that do not suffer themselves from a proliferation of donors and projects. Our specialization subindex is thus conceptually distinct from our sub-index of in-country harmonization practices.

The Paris Declaration calls for a more effective division of labor among donors, declaring that ―[e]xcessive fragmentation of aid at global, country or sector level impairs aid effectiveness.‖ Donors commit to delegating authority to lead donors where appropriate on the basis of comparative advantage. No indicators are specified in the declaration, however, nor are any included in the SMPD. We construct several indicators for the year 2007 from the DAC’s databases, following the methods of Acharya, Fuzzo de Lima, and Moore (2006), Knack and Rahman (2007), Easterly and Pfutze (2008), and Roodman (2006 and 2009).

First, we calculate the _geographic concentration of aid_ for each donor’s 2007 ODA disbursements after subtracting out debt relief, humanitarian aid, and administrative costs. This indicator is calculated as a Herfindahl index, equal to the sum of the squared shares of the donor’s aid going to each of its recipients. Larger donors and those with more expansive geographic mandates tend to have less concentrated aid, probably for justifiable reasons as they are more likely to have sufficient expertise in many countries (and sectors). Geographic concentration is correlated with (the log of) ODA at .49, and using the unadjusted indicator as a performance measure would arguably reward small donors unduly simply for being small.<sup>14</sup> For our donor performance indicator, we therefore take the residuals from a regression on two variables: (1) the log of donor total ODA and (2) the proportion of all aid recipients covered by the donor’s geographic mandate. For most donors this proportion equals one, but for the regional development banks (AFDB, ASDB, IDB, and EBRD), it is less than one.

We similarly compute a Herfindahl-type measure of _aid concentration by sector_ for each donor using commitments data for 2007 from AidData.<sup>15</sup> A donor with lower concentration, by this measure, does not necessarily contribute more to fragmentation measured from the recipient standpoint, because it may be aiding many small countries that receive little or no aid from other donors. However, Acharya, Fuzzo de Lima, and Moore (2006) show that any such cases are the exception and not the rule. Donors that fragment their aid across recipients the most are significantly more likely to be suppliers of ODA to recipients receiving aid from many donors (and hence more fragmented aid, from the recipient’s perspective).

AidData incorporates the DAC’s CRS database, used by Easterly and Pfutze in calculating sectoral fragmentation, but adds information from other donor sources. For our indicator of donor performance indicator we take the residuals from a regression of sectoral concentration on two variables: (1) the log of total 2007 aid commitments by the donor reported in AidData, and (2) a dummy for donors with a limited sectoral mandate (namely, the global or ―vertical‖ funds). The resulting indicator is positively but modestly correlated (at .23) with the performance indicator on geographic concentration.

These concentration indexes capture problems associated with fragmentation very imperfectly, for at least two reasons. First, their values typically decline only slightly if a donor adds recipients or sectors to its portfolio but allocates them only small fractions of its total aid. For example, if a donor with $1 billion in annual disbursements gave 20 new recipients $0.5 million each, it would hardly affect that donor’s concentration index, but it could substantially increase the administrative costs for the donor and the recipient countries. Second, a given value for sectoral concentration

> 14 The specialization index in Easterly and Pfutze (2008) ranks Greece, Portugal, and other small donors as top performers, because it is does not adjust sectoral and geographic fragmentation for donor size.

> 15 Sectors are aggregated to the two-digit level for this purpose.  For example, we treat ―Health‖ as a single sector, rather than treating medical research, health education, health infrastructure etc. as separate sectors.

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