# **Introduction**

Social assistance programs targeted to low-income groups cover nearly two billion people in developing countries (Honorati et al., 2015). Identifying and reaching the intended beneficiaries of these programs can be challenging, especially where a large part of the population works in the informal sector and there are no official income registries. A number of targeting methods have been developed to address these challenges.<sup>1</sup> Traditionally, each program has its own method to select recipients depending on its implementing agency, budget, and benefit package.

In recent years, an increasing number of low- and middle-income countries are moving from fragmented program-specific targeting mechanisms to a single household targeting registry meant to select recipients of multiple social assistance programs often with different eligibility rules. Honorati et al. (2015) report that single registries for social safety nets are fully operational or are being developed in 92 developing countries.<sup>2</sup> For such registries, basic household and individual information is typically collected for a subset of the population that is considered potentially eligible for social assistance.<sup>3</sup> This information is then used to determine eligibility, most commonly based on proxy-means testing (PMT).<sup>4</sup> This paper aims to identify general strategies for improving targeting effectiveness in these new unified registries by evaluating the performance of one of the world’s largest single registries developed recently in Indonesia. Established in 2012, the Unified Database for Social Protection

> 1 Targeting commonly relies on individual assessments (often using means- or proxy-means-testing), broad categorical eligibility (demographic or geographic targeting), self-selection, or some combination therein (see Coady et al., 2004, for a survey). A rich literature on optimal targeting finds that no method clearly dominates in terms of its ability to accurately identify the poorest (e.g., Coady et al., 2004; Banerjee et al, 2007; Coady and Parker, 2009; Alatas et al., 2012, 2013; Karlan and Thuysbaert, forthcoming).

> 2 As argued by Grosh et al. (2008), “a good household targeting system may be complex to develop, but can be used for many programs [...]. The shared overhead is not only efficient, but can lead to a more coherent overall social policy.” Examples of countries currently using single targeting registries include Brazil, Chile, Colombia, India, Mexico and the Philippines (see, for example, Castañeda et al., 2005, for a review of the experience of Latin American countries; Dreze and Khera, 2010, for the Indian Below-Poverty Line Census).

> 3 Compared to common population census questionnaires, targeting registry questionnaires collect more detailed socioeconomic information at household and individual levels. They are therefore generally administered to a subset of the population rather than to the full population, in order to limit costs.

> 4 PMT scores are constructed on the basis of simple socioeconomic indicators that are relatively easy to collect and less prone to misreporting than expenditures or income. These indicators are combined into a single measure of welfare using weights typically derived from consumption regressions estimated from an auxiliary survey. Using these predicted measures of welfare can be a cost-effective way to identify beneficiaries of social programs to the extent that they are sufficiently accurate.

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