Are corruption and corporate tax avoidance in the United States related?

Abstract

We examine whether state-level corruption and corporate tax avoidance in the United States (U.S) are related. Using a sample of 36,078 U.S. firm-year observations from 1998 to 2014, we find that corruption is significantly positively related to tax avoidance. Our main finding is consistent across a series of robustness tests. In additional analysis at the state level, we observe that corruption is significantly positively related to corporate tax avoidance in states that have low levels of litigation risk, irrespective of whether the states rank high or low in terms of corporate governance, social capital, or money laundering. We also correlate state- and firm-level corruption with firm-level corporate tax avoidance and find that the interaction terms are generally significantly positively related to corporate tax avoidance. Finally, we show that state-level corruption and corporate tax avoidance are complementary across industry sectors. Overall, our results indicate that the broader state-level corruption (cultural) effects of where a firm is headquartered have significant consequences for corporate tax avoidance.

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Notes

  1. 1.

    We follow Hanlon and Heitzman (2010) in viewing corporate tax avoidance as a broad range of activities that have outcomes ranging from certain to uncertain, where uncertain (i.e., aggressive or risky) tax positions are supported by a relatively weak set of facts, so are less likely to be sustained when a tax audit is conducted. Tax avoidance therefore differs from tax evasion, which is illegal (Hanlon and Heitzman 2010).

  2. 2.

    Altonji et al. (2005) argue that the selection of unobservables is akin to that of observables. Thus, the probability of the outcome (tax avoidance) related to observables (state-level corruption) has the same relationship with state corruption as the part related to unobservables. Altonji et al. (2005) claim that such a relationship is likely if the following assumptions are adhered to: that the suite of observed variables is chosen at random from the full set of variables that determine (in our case) state-level corruption and firm-level tax avoidance, and that the number of observed and unobserved variables is large enough to not dominate the distribution of the occurrence of corruption in a state and the level of tax avoidance in a firm. As many observed determinants are used in our regression models over a long period, we should be able to adhere to these assumptions. We thus argue that, for corruption to occur in a state and for a firm to engage in tax avoidance, the selection of unobservables is unlikely to be as robust as the selection of observables.

  3. 3.

    The economic effect, based on the average effect of a one-unit increase in CORRP_LN, is computed as the mean pretax income (US$320 million) x state corruption presence (−0.0029), which is equal to a decline of US$0.93 million in GAAP tax expense per firm-year, on average.

  4. 4.

    CASH_ETR is not used as a main measure of corporate tax avoidance in this study, as firms may report nil or negligible amounts of cash taxes paid in some years followed by large absolute amount of cash taxes paid upon IRS audit settlements in other years. However, income tax expense, which is used to calculate GAAP_ETR, comprises both current tax and deferred tax expenses, and the latter may constitute a large proportion of total income tax expenses and take a significant period to reverse (Hanlon and Heitzman 2010).

  5. 5.

    In this study, we set GAAP_ETR as missing when it is greater than 1 or less than 0. We follow the recent studies of Hanlon et al. (2017) and Ling et al. (2017) in retaining loss firms in our sample. We also follow Hanlon et al. (2017) by retaining firms in our sample that have a negative denominator to maximize the sample size. Finally, we truncate GAAP_ETRit to the range [0, 1].

  6. 6.

    We obtain fewer observations of corruption than Smith (2016), because he uses the average of the adjacent years as the conviction number to substitute for any missing corruption observations. However, the DOJ reports disaggregated corruption per state from 1998. Smith (2016) uses overall crime (i.e., bribery, extortion, and election crime) for the years before 1998.

  7. 7.

    Corruption is measured based on the number of state-level convictions issued by the Attorney’s Office (e.g., Dass et al. 2016) or on the number of individual convictions, such as of members of the board of directors, the CEO, or employees of a firm (e.g., Bame-Aldred et al. 2012; Liu 2016).

  8. 8.

