well-established measure of household economic welfare and, indeed, the same variable is typically used in measuring poverty.<sup>5</sup> To distinguish it from other methods of means testing, we will use the term “econometric targeting” to refer to any PMT based on a regression model. An influential early contribution by Grosh (1994) compared numerous social programs in Latin America and concluded that this class of methods produced the best targeting outcomes, measured in terms of reducing inclusion errors, whereby a nonpoor person is counted as poor. Various versions of econometric targeting have since been proposed, and the method has been widely implemented in developing countries.<sup>6</sup> Econometric targeting has also been criticized for its seemingly poor predictions about who is poor and who is not. For example, Kidd and Wylde (2011, p.ii) refer to the method’s “considerable inaccuracy at low levels of coverage.” Transparency has also been a concern. Sometimes the score variables and weights are deliberately kept secret for incentive reasons. In other cases, the method and formula are too complicated, or too poorly explained, for public consumption. Either way, observers on the ground do not always understand why some people are selected and some are not based on these targeting methods. With reference to a conditional cash transfer scheme in Nicaragua using PMT, field work by Adato and Roopnaraine (2004, p.15) led them to write that: “...the targeting process as a whole is poorly understood at the community level in both geographicaland household-targeted communities. When asked why some households were beneficiaries and others not, informants offered a range of explanations, from divine intervention to a random lottery. For example, one informant from a geographically-targeted community noted: ‘ _Well, some people wonder why they weren’t targeted even though they live in this same area. So we tell them that the Bible says that many are called but few are chosen._ ’” In the context of a PMT in Indonesia, Cameron and Shah (2014) argue that considerable local social unrest was generated by this lack of transparency in why some people were deemed beneficiaries and some not. This came with an erosion of local social capital and greater distrust of local administrators.

Another critique relates to the goals of social protection policies, which can be thought of as involving both protection from uninsured risks as well as promotion from poverty over the longer-term.<sup>7</sup> Some observers have questioned the effectiveness of PMT in responding to shocks or targeting insurance. Instead, it is argued that, because it is largely based on long-term assets, PMT is suitable “...for identifying the chronic poor and determining eligibility for programs that provide long-term support” (Del Ninno and Mills, p.22). Nonetheless, PMT is widely used in implementing policies that

> 5 For a critical review of the methods used see Ravallion (2016, Part 2). 6 Useful overviews can be found in Mills et al. (2015) and USAID’s website on <u>Poverty Assessment Tools.</u> 7 On this distinction and the implications for assessing social protection policies see Ravallion et al. (1995).

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