results contrast with those from Indonesia, where CBT performed considerably better than PMT in matching community perceptions of poverty (Alatas et. al. 2012).

Table 3 summarizes the differences in the profile of beneficiary households, compared to non‐ beneficiaries, for each method. The CBT process leads to the selection of households with fewer assets, fewer livestock, lower income per adult equivalent, and lower food stock. It also leads to the selection of households with slightly less consumption, but only based on the adult‐equivalent measure. The FCS method leads to the selection of households with slightly lower consumption, and fewer household assets. The PMT method leads to the selection of households with higher poverty, lower consumption, as well as lower income per adult equivalent and food stock. PMT also selects households with a slightly lower share of underweight children. None of the methods is able to identify food insecure households (as measured by the food consumption score during the harvest season). We return to this issue below. All methods are well‐aligned with subjective well‐being indicators, and this is especially the case for CBT and PMT. For instance, poverty (based on perceptions of others) of CBT and PMT beneficiaries is 28 and 25 percentage points higher than non‐beneficiaries.

# 3.2 Legitimacy among local populations

We now turn to one of the innovations of the study, which is to document whether the legitimacy of the various targeting methods varies among local populations. To analyze legitimacy, we focus on four main questions from the follow‐up survey. The first two questions capture legitimacy based on (i) whether respondents are satisfied with the overall targeting method,<sup>23</sup> and (ii) whether respondents would like the same method to be used again in similar programs in the future. The following two questions capture perceptions of accuracy, namely (i) whether respondents believe that _any_ households were wrongly included; and (iv) whether respondents believe that _any_ households were wrongly excluded from the program. For each of these variables, we estimate an OLS regression that includes indicators for the targeting method assigned to the village (using CBT as the excluded category), as well as commune fixed effects. Robust standard errors are clustered at the village level. We first assess differences across methods by pooling all respondents, and then disaggregate the results between beneficiaries and non‐ beneficiaries.

Legitimacy is found to be relatively high across targeting methods (Table 4). Overall, only 2 percent of households reported to be “unsatisfied”, 10 percent a “little satisfied”, 40 percent “satisfied”, and 49 percent “very satisfied” with the targeting approach. There are no statistical differences in satisfaction across methods. Also, 83 percent of respondents stated that they would like the same approach to be used again in the future. Small differences are observed, suggesting that respondents tend to prefer the formula‐based method to be repeated, when compared to CBT.

Perceptions of the accuracy of each method show that inclusion and exclusion errors are reported, but again with little differences across targeting methods. On average, 36.4 percent of respondents report that at least one non‐poor household has been selected. Exclusion errors are more prevalent, with 55.5 percent of respondents reporting that at least one poor household has been excluded. Note that it is

> 23 This is measured on a scale from 1 to 4 (1, unsatisfied; 2 a little satisfied, 3 satisfied, and 4 very satisfied).

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