (2009-2010) to examine the impact of NREGS on schooling outcomes and child labor. Since this data set has information about the “primary activity” of each household member, this allows the construction of employment and schooling status at the individual level. An individual whose primary activity is going to school is defined as “attends school”. “Child labor” equals one if the child’s primary activity is domestic work, work at home, or work outside the home. For more details about the data and construction of child labor and human capital outcomes, see Shah and Steinberg (2021).<sup>9</sup> _Controlling for SGRY in NSS data:_ To identify the impact of NREGA on an outcome variable, studies using NSS data have relied on an indicator variable for the phase-wise implementation of NREGA. However, controlling for SGRY in the form of an indicator is not possible after acknowledging (in section 2 above) that expenditure under SGRY and NREGA was perfectly negatively correlated with each other. In other words, if _NREGAd,t_ takes the value one (zero) for a district _d_ when NREGA was present (absent) in year _t_ , then an analogous indicator for SGRY is _SGRYd,t_ = 1 _− NREGAd,t_ . Therefore, in the NSS regressions, the key SGRY control is the per capita program expenditure in a district under SGRY during 2004, 2005, and 2007. I match this expenditure data with data from NSS rounds 61 and 64. For human capital regressions, SGRY expenditure is zero for round 66 (2009-2010).

## Rainfall Data

I use remotely sensed rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite. See Fetzer (2014) for a detailed discussion on the consistency and the quality of TRMM data over any other remote sensed or ground-based data.<sup>10</sup>

# **4. Empirical Model and Results**

This section discusses the impact of NREGA after controlling for SGRY on a variety of outcome variables. First, I use AWI data and estimate the effect of public workfares on agricultural wages. Here, I define treatment as a continuous measure using program expenditure. In this specification, I show how the impact of workfares on agricultural wages is underestimated by approximately 43% if we ignore spending under SGRY. Second, I use NSS data and estimate NREGA’s impact on casual earnings. Analogous to agricultural wages, daily casual earnings are underestimated by 34-40% if we do not control for expenditure under SGRY. Third, I estimate NREGA’s impact on employment outcomes: private sector work and public works. The effect on both employment outcomes is again underestimated by 25-34% without SGRY. Finally, I reassess child labor and human capital outcomes with and without SGRY.

> 9 As Shah and Steinberg (2021) report, 71 percent of adolescents aged 13–17 attend school (as main activity), while 25 percent report working. Younger children aged 5–12 are much more likely to attend school (85 percent) and much less likely to report working (about 3 percent).

> 10 Thanks to Thiemo Fetzer for sharing the rainfall data.
# Impact on Agricultural Wages

# _Using binary treatment_

I begin by estimating the general equilibrium effects of public workfares on private wages by estimating equation (1) `a la Berg et al. (2018). In equation (1), _wd,t_ is agricultural wage, _α_ is a constant, and _NREGAd,t_ is an indicator that takes the value one for a district from the year NREGA was introduced and zero before that. Thus, given the staggered implementation of NREGA, _NREGAd,t_ takes the value 1 for different districts at different points in time.

The coefficient _ζ_ is the DD estimate that captures the impact of NREGA on private wages. District and year fixed effects are controlled by _αd_ and _θt_ respectively while _ξdt_ are district-specific trends. The subscripts _d_ and _t_ denote district and year, respectively. To account for serial correlation, all regressions report standard errors clustered at the district-level that are robust to heteroskedasticity as well. Given the heterogeneity in the size of districts, all regressions use weights based on the district population.<sup>11</sup> Consistent, with Berg et al. (2018), I find the DD estimate _ζ_<sup>�</sup> to be positive and significant at 7.2 percent without any controls, but _ζ_<sup>�</sup> becomes statistically insignificant from zero once I control for district and year fixed effects (see table S2). Equation 1 likely underestimates the true impact of the program on wages by overestimating the gap in program provision between treated and control districts.

# _Using program expenditure as treatment_

This section redefines treatment as the amount of real per capita spending under the two programs in any given district. Measuring treatment using program expenditure helps circumvent the overestimation of employment provision between NREGA and SGRY districts in a binary treatment model. Here, I study the effects of public workfares on agricultural wages by estimating the elasticity of wages with respect to program expenditure. I estimate various specifications of the following model where _α_ , _αd_ , _θt_ and _ξdt_ have the same interpretation as in equation 1 and **X** _d,t_ is a vector of controls discussed below. The key regressor, sinh<sup>_−_1</sup> ( _ed,t_ ), is the Inverse Hyperbolic Sine (IHS) transformation of expenditure in a district _d_ and year _t_ under SGRY or NREGA. IHS transformation can be interpreted in exactly the same way as a standard log transformation since sinh<sup>_−_1</sup> ( _ed,t_ ) _≈ log_ (2) + _log_ ( _ed,t_ ). A key advantage of IHS transformation is that it allows for zero-valued observations which is relevant in this case as I will compare equation 2 with and without SGRY expenditure. Therefore, _β_ is the elasticity of wage with respect to expenditure.

An obvious challenge in identifying the policy impact on wages using the expenditure approach is that program expenditure can be endogenous to local economic conditions, district characteristics, and wages. For example, if program expenditure is correlated with time-invariant district effects like demographics, susceptibility to droughts, and general agricultural productivity, then _β_<sup>�</sup> may suffer from omitted variable bias. To illustrate this point, column (1) of table 1 reports _β_<sup>�</sup> obtained from estimating equation 2 with sinh<sup>_−_1</sup> ( _ed,t_ ) and an intercept as the only regressors. As can be seen, _β_<sup>�</sup> is estimated to be around _−_ 0 _._ 045, significant at a 95 percent confidence level. Intuitively, if the level of program expenditure is higher for economically backward districts, then _β_<sup>�</sup> should be expected to suffer from a negative bias.

To address the omitted variable bias, column 2 reports the elasticity estimate after including the district and year fixed effects. While _αd_ controls for any time-invariant district effects, _θt_ controls for any endogenous change in outlays for public workfares at the national level in response to aggregate shocks like widespread droughts. As expected, adding these controls substantially increases _β_<sup>�</sup> from _−_ 0 _._ 045 to 0 _._ 043, where the coefficient estimate is highly significant ( _t ≈_ 3 _._ 5).

> 11 The results largely remain the same if all observations are weighted equally.
10 The specification in column 2 may still be susceptible to omitted variable bias due to district-year shocks that may simultaneously influence field wages and program expenditure. For example, local adverse weather events may adversely affect the agriculture sector (and agricultural wages) while resulting in higher than expected program spending due to increased demand for public works.<sup>12</sup> Such local stresses like conflicts or weather abnormalities may negatively impact wages and result in higher than expected expenditure. If true, the estimated wage elasticity in column 2 is still likely to suffer from a downward bias. I exploit the two programs’ fund allocation process to construct a variable _Utilization ratiod,t_ that captures unexpected increases or decreases in spending that may represent potentially endogenous expenditure fluctuations.

