# 1. Measuring and explaining targeting performance

The literature has often evaluated targeting efficiency based on inclusion or exclusion errors, which aim to measure the extent to which households have been wrongly included or excluded in a program. While informative, these measures are not always well‐understood, and do not necessarily provide robust benchmarks as they can be defined and calculated in different ways.

By construction, targeting errors depend on poverty rates and program eligibility thresholds. Consider a country with a poverty rate of 20 percent where safety nets cover 20 percent of the population. If beneficiaries are randomly selected, 80 percent of the beneficiaries would be wrongly included. If the poverty rate changes to 40 percent, then the error under a random selection process would be 60 percent. Any change in either the poverty rate or program eligibility threshold would affect the levels of errors. The fact that inclusion and exclusion errors are a function of poverty rates and eligibility thresholds is often overlooked in the literature comparing targeting performance across programs.

In addition, program eligibility thresholds used to calculate targeting errors are oftentimes not the same as national poverty lines. Calculating inclusion and exclusion errors based on poverty lines may be more relevant if policy makers are interested in the coverage of the poor. Inclusion and exclusion errors calculated based on program eligibility thresholds are on the other hand often determined by budget constraints.<sup>3</sup> Any analysis of targeting efficiency also needs a clear definition of a welfare benchmark, which often depends on program objectives. Targeting methods may relate to differing concepts of poverty as underlying benchmarks. While social protection policies often assume that targeting households with low levels of consumption will also imply reaching households facing other deprivations, this is not always the case. For example, based on a multi‐country study in Sub‐Saharan Africa, Brown et. al. (2017) show that malnourished individuals are not necessarily in households presenting low consumptions levels. Similarly, Schnitzer (2016) finds that households facing seasonal food insecurity are not necessarily households with the lowest per capita consumption levels. Besides, community definitions of poverty are not always consistent with income or consumption welfare proxies. Increasing evidence is pointing to more nuanced poverty definitions among local populations (Alatas (2012), Schnitzer (2016), Stoeffler et al. (2016)).

Inclusion and exclusion errors represent only one dimension of targeting performance, and other measures are important to consider. Legitimacy or social acceptability of targeting among local populations is often a concern of its own. In addition, targeting is not an end goal but rather a means to effectively achieve program objectives, such as reducing poverty. In this context, assessing the impact of targeting choice on program effectiveness is particularly relevant. Overall, considering targeting legitimacy or the impact of targeting choice on program effectiveness can provide a broader assessment of targeting performance than simple measures of efficiency such as inclusion or exclusion errors.

> 3 While the literature is consistent in describing inclusion errors as those wrongly included, the denominator used in these calculations can differ. For example, Ravallion (2009) defines inclusion errors as the share of the ineligible that are wrongly included in the program, while Devereux et. al. (2017) express them as the share of beneficiaries.

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