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Behavioral dynamics of tax compliance when taxpayer assistance services are available

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Abstract

This research utilizes a laboratory experiment involving a large and diverse set of participants to investigate the behavioral dynamics of tax reporting in a setting where tax liability is uncertain and the tax agency makes a service available to help resolve the uncertainty. Our design varies the level of liability uncertainty, as well as the cost and quality of the information service. We find that, in the absence of an information service regime, the behavioral response to past audits, whether penalizing or not, is to report a lower tax liability. However, with an information service present (regardless of whether it is accessed), behavioral responses to past audits are no longer found. Interestingly, information service acquisition decreases modestly in response to a penalizing audit, although as the experiment progressed a larger proportion of participants were compliant, offsetting this effect. Mirroring the few experimental studies that have investigated tax liability information services, we find that providing these services has a strong and positive effect on tax compliance.

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Notes

  1. See, for instance, Alm et al. (2009), Andreoni et al. (1998), Erard (1992), Gemmell and Ratto (2012), Maciejovsky et al. (2007), and Mittone (2006).

  2. Alm et al. (2009) provide a discussion of the effects of own tax audit experience and of information provided by one’s cohort on audit experience. Other literatures, e.g., Alm et al. (1992a, b), Erard (1992) and Kastlunger et al. (2009), focus only on the effects of past audits on the tax reporting decision. Typical previous findings are that individuals report a lower tax liability following an audit, and various motives have been offered that potentially explain this dynamic response.

  3. For FY 2015 the IRS budget allocated $4.9 billion to “enforcement” and $2.2 billion to “taxpayer services” to provide taxpayer assistance and education regarding tax liability and filing questions. Thus, the service component is a significant element of the interaction between the IRS and taxpayers.

  4. The economics literature on tax compliance is vast. We point the interested reader to Alm (2012), who provides a recent synopsis of the main findings to date. In this article, Alm (2012) puts forward several “frontier questions” regarding tax compliance for which additional evidence is needed. We provide new evidence on a handful of these questions in this study, including the effects of taxpayer uncertainty over taxable income on compliance, the effects of taxpayer services, and interactions between compliance dynamics and government polices.

  5. We note, however, that it is not generally possible to determine in an uncertain liability setting—perhaps except through taxpayer surveys—whether evasion found during an audit is the result of errors or the intent to evade.

  6. In actuality, of course, many tax agencies rely on endogenous audits with audit rates being an increasing function of expected tax underreporting. However, there is some survey evidence that suggests beliefs vary widely across taxpayers, with many believing the process to be purely random and audit rates to be much higher than in actuality (Louis Harris and Associates, Inc. 1988).

  7. The number sorting exercise is designed as a real-effort task to engender a sentiment that experiment income is “earned” rather than endowed. All compensation mechanisms introduce an element of relative performance as a mechanism of determining payment. For example, piece rates result in relative differences since the earning time is fixed and differences in payment may be due to a bad draw from nature (a miner gets a poorer seam to work) or differences in ability. The task was repeated several times, and there was considerable randomness in outcomes, in that the vast majority of participants wound up playing multiple rounds in each of the three income groups.

  8. These are fixed throughout the experiment. Our experimental setting is very contextual and the presence of the income earning task provides, we argue, for the necessary degree of “parallelism” to the naturally occurring world that is crucial to the applicability of experimental results (Smith 1982; Plott 1987). In this regard, our experimental design uses tax language (which is presented via the subject interface), requires that the participants earn income in each period, and also requires that the participants disclose tax liabilities in the same manner as in the typical tax form. As in the naturally occurring setting, there is a time limit on the filing of income. The external validity of the design is investigated in Alm et al. (2015) who show that the observed behavior in the laboratory comports with that in the field. Another view is provided in Choo et al. (2016).

  9. Such information reduces the cognitive burden of computing tax liabilities. The issue of tax liability uncertainty differs from enforcement uncertainty. As Alm et al. (1992a) demonstrate, in a setting where taxes are not used to fund a public good, the tax authority may use enforcement uncertainty to increase compliance. Theory predicts that uncertain penalties increase compliance by risk-averse agents and this is borne out in the experimental data.

  10. Our audit rate is much higher than actual full audit rates in the USA. However, survey evidence suggests there is considerable uncertainty among taxpayers regarding how returns are selected for audit as well as audit rates (Louis Harris and Associates, Inc. 1988). Further, the IRS conducts a range of audits, and for many types of audit the actual rates are quite high. For example, in 2005 only 1.2 million individual returns (or less than 1% of the 131 million individual returns filed) were actually audited. However, in that year the IRS sent 3.1 million “math error notices” and received from third parties nearly 1.5 billion “information returns,” which are used to verify items reported on individual income tax returns. While the financial penalties are smaller for this class of audit, the taxpayer faces costs arising from these queries; transaction costs such as record verification can be considerable.

  11. Certain errors on the part of the taxpayer may not be easily verified in the event of an audit. For example, failure to claim a deduction for a charitable contribution because the taxpayer was uncertain of the status (e.g., not-for-profit or 501c(3) status) of the organization may not be observed by the tax agency even in the event of an audit. In any case, since this condition is in effect throughout the series of experiments it is not likely to affect the responses.

