merging unique geospatial data sets on the subnational distribution of World Bank investment projects and the population identified as the bottom 40 and (2) correlating the two variables.

A relatively high correlation would be consistent with effective geographic targeting insofar as most resources are directed toward areas with a high presence of the bottom 40.<sup>6</sup> However, the presence of the bottom 40 is typically correlated with population size in the areas. Hence, a high correlation may be indicative only that World Bank project funding is concentrated in populous administrative areas rather than explicitly targeting the bottom 40. Likewise, a low positive correlation or even a negative correlation may not necessarily point to poor targeting because there are many other factors potentially affecting the allocation of aid. The paper accounts for some of these effects through the use of control variables in a regression analysis. It does not account, nonetheless, for general equilibrium effects, that is, that different aid allocation patterns within countries may have different effects on economic co-benefits such as higher growth, market and energy access, for example.

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 these show a correlation at above 0.5. Of the total sample, slightly more than a quarter of the countries, mostly in Sub-Saharan Africa, exhibit a negative correlation.

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 for the rest of the world regions. Hence, the significant and positive correlation in the estimations without controlling for population is indicative that World Bank project funding is concentrated in areas where there are more people, including the bottom 40, rather than in poorer areas.

Section 2 discusses how this paper fits within and contributes to the academic debate on the allocation of aid. A number of factors that may affect the within-country allocation of aid are also addressed in the section. Section 3 discusses methodology and data, while section 4 presents the results. Section 5 concludes.

# **2. Background and context**

Donors generally tend to allocate funds across countries following a number of more or less explicit criteria. Within countries, however, little has been produced in the literature on how donor resources are allocated. Nonetheless, because poverty reduction is one of the criteria often at the top of donorstated priorities, the geographical distribution of the projects and programs within countries may be expected to bear a positive correlation with the physical location of the poor in both absolute numbers and relative terms, that is, the share and number of the poor in the population of an administrative area.<sup>7</sup> on the traditional and still dominant form of development assistance, investment projects. See below for details on the reason why the paper does not consider budget support lending.

> 6 Literally targeting the poorest would specifically require the poverty gap or a higher order poverty function to be used as the main allocation criterion, whereas targeting the bottom 40 focuses on the poorest 40 percent in a society, but does not ensure a specific focus on the poorest among the bottom 40 or even all the poor if the bottom 40 fails to encompass all the poor of a society.

> 7 This paper spotlights the bottom 40, who are the focus of the World Bank’s second corporate goal. It thus follows the specific, albeit somewhat arbitrary choice made by the World Bank and the international community (in target 1 of Sustainable Development Goal 10) that growth in the incomes of the bottom 40 is particularly relevant for the overall economic growth and welfare of societies.

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