are provided in Sections II and III. _Source:_ Authors’ analysis based on the variables and data sources described in Section III.

The mean DEA score in the sample comprising both low- and middle-income countries equals 0.62, suggesting that revenue levels are at 62 percent of the estimated potential, on average across these countries. Figure 7 provides additional details on the ranges of DEA scores achieved in each income group (Panel A) and region (Panel B) for the period 2016-2019. According to these results, half of the included LICs feature scores between 0.45 and 0.75, with a median score of 0.54 and a mean of 0.58. This suggests that most LICs currently generate between 45 and 75 percent of their potential revenues.

While the median DEA score among LICs (0.54) is lower than the median score of LMICS (0.62) and UMICS (0.63), Figure 7 also shows that LICs cover almost the entire possible range of DEA scores from zero to one. These results demonstrate that accounting for differences in economic conditions (via DEA scores) leads to important, additional insights about revenue potential of countries at different income levels. In particular, a simple comparison of revenue levels across countries suggests that LICs perform worse than countries in higher income groups (recall the results in Figures 3 and 4). In contrast, the DEA results reveal that there are many LICs that, in fact, perform better than countries from higher income groups _given_ the economic resources and conditions available to them.<sup>17</sup> Hence, just looking at the absolute revenue-to-GDP ratio might be misleading for drawing conclusions about how much more revenues a country can potentially raise, given its economic structure. This puts the efforts of lowincome countries to increase revenue mobilization, i.e., their revenue-to-GDP ratio, in perspective: it may, indeed, be much harder for these countries to raise their revenue mobilization from the low absolute level unless and until their income level rises or economic structure changes substantially, thereby increasing their potential for additional revenue.

Figure 7 also shows that countries with low or high DEA scores are not concentrated in a particular income group or region. Rather, all income groups and regions cover wide ranges of scores, and median scores are relatively similar across income groups and regions (except for ECA and SSA). In particular, these results indicate that in case future efforts to support revenue mobilization were to assign strategic priorities to countries considering their efficiency in DRM as captured by the DEA scores, these priorities would not necessarily end up benefiting countries belonging to a particular income group or geographical region in a disproportional way.<sup>18</sup> Simply put, efficient government revenue mobilization appears to be a challenge across income groups and regions (including across countries with very different revenue-to-GDP ratios).

A series of robustness checks reported in Appendix A indicate that the main results of the DEA are robust to changes in the selection of indicators and the aggregation methodology underlying the construction of the variables used in the DEA. Due to imperfect data quality, however, we stress that the

> 17 An example is Uganda, a LIC, whose revenue-to-GDP ratio (0.12 in 2016-2019) is much smaller than that of, for example, Botswana (an UMIC with revenue-to-GDP ratio of 0.32 in the same period). Despite its lower revenue level, however, Uganda is achieving 81 percent of its potential revenue according to the estimated frontier, slightly more than Botswana (79 percent). The underlying reason is that Botswana features a much more favorable economic structure for generating revenue than Uganda, as captured by the large gap in the DEA input index across these two countries (0.20 in Botswana and 0.03 in Uganda).

> 18 The finding that countries with large untapped revenue potential come from different income groups and geographical regions is generally consistent with the findings of previous studies (Le at al. 2012; Khwaja and Iyer 2014). At the same time, our findings for individual countries differ in several cases from those of previous studies, which is likely due to dynamics over time in these countries that are not captured in previous studies as well as differences in methodology. For example, Le et al. (2012) find that Azerbaijan, Bulgaria, Burkina Faso, Uganda, and Ukraine are part of a group of countries with relatively large potential to increase tax collection, while according to our results these countries are, in the most recent period under study, already achieving revenues close to their potential (above 80 percent of potential revenue in the period 2016-2019).

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