# _C. Regression Analysis_

Why do some countries generate more revenues than other countries with comparable domestic economic structures? To address this question, we estimate two types of panel regressions based on the sample of 118 countries included in the frontier analysis, and the three 4-year periods considered between 2008 to 2019. These regressions feature the efficiency estimates obtained from the DEA as the outcome variable, relating them to various explanatory variables that can potentially account for the observed variation in efficiency scores across countries. Specifically, countries’ quality of tax policy, size of the informal sector, level of technological readiness, natural resources, control of corruption, and social trust are included as regressors.

The results are reported in Table 3. Columns (1) to (6) report the results of bivariate regressions which include each considered factor separately. Columns (7) to (10) report the results of regressions that include different sets of regressors together, and add country-fixed effects (FE) to control for timeinvariant country characteristics such as geographical features, history, and cultural traits.<sup>20</sup> According to the results of the bivariate regressions in columns (1) to (6) of Table 3, countries that are closer to achieving their full revenue potential tend to feature higher levels of technological readiness, natural resources, control of corruption, and social trust, as well as better tax policy and a smaller informal sector. Except for the regression on trust, all the respective coefficients are statistically significant at the 10-percent significant level. However, when several of these factors are included at once and timeinvariant country characteristics are controlled for using country fixed effects, then only technological readiness remains statistically significant across most specifications (see columns (7) to (10) in Table 3).<sup>21</sup> Given the observational nature of the data underlying these estimates, our ability to identify causal relationships between the considered variables is, of course, very limited. We therefore interpret the regression results as suggestive evidence of the factors that are potentially driving the observed differences in efficiency and argue that these results can inform further investigation into the underlying root causes of inefficiency in DRM. Moreover, these results suggest that the findings obtained from the DEA are not merely driven by differences in the roles of the informal economy or natural resources across countries (if this was the case, then the coefficients of these factors should be statistically significant in the regressions in Table 3).

# _D. Does International Support for DRM Target Countries with High Untapped Potential?_

We now turn to the question of how international development cooperation efforts aimed at improving revenue mobilization are distributed across countries with different performances in DRM efficiency. In addressing this question, we focus on the engagement of the World Bank Group (WBG) in developing countries. The main reason for this focus is data availability: For the WBG, we have access to information about the full universe of projects related to DRM, which allows us to get a complete picture of how this institution’s engagement is distributed across countries with respect to DRM efficiency. In addition, the WBG is one of few development agencies that is active in almost all developing countries, and that plays a significant role in the international support to poor countries related to improving revenue mobilization outcomes (World Bank 2021).

In investigating the WBG’s targeting of DRM-related projects across countries, two indicators of engagement are considered. The first indicator is the total amount committed by the WBG for projects

> 20 For tax policy, informal sector employment, and social trust, the data available to us cover only a very limited number of countries, and the covered countries differ relatively strongly across these variables. We therefore include these variables only separately in the regressions.

> 21 The insignificant coefficient of “Technological readiness” in column (10) of Table 3 may be due to the smaller number of observations included in this regression (due to the limited availability of data on the variable _Social trust_ ).

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