GDP, which is based on data from the UNU-WIDER Government Revenue Dataset (GRD), 2020.<sup>12</sup> Panel B in Table 1 describes the four indicators used in the construction of the DEA input index. Panel C lists additional factors which are included in the post-DEA regression analysis (pairwise correlations between these variables are reported in Table A1 in the appendix).

The core sample which is used in the frontier analysis consists of 118 countries, comprising 25 LICs, 45 LMICs, and 48 UMICs. In addition, we have data on 58 HICs as well as 6 countries with revenues above 50 percent of GDP which we exclude as outliers in the frontier analysis, so that the maximum sample size amounts to 182 countries. For most countries, data for the variables listed in Panels A and B of Table 1 are available annually for the years 2008 to 2019. To limit the role of temporary fluctuations and measurement error, most of the analysis is based on values for 4-year periods, which we construct as the mean values of the years 2008-2011, 2012-2015, and 2016-2019 for each country (for ease of exposition, all tables and figures refer to these time periods, even if the data for a particular indicator are only available for a subset of these years).

**Table 1.** List of Variables and Data Sources

|**Indicator**|**Description**|**Source**|
|---|---|---|
|**_Panel A: Revenues_**|**_(DEA output)_**||
|Revenue|Government revenue, excluding grants (% of GDP)|ICTD/UNU-WIDER,<br>Government Revenue Dataset|
|**_Panel B: Economic f_**|**_undamentals (DEA input index)_**||
|Per-capita income|GDP per capita, PPP (constant 2017 international $)|World Bank|
|Share of<br>agriculture in GDP|Agriculture, forestry, and fishing, value added (% of GDP)|World Bank|
|Trade openness|Trade (% of GDP): Sum of exports and imports of goods and<br>services measured as a share of gross domestic product|World Bank|
|Age dependency<br>ratio|Age dependency ratio (% of working-age population): Ratio of<br>dependents (people younger than 15 or older than 64) to the<br>working-age population (ages 15-64)|World Bank|
|**_Panel C: Other fact_**|**_ors (used in regression analysis)_**||
|Control of|Score on ‘Control of corruption’ indicator|Worldwide Governance|
|corruption|(values from -2.5=weak to +2.5=good governance)|Indicators|
|Informal sector|Informal employment (% of total non-agricultural employment)<br>harmonized series|;<br>International Labour<br>Organization|
|Natural resources|Total natural resources rents (% of GDP): Sum of oil rents,<br>natural gas rents, coal rents (hard and soft), mineral rents, and<br>forest rents|World Bank|
|Tax policy|Score on ‘Efficiency of revenue mobilization’ indicator<br>(values from 1=low to 6=high efficiency)|World Bank, Country Policy and<br>Institutional Assessment|
|Technological|Score on ‘Technological Readiness Index (9th Pillar)’|World Economic Forum,|
|readiness|(values from 1=low to 7=high readiness)|Global Competitiveness Report|

> 12 The GRD was launched in 2014 and combines government revenue data from various sources, including OECD Revenue Statistics, IMF Government Finance Statistics, and CEPALSTAT (revenue statistics in Latin America). It is arguably the most updated and comprehensive cross-country data set on government revenues available (for more detailed information on the construction and scope of the data set, see Prichard et al. 2014; McNabb 2017).

9