This paper proposes a new geographic targeting indicator and a related methodology to assess the within-country aid allocations of donors by correlating the geographical distribution of donor funding within countries with the geographical distribution of the bottom 40.

Applying this methodology to World Bank project funding, the correlation analysis indicates that, of the 58 countries in the sample, 42 show a positive correlation between the shares of the bottom 40 and World Bank funding, and almost half of the countries show a correlation above 0.5.<sup>31</sup> Slightly more than a quarter of the countries in the total sample, mostly in Sub-Saharan Africa, exhibit a negative correlation. Indeed, the geographic correlation between the bottom 40 and World Bank investment funding is lowest in Sub-Saharan Africa, with an average correlation coefficient of almost 0, which is significantly lower relative to the entire sample. This result can be explained in the case of many countries in Sub-Saharan Africa by the substantial allocation of project resources to the capital, whereas most of the bottom 40 live elsewhere.

The presence of the bottom 40 is, however, typically correlated with the size of the population in an administrative area. The regression analysis shows that, once one controls for population, the correlation between the bottom 40 and World Bank funding switches sign and becomes significant and negative on average. This is entirely driven by Sub-Saharan Africa because the correlation is insignificant in other world regions. Hence, the significant and positive correlation in the estimates without controls for population indicates that World Bank project funding is concentrated in areas in which more people live, including the bottom 40, rather than in poorer areas. <mark>A regression analysis on a subsample of 11 countries</mark> shows that the subnational allocations of the World Bank and other donors are positively correlated. This suggests that the World Bank and other donors cluster their aid across subnational areas rather than engaging in complementary coordination.

Several factors may explain the imperfect targeting of aid on the poorest. In the regression analyses, controls are applied for remoteness, conflict, and complementarity with domestic and other external financing. Other considerations that are more difficult to measure quantitatively are worth reiterating. First, domestic political economy considerations and recipient country preferences may affect the allocation of development aid. Second, general equilibrium effects may be at play if the returns to poverty reduction are higher in nonpoor areas.<sup>32</sup> Third, the lack of information on where the poor are located may prevent proper targeting.

Given that the interpretation of low (partial) correlations may still be ambiguous, such findings may be useful as an entry point for further analysis rather than as a negative assessment. Nonetheless, given the increasing availability of poverty and aid data associated with small area maps, the World Bank ought to monitor more closely the extent to which the geographical distribution of project funding and the residence of the bottom 40 align.<sup>33</sup> Such an exercise has the potential to enhance substantially the efficiency of increasingly scarce development funds and help maximize the poverty-reducing effects of development assistance.

Similar studies of other development banks and large bilateral donor agencies aimed not only at investigating spatial poverty targeting, but also at other development goals would thus be welcome.

> 31 The generally positive results are supported by two cases of countries on which the correlation analysis was conducted at the second administrative level (districts), in addition to the analysis at the first administrative level. The results show that correlations remain positive.

> 32 The finding that education and health care projects perform better than energy, mining, and transportation projects in within-country allocations is consistent with the fact that the World Bank takes into account general equilibrium effects.

> 33 The results of this paper could serve as a baseline because the analysis covers projects approved between 2005 and 2014. The recent Independent Evaluation Group report (IEG 2017) makes this recommendation based on the research that is described in this paper and that provided input into that report.

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