**Table 1.** Impact of Public Works on Agricultural Wages

|||SGRY +|NREGA||Only NREGA|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_sinh_<sup>_−_1</sup>(_ed,t_)|-4.493<sup>_∗∗_</sup>|4.250<sup>_∗∗∗_</sup>|5.936<sup>_∗∗∗_</sup>|2.342<sup>_∗∗_</sup>|1.341|
||[-2.123]|[1.215]|[1.295]|[1.021]|[1.034]|
|District effects|No|Yes|Yes|Yes|Yes|
|Year effects|No|Yes|Yes|Yes|Yes|
|Oth. controls|No|No|Yes|Yes|Yes|
|Het. trends|No|No|No|Yes|Yes|
|Observations|1340|1340|1340|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . The unit of observation is a district-year. The dependent variable in all the regressions is the log of field wages. Columns 1-5 have _sinh_<sup>_−_1</sup> ( _ed,t_ ) as the key regressor. In columns 1-4, program expenditure is for SGRY (b/w 2001 and 2007) and NREGA (b/w 2006 and 2010). In column 5, only expenditure under NREGA is considered (all expenditure values under SGRY are substituted by zero). The Inverse Hyperbolic Sine (IHS) transformation of the expenditure variable sinh<sup>_−_1</sup> ( _ed,t_ ) has the same interpretation as _log_ ( _ed,t_ ). Other controls include _NREGAd,t_ , _Util. Ratiod,t_ , _Raini,t_ , state elections-year indicator, and density of scheduled caste and scheduled tribe population in a district. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 For both SGRY and NREGA, the central government releases program funds to the states. The states, in turn, distribute the funds at the district level. Since both programs are in principle demand-driven, actual expenditure (or program liabilities) at the district level can be higher or lower relative to the total funds made available in any given year. The central government fulfills any excess expenditure in the next cycle. Hence, financial accounts roll over from one financial year to the next. Since there is information on both fund availability and actual expenditure at the district-year level, I construct _Utilization ratiod,t_ = 100 _∗ Actual expenditured,t/Funds made availabled,t_ . Consistent with public workfares being demand-driven, if deviations in actual expenditure relative to fund availability represent demand shocks, then this may confound the impact of employment policy on wages as reported in column 2 of table 1. Specifically, if over-utilization (under-utilization) of funds reflects more (less) demand for work under public workfares when the private labor market is slack (tight), then the elasticity estimate in column 2, table 1 will be downward biased.

To address these concerns, I estimate _β_<sup>�</sup> after controlling for such _potentially endogenous_ observations by

> 12 For example, Dr`eze (1990) discusses the increased take-up of public employment by laborers under Maharashtra’s public workfare during the famine of 1970-1973.
adding _Utilization ratiod,t_ as an additional regressor in column 3.<sup>13</sup> In addition, the specification in column 3 also adds a vector of other important time-varying district controls like i) average annual rainfall (in millimeters), ii) an indicator for state elections, iii) the density of scheduled castes and scheduled tribes in a district, and iv) the NREGA implementation indicator _NREGAd,t_ which controls for a change in program regime. These variables further control for changes in program expenditure that may be correlated with i) local weather shocks, ii) political motivations at the time of state elections, iii) a change in program regime, or iv) time-varying district demographics that may be correlated with program expenditure and agricultural wages. The inclusion of this vector of additional controls **X** _d,t_ increases the elasticity estimate to 0.06, which is highly significant ( _t >_ 4 _._ 5). Based on the average growth in per capita program expenditure of 16 _._ 1 percent per annum, the specification in column 3 suggests public workfares have increased private wages by approximately 0 _._ 95 percent per annum.

# _Parallel trends assumption_

The specifications in columns 1 _−_ 3 of table 1 assume the existence of parallel trends. However, assuming that all districts experienced a similar growth path between 2000 and 2010 may be a strong assumption. To control for district-specific trends, I add 134 district-specific trends _ξdt_ to the model in equation 2.<sup>14</sup> Adding heterogeneous trends correct any bias in _β_<sup>�</sup> if growth in spending is correlated with economic progress at the district level. For example, if the change in program expenditure is correlated with improvements in general development indicators and institutional capacity to implement government programs, then the elasticity estimate in column 3 of table 1 would be overstated. The inclusion of district-specific trends allows inference independent of the parallel trends assumption. However, adding multiple trends may lead to a very strict specification that fails to distinguish the policy impact from differential trends, especially if treatment effects emerge only gradually (Angrist and Pischke 2014).

Column 4, table 1 reports the elasticity of wage after adding heterogeneous trends as additional controls. Adding district-specific trends lower _β_<sup>�</sup> to 0.023, which is significantly different from zero at the standard 95% confidence level. The elasticity estimate in column 2 suggests wage increases of approximately 0.4% per annum. As explained above, the estimate in column 4 likely represents a lower bound of the impact of workfare programs on the private labor market if the policy does not lead to a sharp contemporaneous jump in private wages. I, however, treat column 4 as the preferred specification as it corrects for any time-varying district controls that may confound the elasticity estimate reported in column 3, table 1.

I further acknowledge the possibility of some residual omitted variable bias even in the preferred specification of column 4. However, using statistics on the selection of observables and unobservables (Altonji et al. 2005; Oster 2019), I show in the online appendix (table S3) that the size of omitted variable bias is inconsequential and unlikely to explain away the impact of public workfares on wages. Specifically, the unobservable variables would need to be an order of magnitude more strongly negatively correlated with program expenditure than all other controls accounted for to explain away the estimated coefficient. This is a very strong assumption, especially since observables in column 4 explain more than 92% of the variation in wages. Indeed, the bias-adjusted coefficient of program expenditure is strictly higher than 0.023. Consistent with the idea of public workfares, if program expenditure is positively correlated with unobservables that, in turn, negatively affect wages in the private sector, then the elasticity estimate in column 4, table 1 represents a lower bound of the true parameter. A case in point is a productivity diminishing weather event that leads to a higher program expenditure on the one hand and lowers agricultural wages on the other. Therefore, based on the elasticities reported in columns 3 and 4 of table 1, public workfares appear to have increased private wages in the agriculture sector between 0 _._ 4 and 1 percent per annum.

> 13 From 2012, funds are directly transferred from the state to the worker’s account through the electronic fund management system (eFMS). Therefore, more recent data have little scope for an accounting design where fund availability and actual expenditure are different. The change in fund management is a key reason I limit the data in this study up to 2010-2011.

> 14 An unbiased estimate of the program impact requires that there are no contemporaneous district-level trends that are correlated with program expenditure and agricultural wages. See, for example, Hoynes et al. (2011) who follow a similar strategy to identify the effect of the WIC program on birth outcomes in the US.
12

# _Ignoring SGRY_

Finally, I estimate the effect of ignoring SGRY when measuring treatment based on program expenditure. Column 5 of table 1 reports the impact of program expenditure on wages but after replacing expenditure values under SGRY by zero. As can be seen, keeping the same set of controls as in column 4, column 5 underestimates the effect of public workfares by approximately 43% when I ignore expenditure under SGRY. In column 5, _β_<sup>�</sup> is equal to 0 _._ 013 which is insignificantly different from zero. Consistent with the result in the baseline model with binary treatment, ignoring SGRY underestimates the impact on wages even with continuous treatment.

