Abstract
We examine how different accounting metrics used to evaluate CEO performance for annual bonuses affect the level of corporate tax planning as well as financial reporting for income taxes. We predict and find that firms using cash flow metrics report lower GAAP and cash effective tax rates (ETR) than firms using earnings metrics. We also find that firms using after-tax earnings metrics report lower GAAP ETRs but similar cash ETRs as firms using pre-tax earnings metrics. Further analyses show that firms using after-tax earnings metrics are more likely to designate foreign earnings as permanently reinvested and have lower discretionary reserves for tax uncertainty. Hence, it appears that both types of firms engage in similar levels of tax planning, but firms evaluating CEOs with after tax-earnings metrics incentivize different financial reporting choices.
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Notes
Among 3309 firms with positive pre-tax income, operating cash flows, tax expense, and taxes paid. All values winsorized at 1 %.
Most CEOs do not have a tax background and are not often directly involved in corporate tax planning (Dyreng et al. 2010). However, CEOs provide input for incentivizing and evaluating other executives and managers, such as the CFO and tax director. CEOs also likely align the incentives of subordinates with their own and reward performance that increases their own incentive compensation. Therefore CEO incentives can influence taxes even without direct CEO involvement.
Annual bonus contracts provide incentives to emphasize (de-emphasize) tax planning incremental to incentives provided by equity. We control for CEO equity incentives but do not speak to the relative importance of short-term bonus and long-term equity incentives or to cash and stock-based incentives.
We assume that all firms engage in some tax planning. For example, many firms claim accelerated tax depreciation. However, not all firms engage in cost segregation studies whereby tax depreciation is further accelerated by advantageously classifying depreciable property into shorter recovery periods. We would consider such a project to be an incremental tax planning strategy of sufficient cost to warrant additional consideration before implementation.
A cost segregation study is an example of a strategy that would not reduce reported tax expense because it is temporary. In contrast, claiming a research and development tax credit is an example of a strategy that would reduce reported tax expense.
Many components of the tax expense accrual require significant judgment. For example, two firms could engage in the same tax planning strategy and accrue different reserves for unrecognized tax benefits due to subjective differences in assessing the inherent risk of the strategy (e.g., De Simone et al. 2014). Our predictions remain unchanged if we allow firms to engage in a combination of tax planning and tax-advantageous financial reporting in each action presented in Exhibit 1.
Action 5 produces an equivalent amount of pre-tax income. However, we believe CEOs compensated on pre-tax earnings will prefer action 1 because making opportunistic financial reporting choices related to tax planning could come at a cost with no corresponding benefit (i.e., no incremental effect on his/her bonus).
One tax advisor revealed to us that a CEO chose not to implement a legal tax planning strategy that would have generated $20 million in tax savings because his bonus was determined exclusively based on pre-tax income. In this situation, the CEO’s performance metric would have reflected the cost of the strategy (i.e., a reduction to pre-tax income reflecting consulting fees paid to implement the strategy) but not the tax benefit.
The firm can also implement a tax planning strategy and make favorable financial reporting choices. This option would exacerbate the effect on ETR, and our predictions would remain unchanged.
If the CEO does not achieve his or her bonus, it is difficult to generate clear predictions about how accounting metrics affected taxes in nonbonus years. For example, if the CEO anticipates missing a target, he or she could be incentivized to take a pre-tax big bath or engage in other forms of downward pre-tax earnings management, which would introduce a denominator effect into our ETR measures. Eliminating firms with nonbonus years allows us to retain 75 % of otherwise includable sample firms. We therefore believe our results are broadly generalizable. Additionally, in an untabulated analysis, we relax this restriction for a sample of S&P 500 firms, and the results remain unchanged.
Companies often modify disclosed performance targets to reflect non-GAAP adjustments. For example, a firm can list EPS as the performance target, but a detailed examination of the metric’s calculation reveals that the actual target adjusts GAAP EPS to exclude income tax expense. We evaluate how each metric is defined in the proxy to ensure our classification reflects the actual characteristics of the performance target.
Because our sample period encompasses three years after the financial crisis of 2007–2008, short-term bonuses paid during our sample may not represent bonuses in other periods. For all firms with nonmissing data on CEO incentive compensation in Execucomp, bonuses were 40 % of incentive compensation during our sample (2009–2011) and 37 % during 2012–2013. Additionally, 88 % of Execucomp firms awarded CEO bonuses during our sample and 91 % awarded bonuses during 2012–2013. Therefore, during the two years since the end of our sample period, we see no substantial changes in CEO bonuses that would cause us to believe our results have limited generalizability.
The receipt of performance-based restricted stock is also often calculated using multi-year averages of the same metrics used for annual bonuses, which increases the salience of these metrics to the CEO. For example, in 2012 Priceline’s compensation committee established adjusted EBITDA as the performance metric to judge performance over both the annual (1-year) and long-term (3-year) periods. In this case, a 3-year average of annual EBITDA determines the long-term incentive, and this compensation is paid in equity rather than cash.
