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.