    We follow research by Hanlon et al. (2017) and Ling et al. (2017) in retaining loss firms (i.e., negative ROA) in our sample. However, to verify that including these firms in our sample does not drive our empirical results, we also estimate our regression models based on a subsample of firms with a positive denominator only (pre-tax income). The results are qualitatively similar (untabulated). Though, the sample size is significantly reduced by up to 21,025 firm-year observations.

  9. 9.

    The data are available at https://www.bjs.gov. Police protection is a function of enforcing the law, preserving order and traffic safety, and catching criminals, and is the main enforcement system in most cities. In addition, these cities operate large judicial and correctional systems and may have significant public defense expenditures (Glaeser and Saks 2006; Goel and Nelson 2011).

  10. 10.

    The weak instrument benchmark proposed by Stock et al. (2002) tests the F-statistic. If the number of instruments is 1, 2, 3, 5, and 10, the suggested critical F-values are 8.96, 11.59, 12.83, 15.09, and 20.88, respectively.

  11. 11.

    For the weak instrument test, if the F-statistic is lower than 10 (the rule of thumb) and the partial R-squared is lower than 0.05%, this indicates the exclusion effects of the instruments as a percentage (Murray 2006). Our results also pass this test.

  12. 12.

    In an additional analysis, we use kernel and radius matching, and find that our empirical results are qualitatively similar to those reported in Table 7 (untabulated).

  13. 13.

    The data required to compute CASH_ETR are missing, which reduces the number of firm-year observations in our sample to 25,776. This is considerably fewer than the 36,078 firm-year observations for the full sample.

  14. 14.

    FIN48 Accounting for Uncertainty in Income Taxes is classified as Accounting Standards Codification (ASC) 740–10-25 under the FASB’s new codification for U.S. GAAP. It was introduced by the FASB to provide financial statement users with information about the uncertainties a firm confronts in computing its tax liability estimates (FASB 2006). FIN48 applied from December 15, 2006, which reduces the number of firm-year observations in our sample to 9780, which is significantly lower than the 36,078 firm-year observations for the full sample.

  15. 15.

    CPI is a country-based corruption index beginning in 1995 (41 countries) and ending in 2014 (175 countries). Refer to https://www.transparency.org

  16. 16.

    We use three databases from BoardEx to match a director’s name, identification, and nationality as well as a firm’s ticker and year. We use: (1) the Individual Profile Detail database to obtain a director’s name, identification and nationality, (2) the Organization Summary-Analytics database to obtain a firm’s ticker, and (3) the Annual Remuneration database to obtain the year.

  17. 17.

    Brochet et al. (2019) find that information on nationality is missing from a large proportion (i.e., more than 70%) of their sample. They state that nationality can be altered for naturalized managers, adding measurement error to the capture of cultural influence.

  18. 18.

    We also use the existence of an AAER securities violation as another measure of firm-level corruption, which is denoted by a dummy variable, coded as 1 if there is a violation, and 0 otherwise. Our results are consistent with RESTATE in that the interaction term between AAER and each of our measures of state-level corruption is significant and negative (p < 0.01) (untabulated), showing that firms headquartered in more corrupt states have a greater propensity to avoid taxes, particularly when they have a recorded AAER violation.

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Acknowledgements

We would like to sincerely thank the editor, Professor Patricia Dechow, for providing excellent and timely feedback on earlier versions of our paper. We would also like to thank the anonymous reviewer for offering many helpful comments and suggestions on our paper, in addition to James Brushwood, Dan S. Dhaliwal, Douglas J. Fairhurst, and Matthew Serfling for providing firms’ headquarters data. Finally, we would like to thank Mohammed Asiri for splendid research assistance. All remaining errors are our own.

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Correspondence to Grant Richardson.

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Appendices

Appendix 1

Examples of Public Corruption

Corruption is a crime that involves the abuse of public trust by government officials. A successful public corruption prosecution requires both the appearance and the reality of fairness and impartiality. Corruption sentencing requires that the public perception is that the conviction was warranted and is not the result of improper motivation by the prosecutor and is free of conflicts of interest. In cases when local conflict of interest is substantial, the local office is removed from the case by a procedure called recusal, which is one of several categories of corruption. A second category is allegations. Examples of allegations are as follows.