Although some studies suggest that firms with more tax avoidance opportunities are more likely to use after-tax metrics, Huang et al. (2015) present evidence that firms with higher GAAP ETRs are more likely to choose EPS, an after-tax metric, as a performance measure in CEO bonus contracts. Therefore it is not obvious how tax planning opportunities are associated with firms’ choice of performance metrics.
Greene (2003) argues that lagged values can also address measurement error.
Because we use robust regressions, we do not winsorize our variables. This design choice significantly affects the mean and standard deviation of variables presented. We therefore focus on median comparisons.
We use robust regressions to deal with influential observations. However, all inferences remain unchanged if we (1) retain all observations and winsorize all variables at 1 and 99 %, (2) eliminate observations where ETR or CETR is outside of [0, 1] before winsorizing at 1 and 99 %, or (3) eliminate observations where ETR or CETR is outside of [0, 1] before estimating robust regression.
Gaertner (2014) conducts his analysis using a sample of S&P 500 firms in 2010. He does not separately identify or control for the presence of after-tax cash flow metrics, and his sample includes firms frequently excluded from tax avoidance research including financial firms, utilities and those reporting losses. We exclude these firms, consistent with prior literature, because the CEO’s incentives to engage in tax avoidance are either limited or unclear. For example, managers of REITs have very little incentive to commit resources to tax avoidance (Manzon and Plesko 2001), and in 2010, managers of banks subject to TARP restrictions were not eligible to receive cash bonuses regardless of firm performance. In an untabulated analysis, we attempt to reconcile our findings to Gaertner’s (2014) and conclude that the difference in CETR results likely stems from differences in sample composition. When we re-estimate Model (4) from Table 4, Panel B, using a sample of 498 observations from S&P 500 firms from 2009 to 2011, including financial firms and utilities, we find a negative coefficient estimate of −0.03 on ATAX (two-tailed p value = 0.03). However, when we eliminate 71 observations from financial firms and utilities, we find an insignificant coefficient estimate on ATAX (−0.015, two-tailed p valued = 0.28).
We also re-estimate our main analysis after dividing the sample into foreign and domestic firms, because Newman (1989) suggests that firms with foreign operations are more likely to use after-tax incentives. Our results (untabulated) hold within both domestic and multinational subsamples, suggesting that foreign operations are not driving our results.
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Acknowledgments
We appreciate comments from Stephen Penman (editor), two anonymous reviewers, James Chyz, Dawn Drnevich (discussant), Scott Dyreng, Charles Enis (discussant), Shane Heitzman, Stacie Laplante, Dan Lynch, Eric Ohrn (discussant), Adam Olson, Tom Omer, John Phillips, Donna Schmitt, Casey Schwab, Doug Shackelford, the University of Georgia tax readings group, workshop participants at the University of Texas at Austin, and participants at the 2013 ATA Midyear Meeting, the 2013 EIASM Tax Symposium, the 2013 AAA Annual Meeting, and the 106th Annual Conference on Taxation. Powers and Stomberg gratefully acknowledge financial support from the AICPA Foundation through the Accounting Doctoral Scholars Program and the Red McCombs School of Business. Robinson gratefully acknowledges research support provided by Mays School of Business, the Sims Eminent Scholar Chair in Business, and the C. Aubrey Smith Professorship. Stomberg gratefully acknowledges financial support from the Deloitte Foundation, the Terry College of Business, and the Tull School of Accounting. We also thank Lydia Byun, Elliott Carson, Yu (Tracy) Fang, Kaitlin Postle, and Mitch Suson for their capable research assistance.