In the online appendix, I check for the stability of the elasticity estimate over the two program regimes in table S4. Thereafter, I test whether there is any sub-sample heterogeneity based on how well the employment programs were implemented (table S5). The results are consistent with previous studies like Imbert and Papp (2015) that report a more significant impact for states that implemented NREGA well. Table S6 reports wage elasticities based on gender and over different program regimes. Finally, table S7 reports table 1 but for wages for relatively skilled labor such as carpenter, cobbler, and blacksmith.<sup>15</sup> We do see a positive impact on wages for skilled labor as well but consistent with the expectation, there is a greater impact on wages for unskilled laborers. Not controlling for SGRY underestimates the impact on wages for skilled labor by 47%.

# Impact on Casual Earnings

I now estimate a baseline model on casual earnings with NSS data. Here, I estimate the impact of NREGA on daily casual earnings by comparing the growth in the outcome variable over time between districts that received NREGA early (before 2008) to those that received it late (from 2008). Similar to Imbert and Papp (2015), I estimate equation 3 where _log_ ( _wi,d,t_ ) refers to the log average daily earnings in casual work for individual _i_ surveyed in district _d_ in quarter _t_ . **Z** _d_ are district controls like rainfall, agricultural productivity, etc.<sup>16</sup> District controls that do not vary over time are interacted with a dummy **1** _{t>_ 2006 _}_ for 2007-08 (post-NREGA). To ease concerns that NREGA’s impact on wages is not driven by worker selection, I control for individual controls **H** _i_ . The individual-level controls include dummy variables for age group, education level, gender, caste, religion, and marital status. **X** _d,t_ represents time-varying district control like rainfall, which is also interacted with an indicator **1** _{Early}_ equal to one for observations within early districts. _θt_ represents year-quarter fixed effects. All other variables have the same interpretation as equation 1.<sup>17</sup> Following Imbert and Papp (2015), I include interactions of _NREGAd,t_ with season dummies to exploit the variation in public employment provision across seasons.

Table 2 reports the results. Panels A and B report regressions where the dependent variable is nominal earnings and real earnings, respectively. In both panels, columns 1, 2, and 3 report NREGA’s impact on the dependent variable without any additional control, with worker controls, and with worker and district controls, respectively. Columns 4, 5, and 6 report estimates from the same specification as columns 1, 2, and 3, respectively but with SGRY expenditure in district _d_ at time _t_ as an additional control. This control takes the value zero once NREGA covers a district. The key regressors in all the regressions are the interaction of _NREGAd,t_ with _Dry_ and _Rainy_ indicators for the first six months and the last six months of a calendar year, respectively.

Consistent with findings reported in Imbert and Papp (2015), NREGA’s effect on private earnings in the earlyphase districts is significantly positive (column 3, panels A and B) during the dry/lean agricultural season when the demand for workers in the private sector (public works) is low (high). The effect on earnings during the rainy/peak agricultural season is positive but insignificantly different from zero (column 3, panels A and B).

> 15 Wages for these occupations are reported only for men. Skilled wage is taken as the average across the three occupations.

> 16 See table 2 for a full list of district and worker controls.

> 17 Strictly speaking, I do not replicate results from Imbert and Papp (2015) as I do not control for some additional district-level controls that are constructed using data that is not freely available. This, however, is not a concern as my baseline NSS results are close to the results reported by Imbert and Papp (2015) and the key objective of this exercise is to compare the change in results once we control for SGRY expenditure.
|With SGRY control<br>6.873_∗∗∗_<br>7.052_∗∗∗_<br>[2.438]<br>[2.202]<br>5.035_∗∗_<br>5.261_∗∗_<br>[2.254]<br>[2.049]<br>59130<br>59130<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>6.621_∗∗∗_<br>7.487_∗∗∗_<br>[2.400]<br>[2.162]<br>3.692<br>4.558_∗∗_<br>[2.284]<br>[2.061]<br>59130<br>59130<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>or SGRY from 2004 to 2007. Program ex-<br>egressions include district and year-quarter<br>dependent variable in panel B is the log of<br>y 2004 to June 2005 and from July 2007 to<br>m the Indian Labour Bureau._NREGAd,t_<br>le equal to one for the frst two quarters of<br>include dummy variables for gender, age<br>tivity, fraction of workers who are catego-<br>District controls that do not vary over time<br>hts. The standard errors in square brackets<br>ployment Guarantee Act.|
|---|
|**Panel A. Dependent Variable: Nominal Earnings**<br>Without SGRY control<br>_NREGAd,t × Dry_<br>4.497_∗∗_<br>4.310_∗_<br>4.173_∗∗_<br>6.794_∗∗∗_<br>[2.218]<br>[2.225]<br>[2.071]<br>[2.441]<br>_NREGAd,t × Rainy_<br>3.540<br>3.590_∗_<br>3.631_∗_<br>4.835_∗∗_<br>[2.234]<br>[2.157]<br>[1.982]<br>[2.322]<br>Observations<br>59130<br>59130<br>59130<br>59130<br>Worker Controls<br>No<br>Yes<br>Yes<br>No<br>District Controls FEs<br>No<br>No<br>Yes<br>No<br>SGRY Control<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Real Earnings**<br>Without SGRY control<br>_NREGAd,t × Dry_<br>4.494_∗∗_<br>4.308_∗∗_<br>4.909_∗∗_<br>6.540_∗∗∗_<br>[2.175]<br>[2.176]<br>[2.016]<br>[2.411]<br>_NREGAd,t × Rainy_<br>2.345<br>2.389<br>3.099<br>3.498<br>[2.257]<br>[2.184]<br>[1.987]<br>[2.351]<br>Observations<br>59130<br>59130<br>59130<br>59130<br>Worker Controls<br>No<br>Yes<br>Yes<br>No<br>District Controls FEs<br>No<br>No<br>Yes<br>No<br>SGRY Control<br>No<br>No<br>No<br>Yes<br>_Source_: Author’s analysis based on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data f<br>penditure data is from the Ministry of Rural Development, Government of India.<br>_Notes_: All estimates are of the order10_−_2. Each column presents results from a separate regression. All r<br>fxed effects. The dependent variable in panel A is the log of nominal daily earnings in casual work. The<br>real daily earnings in casual work. The sample is composed of all adults aged 18 to 60 interviewed from Jul<br>June 2008. Real earnings are defated using the monthly, state-level price index for agricultural labourers fro<br>is a dummy variable equal to one for early districts during July 2007 to June 2008. Dry is a dummy variab<br>the year. Rainy is a dummy variable equal to one for the second two quarters of the year. Worker controls<br>group, education levels, caste, religion and marital status. District controls are rainfall, agricultural produc<br>rized as casual (agriculture) casual (non-agriculture), cultivators, business (non-agriculture), and salaried. <br>are interacted with a dummy for 2007-08 (post-NREGA). All estimates are computed using sampling weig<br>are clustered at the district level. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Em<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p <_0_._01|
14 The results reported in column 3, panel B show real daily casual earnings to be approximately 4.9 (3.1) log points more in early relative to late districts during the dry (rainy) season. These results match very closely with the 4.7 (2.9) log points increase during the dry (rainy) season in Imbert and Papp (2015).

As columns 4-6 (panels A and B) show, controlling for SGRY expenditure substantially increases both the size and significance of NREGA’s impact on daily casual earnings during dry and rainy seasons for all specifications. Comparing the results reported in column 6 and column 3, not controlling for SGRY underestimates NREGA’s impact on earnings in the dry season by approximately 40% and 34% for nominal and real earnings, respectively. The program impact during the rainy season is similarly underestimated in the specifications without SGRY by approximately 31% and 32% for nominal and real earnings, respectively.

