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Stock liquidity and corporate tax avoidance

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Abstract

We show that firms with higher stock liquidity engage less in extreme (i.e., overly aggressive or overly conservative) tax avoidance. The effect of stock liquidity on tax avoidance is economically meaningful and robust across alternative measures of tax avoidance and stock liquidity. The findings also hold after controlling for potential endogenous effects. We further document that the effect of stock liquidity on tax avoidance is amplified for firms with high proportions of activist shareholders and attenuated for firms with high levels of stock price informativeness. Overall, our findings suggest that stock liquidity mitigates extreme tax avoidance by enhancing shareholders’ monitoring over firm management.

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  1. For example, after the revelation in 2012 that Starbucks paid little income tax to the United Kingdom, despite its high growth there, Starbucks faced political attacks and customer boycotts, resulting in the closing of numerous stores (Dyreng et al. 2016). As another example, the publicity of low tax payments by General Electric dissuaded potential customers from purchasing GE products (Graham et al. 2014).

  2. Even if an intervention is private, because of potential information leakage about the intervention, the benefits of the intervention can be reflected in stock prices before any change in firm policies.

  3. Relatedly, because liquidity facilitates accumulation of stocks prior to intervention, liquidity can increase shareholders’ power to influence managers.

  4. Intervention includes communication with managers to influence firm policies, submission of shareholder proposals, voting on corporate issues, and other actions (Becht et al. 2009). While activists intervene more frequently in firm management than other shareholders, other shareholders (including passive institutional investors and retail investors) can also intervene via direct communication with managers, voting on firm policies, voicing through social media (including investor chat rooms and trading forums), and supporting activists in proxy contests (Useem et al. 1993; Daly 2015; Appel et al. 2016; McCahery et al. 2016; Rubio and Vázquez 2016).

  5. Despite the costs of tax shelters documented by Wilson (2009) and Hanlon and Slemrod (2009), it remains an open question as to whether the shelters were positive net present value investments.

  6. The details of the three examples are available at https://www.osc.state.ny.us/pension/ proxyvotingguidelines.pdf, https://www.sbafla.com/fsb/Portals/Internet/CorpGov/ProxyVoting/2015_SBACorporateGovernancePrinciplesProxyVotingGuidelines10162015.pdf, and http://www.sec.gov/Archives/edgar/data/851680/000119312514178926/d7 18969dpx14a6g.htm, respectively.

  7. For instance, Chamberlain (1994) uses quantile regressions to examine the effect of labor unions on manufacturing workers’ wages. He finds that the union wage premium is 28% for low-paid workers (those at the lowest wage decile) but only 0.3% for the high-paid workers (those at the highest wage decile). Thus the OLS estimate of the mean union premium of 15.8% captures mainly the lower tail of the conditional distribution, thereby delivering a misleading impression of the union effect. See Fitzenberger et al. (2002) for a review.

  8. Consistent with the view that shareholders perceive GETR as a primary measure of tax avoidance, Armstrong et al. (2012) find that tax executives’ compensation is linked to GETR but unrelated to other tax avoidance measures, such as cash effective tax rate and book-tax difference. Based on this finding, they conclude that GETR is the most informative measure of executives’ actions. In addition, Graham et al. (2014) report that tax executives in publicly traded firms focus more on GETR, compared to other tax avoidance measures.

  9. All reported p values are for two-tailed tests.

  10. The stock liquidity coefficient difference between the 10th (90th) percentile and the median percentile is statistically significant (largest p value <0.05).

  11. We do not include the additional variables in our main design because of their effects on our sample size. Table 3 has 34,221 observations. Depending on the test, the sample size varies between 7,613 and 12,127 observations after the inclusion of the additional controls. When all the additional controls are included, the sample size is reduced to only 3,431 observations.

  12. The significance levels are slightly lower than those reported in Table 3, most likely because of the substantial reduction in sample size. To assure that lower significance levels are indeed due to the reduction in sample size and not due to the inclusion of board characteristics as additional controls, we re-estimated the model with the same sample but without the board characteristic variables. The (untabulated) results show that both the magnitudes and the t-statistics of the coefficients of liquidity are qualitatively unchanged: the coefficient for the 10-th percentile is 0.028 (t-statistic = 3.455, p value <0.01) and the coefficient for the 90-th percentile is −0.006 (t-statistic = −1.915, p value = 0.055). These results further support the view that the impact of liquidity on extreme tax avoidance occurs through the separate channel than board governance.

