Country variables are drawn from widely used sources. GDP per capita and population data are taken from the World Bank’s World Development Indicators (WDI). Data on aid flows is drawn from the OECD DAC database and migration data is from UNDESA databases. A full set of the definitions and sources of variables used is given in Appendix A.

PEFA scores were obtained for 162 PEFA assessments in 118 countries over the period 2005 to 2011 from the PEFA Secretariat. The PEFA assessment comprises 71 individually assessed dimensions which together form 31 high level indicators (including assessment of donor systems, 68 and 28 respectively excluding them). The assessments are made on an ordinal basis, assigning a score of A to D based on a set of criteria, with A indicating highest performance, with an additional ‘+’ score possible for some indicators To facilitate statistical manipulation, we convert the ordinal scores to a numeric value of 1-4, with 4 being equivalent to ‘A’ and a ‘+’ score ascribed an additional 0.5. This methodology follows existing practice and PEFA Secretariat guidance (PEFA Secretariat 2009). Appendix B provides summary statistics at the PEFA assessment level.

Following Andrews (2010), we exclude from our analysis dimensions that are beyond the direct control of government. Indicators related to aggregate fiscal outturns are omitted (PI-1, PI-2, and PI-3), as are indicators related to donor practices (PI-D1, PI-D2, and PI-D3). Omission of these variables reflects our interest in the success or failure of government administrations in undertaking PFM functions, and avoids results being biased by macroeconomic factors or the different practices of development agencies operating in different countries. These omissions are especially important in any analysis of PICs, which are atypical in both their exposure to macroeconomic volatility and reliance on donor aid flows.

We carry out analysis at the most disaggregated level, using all 65 separate dimensions included in each assessment, rather than relying on the 25 high level performance indicators that aggregate some of the dimensions that have been used in previous analyses.

Missing data is categorized by PEFA assessors as “NA” (not applicable), “NU” (not used for the assessment), or “NR” (not rated due to insufficient information). Our treatment of missing data is in line with suggested practice (PEFA Secretariat 2009). We treat NA and NU scores as missing and exclude them from the analysis, but code NR as a 0 score, since in most cases NR will correspond to a function that is either not being carried out at all, or is being completed to such a poor standard that it would not achieve a D score. This approach avoids a systematic upwards bias in the scores of countries with poorer PFM systems, such as those we are primarily interested in.

Given the limited range of values, we follow the literature on similar constructed, ordinal data (Elbadawi and Randa, 2003; Bates, 2006) in testing for the presence of censoring. As described by Rigobon and Stoker (2006), the presence of censoring can be tested for using a simple Chow test on the stability of the coefficient estimates between a sub-sample of possibly censored data 6