Hence, the program impact is substantially underestimated in tables 1 and 2 when we do not control for SGRY.<sup>18</sup> Given the large sampling variability (standard errors), the coefficients in the specification with SGRY are not statistically different from the corresponding specification without SGRY in tables 1 and 2. This does not undermine the relevance of controlling for SGRY for two reasons. First, the omitted variable bias for both outcomes is economically large, which warrants regressions with the SGRY control as the preferred specification. Second, the estimates with and without SGRY control are different in a statistical sense as well. For example, in table 2, while the impact of NREGA on real earnings during the dry season is statistically different from zero in column 3, the coefficient is not statistically different (at 5% level) from one. On the other hand, the same coefficient in column 6 is statistically different (at 5% level) from three, implying that real earnings increased by _at least_ 3 percentage points during the dry season.

In the online appendix, table S8 compares the effect of NREGA on star states that implemented NREGA well relative to all other states during the dry and rainy season (see Imbert and Papp 2015 for the definition of star states). Overall, the results without SGRY are consistent with Imbert and Papp (2015). The specifications with SGRY remain more pronounced in all cases and the extent of underestimation (without SGRY) is greatest for other states in the dry season.

# Impact on public and private employment

This section compares how controlling for SGRY affects NREGA’s impact on public and private employment outcomes. I estimate various specifications of the following equation where all controls have the same interpretation as equation 3. The outcome variable _Empi,d,t_ can be the percentage of days in the past seven days that an individual _i_ in district _d_ at time _t_ spends in each of the two mutually exclusive activities: private sector work and public works. Private sector work includes waged work, self-employment, and domestic work.

Table 3 reports the results. Panel A and B report results with the dependent variable as public and private employment, respectively. Focusing on columns 1-3, NREGA increases public employment by approximately 1 percentage point in the dry season and decreases private employment by approximately 1.9 percentage points. The program’s impact during the rainy season is small and insignificant for both employment outcomes. These results are broadly consistent with Imbert and Papp (2015). As can be seen from columns 4-6, the impact on both outcome variables is substantially more pronounced once we control for SGRY. Comparing column 6 with column 3, not controlling for SGRY underestimates (in absolute terms) NREGA’s impact on public employment by approximately 25% and private employment by 34% in the dry season.

> 18 Note, the estimates obtained from AWI and NSS data are different and they need not be the same. First, the NSS data is of lower frequency relative to AWI data. Small annual changes in wages may compound when the treatment and control districts are compared over a few years. Second, the AWI data reports agricultural wages, while the NSS data provides information on income from casual work. The two measures, while correlated, are not necessarily the same.
|With SGRY control<br>1.298_∗∗∗_<br>1.316_∗∗∗_<br>[0.270]<br>[0.280]<br>0.345_∗∗∗_<br>0.345_∗∗∗_<br>[0.089]<br>[0.112]<br>315,481<br>315,481<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>-2.859_∗∗∗_<br>-2.700_∗∗∗_<br>[0.753]<br>[0.784]<br>-0.824<br>-0.603<br>[0.667]<br>[0.723]<br>315,481<br>315,481<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>RY from 2004 to 2007. Program expenditure<br>ssions include district and year-quarter fxed<br>July 2004 to June 2005 and from July 2007<br>dual spends in public works. The dependent<br>other controls are the same as in table2. All<br>t level. SGRY: Sampoorna Grameen Rozgar|
|---|
|**Panel A. Dependent Variable: Public Employment**<br>Without SGRY control<br>0.982_∗∗∗_<br>0.970_∗∗∗_<br>0.981_∗∗∗_<br>1.313_∗∗∗_<br>[0.267]<br>[0.266]<br>[0.271]<br>[0.271]<br>0.194_∗∗_<br>0.188_∗∗_<br>0.185_∗_<br>0.351_∗∗∗_<br>[0.077]<br>[0.077]<br>[0.100]<br>[0.090]<br>315,481<br>315,481<br>315,481<br>315,481<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Private Employment**<br>Without SGRY control<br>-1.868_∗∗∗_<br>-1.970_∗∗∗_<br>-1.790_∗∗_<br>-2.780_∗∗∗_<br>[0.710]<br>[0.695]<br>[0.720]<br>[0.773]<br>-0.291<br>-0.400<br>-0.168<br>-0.726<br>[0.707]<br>[0.629]<br>[0.688]<br>[0.744]<br>315,481<br>315,481<br>315,481<br>315,481<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order 10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all adults aged 18 to 60 interviewed from<br> variable in panel A is the percentage of days in the past seven days that an indivi<br>entage of days in the past seven days that an individual spends in private works. All<br>sampling weights. The standard errors in square brackets are clustered at the distric<br>ral Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>to June 2008. The dependent <br>variable in panel B is the perc<br>estimates are computed using<br>Yojana. NREGA: National Ru<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p _|
16 The impact on both outcome variables during the rainy season is similarly more pronounced after controlling for SGRY. In Panel A, the coefficient for _NREGAd,t × Rainy_ with all controls and SGRY is 0.35, which is significantly different from zero at 1% level). The coefficient for _NREGAd,t × Rainy_ in Panel B is more negative in column 6 (relative to column 3), but the estimate is not significantly different from zero. All estimates of column 6 are statistically different from the corresponding estimates in column 3 at the 1% level. Tables S9 and S10 in the online appendix show table 3 separately for men and women, respectively. The results continue to be more pronounced after controlling for SGRY for the sub-samples split by gender.

# Impact on human capital outcomes

Finally, I estimate the impact of human capital outcomes in terms of school enrollments and child labor. It has been established in the literature that income effects result in higher investment in education when returns to education are higher (Bharadwaj et al. 2020; Jacoby and Skoufias 1997; Jensen 2000; Maccini and Yang 2009). On the other hand, there is increasing evidence that increases in low-skill wages can decrease investment in human capital as adolescents substitute education with work to benefit from higher wages (Atkin 2016; Cascio and Narayan ming; Charles et al. 2018; Shah and Steinberg 2017). The direction and magnitude of this effect are relevant for policy as public workfares are a popular means of redistribution in developing economies.

