There are few studies analyzing the legitimacy of targeting methods among local populations. Results from Indonesia suggest high legitimacy of CBT relative to PMT (Alatas (2012)). Again in Indonesia, Cameron and Shah (2014) find that a mixed targeting approach combining CBT and PMT resulted in significant increases in crime and declines in social capital within communities.

Studies on targeting that consider the ultimate impacts on household welfare have usually relied on simulations under different targeting choices.<sup>4</sup> For example, Brown et al. (2017) and Alatas (2012) have found that the choice of targeting method has limited impact on the effect of cash transfer programs on poverty reduction. Based on simulations using LSMS surveys in 9 Sub‐Saharan African countries, Brown et al. (2017) also suggest that, with enough money to cover 20 percent of the population, perfect information and no transactions costs, a well calibrated PMT formula would reduce the Headcount Poverty Index by 23 percent.<sup>5</sup> This compares to 14.5 percent under Universal Basic Income (UBI).<sup>6</sup> Results from such simulations may provide different impacts than actual implementation of targeting methods depending on households’ behavioral responses, among other factors.

The literature has highlighted different mechanisms that can explain targeting performance in community‐based targeting, such as local knowledge, manipulation, and fatigue. Based on 14 studies on the performance of CBT methods, Coady et al. (2004) find large variations in efficiency, which they attribute to differences in implementation. While the consideration of _local knowledge_ has been widely recognized as a key advantage of CBT, there is no direct evidence measuring how information failures at the local level may affect CBT outcomes. _Manipulation_ is one of the main concerns with CBT. While the literature abounds with suggestive reports of corruption (McCord 2013), there are studies finding no evidence of such issues (such as in Alatas 2012). Features such as supervision, increased civic participation, sanctions, and institutionalized structures of competition between rival local leaders may minimize manipulation (Watkins, 2008 and Platteau and Gaspert, 2003). Finally, _community fatigue_ may negatively impact targeting performance in CBT approaches, if the community is unable to sustain attention. Alatas et al. (2010) find that community effort declines as the ranking exercise progresses.

Various issues can also affect the performance of formula‐based methods, such as survey accuracy, external validity, and the incidence of shocks. _Survey accuracy_ issues may arise from simple measurement error or manipulation by respondents. In fact, manipulation has been shown to happen over time in some systems relying on PMT (Camacho and Conover, 2011). _External validity_ may also be a concern as formulas are developed from samples that may not necessarily represent well program areas or may be outdated. Brown et al (2017) find that lags in implementation considerably matter. Finally, formulas relying on variables that change slowly over time (for example, household demographics and dwelling materials)

> 4 Targeted households are assumed to receive a fixed transfer amount. Poverty reduction is then measured by looking at the post‐transfer consumption levels.

> 5 Estimations based on tables 11 and 13 from the paper. Poverty is assumed to be 20% and it is important to note that in reality the poverty rates in the countries studied are usually considerably higher. PMT usually works poorly in predicting the levels of living of the poorest and one may expect that raising the poverty line to a more realistic one may result in better performance. Countries analyzed included Burkina Faso, Ethiopia, Ghana, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda.

> 6 While the improvement of PMT over UBI looks small, in a country like Niger, it would still imply covering approximately an additional 350,000 people (of an estimated population size of 20.67 million) if PMT were to be used as opposed to UBI. Also, if using distribution‐sensitive poverty measures, the difference in the relative performance of PMT and UBI is substantial – using PMT would decrease the Watts index by 48 percent as opposed to 22 percent under UBI.

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