targeting in two anti-poverty programs: (1) the Bihar State Rural Livelihoods Program (BSRLP), and (2) the MNREGS.

The two programs use two very different approaches to reach the poor. The BSRLP requires project implementers to identify possible beneficiaries and offer the program services to them. This approach, which we call “programmatic targeting”, assumes that it is up to the policy maker to induce participation among the population of beneficiaries. MGNREGS, on the other hand, is a “self-targeting” program. It assumes that vulnerable individuals who lack better options will choose to participate on their own.

# 4.1 Program 1: Livelihoods Programs

We first analyze the State Rural Livelihoods Program. These programs are communitydriven development (CDD) projects that organize women into self-help groups (SHGs) that enable them to save and access credit. They are now one of the most important anti-poverty programs run by the Government of India under the umbrella of the National Rural Livelihoods Mission (NRLM). Programs such as these have generally shown to be reasonably effective in mobilizing women, increasing savings and building new institutional platforms in villages (Desai and Joshi, 2014; Khanna, Kochhar and Palaniswamy 2015, Sanyal, Rao and Majumdar 2015, Hoffmann et al 2017).

We obtained information on SHG membership using the endline survey for the evaluation, conducted in 2013.<sup>xv</sup> The SHG membership variable is an indicator for participation in the JEEViKA program. Since the programs treated all the villages in treatment areas, with varying levels of take-up at the individual level, we restrict our analysis to households in treatment villages. Of women in the treatment areas of our sample, 43 percent reported participating in an SHG.<sup>xvi</sup> We use a simple linear probability model to regress a dummy variable for participation in JEEViKA on a set of controls. These include age, age squared, marital status of the woman, age at marriage, a dummy variable for some schooling of the female respondent, a dummy variable for female household-headship, per-capita expenditure and its square, landholdings, education of the household head, and household size. We also include panchayat level fixed-effects. We do this analysis first by adding government defined broad caste categories, with SCs as the omitted category, as additional right-hand side variables. Then we do a separate analysis for groups of jatis – first for SC/ST jatis, with non-SC/ST as the omitted group, and then for non-SC/ST jatis with SC/STs as the omitted group. Regression results are reported in Table 5.1.<sup>xvii</sup> Key regression coefficients are depicted graphically in the three panels of Figure 2.1.

Panel (a) of Figure 2.1 shows that all broad-castes have a lower probability of participating in SHGs than SCs. This result matches the overall program design – the program was actively promoted among the lowest caste groups. Figures 2.1 (b) and (c) however, show that uptake within these groups was far from uniform. We see that while most SC and ST groups in Bihar are more likely to participate in an SHG than non-SC/ST groups, Doms, Adivasis, “Other SCs” and “Other 7