found to have increased female empowerment (Afridi _et al._ 2013), reduced <mark>gender-based violence (Amaral</mark> _<mark>et al.</mark>_ <mark>2015), increased school participation by children in primary school age (</mark> Islam and Sivasankaran 2015), leading to better school outcomes, i.e. grade progression and achievement scores (Mani _et al._ 2014).<sup>7</sup> Given the importance of casual labor for India’s poorest groups, wide inter-temporal fluctuations of rural labor demand, and prevalence of gender discrimination in rural labor markets, wage impacts of MNREGS have been a key focus of study. A Difference-in-Difference design using NSS data points towards a program-induced wage increase of about 5% that was, with 8%, significantly higher for females (Azam _et al._ 2012). NSS data point towards program-induced wage increases of some 5 points overall and 9 points in states with good program implementation. No gender-specific effects are found but estimates’ plausibility is increased by the fact that the increase is confined to the dry season although net effect on employment was zero (Imbert and Papp 2015b). <mark>Monthly wage data for 2000-2011 suggest that MNREGS increased growth of real agricultural wages by 4.3% during the peak season in some states with gender wage gaps and skilled wages remaining unaffected (Berg</mark> _<mark>et al.</mark>_ <mark>2014).</mark><sup>8</sup> <mark>Evidence of p</mark> ositive wage effects is not universal, however: a RD for <mark>the transition from phase II to III, using 64</mark><sup>th</sup> <mark>round NSS data, suggests limited or no impact on labor market outcomes or on closing of the gender wage gap, leading to authors to conclude that MNREGS may have performed an insurance or safety net function (</mark> Zimmermann 2015a) but not contributed to generation of new employment.

Levels of temporary migration may be affected if MNREGS increases local labor demand. Case studies do indeed suggest that the program reduced short-term ‘distress’ migration by unskilled workers in the dry season, in addition to directly and indirectly enhancing the returns to small and medium farmers’ assets through water-conservation structures and improving liquidity without affecting long-term migration by the higher-skilled (Reddy _et al._ 2014). Econometric evidence from data specifically collected for this purpose supports the notion of a program-induced reduction in short-term dry-season migration but no effect on long-term migration (Imbert and Papp 2015a).<sup>9</sup> While anecdotes in the press highlight potentially negative MNREGS effects on agricultural labor supply, a causal relationship beyond general wages increases is unlikely as program rules rule out program work in the agricultural peak season, a restriction which, based on the evidence, seems to have been mostly adhered to in practice.

# **2.3 Program effects on agricultural productivity**

> 7 <mark>The program is also shown to have affected election outcomes (Zimmermann 2015b) and local violence (Khanna and Zimmermann 2014).</mark> 8 A 2005-10 district panel that combines NSS data with information on rainfall and MNREGS-induced asset generation suggests that, partly due to the assets generated by MNREGS, the sensitivity of wages to rainfall shocks decreased by some 10 points (Shah 2013).

> 9 Given the negative impacts of short-term migration on migrants’ children  (Coffey 2013), this is likely generate significant benefits in terms of human capital.

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