calculated at the global level for a donor can be consistent with very different patterns at the recipient level, with very different implications for aid quality. Suppose Donor A’s aid portfolio consists of $1 million in education aid to each of 10 recipients and $1 million in health aid to each of 10 other recipients, while Donor B’s consists of $0.5 million in education and $0.5 million in health aid for each of 20 recipients. Sectoral concentration for each donor is 0.5—even though Donor A assists only 1 sector in each recipient while Donor B aids two sectors in each of its recipients, and Donor B is therefore contributing more to the problems of proliferation of projects and administrative burdens on recipient governments.

To address these shortcomings with the concentration measure, we therefore computed from the PLAID dataset a donor-level indicator of the _average number of sectors aided_ per recipient receiving aid. We then took as a performance indicator the residuals from a regression of the log of this average on the log of total aid commitments by the donor and on a dummy for donors with a limited sectoral mandate. A similar indicator was generated based on the _number of recipients aided_ by each donor in 2007 (using the DAC disbursements data), adjusting for donors’ aid volumes and geographic mandates. The number of sectors and countries aided are correlated at .48 before removing the common effect of aid volumes; even after adjusting for aid volumes and limited mandates, they are correlated at .37.

A fifth indicator on aid specialization is based on the _average size of projects_ in AidData, using 2007 data on commitments.<sup>16</sup> Following Roodman (2006 and 2009) and Knack and Rahman (2007), the premise is that, in general, a proliferation of distinct aid projects imposes unnecessary transactions costs on recipients and unduly taxes their administrative capacity. Roodman uses particular assumptions about project production technology to estimate the efficiency losses associated with breaking aid up into an excessive number of projects. For example, he assumes that the optimal size of projects for a given aid volume is larger in better-governed countries because the marginal benefit from oversight is lower. We are able to take a more flexible approach with the data, because in contrast to Roodman we do not need to generate plausible estimates of efficiency losses to apply a discount to aid volumes. We simply regress the size of project _i_ (in log of current US dollars) on a full set of donor, recipient, and sector dummies and on (the log of) total aid by the donor to the recipient for projects other than project _i_ .<sup>17</sup> If all donors tend to break aid up into smaller pieces in certain recipient countries, whether because of poor governance or other factors, the recipient dummies will control for any such effects. Similarly, the sector dummies will control for any systematic differences in donors’ involvement in activities that have a larger optimal scale. The donor dummy coefficients then reflect donor-specific effects not attributable to either the characteristics of the recipients or sectors in which they happen to be operating.

We find that project size is significantly and positively correlated with the sum of other aid provided by the donor to the recipient. Despite netting out this scale effect, the donor dummy coefficients are still subject to scale effects at the global level. Specifically, even when controlling for donor’s aid volumes in the country where the project is located, projects are significantkly larger for donors with larger global aid budgets. We therefore make one further adjustment to our project size indicator,

> 16 We treat Ireland as missing data on project size, because its records in CRS (which are incorporated in AidData) correspond to expenditure items rather than to projects or programs. There are undoubtedly other significant discrepancies in the way donors report their aid activities in the CRS, but this appears to be the most extreme case of non-comparability in data reporting.  The average size of Ireland’s ―projects‖ in the CRS is smaller than for any other donor, so its score on the specialization sub-index (already its worst among the four sub-indexes) would decline if this indicator were not coded as missing data.

> 17 Following Kilby (2010), we exclude project _i_ to avoid endogeneity in testing for scale effects.  Sector dummies are based on the more detailed 5-digit sector codes in AidData, so average differences between (say) health infrastructure and health education programs can be captured.

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