  • Bill Allen, CEO and part-owner of VECO Corporation, and Richard Smith, vice president of community affairs and government relations, VECO Corporation, pleaded guilty to providing US$400,000 in corrupt payments to Alaska State Legislative officials.

  • Thomas Anderson, former member of the Alaska State House of Representatives, was sentenced to prison after his conviction for extortion, conspiracy, bribery, and money laundering.

Another category of corruption covers fraud, bribery and extortion. Examples are as follows.

  • Robert W. Ney, former congressman, was sentenced to 30 months of imprisonment following his plea of guilty for multiple offenses, including honest services fraud and false statements.

  • J. Steven Griles, the former deputy secretary of the Department of the Interior, was sentenced to 10 months’ imprisonment and a US$30,000 fine after his plea of guilty to obstruction.

  • Lobbyist Neil Volz, who served as chief of staff for former Congressman Robert Ney, was sentenced to two years of probation and a US$2000 fine following his plea of guilty to conspiracy to commit honest services fraud and violation of his one-year lobbying ban.

  • Peter Kott, former Alaska state representative, was sentenced to prison following his conviction for bribery, extortion, and conspiracy for corruptly soliciting and receiving financial benefits from a firm in exchange for performing official acts.

Public corruption cases are often controversial, complex, and highly visible.

