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).