score while budget credibility and policy-based budgeting have the highest scores.<sup>17</sup> For each of the dimensions, and in particular for the accounting and reporting dimension, the spread of country ratings is wide.

To aggregate the PEFA data into a single number for our analysis, we summarize those PEFA indicators that cover the quality of PFM systems on the expenditure side. We therefore exclude PI-1 through P1-4, which measure PFM outcomes, indicators PI-13 to PI-15, which cover transparency and effectiveness of tax administration, and D1 to D3, which are donor-related indicators. This leaves 21 indicators (PI-5 to PI-12 and PI-16 to PI-28) for our analysis (see Annex 3 for a complete list). We then converted the alphabetic indicator ratings included in the PEFA assessments into numerical values, with higher scores denoting better performance (i.e. A = 4 to D = 1), and then calculated a simple average of the selected 21 summary indicator scores, assuming equal weights for each indicator – that is, all indicators have equal importance – in order to arrive at one measure of overall PFM performance.<sup>18</sup> While there are validity concerns about converting ordinary PEFA letter ratings into numbers and aggregating the individual indicators, we believe that this is the best approach for the aims of the analysis presented here.<sup>19</sup> This approach follows De Renzio et al. (2011) with some variation.<sup>20</sup> Alternatively, a multivariate ordered logistic model could be used (cf. Andrews 2009), but results would be more difficult to interpret. Regardless, we undertake sensitivity analysis to highlight the extent to which our findings are robust under alternative ways of aggregating PEFA scores, and found no significant differences in results.

A brief assessment of how our measure of PFM performance is associated with overall governance effectiveness – as measured by the World Governance Indicators – is presented in Figure 2.<sup>21</sup> Given that PFM systems are one part of the overall concept of government effectiveness, we would expect a close relationship between these two variables. Furthermore, given that other aspects of government effectiveness may face even greater reform challenges, 17 The boxes contain the middle 50% of the data (bordered at the 25th and 75th percentiles of the PEFA score with the line representing the median value of the data). The ends of the vertical lines (whiskers) indicate the minimum and maximum data values to a maximum of 1.5 times the inter-quartile range values (over 97% of the data in our case). Outliers beyond this range are excluded from the graph.

> 18 In order to avoid misleading results, we excluded countries that were missing more than 3 indicators or “no scores” due to lack of adequate information or because they were deemed to be inapplicable in the PEFA assessment.

19 The PEFA Secretariat has issued a report summarizing various methods used while taking an agnostic stance as to which are valid or superior. PEFA Secretariat (2009), _Issues in Comparison and Aggregation of PEFA Assessment Results Over Time and Across Countries._ 20 The present approach differs slightly from de Renzio et al. (2011) by averaging the 21 indicators instead of using an average of the 64 sub-indicator scores. The latter approach by de Renzio et al. puts more weight on those indicators with multiple sub-indicators compared with dimensions with few or no subindicators.

> 21 An important caveat is the fact that government effectiveness is a composite concept, and that good measurement of the concept remains a challenge that has not yet been fully met by existing indicators. The WGI GE indicator is a composite of several other indicators, and draws among others on indicators measuring the quality of PFM, but not on PEFA data itself. We rescale the government effectiveness variable (percentile rank) to 0 through 10 for easier comparison with other variables in this paper.  The higher levels denote greater government effectiveness.

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