To analyze short-term labor-market and productivity effects of MNREGS and the underlying channels, we use 1999-00 and 2007-08 ARIS-REDS panel data that include member-level labor supply, and details on agricultural production as well as information on the total amount of employment generated by MNREGS in a subset of villages. A Difference-In-Difference (DID) design with household fixed effects is used to control for time invariant unobservables and 1982 data to test for pre-treatment parallel trends between treated and control groups.

# **3.1 Data and analytical approach**

Our primary data source is the 1981/82, 1999/00, and 2007/8 rounds of ARIS-REDS, a nationally representative panel survey first conducted in 1971 that provides comprehensive information for more than 4,000 panel households in 232 villages from 17 Indian states.<sup>12</sup> The survey contains detailed information on labor supply and wage receipts by each household member and inputs and outputs from agricultural production. To assess MNREGS impacts, we draw primarily on the survey’s labor and agricultural modules to assess impacts of the program being available on labor markets and agricultural productivity.

When the 2007/8 round was administered, MNREGS had been implemented in phase I and II districts (our treated group) but not yet in those to be covered under phase III of the program, allowing us to use the latter as a control.<sup>13</sup> Letting _i_ denote households, _d_ district, and _t_ time (with _t_ =0 for the 1999/2000 and _t_ =1 the 2007/08 round), we use the REDS panel data to estimates the impact of MNREGS on various outcome variables using a regression framework of the form where _Yidt_ is the outcome variable of interest _MNREGSdt_ is an indicator variable that equals 1 if district _d_ was part of phases I or II of the MNREGS roll-out and zero otherwise; _Postt_ is a time dummy; **_X_** is a vector of time varying controls at individual (sex, age, age square, education, education square, marital status), household (land ownership, marital status size, female shares in different age categories, head’s gender and age) and village (population, share of SCs and STs, share of agricultural households, availability of paved road, distance to next town) level; _μi_ a household fixed effect; and _εidt_ an error term.<sup>14</sup> Our main interest is in _β2_ , the estimated impact of MNREGS exposure and we also introduce an additional interaction with

> 12 The sample includes all of a household’s descendants who still reside in the village in case of a split.

> 13 As discussed above, for Andhra Pradesh, Bihar, Chattisgarh, Haryana, Madhya Pradesh, Maharashtra, Rajasthan, and Tamil Nadu, we were also able to obtain administrative data on employment generated by MNREGS overall and for irrigation-related activities, mainly cleaning and rehabilitating canals. If implemented in an effective manner, the latter would quickly improve agricultural productivity even for non-participants while program-induced wage payments would affect risk coping mainly by direct beneficiaries. Due to delays with implementing the survey in Kerala, all of the state’s districts had been covered by MNREGS at the time the survey was administered, forcing us to drop observations from this state. Our final analysis sample thus includes 16 of the original states.

> 14 For households who split between our two periods, successor households resident in the same village are included with the same id in the second period. In practice, dropping them does not affect the substantive results.

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