relief. As we shall see there are interesting differences in our results by subperiod, and for this reason we do not average our data over longer periods. Our sample consists of the set of 62 low-income countries for which we have estimates of debt relief.<sup>9</sup> We regress our discounted total aid variable for each five-year period on log per capita GDP, log population, the quality of domestic policy and institutions as measured by the World Bank's Country Policy and Institutional Assessment (CPIA) ratings, all of which are measured in the year prior to the beginning of each five-year period. Our estimation method is ordinary least squares, and so we interpret the results that follow as capturing partial correlations rather than necessarily causal effects.<sup>10</sup> Not surprisingly, in all periods poor countries receive significantly more aid as a share of GDP, and consistent with the existing literature we also find strong evidence of size effects: small countries are significantly more likely to receive more aid as a share of their GDP. We also find that aid is significantly positively correlated with the quality of domestic policies and institutions in the last period, and marginally so in the middle period, suggesting that the allocation of aid has become increasingly targetted towards countries with good policy over time. Finally we consider a summary statistic of donors' strategic interests, taken from Rajan and Subramanian (2005). Despite its plausibility, however, we do not find this variable to be a very significant predictor of aid in our sample of countries.<sup>11</sup> The next three columns of Table 2 report the same regressions, but now with debt relief relative to GDP as the dependent variable. Since roughly half of the sample 9 Our debt relief panel is unbalanced, and moreover some of our RHS variables are not available for all countries, resulting in somewhat smaller samples in each sub-period.

10 It is not clear however that there is significant reverse causation from aid to our right-hand-side variables, with the possible exception of the CPIA variable. The World Bank is a major provider of aid and also produces the CPIA ratings. It is therefore possible that the Bank generates high CPIA ratings in order to justify a large aid program. To check for this we have re-estimated the regressions in Table 2 using an alternative measure of institutional quality, the Rule of Law index produced by Kaufmann, Kraay, and Mastruzzi (2004), which reflects the views of a very diverse set of respondents, primarily outside of the World Bank. This measure gives broadly similar results, suggesting that this channel of endogeneity is not very important.

11 In particular we use their instrument for aid, which consists of the fitted values from a bilateral aid regression in which the explanatory variables measure bilateral ties between donors and recipients, such as a colonial ties, common languages, and UN voting patterns. This can be interpreted as the component of aid driven by geopolitical considerations rather than recipient needs. We use the value of their instrument based on such a regression for the period 19992000. The weak explanatory power of this variable in our regressions is due to two factors. First, our sample consists mostly of the post-Cold War period where strategic motivations for aid were weaker than in earlier years. Second, it turns out that the Rajan and Subramanian variable is highly correlated with country size (small countries tend to get more politically-motivated aid). Controlling for country size as we do drastically reduces the significance of the Rajan and Subramanian variable.

7