  13. Upward earnings management can lead to lower effective tax rate. We partly mitigate the effects of earnings management by controlling for abnormal accruals. To further address the earnings management concern, we also use the tax avoidance measure based on cash flow items suggested by Guenther et al. (2014), which is unlikely to be contaminated by accrual management.

  14. The data is available on Joel Hasbrouck’s homepage: http://people.stern.nyu.edu/jhasbrou/Research/

  15. We estimate λX,i as the slope from a firm-level regression of changes in firm i’s GETR on changes in a macroeconomic indicator X (where X is a U.S. leading macroeconomic index, a U.S. GDP-based recession indicator index, or Chicago Fed National Financial Conditions Index) over the period 1994–2000. We obtain the data on these macroeconomic indicators from the Federal Reserve Bank of St. Louis (https://www.stlouisfed.org/). We estimate δREVERSION,i, as one minus the degree of persistence of firm i’s GETR, where persistence is estimated as the serial correlation in firm i’s GETR over the period 1994–2000.

  16. As an additional (untabulated) test, we estimate our baseline model (Equation (3)) using an instrumental variable quantile regression method (Chernozhukov et al. 2015). Building upon prior research, we use industry-average stock liquidity as an instrument (Chordia et al. 2000; Brockman and Chung 2002; Brockman et al. 2009; Fang et al. 2009; Jayaraman and Milbourn 2012). The model was estimated cross-sectionally by year with the first-stage estimation conducted using the distribution regression approach. The (untabulated) coefficient of liquidity is still significantly positive for the 10th GETR percentile (p value<0.01) and is significantly negative for the 90th GETR percentile (p value = 0.031). These results provide further support for the causal effect of stock liquidity on tax avoidance.

  17. Prior research suggests that institutional ownership is associated with several firm-level characteristics, such as size, analyst following, cash holdings, leverage, and operating volatility (Del Guercio 1996; Dahlquist and Robertsson 2001; Ferreira and Matos 2008). Hence an omitted correlated variable is a potential concern. To address this concern, we repeat our analyses after orthogonalizing %ACTVIST with respect to the aforementioned factors. The (untabulated) results for the orthogonalized measure are qualitatively similar to those reported in Table 6 for the raw measure of activist ownership.

  18. As the coefficient on the stand-alone %ACTIVIST variable (activist ownership) indicates, we find no evidence that activist ownership mitigates extreme tax avoidance when stock liquidity (LIQ) is zero. However, since by construction LIQ is positive for every firm, the coefficient on %ACTIVIST has limited economic meaning. To facilitate the economic interpretation of the coefficient on the stand-alone %ACTIVIST variable, we conduct two (untabulated) tests. In the first test, we replace the raw values of LIQ with the demeaned values. The results show that the coefficient on the stand-alone %ACTIVIST variable is significantly positive for the 10th percentile of GETR and significantly negative for the 90th percentile of GETR, suggesting that activist ownership mitigates extreme tax avoidance for a typical firm. In the second test, we replace the raw values of LIQ with their quintile-ranked values scaled to [0,1] range. The results of this test indicate a weaker (though still significant) mitigating effect of activist ownership on the 10th percentile of GETR and an insignificant effect on the 90th percentile of GETR, suggesting that, when liquidity is extremely low (and thus the economic benefits of intervention are limited), activists make little effort to intervene (Maug 1998; Collin-Dufrense and Fos 2014).

  19. Prior studies show that stock price informativeness correlates with factors such as firm size, analyst following, forecast dispersions, return volatility, past returns, and stock turnover (Aslan et al. 2011). Consequently, the price informativeness effect might reflect the moderating effect of the correlated variables. In addition, liquidity can directly impact price informativeness (Holmström and Tirole 1993), which could bias the price informativeness effect. To address these issues, we repeat our analysis using the PIN measure orthogonalized with respect to the aforementioned factors. The (untabulated) results for the orthogonalized measure are qualitatively similar to those reported in Table 6 based on the raw measure of stock price informativeness.

  20. We also examine whether the liquidity effect is weaker when managers are more entrenched and thus management turnover is less sensitive to stock market performance. Using Bebchuk et al.’s (2009) entrenchment index measure, we find no evidence that the liquidity effect decreases with managerial entrenchment, consistent with the governance by exit channel not being the driver of the liquidity effect. However, we caution against reading too much from this result because entrenched managers often have large stakes in their firms, which can align their interests with those of shareholders (Jensen and Meckling 1976). As Morck et al. (1988) and De Miguel et al. (2004) argue, while managers are likely to become entrenched at a certain level of ownership, they are also likely to internalize the cost of entrenchment and thus to have less incentive to resist shareholders’ effort to improve firm value.

