(2013) shows that going from 10 to 30 indicators included in a PMT formula does not significantly increase the accuracy of predicted household poverty levels; nor does it reduce targeting errors.<sup>26</sup> Finally, the targeting accuracy results in this paper are based on the assumption of perfect (or strong) correspondence between the beneficiary lists from the UDB registry and the households who actually end up receiving social program benefits. A growing literature examining the political economy of targeting shows that in practice, official beneficiary lists may be modified in the field, which may positively or negatively affect targeting outcomes. Such targeting rule violations may prove beneficial if they allow the community to exercise their greater ability to identify the very poor (Alatas et al., 2012), or if the capture of program benefits by local elites is limited or generates relatively small welfare losses (Alatas et al., 2013). In Indonesia, past research suggests some departures from intended policy, but overall adherence to official beneficiary lists has typically been quite high (World Bank, 2012). In contrast, in the case of India’s Below-Poverty Line (BPL) cards, Niehaus et al. (2013) provide evidence that targeting rule violations by local officials are due to corrupt behavior, which renders BPL’s _de facto_ allocation much less progressive than the _de jure_ allocation. Future research should combine the methods for evaluating targeting effectiveness that we advocate in this paper with an assessment of eligibility adherence in the field to identify which effects prevail overall.

We conclude with an important note about generalizability. Although our results depend on the specific spatial distribution of poverty, the key features of poverty across Indonesia—limited geographic concentration as well as a relatively high degree of within-village inequality even in poor areas<sup>27</sup> —are not unlike those found in other developing countries (see, for example, Elbers et al., 2004; 2007). The success of any particular combination of geographic and individual targeting will

> 26 Dreze and Khera (2010) go even further by proposing to use simplified targeting criteria so that “every household can attribute its inclusion in, or exclusion from, the list to a single criterion”. Results from Niehaus et al. (2013) justify this argument: increasing the number of poverty indicators used to assess household socioeconomic status can have adverse effects in terms of targeting outcomes as it makes eligibility less transparent and therefore more subject to manipulation by corrupt agents at the local level.

> 27 For example, July 2010 _Susenas_ data show that around 85 percent of the variation in household expenditures lies within districts (the nearly 500 administrative units at which the survey is representative). Within-district inequality dominates at similar levels when looking at indicators for households falling below the 10<sup>th</sup> or 30<sup>th</sup> percentiles nationally, which are our cutoffs for defining, respectively, severe undercoverage and undercoverage in targeting. The variation in household poverty status (defined by district-specific poverty lines) is even starker with 96 percent of the variation in poverty status being within-district and only 4 percent between districts. This provides one reason why pure geographic targeting is likely to be ineffective in Indonesia.

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