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The Impact of Corporate Tax Avoidance on Board of Directors and CEO Reputation

Abstract

This study examines the impact of corporate tax avoidance on board of directors and chief executive officer (CEO) reputation. Our regression results show that when firms engage in tax avoidance, both directors and CEOs, on average, are rewarded by improvements in their reputations as proxied by an increased number of outside board seats. In particular, both independent directors and non-CEO executive directors undergo positive changes in reputation. We also find that CEOs of tax-aggressive firms experience enhanced reputations by gaining extra board seats. Our main regression results hold based on additional analyses. Overall, this study provides important empirical evidence confirming an association between tax avoidance and the individual reputations of directors and CEOs.

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Notes

  1. 1.

    The term “tax director” is adopted from Armstrong et al. (2012) and refers to the executive or manager responsible for overseeing a firm’s tax function (Armstrong et al. 2012). It does not necessarily refer to a member of the board of directors, although this may be the case in some cases. Finally, a tax director is responsible for one of the firm’s largest outflows of cash and one of the largest expenses on the income statement, and provides expert advice to senior executives including strategic advice at the board level (Armstrong et al. 2012).

  2. 2.

    The condition of the null hypothesis is where there is no tax planning, tax aggressiveness, and tax avoidance, and the like. In particular, it is the point where the tax response of a firm would be a “random walk.”

  3. 3.

    Securities fraud is typically defined as intentional misstatements of financial information on which information users rely to their detriment (see Hennes et al. 2008, p. 1488; Karpoff et al. 2017, p. 150). Like tax avoidance, there are various empirical measures of fraud in the literature that involve different levels of severity of conduct. Agrawal et al. (1999) uses a quite severe measure of fraud by assigning a value of one to their dummy variable if there is a fraud or crime related to a news article about the firm in the Wall Street Journal and zero otherwise. Helland (2006) and Humphery-Jenner (2012) both employ a more commonly used measure of alleged fraud by computing a dummy variable that equals one if any securities class action is filed against the firm in a given year and zero otherwise. Agrawal and Cooper (2017) and Desai et al. (2006) both use a broad definition of fraud that captures activities of the least severe nature and assign a value of one if the firm has issued any accounting misstatement and zero otherwise. Consequently, all of these studies use dummy variables to capture fraud that is defined at various levels of severity.

  4. 4.

    In an earlier but related study, Hoi et al. (2013) find that firms with excessive irresponsible CSR activities (i.e., a risk management strategy that a firm uses to possibly enhance its CSR reputation) are more aggressive in avoiding taxes.

  5. 5.

    The benefits from tax planning include reduced tax liabilities, increased cash flows, and maintenance of favorable credit ratings, while the costs include explicit direct costs (e.g., audit and litigation costs, consulting fees paid to outside consultants, fines and penalties from tax audits by a tax authority, and salaries and other costs related to running a tax department) (Edwards et al. 2016) and implicit indirect costs (e.g., low rates of return on investments in tax-favored assets as well as reputational costs) (Scholes et al. 2005). An additional indirect cost of tax avoidance for government and society as a whole is the declining tax revenues that create fiscal problems (Hansen et al. 1992).

  6. 6.

    This measure captures all changes in board reputation, attributable to two potential causes: (1) changes in individual directors’ reputations (i.e., the same director gaining or losing board seats), or (2) the departure and appointments of different directors, bringing new reputational capital onto the board. In additional analyses, we isolate the first cause only by excluding the effects of director turnover. We calculate a different measure of board reputation (∆BRD*t|0+2|,t|0+2|), by identifying directors who remain on the board over the entire duration of the observation period |0 + 2| (or |− 1 + 2|), and computing the change in the average number of board seats held by those directors only. We also repeat our analyses for independent director reputation (∆IND*t|0+2|,t|0+2|).

  7. 7.

    Kim et al. (2011) and Rego and Wilson (2012) find that Wilson’s (2009) tax shelter model has construct validity.

  8. 8.

    We use the predicted value of UTB rather than actual UTB as recorded in the Compustat Database for two reasons. First, a significant number of observations of UTB in Compustat contain missing values. Therefore, including this measure in our regressions would result in a reduction of up to 80% of our sample size. Second, predicted UTB is a widely accepted measure of tax avoidance in the relevant literature and has been used in numerous tax studies (e.g., Rego and Wilson 2012; Boone et al. 2013; Olsen and Stekelberg 2016).