A recent and growing literature on NREGA has examined how NREGA has affected human capital outcomes. Using Young Lives data from Andhra Pradesh (one state), Afridi et al. (2016) and Mani et al. (2020) find NREGA to increase human capital investment. On the other hand, Islam and Sivasankaran 2015; Li and Sekhri 2020 and Shah and Steinberg 2021 conclude, using nationally representative data sets, that school enrollment numbers declined while child labor increased due to the spillover effects of NREGA. Using DISE and ASER data, Adukia (2020) also finds small negative effects of NREGA on educational outcomes but concludes the magnitude of these spillovers to be inconsequential, especially relative to the positive spillovers on adult employment outcomes.<sup>19</sup> I replicate results from Shah and Steinberg (2021) to estimate NREGA’s impact on school enrollments and child labor using the following equation where _Sidty_ is an outcome of interest (such as enrollment status or child labor) for individual _i_ in district _d_ in year _t_ who is age _y_ . _ψy_ is a vector of year-child-age fixed effects. All other controls have the same interpretation as before. _β_ is the coefficient of interest, and it measures the effect of NREGA on the outcome variable S. Following Shah and Steinberg (2021), panels A, B, and C of table 4 report results for schooling and child labor for the full sample (children aged 5-17), adolescents (children aged 13-17), and young children (aged 5-12), respectively. Since SGRY data is not available for some states like Jammu and Kashmir, the (NSS and SGRY) matched data set is approximately 90% of the data used by Shah and Steinberg (2021). The comparison of results with and without SGRY is made on the matched sample so that any changes in coefficients can be attributed to the presence or absence of the SGRY control and not to a change in observations.<sup>20</sup> Consistent with Shah and Steinberg (2021), NREGA decreases school enrollments in the full sample by 1.1 percentage points and increases child labor by 1.8 percentage points. The results are mostly driven by the adolescents aged 13-17 while there are no consequential spillovers on young children aged 5-12. Encouragingly, controlling for SGRY makes the impact on school enrollments and child labor more pronounced in the relevant samples (Panel A and B). The extent of underestimation of coefficient estimates without SGRY, however, is not as large as those reported in tables 1, 2, and 3. In Panel B, for example, the impact on schooling and child labor is underestimated by only 3-4%. The smaller “correction” in the presence of SGRY is expected since NREGA’s spillover on adults’ outcomes is much more pronounced relative to outcomes for children (see Adukia 2020 for a detailed discussion on this).

> 19 Das (2018) finds a positive impact of higher days of work and earnings on expenditure on private coaching.

> 20 The “without SGRY” results reported in table 4 are essentially the same as the results reported in table 2 of Shah and Steinberg (2021).
|Without SGRY control<br>With SGRY control<br>Attends School<br>Works<br>Attends School<br>Works<br>**Panel A: Full Sample**<br>NREGA<br>-1.097<br>1.808_∗∗∗_<br>-1.275_∗_<br>1.863_∗∗∗_<br>[0.718]<br>[0.495]<br>[0.748]<br>[0.516]<br>Observations<br>238,479<br>238,479<br>238,479<br>238,479<br>Mean DV<br>0.80<br>0.11<br>0.80<br>0.11<br>**Panel B: Ages 13-17**<br>NREGA<br>-3.541_∗∗∗_<br>4.195_∗∗∗_<br>-3.693_∗∗∗_<br>4.360_∗∗∗_<br>[1.065]<br>[1.003]<br>[1.119]<br>[1.054]<br>Observations<br>84,619<br>84,619<br>84,619<br>84,619<br>Mean DV<br>0.70<br>0.25<br>0.70<br>0.25<br>**Panel C: Ages 5-12**<br>NREGA<br>0.202<br>0.677_∗_<br>0.056<br>0.609<br>[0.787]<br>[0.366]<br>[0.805]<br>[0.384]<br>Observations<br>153,860<br>153,860<br>153,860<br>153,860<br>Mean DV<br>0.85<br>0.027<br>0.85<br>0.027<br>District FEs<br>Yes<br>Yes<br>Yes<br>Yes<br>Year FEs<br>Yes<br>Yes<br>Yes<br>Yes<br>Age FEs<br>Yes<br>Yes<br>Yes<br>Yes<br>_Source_: Author’s analysis based on NSS 2004 – 2010 (Rounds 61, 64, and 66) and program expenditure data for<br>SGRY from 2004 to 2007. Expenditure data is from the Ministry of Rural Development, Government of India.<br>_Notes_: All estimates are of the order 10_−_2. This table reports estimates of _β_, the effect of NREGA on children<br>who report their “primary activity” as school attendance or work. The coeffcients are from the OLS estimation of<br>equation5. The dependent variable in columns 1 and 3 is a dummy if primary activity of the individual is “attends<br>school”. The dependent variable in columns 2 and 4 is a dummy if primary activity of the individual is any kind of<br>work. Other controls include district fxed effects, year fxed effects, and a vector of year-child-age fxed effects.<br>The standard errors in square brackets are clustered at the district level. All regressions include district, year, age,<br>and sex fxed effects. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guaran-<br>tee Act.<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p <_0_._01.|
|---|
18

# **5. Conclusion**

New anti-poverty programs implemented by governments across the world often have similar predecessor programs. Taking evidence from India’s public works programs, this article highlights the need to be cautious while using the initial roll-out of “new” programs as quasi-experimental research designs. This article shows how ignoring an older public works program, SGRY, can substantially underestimate the general equilibrium effect of the new employment policy, NREGA, on a variety of outcomes. The underestimation of program impact can be attributed to the actual gap in employment provision between the treatment and control districts, which is lower than what is initially assumed. Using a novel dataset of program expenditure at the district level for SGRY, I find that ignoring the older program underestimates the general equilibrium impact of public workfares on labor market outcomes for adults by 30-40% in most instances. This paper, therefore, shows how ignoring the continued existence of a similar older program can have first-order effects on measuring the true impact of the policy. A key area for future research is identifying the exact mechanisms through which public workfares affect the private labor market.

# **References**

Adukia, A. 2020. “Spillover Impacts on Education from Employment Guarantees.” _Education Finance and Policy_ . Afridi, F., A. Mukhopadhyay, and S. Sahoo. 2016. “Female labor force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme.” _IZA Journal of Labor and Development_ 5 (7): 164–178. Altonji, J. G., T. E. Elder, and C. R. Taber. 2005. “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ 113 (1): 151–184.

Angrist, J. D., and J.-S. Pischke. 2014. _Mastering ’Metrics: The Path from Cause to Effect_ . Princeton university press. Atkin, D. 2016, August. “Endogenous skill acquisition and export manufacturing in mexico.” _American Economic Review_ 106 (8): 2046–85.

Azam, M. 2012. “The Impact of Indian Job Guarantee Scheme on Labor Market Outcomes: Evidence from a Natural Experiment.” _IZA Discussion Paper No. 6548_ .

Bahal, G. 2016. “Employment Guarantee Schemes and Wages in India.” _Cambridge Economics Working Paper No. 1626_ . Bahal, G. 2020. “Estimating the Impact of Welfare Programs on Agricultural Output: Evidence from India.” _American Journal of Agricultural Economics_ 102 (3): 982–998.

Banerjee, A. V., and E. Duflo. 2007. “The Economic Lives of the Poor.” _Journal of Economic Perspectives_ 21 (1): 141–168. Berg, E., S. Bhattacharyya, R. Durgam, and M. Ramachandra. 2018. “Can Public Works Increase Equilibrium Wages? Evidence from India’s National Rural Employment Guarantee.” _World Development_ 103: 239–254.

Bharadwaj, P., L. K. Lakdawala, and N. Li. 2020, 11. “Perverse Consequences of Well Intentioned Regulation: Evidence from India’s Child Labor Ban.” _Journal of the European Economic Association_ 18 (3): 1158–1195.

Cascio, E. U., and A. Narayan. Forthcoming. “Who Needs a Fracking Education? The Educational Response to Low-SkillBiased Technological Change.” _ILR Review_ .

Charles, K. K., E. Hurst, and M. J. Notowidigdo. 2018, October. “Housing Booms and Busts, Labor Market Opportunities, and College Attendance.” _American Economic Review_ 108 (10): 2947–94.

