during the period 1994-2003, Le et al. (2012) study 110 countries during the period 1994-2009, and Khwaja and Iyer (2014) study 61 countries during the period 2000-2010. We contribute to this literature by providing new evidence on relative efficiency in DRM across countries based on a recently launched, comprehensive data set covering 182 countries (including 118 low- and middle- income countries which we focus on as our core sample) in the period 2008 to 2019.<sup>3</sup> As such, our analysis may be considered as providing an updated view of past analyses of revenue potential across countries based on the largest data set available to date.

In addition, by using non-parametric data envelopment analysis to estimate countries’ domestic revenue potential, we add a new approach to the range of methodologies used in the existing literature on revenue potential. Using non-parametric frontier analysis to evaluate countries’ performance in DRM offers several benefits compared to alternative methods for performance assessments, such as regression-based approaches. In particular, DEA has the advantage that it involves fewer assumptions about the structure of the data than parametric methods (e.g., regression analysis). Consequently, the results generated by DEA tend to be easier to interpret and are not subject to the same econometric challenges that regression-based methods typically face.<sup>4</sup> Moreover, DEA estimates efficiency scores for a given unit (e.g., country) based on the performance of a subset of efficient units in the sample with similar input factors than the considered unit. This differs from the estimates obtained from regressionbased approaches, which rely on comparisons relative to the average performance across all units in the sample. If the observed units are heterogeneous—as is clearly the case when analyzing countries from different parts of the world with different sizes and stages of development—then an important advantage of DEA is that the estimates for any given country are not determined by other countries with fundamentally different characteristics and prospects, but only by those countries that feature comparable economic structures.

Second, we add to the growing body of literature that uses DEA for analyzing efficiency in macroeconomic contexts. Various existing studies in this literature apply DEA to assess efficiency of government expenditure (Gupta and Verhoeven 2001; Afonso et al. 2005; Herrera and Pang 2005) or public spending on education (Clements 2002). In contrast, we focus on efficiency in the financing of public spending through domestic revenue mobilization. Other topics in economics which have been studied by applying DEA include agricultural productivity (Latruffe et al. 2004), efficiency in schools (Ray 1991; Bogetić and Chattopadyay 1995), national transportation and commerce (Rashidi and Cullinane 2019; Wang et al. 2020), and regional economic integration (Naeher 2015; Naeher and Narayanan 2020). In a recent study on private capital mobilization, Naeher and Narayanan (2021) perform a similar analysis as presented here but focusing on countries’ ability to attract (foreign) private capital flows such as foreign direct investment and portfolio equity investment.

Finally, our analysis relates to the theoretical and empirical literature on the determinants of government revenue potential. We contribute to this literature new evidence on factors that are associated with higher efficiency in DRM (see the performed post-DEA panel regression analysis). In addition, our analysis builds on the findings presented in this literature to inform our selection of variables included as input factors in the DEA. Among the many variables that have been considered in this literature, four economic factors are commonly identified as important determinants of revenue potential (see the survey in Gupta 2007): (i) per-capita income levels, (ii) sectoral composition of output,

> 3 Specifically, the analysis is based on data from the UNU-WIDER Government Revenue Dataset which was launched in 2014 and combines government revenue data from various sources (see Section IV for more details).

> 4 DEA and regression analysis represent alternative approaches to performance assessments which each feature their own benefits and weaknesses (for a detailed discussion, we refer to Thanassoulis 1993; Sickles and Zelenyuk 2019). Regression analysis features more tools to identify causal relationships in observational data. However, it also involves stronger assumptions on the structure of the underlying data than non-parametric methods, making the results sensitive to econometric challenges such as endogeneity and omitted variable bias.

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