determined roll-out; and (iii) household panels, often limited to specific states, with data from before and after the intervention for those entering the program at different points in time.

In theory, MNREGS could affect agricultural productivity and rural welfare in the short term via three channels. First, although the program offers less work during the main agricultural season to minimize competition with agricultural labor supply, it is likely to affect wage rates and labor supply and demand. Second, by predictably transferring wages income to individuals in times of need, e.g. if hit by shocks or during the dry season, MNERGS can help smooth consumption and reduce vulnerability.<sup>4</sup> For agents who had earlier been risk-rationed or credit-constrained, access to the program may affect portfolio choices and investment decisions. This may result in adoption of activities, e.g. crop choice and use of inputs with higher risk-return profiles and investment in agricultural technology or human capital and health (e.g. keeping kids in school) that could put individuals on a permanently higher growth trajectory. Finally, the nature and quality of local infrastructure (e.g. for water management or roads) constructed by MNREGS would be expected to increase returns to land and labor, the main assets of small farmers and the rural poor.

# **2.2 Evidence of labor market and socioeconomic impacts**

<mark>Implementing MNREGS in a country the size of India with often clientelist structures was bound to give rise to unique challenges. It is thus not surprising to find evidence of mis-targeting (Dutta</mark> _<mark>et al.</mark>_ <mark>2014; Liu and Barrett 2013), corruption, or distribution of benefits along party lines (Das 2015).</mark> In Orissa, payments to ‘ghost workers’ were found to have accounted to 75-80% of total program cost although expectation of an ability to extract rents in the future may also limit theft according to a ‘golden goose effect’ (Niehaus and Sukhtankar 2013b). Exploiting exogenous wage variation suggests that the marginal rate of corruption was close to 100% (Niehaus and Sukhtankar 2013a).

Where these implementation challenges were dealt with, descriptive accounts suggest that the program had positive impact.<sup>5</sup> Several studies find it improved consumption by the poor. NSS data point towards large season-specific effects for scheduled castes and tribes (SCs and STs) but not the general population who, as a result of MNREGS were able to smooth consumption across seasons (Klonner and Oldiges 2014).<sup>6</sup> In Andhra Pradesh, one of the better performing states, panel data suggest MNREGS was well targeted and affected food consumption and asset accumulation by the poor (Deininger and Liu 2013), with impacts on food security, savings, and health outcomes <mark>(Ravi and Engler 2015). Beyond consumption, M</mark> NREGS was

> 4 Productivity shocks have been shown to cause larger changes in wages when supply is inelastic, e.g. because workers are poorer, less able to migrate, or more credit-constrained. These changes generally hurt workers and benefit land owners (Jayachandran 2006).

> 5 Recent IHDS data suggest that MNREGS increased income and wages for unskilled, reducing poverty by up to 32% and preventing 14 million from falling into poverty (Desai _et al._ 2015). 6 A back-of the envelope cost benefit analysis suggests that consumption smoothing benefits for the SC/ST population would have been sufficient to justify the wage outlays incurred by MNREGS (Klonner and Oldiges 2014). Of course conclusions from such an evaluation might change if the disutility of the work to be performed under the program is taken into account (Alik-Lagrange and Ravallion 2015) and posing the right counterfactual is important (Baird _et al._ 2011).

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