Corporate Effective Tax Rates for Research and Policy

Abstract

How much companies pay in corporate income taxes is often better captured by effective tax rates (ETRs) rather than by statutory ones. Economists further distinguish between those modeled using the law—forward-looking ETRs—and those estimated from actual data on companies’ profits and taxes—backward-looking ETRs. In this article, I move beyond this distinction, and I break down backward-looking ETRs according to the type of data used to estimate them. I focus on backward-looking ETRs that are estimated using companies’ balance sheet databases. Based on my review of recent findings, I argue that backward-looking ETRs—of multinational corporations in particular—have become more frequently estimated thanks to advances in data availability while also becoming more relevant as a result of ongoing global corporate tax reform debates.

Effective tax rates of multinational corporations: Country-level estimates

“…Norway). In the case of Germany, research interest has recently surged, in part in response to this paper’s preliminary results used in a policy report [ 57 ] which sheds further light on why the estimated ETR range is rather wide (see e.g. in German literature [ 58 , 59 ]).…”

“…The second approach uses data available for existing companies to develop backward-looking ETRs (or simply ETRs in this paper), which constitute our focus. In addition to Orbis, which we use in our paper, US-focused sources such as Form 5471 by [ 15 ] or Compustat by [ 16 , 17 ] or [ 18 ], are among other relevant data sources (reviewed more comprehensively by [ 19 ]. We believe that the potential of backward-looking ETRs has gone untapped thus far and that their use in research has been limited by their availability.…”

The long way to tax transparency: lessons from the early publishers of country-by-country reports

“…For research on the taxation of MNEs, CbCR data constitutes the most promising candidate to address the trade-off between the quality of confidential tax returns and the need for comprehensive country coverage (Janský, 2020a). Aggregate CbCR data has been used in recent profit shifting research (Garcia-Bernardo & Janský, 2021) while confidential, country-specific company-level data has been used for Germany (Fuest et al, 2021) and Italy (Bratta et al, 2021).…”

“…Researchers interested in better country coverage and international comparisons have exploited other resources, such as the private databases Orbis (Egger et al, 2009, Fuest & Riedel, 2012 and Compustat (Markle & Shackelford, 2012, Dyreng et al, 2017, official foreign direct investment statistics (Bolwijn et al, 2018, Janský & Palanský, 2019, and foreign affiliate statistics (Tørsløv et al, 2020). Despite increased research interest in recent years, no single data source has emerged as a clear solution to the enduring trade-off between the quality of confidential tax returns data and the need for comprehensive country coverage (Janský, 2020a). Some of the most promising candidates for addressing this trade-off have been, and likely still are, the various types of CbCR data, which have become available in recent years and have been hailed as a potential panacea due to their expected positive impact on corporate behaviour, financial markets and development (Wójcik, 2015).…”

Revenue losses from corporate tax avoidance: Estimations from the UNUWIDER Government Revenue Dataset

“…may be considered a hint of the possible positive effects that the BEPS programme is having on the overall level of tax revenue losses resulting from tax avoidance practices. On the other hand, the difference in the estimated trends using the statutory tax rates and the effective rates suggests the possible ineffectiveness of the STRs to measure recent profit shifting activities, recent studies have shown solid estimations using ETRs and have explained why they should be preferred to be used respect to the STRs from a methodological points of view Garcia-Bernardo and Janský (2022), Janský (2022), Creedy and Gemmell (2011). Additionally, I compute further analysis by running the Crivelli et al model just for the periods 2012-2015 and 2016-2019 in order to observe different dynamics by means of the initial phase of the OECD BEPS process spanning from 2013 to 2015, followed by its adoption and execution among OECD member nations, and subsequently by participants within the newly established Inclusive Framework.…”