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Acknowledgements

We thank Lakshmanan Shivakumar (editor) and two anonymous reviewers for their helpful comments. In addition, we are grateful to Kathleen Bentley, Christine Brown, Viet Cao, Chen Chen, Tarun Chordia, Bin Do, Doug Foster, Jim Frederickson, Phil Gray, Ferdinand Gul, Ole-Kristian Hope, Jungmin Kim, Clive Lennox, Bing Li, Zhenbin Liu, John Lyon, Jeffrey Ng, Doug Skinner, Jamie Tong, Feida (Frank) Zhang, and workshop participants at Monash University, University of New South Wales, and the 2014 UTS Australian Summer Accounting Conference, who commented on earlier versions of this study. Yangyang Chen acknowledges research funding from Hong Kong Polytechnic University (Startup Research Fund: 1-ZE5C). Rui Ge acknowledges research funding from the National Natural Science Foundation of China (project number: 71202091, 71672165).

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Authors and Affiliations

  1. School of Accounting and Finance, Hong Kong Polytechnic University, Hong Kong, China

    Yangyang Chen

  2. Shenzhen Audencia Business School - Shenzhen University, Shenzhen, China

    Rui Ge

  3. Department of Accounting, Pennsylvania State University, State College, PA, USA

    Henock Louis

  4. Melbourne Business School, University of Melbourne, Melbourne, Australia

    Leon Zolotoy

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  1. Yangyang Chen
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  2. Rui Ge
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  3. Henock Louis
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  4. Leon Zolotoy
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Correspondence to Rui Ge.

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Appendix A. Variables and definitions

Appendix A. Variables and definitions

Activist ownership (%ACTIVIST)

Natural logarithm of one plus the proportion of shares held by activist shareholders. Activist shareholders are identified as in Cremers and Nair (2005) and Larcker et al. (2007).

Cash holdings (CASH)

Cash and cash equivalents (CHE) scaled by lagged total assets (AT).

Change in goodwill (POSGDWL)

Change in goodwill (GDWL) scaled by lagged total assets (AT). If the value is negative, then it is set to zero.

Change in loss carryforward (DNOL_AT)

Change in net operating loss carryforward (TLCF) over year t scaled by lagged total assets (AT).

Abnormal accruals (ACCR)

Abnormal accruals estimated following Kothari et al. (2005)

Equity income in earnings (EQINC)

Equity income in earnings (ESUB) scaled by lagged total assets (AT).

Financial constraints (FINCONST)

Hadlock and Pierce (2010) financial constraint index, decile ranked by year.

Firm size (SIZE)

Log of market value of equity (PRCC_F × CSHO).

Foreign assets (FRGNAT)

Proportion of foreign assets, estimated following Oler et al. (2007).

Foreign income (DFI)

An indicator variable that takes the value one for firm observations reporting foreign income (PIFO) in year t and zero otherwise.

GAAP effective tax rate (GETR)

Income taxes (TXT) scaled by pre-tax income (PI).

Intangible assets (INTANG)

Intangible assets (INTAN) scaled by lagged total assets (AT).

Leverage (LEV)

Long-term debt (DLTT) scaled by total assets (AT).

Loss carry-forward (NOL)

An indicator variable that equals one if net operating loss carryforward (TLCF) is positive for year t-1.

Tobin’s Q (TBQ)

Market value of assets divided by book value of assets (AT). Market value of assets is estimated following Fang et al. (2009).

Number of operating segments (SEG)

Natural log of a firm’s number of operating segments as per Compustat.

New investments (NEWINV)

New investment, calculated as per Compustat (XRD + CAPX+AQC-SPPE-DPC) scaled by lagged total assets (AT).

PPE assets (PPE)

Net property, plant, and equipment (PPENT) scaled by lagged total assets (AT).

Probability of informed trading (PIN)

Natural logarithm of one plus probability of informed trading measure of Easley et al. (2002) as modified by Brown and Hillegeist (2007).

ROA volatility (STDROA)

Standard deviation of return on assets (ROA) over the past five years.

Return on assets (ROA)

Pre-tax income (PI) divided by lagged total assets (AT).

Stock liquidity (LIQ)

The additive inverse of the natural logarithm of the relative effective spread (the absolute difference between the trade price and the midpoint of the bid-ask quote divided by the trade price).

  1. The characters in the parentheses in the right column of the table refer to the item names in the Compustat database.

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Chen, Y., Ge, R., Louis, H. et al. Stock liquidity and corporate tax avoidance. Rev Account Stud 24, 309–340 (2019). https://doi.org/10.1007/s11142-018-9479-6

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