Das, S., and A. Singh. 2013. “The Impact of Temporary Work Guarantee Programs on Children’s Education: Evidence from the Mahatma Gandhi National Rural Guarantee Act from India.” _Working Papers 13-03, UW-Whitewater, Department of Economics_ .

Das, U. 2018. “Rural Employment Guarantee Programme in India and Its Impact on Household Educational Decisions: A Focus on Private Coaching.” _Unpublished_ .

Dr`eze, J. 1990. “Famine Prevention in India.” In _The Political Economy of Hunger_ , edited by J. Dr`eze and A. Sen, 13–122. Oxford University Press.

Dutta, P., R. Murgai, M. Ravallion, and D. van de Walle. 2012. “Does India’s Employment Guarantee Scheme Guarantee Employment?.” _World Bank Policy Research Working Paper no. 6003_ .

Fetzer, T. 2014. “Can Workfare Programs Moderate Violence? Evidence from India.” _EOPP Working Paper Number 53_ . Hoynes, H., M. Page, and A. H. Stevens. 2011. “Can Targeted Transfers Improve Birth Outcomes?: Evidence from the Introduction of the WIC program.” _Journal of Public Economics_ 95 (7): 813 – 827.

Imbert, C., and J. Papp. 2015. “Labor Market Effects of Social Programs: Evidence from India’s Employment Guarantee.” _American Economic Journal: Applied Economics_ 7 (2): 233–63.
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Li, T., and S. Sekhri. 2020, 08. “The Spillovers of Employment Guarantee Programs on Child Labor and Education.” _The World Bank Economic Review_ 34 (1): 164–178.

Maccini, S., and D. Yang. 2009, June. “Under the Weather: Health, Schooling, and Economic Consequences of Early-Life Rainfall.” _American Economic Review_ 99 (3): 1006–26.

- Mani, S., J. R. Behrman, S. Galab, P. Reddy, Y. L. Determinants, and C. of Child Growth Project Team. 2020. “Impact of the NREGS on Children’s Intellectual Human Capital.” _The Journal of Development Studies_ 56 (5): 929–945.

- Muralidharan, K., P. Niehaus, and S. Sukhtankar. 2016. “Building State Capacity: Evidence from Biometric Smartcards in India.” _American Economic Review_ 106 (10): 2895–2929.

- Muralidharan, K., P. Niehaus, and S. Sukhtankar. 2017. “General Equilibrium Effects of (Improving) Public Employment Programs: Experimental Evidence from India.”. Working Paper 23838, National Bureau of Economic Research.

- Oster, E. 2019. “Unobservable Selection and Coefficient Stability: Theory and Evidence.” _Journal of Business & Economic Statistics_ 37 (2): 187–204.

̈Ozler, B., G. Dutt, and M. Ravallion. 1996. “A Database on Growth and Poverty in India.” _The World Bank_ .

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# **Supplementary Online Appendix**

# **A Tale of Two Programs: Assessing Treatment and Control in NREGA Studies**

# **Girish Bahal**

## **S1. Oster (2019) test**

Here, I conduct Oster (2019) test for omitted variable bias that is based on information contained in the change in coefficient and the change in R-squared when moving from an uncontrolled regression (column 1 of table 1 in the main article) to a controlled regression (column 4 of table 1). The test is based on Altonji et al. (2005) and shows that given the degree of selection on unobservables relative to observables ( _δ_ ), one can estimate an identified set of the true treatment effect [ _βcontrolled, β_ ( _δ, Rmax_ )]. Alternatively, one can estimate the coefficient of proportionality that is required to explain away the result (under a hypothetical regression with all controls). Following Oster (2019), I establish a theoretical _Rmax_ of 0 _._ 9402 by regressing log wage measure on its lag (together with fixed effects and heterogeneous trends). As can be seen from table S3, if we assume unobservables to be equally important as observables ( _δ_ = 1), the true _β_ is estimated to be between [0 _._ 023 _,_ 0 _._ 026] which suggests that the _β_<sup>�</sup> reported in column 4, table 1 is likely a lower bound of the true general equilibrium effect of public workfares on private wages. Alternatively, if I assume the true _β_ to be equal to zero, this requires _δ_ = _−_ 12. In other words, unobservables need to be an order of magnitude more important than observables. This is a very strong assumption, especially because observables explain more than 92% of the variation in wages.

## **S2. Stability of elasticity estimates**

In this section, I check for the stability of the elasticity estimate as obtained in equation 2 in the main article). Until now, the effect of program expenditure on wages is assumed to be similar for both programs. Although I allow the level of wages to be different (on average) under the NREGA regime by controlling for _NREGAd,t_ , the elasticity of wage is assumed to be constant for both the programs. I now check whether wages have become more or less responsive under NREGA by estimating separate elasticities for SGRY ( _NREGAd,t_ = 0) and NREGA ( _NREGAd,t_ = 1) regimes. For comparability, column 1 of table S4 reports the elasticity of wage estimated under the preferred specification in table 1 in the main article. Column 2 reports separate elasticities for the two programs by interacting _NREGAd,t_ with _sinh_<sup>_−_1</sup> ( _ed,t_ ).

The elasticity of wage under the SGRY regime is 0 _._ 06 (significant at 1% level). In contrast, the elasticity of wage under NREGA is approximately 0.02 (insignificant at 5% level). Prima facie, it appears that expenditure under the older program drives the overall results. However, it is important to note the larger standard errors in the second specification due to the smaller sub-samples. I cannot reject the equality of the two coefficients, even at 90% percent confidence level. Hence, we cannot reject the null of similar aggregate effects due to the two programs.

Next, I test whether there is any sub-sample heterogeneity based on how well the employment programs were implemented. A useful criterion to measure program implementation’s intensity is the annual average per-capita expenditure incurred in a district. Table S5 reports separate elasticities for sub-samples divided on the basis of district-wise _low_ and _high_ annual average (per capita) expenditure, respectively. This is achieved by interacting an indicator variable _Iexp_ which takes the value one (zero) if the average expenditure of a district is above (below) the median value of the series (<sup>�2010</sup> _t_ =2001<sup>_ed,t_)</sup><sup>_/_10.The elasticities for</sup><sup>_low_and</sup><sup>_high_average expenditure groups</sup> are estimated to be around 0.01 and 0.03, respectively. Based on the coefficient size alone, this suggests the
One, Two, and Three 2 general equilibrium effect of the workfares is approximately three times larger in the high-expenditure districts. However, owing to the large standard errors, the two coefficients’ equality cannot be rejected ( _p_ - _value_ = 0 _._ 44).

The differences in elasticity estimates are consistent with previous studies like Imbert and Papp (2015) who find a more significant general equilibrium effect for the “star-states” that implemented NREGA well. It is worth mentioning that the above differences in elasticities represent a lower bound on the difference between the program’s impact due to the heterogeneity in program implementation across districts. Any scale effects based on how well the program is implemented are partialled out by fixed effects and differential trends in equation 2 in the main article..

