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Taxes and director independence: evidence from board reforms worldwide

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Abstract

We examine whether changes to corporate governance resulting from board reforms affect corporate tax behavior. While the connection between corporate governance and tax behavior has been the subject of intense interest in the literature, a lack of exogenous variation in governance has hampered inferences. Our inquiry exploits a set of major board reforms that capture shocks to board reforms for firms in 31 countries. The results indicate that corporate tax avoidance decreases significantly following major board reforms. We find that the influence of board reforms on corporate tax behavior is stronger in firms with relatively higher agency conflicts and more opaque information environments.

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Notes

  1. Following Leone et al. (2019) and Li et al. (2020), we implement several methods to address influential observations, including truncation based on influence diagnostics and robust regression. The conclusions are unchanged.

  2. The percentage of global market capitalization for each year is calculated by dividing the calendar year-end total market capitalization of listed companies of these 31 countries (in USD) by the total market capitalization of listed companies of all countries listed in the World Bank’s World Development Indictors (in USD). The mean percentage of global market capitalization from 1995 to 2010 is reported.

  3. Cash ETR is annual cash effective tax rates. We calculate Cash ETR as Cash taxes paid (WC04150) divided by pre-tax income before discontinued operations and extraordinary items (WC01401 – WC04054 – WC04225). We constrain these values to fall between 0 and 1. We exclude discontinued operations and extraordinary items in the Cash ETR calculation because these items are usually income decreasing and introduce significant volatility into the Cash ETR measure. However, recalculating Cash ETR to include these items in the denominator calculation does not change our conclusions. Specifically, the coefficient estimate on Post in Table 4 is 0.032 with t-statistic 2.04 (two-tailed p < 0.05).

  4. Studies have cautioned the use of a single-coefficient two-way fixed effects difference-in-differences (TWFEDID) specification to summarize time-varying effects when there is treatment effects heterogeneity and variation in treatment timing (Chasemartin and D’Haultfoeuille 2020). Two-way fixed effect difference-in-differences models with differential treatment timing can result in nonconvex weights (Chasemartin and D’Haultfoeuille 2020), with certain treatment effects receiving more weight than others (Goodman-Bacon 2018). In some cases, heterogeneity can cause estimates of the average treatment effects to be negative, even though the individual treatment effects are positive. Chasemartin and D’Haultfoeuille (2020) recommend examining the number of treatments with negative weights and the ratio of negative to positive treatments. A large number of negative weights and high negative-to-positive ratio may indicate that the estimator in the TWFEDID model is a biased estimator of the overall treatment effect. Another diagnostic is to regress the weights on a variable that is associated with the size of the treatment effect. A significant correlation indicates that the estimator in the TWFEDID model is a biased estimator of the overall treatment effect. We calculate the number of treatments that receive a negative weight using Chaisemartin and D’Haultfoeuille’s (2020) “twowayfeweights” Stata command. The negative weights present in our regressions are minimal (98 out of 5318) and sum to approximately zero weight (−0.0031). The regression coefficient of the weights of the fixed effects is not significant (t-statistic = 1.60). We conclude that the corresponding weights are not correlated with the treatment effects, and our main results are not biased.

  5. Bertrand et al. (2004) demonstrate that clustered robust standard errors exhibit downward-bias that asymptotically resolves as the number of clusters exceeds 50. As a check, we implement two-way clustering of standard errors by firm and country-year. This approach mitigates correlated errors stemming from a reform year in given country. It yields more than 200 clusters, reducing the likelihood that our conclusions are affected by downward bias associated with robust standard errors. The conclusions are unchanged.

  6. When tax avoidance is measured using Cash ETR (as in our paper), most of the studies find a negative association between tax avoidance and ROA (see Donohoe 2015 [Table 4, p. 14]; Cen et al. 2017 [Table 4, p. 385]; Chen et al. 2010 [Table 4, p. 52]; Rego and Wilson 2012 [Table 4, p. 759]; McGuire, Wang and Wilson 2014 [Table 3, p. 1502]. However, when tax avoidance is measured using other measures, such as GAAP ETR or BTD, most studies find a positive association between tax avoidance and ROA.

