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.