which were read one by one, in a randomly provided order. As each card was read, committee members were asked to place it on a long board containing 3 different colors (red, yellow and green), according to the capacity of each household to satisfy needs during the entire year.<sup>12</sup> When disagreements were observed, the facilitator would recall the characteristics defined during the discussion to help members find a consensus.

Once the process was completed, ranks from each committee were standardized and an average was obtained. If a score for any given household deviated by more than 2 standard deviations from the other two ranks, it was not used. Finally, within each village, households were ranked according to their average score, which together with the village quotas, and after community validation, determined their beneficiary status.

Food Consumption Score Formula (FCS) The FCS method relies on a formula to identify households at risk of food insecurity during the lean season based on a limited set of household characteristics. In Niger, food insecure households during the lean season are not necessarily those with low consumption levels throughout the year. For instance, based on nationally representative data in Niger, Schnitzer (2016) highlights the relatively weak correlation between consumption and food insecurity during the lean season. Trade‐offs may arise in the design of targeting mechanisms for social policy if the poor differ from the seasonally food insecure. The consideration of transient vulnerabilities and shocks in targeting remains a challenge (Del Ninno and Mills, 2014). In the Sahel, many humanitarian actors use a method called the Household Economist Analysis (HEA), which tends to identify households at risk of being seasonally food insecure. Schnitzer (2016) shows that the formula on which the HEA method is based can be further improved to identify food‐insecure households. The third method we use here is thus a formula that aims to estimate the food consumption score indicator based on a limited number of easily verifiable variables. The food consumption score is widely used by agencies addressing food crises and aims to reflect both the quantity and quality of food consumed. Specifically, the food consumption score is based on a set of questions asking about the consumption frequency of different food groups during the past seven days (WFP, 2008).<sup>13</sup> The FCS formula was developed based on data from the lean season of the 2011 LSMS survey. A separate model was estimated for each of the three livelihood zones. The formula was derived from a step‐wise regression model and relied on a set of variables including household demographics, dwelling characteristics, land ownership, livestock, and assets.

> 12 In practice, based on the door‐to‐door registry survey data, around 7 percent of households in the top part of the PMT distribution were filtered out from the selection process. This step aimed to reduce the burden of the ranking process (by reducing the number of households to be ranked), while at the same time improve targeting efficiency and minimize manipulation. As shown in Schnitzer (2016), PMT can be highly effective at excluding households in the top part of the consumption distribution. Section 5.3 below discusses that this step does not affect the results on targeting performance.

> 13 Food groups included cereals and cereal products, tubers and plantains, legumes and seeds, vegetables, fish and meat, fruits, milk and milk products, oil and grease, sugar products, spices and condiments.

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