Appendix 2

Variable Definitions and Measurement

Variables   Definition and measurement Data source
Dependent Variable  
GAAP_ETR = Total income tax expense scaled by pre-tax book income less special items. Negative pre-tax book income values are retained in the calculation of GAAP_ETRit. We truncate GAAP_ETRit to the range [0, 1] Compustat
CASH_ETR = Total income tax paid scaled by total pre-tax income net of total special items Compustat
UTB = Total unrecognized tax benefits for firm i at the end of year t scaled by total assets at the beginning of year t Compustat
SHELTER = The Wilson (2009) sheltering probability equation is estimated as follows.
SHELTER_PROBi,t = −4.86 + 5.20 × BTDi,t + 4.08 × DAi,t - 0.41 × LEVi,t + 0.76 × ATit + 3.51 × ROAi,t + 1.72 × FOREIGN INCOMEi,t + 2.43 × R&Di,t
where SHELTER_PROBi,t is the sheltering probability for firm i in year t, BTD is the book-tax difference measure as defined by Kim et al. (2011), DA is discretionary accruals from the performance-adjusted modified cross-sectional Jones Model, LEV is firm leverage, AT is the log of total assets, ROA is return on assets, FOREIGN INCOME is a dummy variable, coded as 1 for firm-years that report foreign income, and 0 otherwise, and R&D is research and development expense. Following Kim et al. (2011), we define BTD as book income less taxable income scaled by lagged total assets (AT). Book income is pre-tax income (PI). Taxable income is calculated by summing current federal tax expense (TXFED) and current foreign tax expense (TXFO) and dividing by the statutory tax rate and then subtracting the change in NOL carryforwards (TLCF). If current federal tax expense is missing, total current tax expense is calculated by subtracting deferred taxes (TXDI), state income taxes (TXS), and other income taxes (TXO) from total income taxes (TXT). Following Rego and Wilson (2012), we rank SHELTER_PROB each year and create a dummy variable to capture those firms that have a high sheltering probability. SHELTER is a dummy variable, coded as 1 if a firm’s estimated sheltering probability is in the top quartile in that year, and 0 otherwise.
Compustat
Independent Variables  
CORRP_LN = Natural logarithm of the total crime reported by the DOJ for each state and year Depart of Justice: Report to Congress on the Activities and Operations
of the Public Integrity Section
https://www.justice.gov/
CORRP_POP = Corruption cases reported for each state and year scaled by the natural logarithm of state population for each state and year https://www.justice.gov/
Control Variables  
SIZE = Natural logarithm of the market value of equity at the beginning of a year Compustat
MTB = Market value of equity scaled by the book value of equity Compustat
LEV = Long-term debt scaled by lagged total assets Compustat
CASH = Cash and marketable securities scaled by lagged total assets Compustat
ROA = Operating income scaled by lagged total assets Compustat
NOL = Dummy variable, coded as 1 if loss carryforward is positive as of the beginning of the year, and 0 otherwise Compustat
∆NOL = Change in loss carries forward scaled by lagged total assets Compustat
FI = Foreign income scaled by lagged total assets. Missing values are set to 0 Compustat
CAPINT = Property, plant, and equipment scaled by lagged total assets Compustat
RDINT = Research and development expense scaled by lagged total assets. Missing values are set to 0 Compustat
EQINC = Equity income in earnings scaled by lagged total assets Compustat
ST_GDP_LN = Natural Logarithm of state gross domestic product United States Census Bureau https://www.census.gov
ST_FRAUD_LN = Natural Logarithm of state total fraud cases sentenced by the USSC USSC
https://www.ussc.gov
ST_LIT = Literacy rate by total higher education (Masters and higher) in each state https://www.census.gov
FIRM_LN = Natural logarithm of the number of firms in each state https://www.census.gov
YEAR = Year dummy variables to control for year effects Compustat
IND = FF12 industry dummy variables to control for industry effects Compustat
STATE = State relocation Brushwood et al. (2016) https://sraf.nd.edu/data/
Variables for Additional Analysis  
ST_CORR_MEAN = Mean level of corruption for each state https://www.ussc.gov
CORRUPT = Dummy variable, coded as 1 if firms are headquartered in a state subject to greater than the median risk of committing corruption offenses, and 0 otherwise https://www.ussc.gov
SC_MEAN = Mean number of environmental sentences in each state https://www.ussc.gov
ST_WAGES_LN = Natural logarithm of state-level employee wages https://www.census.gov
ST_INCOME_LN = Natural logarithm of total state-level wages and salaries https://www.census.gov
ST_BACHELOR_LN = Natural logarithm of the number of bachelor’s degrees https://www.census.gov
ST_ADVANCED_LN = Natural logarithm of the number of master’s degrees https://www.census.gov
SC = Dummy variable, coded as 1 if state-level social capital is greater than median state-level social capital, and 0 otherwise https://www.census.gov
CG = Dummy variable, coded as 1 if state-level corporate governance is greater than median state-level corporate governance, and 0 otherwise Gompers Governance Index Data by Firm from Andrew Metrick website: https://faculty.som.yale.edu/andrewmetrick/data/
ML = Total number of money laundering sentences reported in a firm’s headquarters state scaled by the total state population for that state https://www.ussc.gov
LR = Dummy variable, coded as 1 if the state-level litigation risk is greater than the median state-level litigation risk, and 0 otherwise Compustat
RESTATE = Dummy variable, coded as 1 if the firm has an accounting restatement, and 0 otherwise Audit Analytics
CORR_NAT = The CPI scores ranks countries/territories based on how corrupt a country’s public sector is perceived to be. This is a composite index, a combination of surveys and assessments of corruption that are collected by a variety of reputable institutions www.transparency.org.
and BoardEx
POLICEPROEXP_LN = Natural logarithm of police protection expenditures for each state and year. Police protection expenditures are total direct expenditure + direct current + capital outlay + intergovernmental expenditure. Justice Expenditure and Employment Extracts Program
https://www.bjs.gov
JUSTICESYE_EXP_LN = Natural logarithm of total justice system expenditures for each state and year. Total justice system expenditures are total direct expenditure + direct current + capital outlay + intergovernmental expenditure https://www.bjs.gov

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Al-Hadi, A., Taylor, G. & Richardson, G. Are corruption and corporate tax avoidance in the United States related?. Rev Account Stud (2021). https://doi.org/10.1007/s11142-021-09587-8

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Keywords

  • Corruption
  • Corporate tax avoidance

JEL classification

  • G30
  • H20