The results show that in the context of the study PMT is more efficient than other methods in identifying households with lower consumption per capita. The methods perform similarly with respect to other welfare benchmarks, including community perceptions of poverty. Legitimacy is high across all methods. Formula‐based methods are not found to be less legitimate that community‐based targeting among local populations, but in fact seem to be preferred. The analysis of a range of determinants of targeting performance contributes to explain why. While community‐based targeting can incorporate information available at the local level, for instance related to the incidence of shocks, it is affected by imperfect information, manipulation, and, to a lower extent, fatigue. Local populations recognize some of these issues. While concerns about manipulation and imperfect information in community‐based targeting are in part justified, the associated risks can be largely addressed through careful implementation, including the triangulation of results across several independent selection committees. Implementation issues and measurement error are not found to strongly affect the performance of formula‐based methods, but these methods have limited ability to take into account determinants of transient poverty and seasonality.

In the end, the decision on which targeting method to use should be driven by a cost‐benefit analysis. In the context of the study, the results suggest that PMT is the method that may maximize program effectiveness, at least in the short term and based on a limited set of outcomes. Households selected by PMT tend to display significantly higher short‐term program impact on food security and food stock than households selected through CBT, and, to a lesser extent, FCS. On the other hand, the costs of methods studied in this paper are largely similar; the cost of PMT or FCS per household is roughly $6.8 USD, while the cost of CBT per household is $5.4 USD, which represents lower bound estimate.<sup>31</sup> The results obtained in the context of Niger contrast with those from other settings, most notably from Indonesia (Alatas et al., 2012). While both studies do not find large differences in targeting efficiency between methods, the results on legitimacy among local populations differ. In Indonesia, communities seemed to have a preference for CBT over PMT, which is not the case in Niger. The factors driving targeting performance can help explain the differences. For instance, in the context of Niger, imperfect information at the local level and manipulation are shown to affect results from community‐based targeting, while these factors were not observed in Indonesia. Other factors may also play a role, including the prevalence of poverty, local inequalities or the degree of trust among communities, in addition to the way targeting methods are communicated to local communities and implemented. As such, the results highlight the importance of carefully considering factors that may affect the relative performance of alternative targeting methods in a given context, as well as taking a broad view in analyzing targeting performance beyond efficiency.

Many low‐income countries, like Niger, have limited funding for social programs and low coverage of safety nets, despite high prevalence of poverty. Although PMT can be seen as performing slightly better than CBT and FCS in the context of the study, differences in efficiency, legitimacy and effectiveness remain limited. The results can thus also be interpreted as suggesting that the choice of methods to select safety net beneficiaries does not make a large difference. Variations in performance across targeting methods are small relative to the overall level of exclusion stemming from limited funding for social programs. In

> 31 This is a lower bound estimate as household listings were based on a prior registry survey, and any CBT process would also require collecting complementary data on households to be registered. Estimates for PMT, FCS, and CBT are based on variable costs (including costs related to field staff and logistics), and exclude fixed costs related to program implementation (other than targeting), such as program staffing costs.

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