eligible and random sample in Table 1. One important difference in the construction of these z-scores is that now we exclude variables used to construct the PMT index. Including these variables would give unfair advantage to the PMT method: we would expect to observe larger differences between eligible and non-eligible according to PMT on the variables underlying its construction.<sup>14</sup> Figure 1 plots the differences in z-scores between RMG-eligible and RS, against the differences between poor and non-poor according to PMT, including 90% confidence intervals. The gaps between groups according to the two methods suggest that PMT outperforms RMG-eligible selection in identifying poor households. The differences in means are significantly larger under PMT for 4 out of 8 analyzed indicators.<sup>15</sup> RMG-eligible selection performs better than pure random assignment to eligible vs. ineligible groups, but it is less accurate than PMT. T-tests for differences in z-scores for eligible and not eligible according to two selection methods are shown in Table A3 of the Appendix.

However, village heads were identifying the poorest households from within a village, not across the sample. If we replicate this analysis by calculating z-scores within village, rather than across the entire sample, village head selection and PMT appear to provide equivalent levels of accuracy. Figure 2 shows the differences in z-scores between eligible and non-eligible according to the two methods, including confidence intervals.<sup>16</sup> The overlap of confidence intervals suggests that we cannot reject the hypothesis of no difference in the magnitude of the welfare gap between eligible and non-eligible according to the two methods. Table A4 in the Appendix shows corresponding t-tests.

In the analysis based on village-level z-scores, easily observed variables perform better under village heads selection, while factors less visible to the public eye perform better under PMT. For example, livestock and durables ownership are negative and significant for both methodologies, but the difference in means is greater under RMG-eligible selection. Conversely, nutrition and income, the status of which might be known only to household members, perform better under PMT; the difference in the nutrition composite z-score t-test is negative and significant only for PMT, and the difference in means for income is significantly larger under PMT.

The village-level results suggest that when organizing beneficiary selection to meet the needs of the local context, the village heads selection was effective through leveraging the heads’ familiarity with the households. This familiarity, however, was limited when it came to assessing more obscure or private household characteristics. In such cases, the rigorous PMT methodology based on detailed survey data performed better.

We must also note that less-poor households (ranked ‘Middle-income’ or ‘Better-off’) were included in the RMG-eligible list in some villages in order to satisfy village quotas for the program. We would expect the inclusion of these non-poor households to worsen the average performance of the village heads selection. In a setting where the quotas for road maintenance were satisfied by the households from the

> 14 Comparison of RMG eligible observations and random sample using this restricted selection of variables provides results similar to those shown in Table 1. See Table A5 in the Appendix for all comparisons.

> 15 Please note, income per household member is shown as z-scores in Figure 1 in order to display the measure along with the other indicators in the graph, but the actual difference for the RMG-eligible vs. RS is -0.1426 (p<0.01) and, for PMT poor vs. nonpoor, -0.8213 (p<0.01).

> 16 Income per household member is again shown as a z-score to aid the graph. Actual difference between RMGeligible and RS is -0.2160 (p<0.01). Between PMT poor and nonpoor, the difference is -0.5417 (p<0.01).

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