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Appendix: Variable definitions
Appendix: Variable definitions
Tax rate variables |
ETR = Tax expense (TXT) in year t divided by pre-tax income (PI) in year t |
CETR = Cash taxes paid (TXPD) in year t divided by pre-tax income (PI) in year t |
Performance metrics |
ATAX = A binary variable that equals one for firm-years where the CEO annual incentive is determined using an after-tax earnings performance metric and zero otherwise |
CFLOW = A binary variable that equals one for firm-years where the CEO annual incentive is determined using a cash flow performance metric and zero otherwise |
Compensation variables |
DELTA = The change in CEO wealth from stock and option holdings given a 1 % change in stock price. Calculated following Core and Guay (1999) |
VEGA = The sensitivity of the CEO’s wealth to a 1 % change in stock return volatility. Calculated following Guay (1999) |
SALARY = CEO salary in thousands of US$ as reported in Execucomp |
%SALARY = SALARY as a percentage of total compensation as reported in Execucomp |
BONUS = CEO discretionary and non-equity plan bonus in thousands of US$ as reported in Execucomp |
%BONUS = BONUS as a percentage of total compensation as reported in Execucomp |
STOCK = The value of all stock-based compensation (restricted stock and options) in thousands of US$ granted to the CEO as reported in Execucomp |
%STOCK = STOCK as a percentage of total compensation as reported in Execucomp |
Control variables |
SIZE = Natural log of total assets (AT) at the beginning of year t |
ROA = Pre-tax income (PI) divided by total assets (AT) at the beginning of year t |
LEV = Total debt divided by total assets (AT) at the beginning of year t |
FOR = A binary variable that equals one if foreign pre-tax income (PIFO) is not missing or zero and zero otherwise |
CAP = Property, plant, and equipment (PPENT) divided by total assets (AT) at the beginning of year t |
RD = Research and development expense (XRD set to zero if missing) divided by sales (SALE) in year t |
INTAN = Intangible assets divided by total assets (AT) at the beginning of year t |
BM = Common equity (CEQ) divided by market value of equity (PRCC_F × CSHO) |
GROW = The change in sales (SALE) from year t − 1 to year t deflated by sales in year t – 1 |
NOL = A binary variable that equals one if tax carryovers (TLCF) are greater than zero and zero otherwise |
NOLC = The change in tax carryovers (TLCF) from year t − 1 to year t deflated by total assets (AT) at the beginning of year t |
LIQ = Cash and investments (CHE) divided by total assets (AT) at the beginning of year t |
L.CETR3 = Cash taxes paid (TXPD) aggregated from year t − 3 to year t − 1 divided by pre-tax income (PI) aggregated from year t − 3 to year t − 1 |
Variables related to propensity matching |
AGE = The number of years since a firm entered Compustat |
CFVOL = The time-series standard deviation of the ratio of operating cash flows to average assets, calculated using the previous 10 years of data |
CFPERSIST = The estimate of θ for the following AR(1) process using the previous 10 years of data, X t = μtΦX t−1 + μt, where X t is the ratio of operating cash flows to average assets in year t |
WERANK = The decile ranking of the Whited–Wu Index |
TRADECY = The sum of average accounts receivable, inventory, and accounts payable, deflated by average daily sales, cost of goods sold, and purchases of inventory, respectively |
SALEAVE = The natural log of average sales from year t − 4 through year t |
MNE = An indicator variable equal to one if the firm has nonmissing, nonzero foreign income in year t and zero otherwise |
CAPAVE = the average ratio of gross property, plant, and equipment to total assets from year t − 4 through year t |
INVAVE = The average ratio of inventory to total assets from year t − 4 through year t |
LEVAVE = The average ratio of long-term and current debt to total assets from year t − 4 through year t |
GSEG = The natural log of the number of operating or geographic segments reported in the Compustat Segments database in year t |
BONINTAVE = The average industry (one-digit SIC) bonus intensity for year t computed by each firm by dividing the bonus paid to the CEO by the CEO’s total compensation |
Variables related to permanently reinvested foreign earnings |
PRE = Amount of foreign earnings asserted to be permanently reinvested in accordance with APB 23 as reported in Audit Analytics in millions USD |
PRE_AT = PRE scaled by total asset (AT) at the beginning of year t |
CH_PRE = Change in foreign earnings designated as permanently reinvested from year t − 1 to year t deflated by total sales in year t |
ROS_Diff = Difference between the foreign and domestic return on sales for firm i in year t, where foreign return on sales is calculated as foreign net income divided by foreign sales and domestic return on sales is domestic net income divided by domestic sales |
FORSALES = Total foreign sales reported in the Compustat Geographic Segment file for firm i in fiscal year t divided by total sales in year t |
CH_FSALES = The change in foreign sales reported in the Compustat Geographic Segment file from year t − 1 to year t deflated by total foreign sales in year t |
FTR = Average current foreign tax rate for firm i in fiscal year t, where current foreign income tax expense (TXFO) is divided by foreign pre-tax income (PIFO) |
DIVYIELD = Total dividends paid by firm i in fiscal year t divided by the market value of equity (PRCC_F × CSHO) |
NONBIND = A binary variable that equals one for firm-years where the 5-year FTR is less than the U.S. statutory tax rate of 35 % and zero otherwise |
Variables related to reserves for unrecognized tax benefits |
UTB = Reserve for unrecognized tax benefits recorded in accordance with ASC 740 at the end of year t, in millions USD |
UTB_AT = UTB scaled by total assets (AT) at the end of year t |
SGA = Selling, general, and administrative expense (XGA set to zero if missing) divided by sales (SALE) in year t |
DISC_ACC = Discretionary accruals calculated using performance-adjusted modified Jones model |
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Powers, K., Robinson, J.R. & Stomberg, B. How do CEO incentives affect corporate tax planning and financial reporting of income taxes?. Rev Account Stud 21, 672–710 (2016). https://doi.org/10.1007/s11142-016-9350-6
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DOI: https://doi.org/10.1007/s11142-016-9350-6
Keywords
- Effective tax rate
- Performance metrics
- Executive compensation
- After-tax compensation
JEL Classification
- H25
- M41
- M52