improve their voices in the community by taking a stand against alcoholism, domestic violence, etc. However, given the close working relationship with the government, and the time pressure to meet narrow NRLM goals while in Rajasthan, we expected _fewer_ examples of collective action on political issues or public service delivery—those take longer to develop. Previous work on SHGs in Rajasthan found that it took more than two years to see improvements in water and village infrastructure (Deininger and Liu, 2009; Desai and Joshi, 2013).

Results of logit regressions are presented in Table 5. This table presents the odds ratios of the coefficients from each of the individual regressions. We note that—as predicted—external facilitators are considerably less likely in their responses to provide examples of collective action related to politics or the delivery of public services, and these estimates are statistically significant at the 1% level. Specifically, once the other variables in the model are held constant, the relative odds of external facilitators witnessing collective action on issues related to politics and public service delivery are _lower_ by a factor of 0.05 and 0.02 respectively. The relative odds of collective action on women-specific issues is higher for external facilitators, but the coefficient is not statistically significant at the 1% level.

We can conclude from this that even though external facilitators are quite successful in mobilizing women and forming groups, their alignment with the priorities of the state and their narrow focus on group formation may limit their effectiveness in mobilizing women toward deeper political and social change. Local facilitators were better able to connect women to other political organizations and enable them to achieve broader goals.

# Differences in Outcomes

One of the most basic questions facing the RRLP is whether the strategy used to mobilize the poor has an impact on the women who were targeted by the program. Moreover, it is important to know whether the differences in the types of facilitators actually translated into differences in outcomes. To answer this question, we examine two types of data: block-level outcomes reported by the project, and results of a small survey of SHG women in the two types of areas. Given that the two areas were for the most part identical prior to the rollout of the RRLP, and that the program was rolled out in the two areas with almost the same intensity, we can, to some extent, interpret these estimates as causally related to the mobilization effort.<sup>28</sup> We present two sets of results for the block-level outcomes. The first is aggregate data taken from the website of the RRLP program. (The raw data are reported in the Appendix.) Analysis of these data suggests that Intensive and Resource blocks performed very similarly along a number of different metrics: both types of facilitators were able to set up approximately 4-5 SHGs per

> 28 Official data are collected by a strong team led by a statistician, Mr. Hardeep Chopra, who has no vested interest in either block. He was brought into the project right at the time that the Resource and Intensive blocks were being decided in 2012.

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