  7. The coefficient on that statutory tax rate is not significant in Table 4. We suspect that firm, country, year fixed effects absorb most of the variance in statutory tax rate. To check whether statutory tax rate impacts Cash ETR, we run the following two tests. First, we exclude all fixed effects from the regressions, and the untabulated results show that, after excluding all fixed effects, the statutory tax rate is significantly positively associated with Cash ETR. Second, to further explore the proportion of variance in statutory tax rate are absorbed by the fixed effects, we regress statutory tax rate on fixed effects only. As suspected, the untabulated R-squared results show that firm and year fixed effects can explain 94.5% of the variance for the statutory tax rate, and country and year fixed effects can explain 80.7% of the variance for the statutory tax rate.

  8. For example, the treatment group for 2006 consists of firms incorporated in Italy and Sweden, the sample countries that adopted a board reform in 2006. The control group for 2006 consists of firms from countries that do not adopt board reforms during 2003–2009.

  9. We obtain analyst data from Capital IQ, which provides analyst data for North America since 1999 and for the rest of world since 1996. If analyst data are unavailable, we calculate Transparency using the remaining measures.

  10. To assess whether our results may be affected by non-U.S. firms listed on a U.S. exchange that must comply with Sarbanes-Oxley we remove all American Depository Receipt (ADR) firms from the sample and reestimate Eq. (1). We obtain data regarding ADRs listed in the United States from the Center for Research in Security Prices (CRSP) database. Only Level II and Level III ADRs are included in CRSP. Level I ADRs trade over the counter (OTC) on “pink sheets” and are subject to minimal disclosure. The first day an ADR appears in CRSP is used as the listing date. The coefficient estimate on Post is statistically negative at two-tailed p < 0.05 (untabulated).

  11. We also adapt the Faccio data to measure political connections at the country level by including all observations. We first rank the firms based on the percentage of top 50 firms connected with a minister or member of Parliament, as identified by Faccio (2006). We define high-connection countries as those ranked at or above the median (i.e., countries for which more than 4% of the firms in the country are politically connected). We then exclude all high-connection countries from the sample; the conclusions are unchanged.

  12. The statutory tax rate data show a large tax change for Italy in 1998, apparently attributable to the OECD data excluding regional business taxes in 1998. To be cautious, we treat that year as a large tax change and exclude it.

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Acknowledgements

We appreciate comments from Andrew Belnap, Jennifer Blouin (editor), Xin Cheng (discussant), Travis Chow (discussant), Scott Dyreng, Jeff Hoopes, Michele Mullaney, João M. C. Santos Silva, Luo Zuo (discussant), an anonymous referee, and the Texas A&M tax reading group, the Otto Beisheim School of Management tax reading group, and seminar participants at the American Taxation Association Midyear Meeting, the China International Conference in Finance, the MIT Asia Conference in Accounting, Peking University, and Tsinghua University. We thank workshop participants at Washington State University for helpful comments and discussions. We thank Shawn Nakayama for research assistance. We appreciate the financial support of the School of Economics and Management at Wuhan University, the Kenan-Flagler Business School at the University of North Carolina at Chapel Hill, the Owen Graduate School of Management at Vanderbilt University, and the College of Business and the Hoops Institute of Taxation Research and Policy at Washington State University, respectively. Qingyuan Li also is grateful for financial support from major projects of the National Social Science Fund of China (no. 18ZDA113). Errors or omissions are our responsibility.

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Authors and Affiliations

  1. School of Economics and Management, Wuhan University, Luojia Hill, Wuchang District, 430072, China

    Qingyuan Li

  2. Kenan-Flagler Business School, University of North Carolina at Chapel Hill, McColl Building—CB 3490, Chapel Hill, NC, 27599, USA

    Edward L. Maydew

  3. Owen Graduate School of Management, Vanderbilt University, 401 21st Avenue South, Nashville, TN, 37203, USA

    Richard H. Willis

  4. College of Business, Washington State University, WA, 98686, Vancouver, USA

    Li Xu

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  1. Qingyuan Li
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Correspondence to Richard H. Willis.

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Li, Q., Maydew, E.L., Willis, R.H. et al. Taxes and director independence: evidence from board reforms worldwide. Rev Account Stud 28, 910–957 (2023). https://doi.org/10.1007/s11142-021-09660-2

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