the obtained efficiency scores provide a more adequate picture of revenue mobilization potential in developing countries. In addition, we estimate a second frontier based exclusively on the set of lowincome countries (LICs) in our data set. The purpose of this second frontier is to provide an upper bound estimate of the efficiency scores for LICs. In particular, the second frontier assumes that LICs can generate the same revenue levels as other LICs with comparable economic conditions (as captured by the DEA input index) but not necessarily the revenue levels achieved by lower-middle income countries (LMICs) and upper-middle income countries (UMICs) with such conditions, thus accounting for the special challenges to revenue mobilization facing many LICs.

It should be noted that the results derived from DEA are subject to the following limitations. Given that DEA is a non-parametric method of estimating efficiency scores, the derived results are of a descriptive nature and should not be interpreted as identifying causal links between the included variables. Instead, the performed DEA compares revenues among a set of countries at a given time and identifies those countries that, relative to others with similar economic conditions, are currently performing below the level which they should potentially be able to. This also implies that the analysis is based exclusively on current conditions and thus not designed to provide forecasts of revenues under possible scenarios of future changes in economic or other conditions. Finally, it should be noted that the obtained results relate only to the considered outcome in government revenues relative to GDP and do not provide direct implications for potential welfare gains (or losses) associated with increased revenues.

# _B. Panel Regression Analysis_

To investigate the factors that potentially account for variation in efficiency scores across countries, we estimate a panel regression framework using the obtained DEA efficiency scores as dependent variable. The purpose of this analysis is to examine which factors are associated with higher or lower relative performance in achieving domestic revenue potential (as captured by the DEA scores), i.e., why some countries generate more revenues than other countries with comparable domestic economic structures. While the panel structure of our data allows us to control for both observable and unobservable, timeinvariant country characteristics (e.g., geographical features, history, cultural traits) through the inclusion of country-fixed effects, we stress that the observational nature of the underlying data clearly limits our ability to identify causal relationships. The (time-variant) factors included as regressors are selected to capture countries’ quality of tax policy, size of the informal sector, level of technological readiness, natural resources, control of corruption, and social trust, since these factors are commonly argued to play a role in determining countries’ ability to convert their tax base into revenues.

In addition, we estimate a second set of regressions where the DEA efficiency scores enter as an independent variable to potentially explain the allocation of international development cooperation efforts aimed at improving revenue mobilization (focusing on projects related to DRM financed by the World Bank Group) across countries with different performances in DRM efficiency. The purpose behind this specification is to obtain insights on the extent to which these efforts tend to be targeted at countries with large untapped revenue potential (and thus likely higher marginal benefits to such projects).

# **IV. Data**

All data used in this study are publicly available from the sources described in Table 1. The table provides a list of the variables and respective data sources used in the analysis, grouped into four categories. Panel A describes our main outcome variable, government revenue (excluding grants) as a percentage of 8