when programs are implemented (Devereux et al., 2017). While implementation flaws (including implementation lags) have been mentioned as caveats emerging from basic simulations (see for example Brown et al., 2018), other factors such as geographical targeting and the arbitrary choice of selection thresholds have been largely ignored. In contexts such as the Sahel, PMT or CBT methods are almost always applied after geographical targeting has been applied to focus the program on the neediest areas. Because of this, PMT targeting is implemented in places that significantly differ from those that are used to simulate their performance – usually based on national or regional samples. If geographical targeting selects areas that are poorer and more homogenous, then simulations will likely overstate the contributions that PMT targeting or other household-level selection methods can make. Besides, a series of implementation issues affect the actual targeting outcome. This is well-documented in the literature (Devereux et al., 2017; de Sardan & Piccoli, 2018; Stoeffler et al., 2020) and in the reports we reviewed (see Table A1). Finally, simulations often rely on selection thresholds that differ from practice. Given the significant role of selection thresholds in the performance of targeting, this is not trivial.

As an illustration, we compute targeting errors for selection thresholds set at 20% and 40% and provide a comparison with results from Brown et al. (2018) who use these thresholds in Burkina Faso, Mali and Niger (Table A3 and Table A4). As expected, the level of targeting errors is much larger in our data sets than in their study based on nationally representative data sets: our targeting error rates are approximatively 50% larger, based on the same selection thresholds in the same country. In sum, our results suggest that the role of geographical targeting and other implementation issues need to be carefully considered when conducting simulations, and that results obtained based on program data are largely different from those obtained from nationally representative surveys. For future studies based on simulations, our results imply that samples should aim at mimicking areas where PMT will be applied, and to use selection thresholds similar to those employed in practice by social programs (or a wide range of thresholds).<sup>30</sup>

# _5.2 Mechanisms: Determinants of CBT and PMT selection_

To explore what drives the differences between CBT and PMT results, we explore the determinants of the selection of households with each selection method. Table 7 shows simple probit models where the dependent variable is the selection by CBT or PMT in each data set. A few independent variables that were found in most data sets and discussed in the targeting literature are included. These probit regressions are conducted for the seven data sets where we have information on both CBT and PMT selection, and PMT selection thresholds are adjusted to match CBT selection rates (see section 4).

> 30 Yet, it is not always possible to conduct simulations based on the narrow region where PMT will be applied based on nationally representative surveys due to sample size issues.

20