However, targeting is expensive in monetary, administrative, and political terms. The production of data is costly; the collection of data takes time; and the exclusion of some applicants may have political and social consequences. Therefore, targeting always involves a trade-off between errors of inclusion, errors of exclusion, and higher implementation costs (Morley and Coady 2003). Among the strategies in social program targeting, geographical targeting exploits information on variations in poverty rates by location to channel resources toward regions with the highest poverty rates.

Nonetheless, the implementation of geographical targeting is not a trivial endeavor because household surveys alone do not usually provide information to support accurate poverty estimates across small administrative units. Poverty measures derived from survey data are usually either too noisy or lack adequate coverage. Small area estimation techniques have been developed as a way to produce highly disaggregated poverty rates by imputing poverty into a population census.<sup>1</sup> The poverty maps created through these techniques provide relatively precise estimates of poverty and have been used in several countries to channel resources toward localities in which poverty is more concentrated.

There are two main approaches to the application of poverty maps in social program targeting. In the single-step approach, localities with the highest poverty rates are identified and prioritized in the disbursement of resources.<sup>2</sup> Evidence shows that the effectiveness of this process increases in cases where poverty is more spatially concentrated (Elbers et al. 2007). An alternative approach, which is used in the case of the national social registry (RNU) in Senegal, consists of two steps. First, the information supplied through the poverty map is used to develop eligibility quotas by location. These quotas are then processed using additional tools to identify program recipients. Including vulnerability on the targeting raises program’s effectiveness because the share of the population living above the poverty line that may be only a shock away from becoming poor. Moreover, this share is likely to increase if the poverty rate has recently declined.

The analysis incorporated the notion of vulnerability to poverty into the estimation of poverty maps by combining small area estimation techniques with methods derived from the literature on vulnerability to poverty. Among the many approaches available for estimating vulnerability, the analysis drew on the approach of Günther and Harttgen (2009), which enables the calculation of welfare variability based solely on cross-sectional data. This approach divides vulnerable households into two groups: first, poverty-

> 1 For a complete review of the relevant literature, see Rao and Molina (2015). See Molina, Corral Rodas, and Nguyen (2022) for a review of the application to poverty measurement.

> 2 Targeting areas where poverty rates are highest helps minimize errors of inclusion—that is, some households that are not poor are included in a program by error—because these areas have smaller shares of nonpoor households.

4