# **S2. Effect on Male and Female Wages**

Here I check whether the public workfares affected male and female wages differently. Using the same set of controls as in equation 2, table S6 reports wage elasticities based on gender as well as over different program regimes. Column 1 of table S6 reports the elasticity of male wages over the aggregate sample, while column 2 shows male wage elasticities separately for SGRY and NREGA regimes. Columns 3 and 4 show the same set of results but for female wages. Based on the average growth in _ed,t_ and the elasticities of male and female wages reported in columns (1) and (3), respectively, public workfares are shown to increase male wages by 0.3 percent ( _t_ = 1 _._ 92) and female wages by 0.4 percent ( _t >_ 2) per annum on average.

The more significant impact of employment programs on female wage rates is expected since, relative to men, women earn 18-20 percent lower on average. On the other hand, employment programs offer equal wages for men and women, inducing a higher growth in female wage rates. Columns (2) and (4) show that the wage elasticities for male and female wages under the SGRY regime are noticeably larger than the respective wage elasticities during NREGA. However, a test on the equality of coefficients over the two program regimes cannot be rejected at a 95 percent confidence level for both male and female wages.

# **References**

> Altonji, J. G., T. E. Elder, and C. R. Taber. 2005. “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ 113 (1): 151–184.

- Imbert, C., and J. Papp. 2015. “Labor Market Effects of Social Programs: Evidence from India’s Employment Guarantee.” _American Economic Journal: Applied Economics_ 7 (2): 233–63.

- Oster, E. 2019. “Unobservable Selection and Coefficient Stability: Theory and Evidence.” _Journal of Business & Economic Statistics_ 37 (2): 187–204.
**Table S1.** Comparison of SGRY and NREGA

|Description|SGRY|NREGA|
|---|---|---|
|Centrally Sponsored Scheme|Yes|Yes|
|Centre:State Cost Ratio|75:25|90:10|
|Female participation target|33%|33%|
|Districts covered|All|All|
|Primary Objectives:|||
|a) Wage Employment|Yes|Yes|
|b) Infrastructure Creation|Yes|Yes|
|Restrictions on public works:|||
|a) Ban on contractors|Yes|Yes|
|b) Ban on heavy machinery|Yes|Yes|
|c) Wage:Capital Cost Ratio|60:40|60:40|
|Implementing Authority|Village<br>Panchayat|Village<br>Panchayat|

_Source_ : Annual reports (2001 – 2010), Ministry of Rural Development, Government of India. _Note_ : The table compares various program attributes of SGRY and NREGA. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act.

**Table S2.** Results with AWI data: Impact of public works on wages with binary treatment

||(1)|(2)|(3)|
|---|---|---|---|
|_NREGAd,t_|7.197<sup>_∗∗∗_</sup>|-2.302|-3.045|
||[2.319]|[1.946]|[1.989]|
|District effects|No|Yes|Yes|
|Year effects|No|Yes|Yes|
|Trend effects|No|No|Yes|
|Observations|1340|1340|1340|
|Adjusted_R_<sup>2</sup>|0.009|0.853|0.918|

_Source_ : Author’s analysis based on AWI data (for field wages). _Notes_ : All estimates are of the order 10<sup>_−_2</sup> . For a district, _NREGAd,t_ takes the value zero (one) before (after) the implementation of NREGA. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
One, Two, and Three 4

**Table S3.** Oster Test

|(1)|(2)|
|---|---|
|�<br>**_βc, β_(****_δ, Rmax_)**<br>�|**_δ|β_=0**|
|�<br>0_._023_,_ 0_._026<br>�|-12.00|

_Source_ : Author’s postestimation analysis based on table 1. _Note_ : Column 1 reports the identified set of elasticity estimates. _βc_ is the elasticity estimate obtained after adding all controls (column 4, table 1 while _β_ ( _δ, Rmax_ ) is computed at _δ_ = 1 and _Rmax_ = 0 _._ 9402. Column 2 reports the coefficient of proportionality that is required to explain away the treatment effect after adding the unobservables in a hypothetical regression.

**Table S4.** Effect over different program regimes

||(1)|(2)|
|---|---|---|
|_sinh_<sup>_−_1</sup>(_ed,t_)|2.342<sup>_∗∗_</sup>||
||[1.021]||
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=0||5.556<sup>_∗∗∗_</sup><br>[2.032]|
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=1||1.724|
|||[1.063]|
|All Controls|Yes|Yes|
|Observations|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . Column 1 shows the wage elasticity as reported in column 4 of table 1. Column 2 reports separate elasticities for SGRY and NREGA regimes. All other controls are the same as in table 1 in the main article. Regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district-level and robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
**Table S5.** Stability of Estimates

||(1)|(2)|
|---|---|---|
|_sinh_<sup>_−_1</sup>(_ed,t_)|2.342<sup>_∗∗_</sup>||
||[1.021]||
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_Iexp_=0||1.129<br>[1.538]|
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_Iexp_=1||2.839<sup>_∗_</sup>|
|||[1.499]|
|All Controls|Yes|Yes|
|Observations|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . Column 1 shows the wage elasticity as reported in column 4 of table 1. Column 2 reports separate elasticities for the sample of districts below and above the median value of average annual expenditure in a district. All other controls are the same as in table 1 in the main article. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district-level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act.

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01

**Table S6.** Effect on Field Wages for Men and Women

|Dependent variable:|_log_(_Mal_|_e Wages_)|_log_(_Fema_|_le Wages_)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_sinh_<sup>_−_1</sup>(_ed,t_)|1.861<sup>_∗_</sup>||2.500<sup>_∗∗_</sup>||
||[0.968]||[1.159]||
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=0||5.733<sup>_∗∗∗_</sup>||5.480<sup>_∗∗_</sup>|
|||[2.152]||[2.144]|
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=1||1.427||2.166<sup>_∗_</sup>|
|||[1.044]||[1.219]|
|All Controls|Yes|Yes|Yes|Yes|
|Observations|1340|1340|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . All other controls are the same as in table 1 in the main article. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
One, Two, and Three 6

**Table S7.** Impact of Public Works on Skilled Wages

|||SGRY +|NREGA||Only NREGA|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_sinh_<sup>_−_1</sup>(_ed,t_)|-9.270<sup>_∗∗∗_</sup>|2.852<sup>_∗∗∗_</sup>|3.717<sup>_∗∗∗_</sup>|1.935<sup>_∗∗_</sup>|1.028|
||[1.992]|[1.188]|[1.390]|[0.885]|[0.889]|
|District effects|No|Yes|Yes|Yes|Yes|
|Year effects|No|Yes|Yes|Yes|Yes|
|Oth. controls|No|No|Yes|Yes|Yes|
|Het. trends|No|No|No|Yes|Yes|
|Observations|1313|1313|1313|1313|1313|

