practical benefits in logistics, security, enhanced local capacities, more leverage on local authorities, and even a greater social awareness of aid practices across officials, communities, and beneficiaries.

Security considerations are likely to affect the allocation of aid in countries where security risks are an issue. This presents a clear trade-off as areas exhibiting less security tend also to be poorer so that allocation patterns may positively correlate with more prosperous, safer areas. More generally, donors may shy away from difficult environments with weak local institutions and entrenched forms of poverty where the likelihood of success is low (Nunnenkamp, Öhler, and Sosa Andrés 2017).

Imperfect local information on poverty may prevent proper targeting. While many recipient governments have a general idea where the poor are located, local poverty estimates may differ from government expectations. Given the infrequency of surveys in poorer countries and the fact that small area poverty mapping is a relatively new technology and requires substantial technical skill, the necessary information for adequate poverty targeting is limited in many developing countries.

# **3. Data and methodology**

For the purpose of the examination of aid allocations and targeting effectiveness, we use data on the bottom 40 based on representative household surveys in 58 countries on which sufficiently disaggregated geospatial data are available on the bottom 40.<sup>12</sup> More specifically, this study has relied on harmonized survey collections produced by the World Bank’s Poverty and Equity Global Practice.<sup>13</sup> These surveys contain a welfare indicator (income or consumption), a geographic identifier, and a sample weight for each household. The variables allow us to calculate the number of individuals belonging to the bottom 40 in each first-level administrative division.

Subnational information on the locations of World Bank projects within recipient countries is mined from AidData.<sup>14</sup> The database lists 1,517 World Bank investment projects approved in the sample of 58 countries between 2005 and 2014. Taken together, these projects account for commitments of US$156 billion. The data set does not provide a geographic breakdown of the overall amount of project commitments. However, the entries in the database typically contain information on the locations (administrative areas) where (part of) a project takes place.<sup>15</sup> This information is used to split total project commitments either equally or on a population-weighted basis across the subnational administrative areas in which each project is active.

Subsequently, the share of World Bank funding—measured as commitments of investment projects— each area receives and the share of the nationwide bottom 40 population located in each subnational area are calculated. Simple bivariate correlations between the two variables are calculated to assess

> 12 Not all household surveys relied on may be perfectly representative at the first administrative level. In case some were not, our estimates would be less precise. However, there is no particular reason to assume that they are biased.

> 13 Global Monitoring Database (internal database), Poverty and Equity Global Practice, World Bank, Washington, DC.

> 14 World Bank Geocoded Research Release, Version 1.4.2 (database), AidData, College of William and Mary, Williamsburg, VA, http://aiddata.org/data/world-bank-geocoded-research-release-level-1-v1-4-2.

15 Data in the GADM database are used to assign project locations to the first-level administrative areas. For a few countries in which the first-level administrative areas in the GADM shapefiles do not match the areas in the household surveys, the Global Administrative Unit Layers data set of the Food and Agriculture Organization of the United Nations is used. Here, in the sample of 58 countries, there are 1,084 first-level administrative areas. For the GADM data, see GADM Database of Global Administrative Areas, Environmental Science and Policy, University of California, Davis, CA, http://www.gadm.org/. For the Global Administrative Unit Layers dataset, see GAUL (Global Administrative Unit Layers) (database), Food and Agriculture Organization of the United Nations, Rome, http://www.fao.org/geonetwork/srv/en/metadata.show?id=12691.

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