(PMT) and community-based targeting (CBT), two targeting methods that are commonly employed, and compare their performance as implemented in practice, within and across countries. Since in low-income settings, PMT and CBT are almost always applied after geographical targeting, our analysis allows us to understand the actual contributions of PMT and CBT once _geographical_ targeting has been applied. In addition, we explore the role of measurement choices across multiple dimensions, including three key ones. First, we consider both per-capita consumption and food security as well-being metrics, the latter being a major policy objective in humanitarian interventions. Second, we explore the role of program coverage (or the share of beneficiaries selected) in the performance of targeting. Third, we rely on multiple measures used in the literature to evaluate targeting, with a preference for distribution-sensitive indices (i.e. measures that consider distances to poverty lines). To provide additional insights, we conduct simple regressions to identify whether distinct types of households are selected or excluded by different targeting methods. Finally, we collect cost data, an area where evidence is particularly limited, although the cost of targeting instruments is often cited as an argument against targeting.

Several important results emerge from this study. We show that measurement choices play a decisive role in the performance of targeting. At first sight, PMT seems to perform well in reaching the poorest households.<sup>2</sup> The median PMT-targeted program provides 20 percent more resources to the poorest than would random allocations. This is only 6 percent for CBT, which performs systematically worse than PMT based on consumption per capita across all programs studied. Nonetheless, when considering distributionsensitive measures of performance, none of the methods seems to perform significantly differently from each other and from random or budget-neutral universal delivery of benefits. This result is driven by the high poverty rates and the significant homogeneity present in the program areas studied. A different picture emerges when relying on food insecurity as a well-being metric: only four targeting schemes perform better than a random allocation of benefits, and there are no systematic differences between PMT and CBT. Again, when relying on distribution-sensitive metrics, no targeting scheme makes a significant contribution toward reaching the most food-insecure households.

While differences between targeting methods are relatively small, we also find that for a given method, the program coverage (i.e. the share of beneficiaries selected) plays a crucial role in explaining targeting performance. Higher program coverage usually implies a lower contribution that targeting makes relative to a random allocation of benefits. As such, comparisons of targeting performance that do not account for this parameter can be misleading, and the adjustments that we make in our primary data analysis by harmonizing program coverage rates are not trivial. In addition, our results highlight the critical role of

> 2 Where ‘poorest’ is defined based on program coverage rates. For instance, if a program covers 30 percent of the population, the poorest are then defined as those in the bottom 30 percent of the consumption per capita distribution.

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