A downside of this harmonization exercise is that it cannot be applied to CBT, given the lack of a welfarebased ranking for all data sets except Niger 2.

The performance of PMT under a harmonized threshold tells a different story, compared to the analysis based on different thresholds (i.e. using CBT selection rates for comparison between PMT and CBT). Threshold adjustment affects both the levels of performance and the variations in performance across data sets (Table 6). The median PMT scheme delivers 38% more resources to the poorest households than a random allocation would, compared to 20% for the median PMT scheme under CBT selection rates (see Table 3). The median targeting error rates based on per-capita consumption also increases from 38.6% (see Table 3) to 51.9%. Both results are a mechanical effect of the lower threshold (35%) used in Table 6.

Figure 2, which computes targeting error rates for each selection threshold between 5% and 100% of the population, confirms the decisive role played by the selection thresholds in targeting performance. In fact, the figure illustrates that the performance of targeting varies more across selection thresholds than across targeting PMT schemes. Targeting errors decrease at a quasi-constant rate with the increase of the percentage selection in all data sets. But decreasing targeting errors do not mean an increasing contribution of targeting relative to a random allocation (as illustrated by the 45-degree line). After a selection threshold of around 30 percent, the larger the selection threshold, the smaller the contribution targeting can make relative to a random allocation.<sup>28</sup> A similar result is obtained when measuring PMT performance based on the FCS, although in most data sets targeting errors are indistinguishable from errors obtained from random selection (Figure 5).<sup>29</sup> Using a harmonized selection threshold of 35% (Table 6) confirms that the differences in performance across PMT methods exist, but these are substantially smaller than the observed differences under actual (unharmonized, CBT-based) selection thresholds. Moreover, performance systematically varies, depending on whether methods were applied in areas undergoing narrower or broader geographical selection of poor areas. For instance, where geographical targeting was relatively broader (in Senegal 1, Senegal 2, and

> 28 Figures 3 and 4 show that the poverty gap and poverty severity decrease at quasi-constant rate with the selection threshold for most countries. For datasets with lower initial poverty levels (e.g. Senegal 2), PMT performs slightly better than random or universal transfers at addressing poverty severity, but for the poorest datasets (e.g. Cameroon), universal and random selection perform just as well as PMT.

> 29 We compute the DCI index for each data set in Figure A4 in the Addendum, available upon request. Mechanically, the index has a larger value for low selection rates, because the difference between selected and non-selected households is greater when few households are selected. The DCI values depend critically on the initial distribution in each data set: it is higher in countries with more heterogeneity (Burkina Faso 2 and Mali). For other countries, DCI values are closer, and relatively flat within a data set for selection rates from 20% to 100%. For selected countries, the DCI from PMT targeting is compared with perfect targeting (only the poorest households are rightly selected), random targeting and universal selection (Figure A5 in the Addendum, available upon request). PMT targeting results in a DCI that is substantially larger than random and universal transfers (which, by definition, treat all households equally) in Burkina Faso 2 but in Mali, PMT, random and universal targeting achieve very similar results.

18