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The Effect of Interactional Fairness and Detection on Taxpayers’ Compliance Intentions

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Abstract

Although the role of fairness in tax compliance has been of increasing interest among the academic and professional tax communities, very little is known about the role of interactional fairness. Interactional fairness refers to the quality of the treatment provided to individuals from authority figures, such as tax authority representatives. We conduct an experiment using US taxpayers to examine the role of interactional fairness on tax compliance intentions, and how detection influences this relation. Taxpayers’ detection salience reflects their perceptions that they will be audited by the tax authority. Using insights from conditional cooperation theory, we predict and find that detection moderates the relation between interactional fairness and tax compliance intentions, such that the effect of interactional fairness on tax compliance intentions diminishes with higher detection. We discuss the implications of our results for tax policy.

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Notes

  1. Using dummy responses, the firm initially tested all web links to ensure they were working properly. We verified that the dummy data were accurately populated into the software. The firm then launched the data collection. The firm provided us with an update at the end of each day of the number of complete responses they had collected. The firm also recalibrated the sample at the end of each day so that our gender and age parameters were as accurate as possible.

  2. We checked our sample against U.S. census data (U.S. Census Bureau, Current Population Survey, 2014 Annual Social and Economic Supplement) that segmented the U.S. population according to income and education level. Our income segments matched those used in this population survey. Our sample was similarly weighted to that of the US population with two exceptions: (1) The income category below $50,000 was underweight (by about 8%) relative to the census data, and (2) our income category above $75,000 was weighted 6% more than the census data. Given that the population of the income category below $50,000 is the one least likely to pay tax, we believe that our sample is relatively representative of the broader population of US taxpayers.

  3. As discussed earlier, we chose a phone call because it is a common and widespread method of contacting the IRS. For example, in the period from January 1 through March 5, 2016, the IRS received 46.1 million telephone calls (https://www.treasury.gov/tigta/auditreports/2016reports/201640034fr.pdf, p. 16).

  4. Consistent with the tax compliance literature (Blanthorne and Kaplan 2008; Bobek and Hatfield 2003; Carnes and Englebrecht 1995; Sanders et al. 2008; Verboon and van Dijke 2007), we use compliance intentions as a proxy for taxpayers’ compliance.

  5. We also tested whether any demographic variables were associated with either independent variable or the interaction between the two independent variables. With the exception of age, the demographic variables were not associated with the independent variables. Age was significantly higher in the lower condition of detection expectations (M = 43.6, SD = 14.6) compared to the higher condition of detection expectations (M = 39.04, SD = 15.0), F(1203) = 4.80, p = .03). We do not include age in the model because it was not associated with the dependent variable, compliance intentions.

  6. Contrast coding represents a more powerful test than ANCOVA and is appropriate when there is a specific interaction based on theoretical predictions (Buckless and Ravenscroft 1990; Cohen et al. 2015; Rosenthal and Rosnow 1985).

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Acknowledgements

Funding was provided by Schulich CPA Research Alliance, Canadian Accounting Association.

Author information

Authors and Affiliations

  1. School of Accounting and Finance, Ted Rogers School of Management, Ryerson University, Toronto, Canada

    Jonathan Farrar

  2. School of Accountancy, W. P. Carey School of Business, Arizona State University, Tempe, AZ, USA

    Steven E. Kaplan

  3. Schulich School of Business, York University, Toronto, Canada

    Linda Thorne

Authors
  1. Jonathan Farrar
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  2. Steven E. Kaplan
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  3. Linda Thorne
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Corresponding author

Correspondence to Linda Thorne.

Appendices

Appendix: Experimental Instrument

Part 1: Basic Tax Scenario, Common to all Experimental Materials

Below is a story about a barber named Jason and his experiences with the IRS. Imagine that you are Jason. Please read it carefully, as you will be asked some follow-up questions.