  12. Although the choice is framed as a credit, it qualitatively captures any reductions to liability, such as charitable contributions and business expenses.

  13. Since the intercept is excluded, the reported \(R^{2}\) does not have the usual interpretation.

  14. Unless otherwise indicated, a 5% significance level is implied when discussing statistical results. Supporting test statistics are available upon request, but omitted here for ease of exposition.

  15. For the case of the sequential information treatment, with only one source requested, we assume the WTP interval spans from zero to the cost of acquiring both sources.

  16. One possible reason for this result is that the fraction of compliers increases with repetition, which would offset the increase in reporting triggered by a past audit. However, the fraction of compliers is stable.

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Acknowledgements

The authors thank Michael Jones for excellent software development. This research was undertaken in partial fulfillment of IRS contract TIRNO-09-Z-00019. The views expressed are those of the authors and do not reflect the opinions of the IRS or of any researchers working within the IRS.

Author information

Authors and Affiliations

  1. Department of Economics, Appalachian State University, Boone, NC, 28608, USA

    Michael McKee

  2. Department of Economics, University of Tennessee, Knoxville, TN, 37996, USA

    Caleb A. Siladke

  3. Department of Economics and Howard H. Baker Jr. Center for Public Policy, University of Tennessee, Knoxville, TN, 37996, USA

    Christian A. Vossler

Authors
  1. Michael McKee
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  2. Caleb A. Siladke
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  3. Christian A. Vossler
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Corresponding author

Correspondence to Christian A. Vossler.

Appendices

Appendix A: Selected experiment screenshots (Treatment 3, cost of $50)

See Figs.1, 2, 3, 4, and 5

Fig. 1
figure 1

Income earnings task

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Fig. 2
figure 2

Treatment 3, tax decision screen, information requested

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Fig. 3
figure 3

Treatment 3, tax decision screen, after information acquired

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Fig. 4
figure 4

Audit selection process

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Fig. 5
figure 5

Results screen

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Appendix B: Example experiment summary sheet (Treatment 3, cost of $50)

Experiment overview

  • You will be participating in a market simulation that lasts several decision “rounds”. In each round, you first play an earnings game and then face a tax reporting decision.

  • In the earnings game, you sort the numbers 1 through 9. Your Income earned is determined by how fast you sort the numbers relative to others. The participant in your group with the fastest time receives the highest Income earned.

  • In the tax reporting stage, you fill out and file a tax form. How much you earn from the tax reporting decision depends on how much you claim in Tax Credit and whether or not you are audited. Note that the on-screen instructions do not specify the tax policy parameters (e.g., tax rate, penalty rate, etc.), but those specified below will be in effect for this experiment.

  • Each round is completely independent from the others, which means your decisions in one round in no way affect the outcome of any other round.

How your earnings are determined each round

  • On the tax form, your Initial Taxes will be calculated automatically. This amount is determined by multiplying your Income earned by a tax rate of 50%.

  • You decide how much to claim in Tax Credit on the tax form. Each dollar you claim in credits reduces your Final taxes by one dollar. This amount is subtracted from the Initial Taxes to determine your Final Taxes. If Final Taxes is a negative number, this reflects a tax refund.

  • You will be shown a range of tax credits (this range is highlighted in white on the left side of the decision screen), which depends on your Income earned. Each amount within the range has an equal chance of being your actual tax credit, which is the highest amount you can claim without possible penalty. You can choose to claim any amount between 0 and 1000.

  • You have an information service available to you at a cost of $50. By clicking on the “Information Request” button you will know the exact amount of your actual tax credit.

  • You have a 30% chance of being audited. Audits are determined completely at random and do not depend on how much you or anyone else claims in tax credits.

  • If you are not audited, your earnings for the round are your Income earned minus Final taxes.

  • If you are audited, but claimed less than or equal to the actual tax credit, your earnings for the round are your Income earned minus Final taxes. Know that if you under-reported the credit you will not receive additional money through the audit process.

  • If you are audited, and claimed more than the actual tax credit, you pay back the extra tax credit you claimed and also pay a penalty.

    • The penalty is equal to 300% multiplied by the amount of extra tax credit you claimed. Thus, if you claimed an extra $100 your penalty is $100*300% or $300.

    • Your earnings for the round are then Income earned minus Final taxes minus the extra tax credit you claimed minus the penalty.

Appendix C: Supplemental data analysis

See Tables 7, 8, 9, 10, 11, 12, and 13.

Table 7 Compliance models (dependent variable is tax credit reported—actual credit)
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Table 8 Information service acquisition models (probit)
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Table 9 Tax credit reporting models (rounds 1–10)
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Table 10 Tax credit reporting models (rounds 11–20)
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Table 11 Information service acquisition models (rounds 1–10)
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Table 12 Information service acquisition models (rounds 11–20)
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Table 13 Dynamic tax credit reporting models
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McKee, M., Siladke, C.A. & Vossler, C.A. Behavioral dynamics of tax compliance when taxpayer assistance services are available. Int Tax Public Finance 25, 722–756 (2018). https://doi.org/10.1007/s10797-017-9466-z

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