balanced, i.e., on average, similar in their baseline characteristics. Table 4-7 compare the sample means of household economic and demographic characteristics, village infrastructure, and activity from the baseline survey in 2007. Table 4 shows that households in treatment and control GPs are statistically indistinguishable based on household demographic and economic characteristics such as gender, education, caste and asset ownership. 6 Tables 5 and 6 show that, on most measures of village infrastructure and activity, the treatment and control villages are reasonably well balanced, but a few variables are significantly higher for treatment villages, and others significantly lower. Treatment villages have a greater number of government schools and teachers, road construction, irrigation activity, and lower numbers of hand pumps, electricity construction activity and male agricultural wages. The results in Table 7, however, suggest a more systematic kind of difference; households in treatment GPs had greater pre-existing levels of awareness and participation compared to those in control GPs. The likely reason for this is that poor coordination between the intervention and survey teams resulted in KSIRD facilitators initiating their training in some treatment villages prior to the collection of the baseline data.

Since the results from Tables 4-7 indicate that there may be baseline differences between the treatment and control groups, we need to account for these differences in estimating the treatment’s impact. To do this we estimate a difference-in-difference model first without, and then with, individual or village fixed effects depending on the nature of the data. Table 8, columns 1 and 3, show the results from an estimation of equation (1) for household awareness, participation and government transfers with individual data. There is no evidence that the intervention had a significant impact— both with and without individual fixed effects. On some measures, such as heard of MNREGS, and heard of gram or ward sabhas, the coefficient is positive and meaningful in magnitude, but very imprecisely estimated.

For village outcomes, presented in Table 9 and 10, the results also show no impact from the treatment on most measures of village infrastructure and activity. We do, however, find a negative treatment effect for government schools and road construction and a positive treatment impact for number of hand pumps in the village. The standard errors of these estimates increase considerably with the inclusion of fixed effects making them significant only at the 10 percent level. The one exception is the male agricultural wage7 that increases by 10 percent and retains its significance even after adding GP fixed effects. This suggests that MNREGA, which guarantees employment for 100 days at a fixed wage, was implemented more effectively in treatment villages and that this had a general equilibrium effect on male agricultural wages.

> 6 Only one variable, TV ownership, is significantly more for the treatment households at the 10 percent level of significance.

> 7 Agricultural wages in India are determined at the village level with almost no individual variation.

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