  9. 9.

    Unrecognized tax benefits (UTBs) are accrued balance sheet liabilities that are identified as per FIN 48.18. They are that part of the tax benefit listed in a firm’s tax return that is not expected to be sustained following a tax audit. Hanlon and Heitzman (2010) claim that higher UTBs infer more uncertainty in a firm’s tax position and are indicative of the level of uncertainty in its tax avoidance. As an accounting accrual, UTBs are reliant on management judgement and may be affected by financial reporting incentives, so UTBs are a composite measure that reflect both tax avoidance and tax-based earnings management activity (Boone et al. 2013).

  10. 10.

    In our additional analyses where we employ a continuous measure of ETR, we adjust the sign of the ETR variable by multiplying it with − 1, so that the direction of the expected sign is consistent with those of the other two measures of tax avoidance (SHELTER and P_UTB), which makes the results easier to interpret.

  11. 11.

    In our additional analyses, we also use a series of continuous tax-avoidance variables (SHELTER, P_UTB, and ETR) as independent variables instead of the tax-avoidance dummy variables, producing substantially similar results (see Table 12).

  12. 12.

    We also compute variance inflation factors (VIFs) when estimating our regression models to test for multicollinearity between the explanatory variables. We observe that no VIFs exceed five and thus conclude that multicollinearity is not a problem in our study (e.g., Hair et al. 2010).

  13. 13.

    For reasons of brevity, we only report the additional analyses corresponding to those main regression results that are found to be statistically significant in prior analyses. However, the full set of analyses are available from the corresponding author upon request.

  14. 14.

    The number of observations included in each regression model is subject to missing values in the control variables. Thus, the final N reported for each regression model may be smaller than the total number of pair-matched observations using the propensity score.

  15. 15.

    We transform our ETR variable into negative values by multiplying it by − 1, so that the expected sign of the estimated coefficient of ETR is consistent with the other two proxies of tax avoidance, SHELTER and P_UTB (i.e., a higher value indicating a higher level of tax avoidance).

  16. 16.

    We also carry out additional analyses by re-specifying the levels model using dummy-dependent and dummy-independent variables. As per the calculation of the TA variables by Donohoe and Knechel (2014), we also compute the dependent variables of independent director, executive director, and CEO reputation as dummy variables. Our untabulated results show that the coefficients for the TA variables (TA_SHELTER, TA_PUTB, and TA_ETR) are uniformly significant in predicting the dummy reputation variables based on terciles computed in each year and industry (p < 0.10 or better).

  17. 17.

    In addition, we carry out further analyses using levels regression model specifications based on the propensity score matching approach and the Fama–Macbeth (1973) estimation method. The untabulated results from these robustness tests are qualitatively similar to those reported in Table 12.

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Correspondence to Roman Lanis.

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Appendix 1: Variable Definitions

