level data on PFM quality and associated country characteristics. Further work under the study will look at how political economy factors interact with key socio-economic and political drivers of PFM quality.

The paper proceeds as follows. In Section 2, we present our analytical framework for the paper, including a brief overview of relevant literature. Sections 3 and 4 describe the empirical models to be tested, our methodology and assumptions, and the data sources used in the analysis. In Section 5, we present the results of the econometric analysis. Section 6 follows with concluding remarks.

**2. The state of knowledge on drivers for the quality of public financial management**

# **2.1 Drivers of PFM performance**

The relationship between country characteristics and PFM performance has begun to be explored by some recent analysis as relevant cross-country data on the latter has become available. This paper builds on these efforts. De Renzio et al. (2011) undertook an initial analysis using accumulating PEFA data as part of a major donor evaluation effort concerning the effectiveness of PFM reform support, based on a global sample of 90+ countries with available PEFA scores. The main relationships diagnosed as significant are GDP per capita, recent GDP growth, population size, natural resource dependency, state fragility, and the level of PFM support provided by donor agencies. Among these, the two stronger effects emphasized by de Renzio et al. are levels of GDP per capita (positive) and state fragility (negative). Population size is also a significant factor, while PFM donor support, though statistically significant, has only a small explanatory effect. The paper also rejects a number of hypotheses, most importantly trade openness, colonial heritage, regime type, and aid dependency, which are all shown not to have significant effects in the multivariate cross-country analysis.

Another related paper, by Andrews (2010) focuses on a narrower sample of 31 Sub-Saharan African countries. For this group, Andrews finds a strong relationship between recent growth rates (over the decade 1996 to 2006 – that is, up to the years prior to the global financial crisis) and average PEFA scores. With regards to income levels, he notes that for the full sample of 73 countries for which PEFA scores were available at the time, higher income countries (with per capita incomes above $4,000 per capita) are more likely to attain top PEFA scores than those with lower incomes. But, this relationship between better PEFA scores and higher per capita incomes did not hold within the SSA sub-sample of 31 countries. A second main finding is that fragile states within the Sub-Saharan African group were among those with the lowest PEFA scores. Andrews also notes, however, that some post-conflict countries such as Sierra Leone and earlier Mozambique have shown an ability to rapidly improve their PFM systems once stability was regained.

Furthermore, Andrews (2009) quantifies a number of country factors to analyze their effect on PFM change in Africa through a multivariate ordered logistic model.<sup>5</sup> He finds several country-

> 5 Andrews derives findings on drivers of PFM quality change from cross-country comparisons. This paper, in contrast, looks at changes in PFM quality within countries over time.

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