not only on program participation but also on the behavior of peers and support from them; this link is studied in greater detail in Kandpal and Baylis (2011).

A potential concern related to the use of PSM in comparing participants to women in untreated districts is that our treated sample is truncated by only including those women who participate. The true propensity of having access to _Mahila Samakhya_ for women living in the treated districts is one, so the best control-group matches would be women with high propensity scores in the untreated districts. Therefore, by definition, women in villages without the program represent the full distribution of outcomes, while treated women represent a left-truncated sample of this full distribution. However, in the above matching process, we are comparing the full distribution of women in untreated districts to a subset in treated districts that has chosen to be treated, and therefore likely has higher propensity scores. If uncorrected, this truncation could bias our treatment results upward.

Thus, we may have a right-truncated distribution of non-participants and a left-truncated distribution of participants in treated districts. The distributions of propensity scores for program participation of treated and untreated women highlight the problem of truncation (figure 1). The distribution of the untreated is bimodal, so we should not compare individuals in or around the lower mode of the untreated distribution to participants. Similarly, comparing non-participants to individuals around the higher mode of the propensity scores for the untreated would also be misleading. We correct for truncation by re-estimating the treatment effects comparing non-participants to the untreated (presented in the upper panel table 11) with the sample of the untreated limited to those whose propensity scores are below the lowest percentile of propensity scores for participants. For the comparison between participants and the untreated, we limit the sample of untreated to women with propensity scores for program participation greater than the lowest percentile of propensity scores for participants. Results presented in table 14 indicate that truncation does not influence the outcomes presented in table 11: the sign, size, and significance of the estimates do not change for either comparison set (non-participants versus untreated, and participants versus untreated).

# **7.4 Comparing PSM and 2SLS Estimates**

The marginal effect estimates obtained from 2SLS cannot be interpreted as average treatment effects, and thus cannot be compared directly to the estimates obtained from PSM. Multiplying 2SLS marginal effect estimates with individual propensity scores for participation gives us the distribution of treatment effects. The average of this interaction is, then, the average treatment effect. In table 10, we present the 2SLS estimates for average treatment effects of participation for the entire sample, participants, non-participants, and the untreated.

Marginal effect estimates point to the significant benefits from participation. In the untreated districts in our sample, 57.5 percent of all women (69 of 120 women) can go out without permission. If these districts were to be covered by _Mahila Samakhya_ , our results suggest that 75 percent (89 of 120 women) would be able to do so. Similarly, only 19 percent of all untreated women (25 of 131 women) participate in village council meetings, whereas if the program covered these districts, 41 percent of these women (55 of 131 women) would participate in village council meetings. Finally, only 19 percent of the women had NREGS cards (27 of 140 women), but if they were to receive the program, 88 percent (122 of 140 women) would have access to outside employment.

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