Dobhas, Dushads, STs, Musahars, Sardars, and Yadavs -- we reject the null hypothesis of equality of coefficients at least at the 10 percent level of significance. We interpret this as evidence that these groups are distinct from others in our sample. This is consistent with what we see in Figures 1.1 and 1.2.

Next, we decompose inequality into within- and between-group components. We use two measures. The first decomposes Theil’s L or GE(0) which belongs to the additively decomposable General Entropy class of inequality measures (Bourguignon 1979; Cowell 1980; Shorrocks 1980).<sup>xii</sup> Our second measure, the ‘ELMO’ statistic (Elbers, et al. 2008), normalizes betweengroup inequality with the maximum possible between-group inequality given the current income distribution, relative sub-group size, and their rank order. A key advantage of this measure over conventional decomposition techniques is that it allows for comparisons between populations with different numbers of groups and different population sizes. For these reasons, the discussion that follows focuses on the results from the ELMO measure.<sup>xiii</sup> Results on between-group inequality in MPCE are presented in Table 3. Note that in panel (a), which features broad caste groups, the contribution of _between_ caste inequality to total inequality is just 0.6 percent using the conventional measure (column b) and 0.8 percent using the ELMO measure (column c). When we use jati-level groupings however, these go up more than 300 percent to 2.9 and 3.1 percent, respectively. Thus, considerably more inequality is explained by variations between jatis than variations across government caste categories.<sup>xiv</sup> Finally, we examine how much inequality _between_ jatis drives inequality within broad caste groups. Panel (b) of Table 3 presents decompositions of inequality by jati for each broad caste group separately. Here we see that the contribution of between-jati inequality to within broad caste inequality is relatively low, varying from 0.03 percent for Muslims to 6.5 percent for Forward Castes. Taken together, these estimates suggest that in the poorest communities of rural Bihar, inequality appears to emerge from variations _within_ caste and jati groups, rather than _between_ them, which is consistent with other literature on the subject (Lanjouw and Rao 2011; Lanjouw, Murgai and Stern 2013).

This observation is critical for an informed debate about targeting poverty alleviation in India. As discussed earlier, affiliation to a broad caste group determines eligibility for a wide range of government programs and services. Our results suggest that some SC groups, for example, are truly vulnerable on a range of indicators, and they are a small sub-group of the broader SC category. Conversely, many non-SC/ST groups are also equally vulnerable.

# **4. Results: How effective is targeting based on caste?**

The results shown above raise the question of whether aggregate caste groupings are an effective strategy for targeting poverty-alleviation programs, and whether benefits disproportionately accrue to specific jatis within these caste groupings. We now address this question directly by examining 6