_Source_ : Author’s analysis based on AWI data (for skilled wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . The unit of observation is a district-year. The dependent variable in all the regressions is the log of skilled wages. Columns 1-5 have _sinh_<sup>_−_1</sup> ( _ed,t_ ) as the key regressor. In columns 1-4, program expenditure is for SGRY (b/w 2001 and 2007) and NREGA (b/w 2006 and 2010). In column 5, only expenditure under NREGA is considered (all expenditure values under SGRY are substituted by zero). The Inverse Hyperbolic Sine (IHS) transformation of the expenditure variable sinh<sup>_−_1</sup> ( _ed,t_ ) has the same interpretation as _log_ ( _ed,t_ ). All other controls are the same as in table 1 in the main article. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
|11.507_∗∗∗_<br>[2.708]|7.377_∗∗∗_<br>[2.763]|4.941_∗∗_|[2.386]|2.634<br>[2.237]<br>59,130|Yes<br>Yes<br>Yes<br>is from the Min-<br>. The dependent<br>2008. All other<br>he district level.|
|---|---|---|---|---|---|
|With SGRY control<br>13.650_∗∗∗_<br>[3.126]|9.753_∗∗∗_<br>[3.166]|2.505|[2.395]|0.179<br>[2.318]<br>59,130|Yes<br>No<br>Yes<br>07. Program expenditure data<br>t and year-quarter fxed effects<br>05 and from July 2007 to June<br>uare brackets are clustered at t|
|13.322_∗∗∗_<br>[3.233]|9.215_∗∗∗_<br>[3.305]|2.574|[2.399]|0.177<br>[2.403]<br>59,130|No<br>No<br>Yes<br>from 2001 to 20<br>s include distric<br>2004 to June 20<br>dard errors in sq|
|**Dependent Variable: Real Earnings**<br>Without SGRY control<br>11.544_∗∗∗_<br>11.627_∗∗∗_<br>9.295_∗∗∗_<br>[3.108]<br>[3.016]<br>[2.635]|8.258_∗∗_<br>8.664_∗∗∗_<br>6.224_∗∗_<br>[3.279]<br>[3.143]<br>[2.746]|0.656<br>0.322<br>2.378|[2.208]<br>[2.216]<br>[2.247]|-0.930<br>-1.080<br>1.119<br>[2.327]<br>[2.235]<br>[2.162]<br>59,130<br>59,130<br>59,130|No<br>Yes<br>Yes<br>No<br>No<br>Yes<br>No<br>No<br>No<br>– 2008 (Rounds 61 and 64) and program expenditure data for SGRY<br>ia.<br>ch column presents results from a separate regression. All regression<br>sample is composed of all adults aged 18 to 60 interviewed from July<br>article. All estimates are computed using sampling weights. The stan<br>REGA: National Rural Employment Guarantee Act.|
|_× Dry × Star states_|_× Rainy × Star states_|_× Dry × Oth states_||_× Rainy × Oth states_|ols<br>ols FEs<br>l<br>analysis based on NSS 2004<br>elopment, Government of Ind<br>tes are of the order10_−_2. Ea<br>of real casual earnings. The<br>me as in table 2 in the main<br>a Grameen Rozgar Yojana. N<br> 0_._05_,∗∗∗p <_0_._01|
|_NREGAd,t _|_NREGAd,t _|_NREGAd,t _||_NREGAd,t _<br>Observations|Worker Contr<br>District Contr<br>SGRY Contro<br>_Source_: Author’s<br>istry of Rural Dev<br>_Notes_: All estima<br>variable is the log<br>controls are the sa<br>SGRY: Sampoorn<br>_∗p <_0_._10_,∗∗p <_|
One, Two, and Three 8

|With SGRY control<br>1.426_∗∗∗_<br>1.428_∗∗∗_<br>[0.320]<br>[0.338]<br>0.349_∗∗∗_<br>0.313_∗∗_<br>[0.125]<br>[0.146]<br>156,133<br>156,133<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>-3.129_∗∗∗_<br>-3.040_∗∗∗_<br>[1.042]<br>[1.110]<br>-0.874<br>-0.705<br>[1.017]<br>[1.116]<br>156,133<br>156,133<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>RY from 2004 to 2007. Program expenditure<br>ssions include district and year-quarter fxed<br>July 2004 to June 2005 and from July 2007<br>dual spends in public works. The dependent<br>ll other controls are the same as in table 2 in<br>e clustered at the district level. SGRY: Sam-|
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|**Panel A. Dependent Variable: Public Employment**<br>Without SGRY control<br>1.174_∗∗∗_<br>1.161_∗∗∗_<br>1.157_∗∗∗_<br>1.442_∗∗∗_<br>[0.316]<br>[0.315]<br>[0.329]<br>[0.321]<br>0.231_∗∗_<br>0.222_∗_<br>0.183<br>0.360_∗∗∗_<br>[0.115]<br>[0.115]<br>[0.135]<br>[0.125]<br>156,133<br>156,133<br>156,133<br>156,133<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Private Employment**<br>Without SGRY control<br>-2.164_∗∗_<br>-2.237_∗∗_<br>-2.137_∗∗_<br>-3.104_∗∗∗_<br>[0.982]<br>[0.956]<br>[1.011]<br>[1.070]<br>-0.321<br>-0.445<br>-0.270<br>-0.773<br>[1.076]<br>[0.960]<br>[1.062]<br>[1.131]<br>156,133<br>156,133<br>156,133<br>156,133<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all men aged 18 to 60 interviewed from<br>variable in panel A is the percentage of days in the past seven days that an indivi<br>entage of days in the past seven days that an individual spends in private works. A<br>s are computed using sampling weights. The standard errors in square brackets ar<br>na. NREGA: National Rural Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>to June 2008. The dependent<br>variable in panel B is the perc<br>the main article. All estimate<br>poorna Grameen Rozgar Yoja<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p _|
|With SGRY control<br>1.181_∗∗∗_<br>1.211_∗∗∗_<br>[0.256]<br>[0.263]<br>0.342_∗∗∗_<br>0.377_∗∗∗_<br>[0.096]<br>[0.119]<br>159,348<br>159,348<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>-2.606_∗∗∗_<br>-2.422_∗∗∗_<br>[0.751]<br>[0.776]<br>-0.812<br>-0.576<br>[0.591]<br>[0.618]<br>159,348<br>159,348<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>RY from 2004 to 2007. Program expenditure<br>ssions include district and year-quarter fxed<br> from July 2004 to June 2005 and from July<br>dividual spends in public works. The depen-<br>s. All other controls are the same as in table<br>ets are clustered at the district level. SGRY:|
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|**Panel A. Dependent Variable: Public Employment**<br>Without SGRY control<br>0.796_∗∗∗_<br>0.789_∗∗∗_<br>0.812_∗∗∗_<br>1.189_∗∗∗_<br>[0.257]<br>[0.256]<br>[0.257]<br>[0.257]<br>0.159_∗∗_<br>0.157_∗∗_<br>0.187_∗_<br>0.344_∗∗∗_<br>[0.069]<br>[0.069]<br>[0.097]<br>[0.096]<br>159,348<br>159,348<br>159,348<br>159,348<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Private Employment**<br>Without SGRY control<br>-1.443_∗∗_<br>-1.706_∗∗_<br>-1.491_∗∗_<br>-2.330_∗∗∗_<br>[0.721]<br>[0.703]<br>[0.728]<br>[0.776]<br>-0.309<br>-0.387<br>-0.133<br>-0.727<br>[0.577]<br>[0.562]<br>[0.591]<br>[0.609]<br>159,348<br>159,348<br>159,348<br>159,348<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all women aged 18 to 60 interviewed <br>dent variable in panel A is the percentage of days in the past seven days that an in<br>percentage of days in the past seven days that an individual spends in private work<br>mates are computed using sampling weights. The standard errors in square brack<br>Yojana. NREGA: National Rural Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>2007 to June 2008. The depen<br>dent variable in panel B is the<br>2 in the main article. All esti<br>Sampoorna Grameen Rozgar<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p _|