We further assess whether formulas based on the nationally representative survey are valid for the project area. We find that external validity of the formulas does not affect targeting efficiency. The PMT and FCS methods are based on a formula developed from a nationally representative survey. The survey was collected a few years earlier, and as such some of the determinants of poverty may have changed. At the same time, there may be specificities in the poor communes where the project operates, and the formula may not be as effective at differentiating the poor from the non‐poor in these areas. To assess whether external validity of the formula is an issue, we develop new weights for the formula based on the baseline survey, and check if performance changes. Tables 13 and 14 provide the results, and show that external validity does not seem to be a concern. In fact, if the recalibrated model were to be used, inclusion errors would not be significantly different for any of the models.

Finally, we also tested whether the exclusion criteria that were used prior to applying the CBT and CFS formulas explain part of their performance. The filter does not seem to matter for any of the key results.<sup>28</sup>

# 5. Short‐term impacts of cash transfers across targeting methods

In this section, we analyze whether short‐term impacts of the cash transfer program vary across beneficiaries selected by different targeting methods. This analysis complements earlier analysis of targeting performance related to efficiency and legitimacy. It tests whether the choice of targeting method has implications for program effectiveness. The analysis is based on a short‐term follow‐up survey collected a few months after the start of the cash transfer program. The survey also included a few short‐ term outcome indicators. It did not include longer‐term measures or a full consumption aggregate. The results do not seek to provide a full picture of program effectiveness (see Stoeffler et al. (2016) or Premand et al. (2016) for analysis of the impacts of earlier phases of the program), but rather to highlight if there are some differences in short‐term impacts across targeting methods.

We start by analyzing whether households selected based on alternative targeting methods report using cash transfers differently, and find no statistically significant differences. Table 15 documents (i) the share of households declaring spending cash transfers across various categories, (ii) average spending amount, and (iii) average spending shares by categories. Households report spending between 65 and 69 percent of the transfers on food. Savings account for an additional 11‐12 percent. This is followed by smaller expenditures for the purchase of durable goods, health expenditures, transfer to other households and education expenditures. Importantly, there are no significant differences in the use of transfers across the groups selected by alternative targeting methods.

We proceed to analyze short‐term impacts of the cash transfer program across groups selected by the various targeting methods. We estimate program impacts on beneficiaries only. While targeting methods were randomized at the village level, only on average 43 percent of households per village were selected as beneficiaries. Although average differences at the village level could be estimated relying on the randomized design, they would not be sufficiently powered to detect meaningful differences across groups since only a sub‐set of each village is covered by the program. Therefore, we focus on analyzing

> 28 See tables A.6 and A.7 in the supplementary appendix.

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