Appendix 1: Variable Definitions

Variable name Variable definition
∆BRD|0 + 2| The change in the average number of external board seats held by all directors, from year 0 to year + 2, as a measure of the change in the collective reputation of the board
∆BRD|− 1 + 2| The change in the average number of external board seats held by all directors, from year − 1 to year + 2, as a measure of the change in the collective reputation of the board
∆IND|0 + 2| The change in the average number of external board seats held by independent directors, from year 0 to year + 2, as a measure of the change in the collective reputation of independent directors on the board
∆IND|− 1 + 2| The change in the average number of external board seats held by independent directors, from year − 1 to year + 2, as a measure of the change in the collective reputation of independent directors on the board
∆EXE|0 + 2| The change in the average number of external board seats held by executive and linked directors (excluding the CEO), from year 0 to year + 2, as a measure of the change in the collective reputation of executive directors on the board
∆EXE|− 1 + 2| The change in the average number of external board seats held by executive and linked directors (excluding the CEO), from year − 1 to year + 2, as a measure of the change in the collective reputation of executive directors on the board
∆EXECEO|0 + 2| The change in the average number of external board seats held by executive and linked directors (including the CEO), from year 0 to year + 2, as an alternative measure of the change in the collective reputation of executive directors on the board
∆EXECEO|− 1 + 2| The change in the average number of external board seats held by executive and linked directors (including the CEO), from year − 1 to year + 2, as an alternative measure of the change in the collective reputation of executive directors on the board
∆CEO|0 + 2| The change in the number of external board seats held by the CEO from year 0 to year + 2, as a measure of the change in CEO reputation
∆CEO|− 1 + 2| The change in the number of external board seats held by the CEO from year 0 to year + 2, as a measure of the change in CEO reputation
∆BRD*|0 + 2| The change in the number of external board seats held by all directors who remain on the board from year 0 to year + 2, scaled by the number of these directors in year 0
∆BRD*|− 1 + 2| The change in the number of external board seats held by all directors who remain on the board from year − 1 to year + 2, scaled by the number of these directors in year − 1
∆IND*|0 + 2| The change in the number of external board seats held by independent directors who remain on the board from year 0 to year + 2, scaled by the number of these independent directors in year 0
∆IND*|− 1 + 2| The change in the number of external board seats held by independent directors who remain on the board from year − 1 to year + 2, scaled by the number of these independent directors in year − 1
SHELTER Propensity of tax sheltering calculated by using the following model obtained from Wilson’s (2009) study: SHELTER = − 4.30 + 6.63 * BTD − 1.72 * LEV + 0.66 * SIZE + 2.26 * ROA + 1.62 * FOREIGN_ INCOME + 1.56 * RD, where BTD = book-tax difference: book income less taxable income scaled by total assets; LEV = long-term debt scaled by total assets; SIZE = log of total assets; ROA = net-income scaled by total assets; FOREIGN_INCOME = a dummy variable, coded 1 for firms with foreign income, otherwise 0; and RD = research and development (R&D) expenses scaled by total assets
TA_SHELTER Dummy variable denoting tax aggressiveness, which is assigned the value of one if the firm falls into the highest tercile of SHELTER by year within a two-digit GlCS industry, and zero otherwise
P_UTB Predicted unrecognized tax benefits computed from the following equation obtained from the study by Rego and Wilson (2012): P_UTB = − 0.004 + 0.011 * ROA + 0.001 * SIZE + 0.010 * FOR_SALE + 0.0928 * RD − 0.002 * DISC_ACCR + 0.003 * LEV + 0.000 * MB + 0.014 * SGA − 0.018 * SALES_GR, where ROA = net-income scaled by total assets; SIZE = log of total assets; FOR_SALE = foreign sales scaled by total sales; RD = R&D expenses scaled by total assets; DISC_ACCR = discretionary accruals, computed using the performance-adjusted modified Jones (1991) model; LEV = long-term debt scaled by total assets; MB = the market-to-book ratio; SGA = selling and general administrative expenses; and SALES_GR = annual growth rate in sales
TA_PUTB Dummy variable denoting tax aggressiveness, which is assigned the value of one if the firm falls into the highest tercile of P_UTB by year within a two-digit Global Industry Classification Standard industry, and zero otherwise
ETR Effective tax rate calculated as tax liability divided by pre-tax accounting income, multiplied by − 1
TA_ETR Dummy variable denoting tax aggressiveness, which is assigned the value of one if the firm falls into the lowest tercile of ETR by year within a two-digit Global Industry Classification Standard industry, and zero otherwise
BRD_GENDER Proportion of female directors amongst all directors on the board at the beginning of the observation period year 0 (or year − 1)
BRD_AGE Average age of all directors on the board at the beginning of the observation period year 0 (or year − 1)
BRD_OWN Average percentage point of common shares outstanding owned by all directors at the beginning of the observation period year 0 (or year − 1)
BRD_TENURE Average number of years of service in the firm for all directors on the board at the beginning of the observation period year 0 (or year − 1)
BRD_SEATS Average number of external board seats in other firms held by all directors on the board at the beginning of the observation period year 0 (or year − 1)
