EBRD is targeting its efforts at poorer countries. The methodological sections below describe in greater detail how and why we adjusted for constraints on the donor agencies.

Third, in computing indicators of sectoral fragmentation and project proliferation, we take advantage of the new AidData database produced by researchers at the College of William and Mary and Brigham Young University. AidData is based on the CRS data from the OECD-DAC used previously for this purpose by Easterly and Pfutze (2008) and Roodman (2006 and 2009), but includes data on additional donors, recipients and projects obtained directly from donor agencies.

Finally, we provide a database of our aid quality indicators for 2007, including the adjusted indicators and the indexes and sub-indexes we construct. This database will be updated with indicators for 2009, when data from the third round of the Paris Declaration monitoring survey become available. Note that our sample of donors is similar to the Easterly-Pfutze sample, in that it includes all DAC bilaterals and the most significant multilaterals and global funds. We exclude any donors missing data on more than half of the indicators or on more than one sub-index; those excluded tend to be newer, smaller, and non-DAC donors. For consistency with the Paris Declaration monitoring survey—the source of 8 of our 18 indicators—we treat the UN as a single donor, aggregating data over its various agencies.

The results from this exercise in collecting and combining aid quality indicators are both encouraging and instructive. First, we show that it is possible to group these indicators into logical sub-indexes that capture key aspects of aid quality: selectivity, alignment with country systems, harmonization in country, and specialization. Each of these aspects has been a focus of efforts to improve quality, and we develop sub-indexes of indicators that are sufficiently highly correlated with each other that that appear to be measuring a common underlying aspect of quality. Donor rankings within these sub-indexes should be useful in and of themselves, by helping donors to identify areas of relative strength and weakness.

Second, we offer a new overall ranking of donor quality, constructed from these sub-indexes. This ranking has the advantage of incorporating more aspects of quality than other rankings, so that it is likely to be a fairer representation of overall donor quality.

Third, we show that weightings do matter to the rankings: because donor rankings vary among the different sub-indexes, the ultimate overall ranking will be sensitive to the weightings of those subindexes. We argue that ranking exercises need to take into account this sensitivity to weightings and should make their weighting choices explicit, to allow the reader to decide whether he or she agrees with them. In the interest of transparency, we aggregate our sub-indexes several different ways and show how the rankings change with each reweighting.

# **Ranking donors:  Challenges, motivations, and methods**

Any effort to rank donors on aid quality faces a number of challenges. First, most indicators of donor performance are based on plausible but largely untested beliefs about best practices in aid management. Policy selectivity has received the most attention in the literature (World Bank 1998; Burnside and Dollar 2000). Nevertheless, the evidence linking the quality of policies to the effectiveness of aid has been disputed by some studies (Hansen and Tarp 2000; 2001; Easterly, Levine, and Roodman 2003; 2004), while others have questioned the criteria for assessing selectivity (Amprou, Guillaumont, and Guillaumont Jeanneney 2007). In the case of other aid quality indicators, such as donor use of country systems, there is an even stronger consensus in the donor community on their importance, despite an even weaker base of empirical evidence. Despite some 3