Jason is a barber, and while he used to work in construction, he decided to open his own barber shop last year. He rented a small store on the main street of his home town and bought an antique barber chair on eBay for $2200. He did not accept credit cards or checks, so his customers paid him in cash only. He kept records of customers’ appointments and haircuts by writing on a calendar with a pencil.

Jason was preparing his own tax return shortly before the April 15th deadline. He understood that the total amount of cash received from customers was part of his business income. But, he had a question about how to treat the barber chair for tax purposes. He wasn’t sure whether he should deduct the entire cost, or only a portion.

Specific Scenario Information

Higher interactional fairness, Higher Detection

Jason had several barber friends who were audited last year by the IRS. They told him the IRS was devoting more time and effort to auditing cash-based businesses.

Jason decided to call the IRS to ask about the tax rules for deducting the barber chair. The IRS spokesperson was very polite and respectful, answered his question simply and thoroughly, and asked whether there was anything else she could help him with.

Higher Interactional Fairness, Lower Detection

Jason decided to call the IRS to ask about the tax rules for deducting the barber chair. The IRS spokesperson was very polite and respectful, answered his question simply and thoroughly, and asked whether there was anything else she could help him with.

Lower Interactional Fairness, Higher Detection

Jason had several barber friends who were audited last year by the IRS. They told him the IRS was devoting more time and effort to auditing cash-based businesses.

Jason decided to call the IRS to ask about the tax rules for deducting the barber chair. The IRS spokesperson was very rude and disrespectful, said that it was not their responsibility to answer his question, and immediately disconnected his call.

Lower Interactional Fairness, Lower Detection

Jason decided to call the IRS to ask about the tax rules for deducting the barber chair. The IRS spokesperson was very rude and disrespectful, said that it was not their responsibility to answer his question, and immediately disconnected his call.

Part 2: Questions

Dependent Variable

Please read the following statements and indicate your level of agreement by clicking on the appropriate response, where 1 = strongly agree, and 7 = strongly disagree.

  1. 1.

    Jason will not declare all the cash to the IRS.

  2. 2.

    Jason would be tempted to not report all of his cash receipts on his tax return.

  3. 3.

    Jason is unlikely to report all his cash earnings to the IRS.

  4. 4.

    Under the circumstances, Jason might not report all of his cash earnings on his tax return.

Manipulation checks

Interactional Fairness

  1. 1.

    Jason was treated well when he phoned the IRS (1 = strongly agree; 7 = strongly disagree).

Detection

  1. 1.

    Jason expects to be audited by the IRS (1 = strongly agree; 7 = strongly disagree).

Demographic Questions

Please answer the following demographic questions.

  1. 1.

    Your gender: male female

  2. 2.

    Have you ever had a problem with the IRS? yes no

  3. 3.

    Your present age in years: __________

  4. 4.

    The number of years of your full-time work experience: __________.

  5. 5.

    Who usually prepares your tax return?

    • I do

    • My spouse/partner

    • Paid preparer

    • Other

  6. 6.

    Please indicate your highest level of education completed:

    • High School

    • Community College diploma

    • Undergraduate degree

    • Graduate degree

    • Other

  7. 7.

    How would you categorize your political beliefs?

    • Very conservative

    • Moderately conservative

    • Slightly conservative

    • Middle of political spectrum

    • Slightly liberal

    • Moderately liberal

    • Very liberal

  8. 8.

    Please indicate your approximate annual income:

    • Less than $25,000

    • Between $25,000 and $50,000

    • Between $50,001 and $75,000

    • Between $75,001 and $100,000

    • ≥$100,000

    • Prefer not to answer

________________________________________

Thank you for your participation in this study.

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Farrar, J., Kaplan, S.E. & Thorne, L. The Effect of Interactional Fairness and Detection on Taxpayers’ Compliance Intentions. J Bus Ethics 154, 167–180 (2019). https://doi.org/10.1007/s10551-017-3458-x

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