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An examination of firms’ responses to tax forgiveness


This study uses state tax amnesties to examine how firms respond to forgiveness—particularly repeated forgiveness—by a taxing authority. We posit that tax forgiveness programs alter taxpayer perceptions of the probability of detection by enforcers or the probability of future forgiveness programs, either of which could affect future tax aggressiveness. We find that firms headquartered in an amnesty-granting state increase state income tax aggressiveness following the first instance of tax amnesty, relative to control firms in other states. Moreover, we find evidence that tax aggressiveness incrementally increases with each additional repetition of a tax amnesty. Finally, we find that the effect of amnesties on tax aggressiveness is more prominent for small firms, which face less scrutiny and for which the tax aggressiveness measures are less confounded. Our findings suggest that repeated programs of tax forgiveness have increasingly negative implications for corporate tax collections.

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Fig. 1


  1. We consider tax authorities to be the joint team of those responsible for creating tax laws (e.g., legislatures) and those responsible for enforcing them (e.g., tax collection agencies).

  2. Recent uses of this tactic have occurred at the individual taxpayer level (e.g., forgiveness programs offered to individual taxpayers accused of using Swiss bank accounts to avoid U.S. taxes) and at the corporate taxpayer level (e.g., various state tax amnesty programs).

  3. We use the term “tax aggressiveness” to reflect increased noncompliance among taxpayers who are already in the tax system. It captures the aggressive activities and strategies that a firm may undertake to reduce its explicit tax burden, which includes interpreting state tax rules more aggressively, engaging in more tax aggressive tax planning to avoid state taxes (such as becoming more aggressive in shifting income out of high tax states into lower tax states), or both. The inverse of this concept is analogous to the term “tax compliance” used in the economics literature. We acknowledge at the outset that empirical measurement of tax aggressiveness is challenging, especially in separating tax aggressiveness from more benign tax avoidance. We provide an important set of limitations and robustness tests in the conclusion of this section and in the online appendix.

  4. For example, Mikesell and Ross (2012) note that recent state tax amnesties have been accompanied by increased fines and penalties (many states, including Maryland, New Jersey, Virginia, and California), potential publication of a list of delinquent taxpayers (New Jersey), stricter enforcement (Indiana), increased jail time (Mississippi), and major tax reform (Ohio).

  5. For states that have multiple amnesties, the average (median) elapsed time between amnesties is 10 (nine) years.

  6. Boyd (2011) examines how required disclosure influences firms’ tendency to participate in voluntary state tax amnesty programs. In contrast, we focus on firms’ reactions to these programs being offered in terms of their state tax planning.

  7. This research design is subject to both strengths and weaknesses, which are discussed in detail in Section 5 and the online appendix.

  8. The details supporting this estimate are discussed at the end of Section 4.

  9. The online appendix contains a detailed discussion of the empirical limitations we face, as well as many of the robustness tests we employ to address them.

  10. Barnwell (2009) provides a history of state tax-planning strategies over the past three decades and details a number of them.

  11. This can present an empirical challenge because the effect of an amnesty can be confounded by unknown macroeconomic and political effects—we discuss this challenge in the online appendix.


  13. The early tax compliance models primarily address risk averse individuals, but see Mills and Sansing (2000), Mills et al. (2010) and De Simone et al. (2013) for applications of tax compliance models to firms that incorporate both tax evasion and financial reporting incentives. None of these single-period models consider amnesty programs or repeated game incentives.

  14. This listing of amnesties has also been used in other studies (e.g., Mikesell and Ross 2012).

  15. Using the firm’s headquarters STATE ETR can be problematic because firms often operate and pay taxes in multiple states, not just the headquarters state—we further discuss this issue and present alternate approaches in Section 5 and the online appendix.

  16. Compustat variable mnemonics are listed in all caps in the parentheses, while computed variables are always shown in italics.

  17. Observations outside this range represent extreme tax rates not driven by conscious tax planning; rather, these observations are likely artifacts of differences in book-tax accounting, operational changes (e.g., restructuring or goodwill charges), or a small denominator effect.

  18. By allowing a single control firm to serve as a match for multiple treatment firms, we may be susceptible to undue influence by the repeated control firms. However, by allowing the firms in the control group to serve as matches for multiple treatment firms, we greatly increase the number of usable observations in our sample. We also note that our fixed effects estimation described below should alleviate the effects of industries or states that have a disproportionate number of observations in the control group. We cluster standard errors by firm to reflect the multiple occurrences of the same control firms.

  19. In Panel A of Table 3, we assess statistical significance by median regression of the test variable on an amnesty indicator and clustering by firm to account for serial correlation.

