Our sample selection process yields a sample of 16,483 firm-year observations. Table 4 reports descriptive statistics for our regression variables on the final sample. The distribution of our control variables are comparable with prior studies (e.g., Dyreng et al., 2010; Koester et al., 2017), but differences between American and European firms are to be expected. For this sample, cash ETR has a mean of 26.7%, with an interquartile range of 13.6% to 32.3%.
Consistent with what is observed in prior studies, values for long-run cash ETR measures are higher than for a one-year measure. Our mean cash ETRs are marginally lower than what is obtained by Koester et al. (2017) for US firms, but the difference is smaller than the gap in
corporate tax rate should indicate19. We also note that the interquartile range for one-year cash ETR is wider for US firms, ranging from 8.8% to 36.9%. These findings are consistent with those obtained by Avi-Yonah and Lahav (2011) and imply a greater opportunity for corporate tax avoidance in the US. The mean MASCORE for the sample is -3.6%, with an interquartile range of -16.3% to 6.4%. The mean MASCORE is lower than for prior studies, while the standard deviation is higher, likely due to calculation groups for our DEA model being divided by years, and greater differences in managerial ability across European firms than within the US. An untabulated analysis reveals that MASCORE values are relatively stable within firms from year to year, with a within-firm correlation of 56.0%. There may be several reasons as to why firms experience varying values of MASCORE over time according to Koester et al.
(2017). Firstly, there may be a change in the composition of the management team. Secondly, a management team may tackle different macroeconomic conditions with varying degrees of competence, and finally, changes in the societies demand for products delivered by a firm may lead to managers reallocating resources.
NOL_DECREASE 16,483 0.0805 0.0000 0.2721 0.0000 0.0000
Notes. This table presents descriptive statistics for our main regression variables. ETR measures winsorized at [0,1], and all continuous variables are winsorized at the 1st and 99th percentiles (pooled). All variables defined in Appendix A.
The remaining control variables introduce some differences compared to prior American studies (e.g., Dyreng et al., 2010; Koester et al., 2017). Our sample consists of bigger firms who spend more on R&D. Furthermore, a bigger percentage of firms in our sample has foreign
19 The average corporate tax rate in our sample is 22.5% while the average corporate tax rate in the sample of Koester et al.
operation, 69.7%, likely due to our sample containing larger firms and the European Union facilitating for foreign operations within much of Europe. Finally, our NOL_DECREASE proxy has a lower mean value, likely due to differences in estimation, and our sample not Notes. This table presents Pearson product–moment correlations. ETR measures winsorized at [0,1], and all continuous variables are winsorized at the 1st and 99th percentiles (pooled). All variables defined in Appendix A. Column (2) and (3) contain data on variables averaged over the respective period (t+1 and t+3). Correlation coefficients who are significant at the 10% level or stronger (two-tailed tests) are presented in italic.
In Table 5, the results from Pearson correlations are presented. The results show that CASHETR, CASHTER2, and CASHETR4 are all negatively correlated with MASCORE20. This implies that higher ability managers engage in more tax avoidance, not considering other explanatory factors. Most of the control variables are significantly correlated with both short- and long-run proxies for tax avoidance, a result which states the importance of including these control variables in our model. Short- and long-run cash ETR proxies are significantly correlated, but looking at the coefficient magnitudes we observe what was stated by Dyreng et al. (2008), that one-year cash ETR is an inaccurate measure for long-run cash ETRs.
Table 17, Table 18 and Table 19, all of whom can be found in Appendix B, present summary statistics grouped by years, country, and industry. All variables are similarly distributed over the years of the sample. Across industries, the main variables of interest, cash ETR and MASCORE, are evenly distributed. However, there are significant differences in some control variables such as R&D. We observe more variation in the main variables of interest across countries, especially for MASORE, where several outliers are present. Most of these outliers are countries of less reliable reporting or are countries that have few firm-year observations,
20 Both short- and long-run cash ETRs are significant with their respective MASCORE at the 1% level using a two-tailed test (untabulated).
but some of these outliers such as Norway and Belgium are of both reliable reporting and have a substantial amount of observations. A plausible explanation for these particular outliers could be that some countries have stronger efficiency focus than others as it is important and challenging for export firms in relatively small open economies in well-off countries to be competitive in an international market. Another plausible explanation is that for the smaller economies in our sample, the companies who meet our variable requirements might be a skewed sample in terms of management ability. Note that our primary model includes country-year fixed effects and that as a robustness test we run the model excluding countries of unreliable financial reporting.
Primary Findings
In this section, we present our primary findings. We first present the results from our primary model in the form of five regressions, including both short- and long-run ETR proxies, with differing levels of fixed effects included. We then present several alternative tests, including a variety of results from differing methodological considerations and several robustness tests.