Data Sources: <mark>Tax revenues as a share of GDP measure</mark> drawn from the International Survey on Revenue Administration (CIAT, IMF, IOTA, OECD 2022) using values for 2018. Taxation technology measure drawn from <mark>OECD Inventory of Tax Technology Initiatives (2023) Global Survey on Digitalisation.</mark> <mark>Notes: This figure displays the relationship between tax revenues as a share of GDP and an index of taxation technology for 75 individual countries. The tax revenues as a share of GDP measure is drawn from the International Survey on Revenue Administration (CIAT, IMF, IOTA, OECD 2022) using values for 2018. We fill in missing values for 7 out of 75 countries using UNU-WIDER (2022) data in 2018. The index of taxation technology is computed from individual measures drawn from the OECD Inventory of Tax Technology Initiatives (ITTI) (2023) Global Survey on Digitalisation. The index includes variables capturing tax authorities’ use of third-party information, taxpayer identification systems, taxpayer services, and innovation in technology use. More details on these measures are provided in the text and Appendix I. Classifications of country income level are drawn from World Bank Development Indicators. Low-income countries are represented by green triangles, lower-middle-income by blue diamonds, uppermiddle by gray squares, and high-income by black circles. The line of best fit is estimated using all observations and has slope 2.32 (robust standard error 1.10).</mark> For each dimension of tax capacity, we construct an index that counts how many of the possible technologies a country uses, as well as an overall index that sums across the three groups.<sup>6</sup> The horizontal axis of Figure 2 shows the standardized scores for each country (in standard deviation units relative to mean zero). Figure 2 reveals a positive correlation between the extent of technology use (captured by the overall index) and tax revenue as a share of GDP, consistent with the case-specific evidence we discuss above. We examine these relationships using exploratory regressions reported in Appendix Table 1 and find that this correlation persists while holding constant country income level. Further, the relationship appears driven by technology applications for identification and detection in contrast to the collection index, which does not have a detectable relationship with tax performance. Of course, these relationships are simply correlations and should by no means be interpreted as causal as many factors can affect both the use of technology as well as tax performance. Nevertheless, the patterns in cross-country data are consistent with findings from well-identified studies at the level of individual countries.

# **The Role of Tax Officials**

Technology supplies data and tools that, ultimately, will assist tax personnel in the work of tax collection. What this work entails depends on the underlying tax system. In more advanced tax systems, tax personnel are deployed mostly in the higher-level tasks of maintaining and updating information technology systems, processing returns, analyzing data, and undertaking audits. In less advanced systems, particularly in the context of local taxation in many lower-income countries, tax collection proceeds manually, with officials soliciting payments and undertaking enforcement efforts in-person.

As a starting point, we examine how the relative strength of a nation’s tax staff relates to its fiscal capacity. Figure 3 illustrates a negative relationship between tax-to-GDP and population-to-taxstaff, using data from the International Survey on Revenue Administration (CIAT, IMF, IOTA,

> 6 Appendix I provides further details on the construction of these variables and summary statistics.

10