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.