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.