on the one hand, and PFM quality, on the other hand. While we do mitigate concerns about endogeneity and omitted variables by employing the first differences analysis, the conclusions should still be interpreted with this caveat in mind. Findings on statistical correlation are nevertheless valuable for answering the questions that motivate this paper. Most importantly, the relative strength of the associations indicate to what degree PFM performance is likely to be ‘bounded’ by the set of variables explored here.

# **3.2 PFM quality and outcomes**

Next, the linkages between PFM performance and key outcome variables are examined (question 3 of the Introduction). We hypothesize that higher PFM quality is associated with better fiscal discipline (i.e., lower deficits to GDP as well as aggregate budget execution rates close to planned levels), ceteris paribus. We also expect that better PFM systems result in better allocative and operational efficiency, consistent with the literature outlined in Section 2.

We investigate these relationships using simple OLS cross-sectional regressions by estimating the following model: 1 2 ii ii ii ii where (2) YY _i_ indexes countries, = ∝+ γγ PP + γγ _Y_ II is the dependent variable of interest (measures of aggregate fiscal + εε where _i_ indexes countries, _Y_ is the dependent variable of interest (measures of aggregate fiscal discipline, allocative efficiency and operational efficiency), _P_ is PFM Quality, measured by the most recent PEFA assessment (an average of 21 selected PEFA sub-indicators – see Section 4.1), the control variable _I_ is log GDP per capita, and is the error term. Similar to the first part of this analysis, we start with a dataset of over 100 countries to study the εε Similar to the first part of this analysis, we start with a dataset of over 100 countries to study the cross-country relationship between PEFA scores and a variety of outcome variables.<sup>11</sup> We assume that the quality of PFM systems as measured by PEFA assessments measures the accumulated capabilities and efforts at system reforms. For each country, we use the earliest PEFA score available (ranging from 2005 to 2012). On the dependent side, as described in section 2 above, we use available proxies for the three broad categories of expected effects: (i) for aggregate fiscal discipline – the annual deficit to GDP ratio and the overall budget execution rate (PEFA indicator P-1), (ii) for allocative efficiency – the sectoral variance in budget execution (PEFA indicator P-2), (iii) and for operational efficiency – the cost effectiveness of health and education expenditures.<sup>12</sup>

> 11 The number of observations available for the dependent variables ranges from 56 to 102; see Annex 1, table A1.2.

12 These variables are imperfect proxies for the three dimensions of fiscal performance. For example, fiscal discipline is not directly proportional to deficits—fiscally responsible countries with lower fiscal liabilities (explicit and contingent) and interest rates or facing a temporary shortfall in aggregate demand may run larger deficits than their comparators. Similarly, in select cases a low score on PEFA indicator P-1 for the budget execution rate may reflect that the country frequently adjusts budget implementation when macro-economic conditions change. These may be signs of good fiscal discipline instead of profligacy. Also, the deviation from sectoral budget plans during budget implementation (PEFA Indicator P-2) may reflect inefficiencies in budget planning—although this is more often an indicator of skewed allocative decision making.. But even with imperfect proxies, we can start a process of empirically exploring these relationships with the most relevant empirical data available.

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