  20. There are several concerns related to the difference in STATE ETR between the treatment and control samples. First, there is the concern that treatment firms face higher statutory state rates and therefore have much more incentive for tax aggressiveness. Second, and relatedly, treatment firms also have much room for tax aggressiveness, in that their opportunities for tax aggressiveness are likely greater. We attempt to account for these concerns with a difference-in-differences research design, which accounts for temporal changes in STATE ETR, and with state-level controls (including the state statutory tax rate and headquarters-state fixed effects) and the firm’s FEDERAL ETR, which account for the firm’s tax aggressiveness incentives and activity.

  21. Note that a given firm’s headquarters can change, but Compustat’s identification of headquarters state is static because it is based on the most recent one. We consider the effect of this measurement error in the online appendix.

  22. For a given firm in the treatment group, POST FIRST AMNESTY is set equal to one for all years after the firm’s headquarters state grants their initial amnesty program. For a given firm in the control group, POST FIRST AMNESTY is artificially set to one when the corresponding matched firm in the treatment group is also set to one.

  23. The headquarters-state statutory tax rates account for over-time variation in the state’s statutory tax rate for corporations. However, one shortcoming of statutory tax rates is that they do not account for changes in tax base or in tax credits. BUSINESS FRIENDLINESS rankings are largely a function of tax incentives and should help control for changes to the tax base. Also, because of limited data in the early part of our sample, we omit control variables that proxy for several determinants of tax avoidance studied in prior literature (e.g., incentive compensation (Rego and Wilson 2012), managerial style (Dyreng et al. 2010, etc.). To the extent that these omitted and unavailable control variables are not systematically correlated with states’ amnesty schedules, our results should be unbiased.

  24. When the model includes state fixed effects, we drop AMNESTY STATE from the estimation because it does not vary over time and is therefore perfectly collinear with the state fixed effects.

  25. While we cannot control for every change in a state tax regime that can affect the tax base (e.g., apportionment, unitary filing, throwback rules, etc.), it is unlikely that any single correlated omitted factor is driving our results because of the repeated nature of the amnesties and the non-repeated nature of the regime changes. In other words, a state can offer amnesty multiple times, but it can only switch to unitary filing once. Because we see incremental results from each amnesty, it is unlikely that an omitted characteristic of the state tax system is driving our results.

  26. In addition to the small versus large firm tests, we attempt to overcome this issue with three untabulated tests based on three different sample partitions to approximate the concentration of the firm’s operations using location information as reported in the firm’s 10-K, as in Dyreng et al. (2013). The three partitions are firms with high vs. low proportions of subsidiaries in the headquarters state, firms with high vs. low total numbers of subsidiaries, firms with high vs. low numbers of states where they operate. We expect that a tax forgiveness program in a particular state will be more salient for firms that have highly concentrated operations in the headquarters state. We find that most of our results continue to persist, though with lower statistical significance, in the high concentration subsamples while the results do not persist in the low concentration subsamples. These results are untabulated (but available upon request) because these data are only available after 1995 and only for a subset of firms, which removes a substantial portion of the primary sample (i.e., our sample size decreases by more than 70%).

  27. However, we emphasize the existence of several clear differences between a tax holiday and a tax amnesty, such as possible differences in taxpayers’ level of presumed tax avoidance, differences in financial reporting incentives, and differences in the tax complexity between state and international taxation. Hence we acknowledge substantial differences in the tax-holiday and tax-amnesty settings and urge caution in over-interpreting our results in the international setting.


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We are grateful to Richard Sloan (editor) and two anonymous referees for their constructive comments on this paper. We also thank Darren Bernard, Nicole Cade, Ed deHaan, Frank Hodge, Jeff Hoopes, David Kenchington, Landon Mauler, Ed Maydew, Lillian Mills, Adam Olson, Miles Romney, D. Shores, Brian Spilker, Ryan Wilson and workshop participants at the University of Washington and the BYU Accounting Research Symposium for helpful comments and insights on this paper. Shevlin acknowledges financial support from the Paul Merage School of Business at the University of California-Irvine. Thornock acknowledges financial support from the Marriott School of Management at Brigham Young University. Williams acknowledges support from the McCombs School of Business at the University of Texas. Finally, we are grateful for the support of the Foster School of Business at the University of Washington, where this project began.

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Correspondence to Jacob Thornock.

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Table 8 Definition and source of variables

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Shevlin, T., Thornock, J. & Williams, B. An examination of firms’ responses to tax forgiveness. Rev Account Stud 22, 577–607 (2017).

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  • Tax amnesty
  • Tax forgiveness
  • Tax avoidance
  • State taxation
  • Effective tax rate

JEL classification

  • H25
  • H26
  • H71
  • K34