key driver in previous studies. Next, we explore whether other macro-level country characteristics are associated with PFM quality by building on the previous literature. Our interest is to explore whether these variables show a relationship with the variance in the quality of PFM systems (across countries and within countries over time). We hypothesize that overall, macro-level country characteristics are relatively weak or insignificant in explaining the level and change PFM quality across countries and over time, ceteris paribus. This is based on the anecdotal observation that PFM reforms appear to have succeeded in a substantial range of different country contexts.

The cross-sectional model, across countries, is estimated as follows: ii 1ii ii 2ii ii ii (1a) PP = ∝+ ββ II + ββ XX + εε The first-differences model focusing on within-country changes over time is as follows: i 1i i 2i i i ii where (1b) PP _i_ indexes countries, = αα+ ββ II _P_ + ββ is PFM quality, measured by the most recent PEFA assessment XX + δδ + εε (using an average of 21 selected PEFA sub-indicators – see Section 4.1), _I_ is the log of real GDP per capita, _X_ is a matrix of socio-economic and political macro-level variables, and is the error term. These equations are estimated using ordinary least squares (OLS). δδ is fixed effects, For the first model, we are able to use a dataset of 120 countries to study the cross-country εε For the first model, we are able to use a dataset of 120 countries to study the cross-country relationship between PEFA scores and a variety of country characteristics in levels. The dependent variable is the most recent PEFA score available (ranging from 2008 to 2012 for each country), and the independent and control variables cover a five-year lagged average prior to the most recent PEFA score for each respective country.<sup>9</sup> Second, we examine changes in PFM quality for almost 50 countries for which we have at least two PEFA scores using first differences. We estimate this second model by computing the average annual changes for our dependent and independent variables between the first and most recent PEFA measurement.<sup>10</sup> We also perform a robustness check using CPIA-13 as our dependent variable instead of PEFA. Looking at changes within-countries over time indicates whether macro variables associated with the differences in PFM quality between countries also matter for changes over time (question 2). In other words, we are asking if X improves within a country, should we expect P to improve, and if so, by how much?

The econometric strategy outlined here does not demonstrate a _causal_ link between macrolevel economic and population variables, sources of revenues, political and institutional factors,

> 9 This differs from previous work, which calculated a five-year average from 2002 to 2006 for all countries, despite the fact that the year of the assessment was not the same for all of the countries being compared, and in fact the range was quite extensive from 2005 through 2010.  We only include those countries that have data for at least three of the five years.

> 10 Since only a limited number of countries completed multiple PEFA assessments until recently, this paper is the first to undertake such an econometric analysis. The analysis includes PEFA assessments up until 2012. Assessments completed in 2013 are not included, to ensure availability of data for other variables.

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