IND_GENDER Proportion of female directors amongst independent directors on the board at the beginning of the observation period year 0 (or year − 1)
IND_AGE Average age of independent directors on the board at the beginning of the observation period year 0 (or year − 1)
IND_OWN Average percentage point of common shares outstanding owned by independent directors at the beginning of the observation period year 0 (or year − 1)
IND_TENURE Average number of years of service in the firm for independent directors on the board at the beginning of the observation period year 0 (or year − 1)
IND_SEATS Average number of external board seats in other firms held by independent directors on the board at the beginning of the observation period year 0 (or year − 1)
EXE_GENDER Proportion of female directors amongst executive and linked directors on the board (excluding the CEO) at the beginning of the observation period year 0 (or year − 1)
EXE_AGE Average age of executive and linked directors on the board (excluding the CEO) at the beginning of the observation period year 0 (or year − 1)
EXE_OWN Average percentage point of common shares outstanding owned by executive and linked directors on the board (excluding the CEO), at the beginning of the observation period year 0 (or year − 1)
EXE_TENURE Average number of years of service in the firm for executive and linked directors on the board (excluding the CEO) at the beginning of the observation period year 0 (or year − 1)
EXE_SEATS Average number of external board seats in other firms held by executive and linked directors on the board (excluding the CEO) at the beginning of the observation period year 0 (or year − 1)
EXECEO_GENDER Proportion of female directors amongst executive and linked directors on the board (including the CEO) at the beginning of the observation period year 0 (or year − 1)
EXECEO_AGE Average age of executive and linked directors on the board (including the CEO) at the beginning of the observation period year 0 (or year − 1)
EXECEO_OWN Average percentage point of common shares outstanding owned by executive and linked directors on the board (including the CEO), at the beginning of the observation period year 0 (or year − 1)
EXECEO_TENURE Average number of years of service in the firm for executive and linked directors on the board (including the CEO) at the beginning of the observation period year 0 (or year − 1)
EXECEO_SEATS Average number of external board seats in other firms held by executive and linked directors on the board (including the CEO) at the beginning of the observation period year 0 (or year − 1)
CEO_GENDER Dummy variable equals one if the CEO is female, and zero otherwise
CEO_AGE The age of the CEO in year 0
CEO_OWN The percentage point of common shares outstanding owned by the CEO, calculated as the number of shares owned by the CEO scaled by the number of total common shares outstanding in year 0
CEO_TENURE The number of years during which the CEO has served in the current capacity as at year 0
INTERNAL Dummy variable equals one if the CEO is internally appointed (i.e., having been employed by the firm for a period of 12 months or longer prior to his or her appointment as the CEO), and zero otherwise
CEO_SEATS The number of existing board seats in other firms held by the CEO as at year 0 (or year − 1)
logTA Natural logarithm of total assets in year − 1
ROA Return on assets for year − 1, measured as the ratio of income before extraordinary items to the average of total assets for the year
%OUTSIDE Proportion of independent directors on the board (number of independent directors on the board in year − 1, scaled by the total number of directors on the board in year − 1)
SIZE Natural logarithm of market value of equity at the beginning of year 0
DISC_ACCR Discretionary accruals in year 0 computed using the performance-adjusted modified Jones (1991) model
NOL Dummy variable equal to one if there is a tax loss carry forward during year 0 and zero otherwise
CNOL Change in tax loss carry forward from year − 1 to year 0 scaled by total assets at the beginning of the year
EQINC Equity income for year 0 scaled by total assets at the beginning of the year
FINC Pre-tax foreign income for year 0 scaled by total assets at the beginning of the year
RD Research and development expense for year 0 scaled by total assets at the beginning of the year
LEV Long-term debt-to-asset ratio at the end of year 0
BTM Book-to-market ratio at the end of year 0, measured as book value of equity divided by market value of equity
PPE Net property plant and equipment for year 0 scaled by total assets at the beginning of the year
ROA0 Return on assets for year 0, measured as the ratio of income before extraordinary items to the average of total assets for the year
CASH Cash holding at the end of year 0 divided by total assets at the beginning of the year
DEPRE Depreciation and amortization expense for year 0 divided by total assets at the beginning of the year
BIG4 Dummy variable equals one if the firm is audited by one of the Big 4 accounting firms, and zero otherwise
SECTIER Dummy variable equals one if the firm is audited by one of the second-tier accounting firms: Grant Thornton or BDO Seidman, and zero otherwise

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Lanis, R., Richardson, G., Liu, C. et al. The Impact of Corporate Tax Avoidance on Board of Directors and CEO Reputation. J Bus Ethics 160, 463–498 (2019). https://doi.org/10.1007/s10551-018-3949-4

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Keywords

  • Tax avoidance
  • Corporate reputation
  • Board of directors
  • Chief executive officer (CEO)

JEL Classification

  • H25
  • H26
  • M14