identify “predicted (potential) beneficiary households” by applying the PMT targeting formula to the baseline data collected in the treatment and control groups. In practice, there are differences between the PMT score predicted from the baseline survey and the actual PMT score calculated from the targeting registry census. As such, the set of predicted beneficiaries does not fully overlap with actual beneficiaries in the treatment groups. This approach should thus be interpreted as providing intent-to-treat estimates. Figure A.1 illustrates that the PMT score from the registry census accurately predicts beneficiary status: the probability of being a beneficiary drops sharply around the eligibility cut-off. This shows that the PMT selection procedure was faithfully implemented based on the registry census data. However, the “predicted” PMT score based on the baseline survey variables does not perfectly match the PMT score from the registry census: the likelihood of being predicted beneficiary is strongly associated with the PMT score from the registry census, but no sharp drop is observed at the cut-off.<sup>21</sup> Table 1 (Panel C) illustrates the composition of the sample by “predicted” beneficiary status for all baseline households included in the follow-up sample. (Note that predicted beneficiary status cannot be estimated for households in the booster sample since they were not interviewed at baseline. Booster sample households are not included in specifications involving comparisons with the control group.) The effect of the cash transfers can be separated from the effect of the behavioral change component by comparing “predicted beneficiaries” between the treatment and control groups. ITT estimates are obtained through the following regression model based on follow-up data on the sub-sample of “predicted beneficiary households” (PB=1): _i_ refers to an individual or household (depending on whether the outcome of interest is a household-level or individual-level outcome), _Y_ is the outcome of interest, _Z_ is a set of randomization strata fixed effects, and _ε_ is the error term. Robust (White-Huber) standard-errors are clustered. This specification compares outcomes between observations in cells H, I and J in Table 1 (Panel C). We include sampling weights to ensure results are representative of the overall population in sample villages.

> 21 This is likely due to measurement errors in household survey variables that enter the PMT score calculation (possibly in both the registry census and baseline surveys). For instance, defining household units in the context of large, frequently polygamous households in rural Niger is challenging, and some discrepancies in the application of these definitions may have taken place between surveys. This measurement error likely reflects teething pains during the establishment of the Niger safety net system, including with the quality of large-scale data collection for the registry census in a context characterized by low administrative capacity. Indeed, the PMT targeting was shown to be relatively efficient and to suffer from little measurement errors in subsequent phases of the program (Premand and Schnitzer, forthcoming).

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