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Selfish Sharing? The Impact of the Sharing Economy on Tax Reporting Honesty

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A Correction to this article was published on 07 February 2020

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Abstract

In the last decade, advances in technology have significantly disrupted the way firms provide goods and services. At the forefront of this technological disruption is the sharing economy, where individuals earn income by providing services or sharing assets through peer-to-peer (P2P) platforms. With global revenues in the sharing economy projected to increase substantially in the next decade, income from this economy will continue to be an important source of tax revenues for governments around the world. However, sceptics argue that the sharing economy inherently lends itself to dishonest reporting of taxable income. We employ an online experiment, using 746 taxpayers, to observe whether the prosocial benefits often promoted by P2P platforms reduce honest reporting of taxable sharing economy income. Consistent with moral licensing theory, we find that earning income from a prosocial-oriented P2P platform liberates taxpayers to dishonestly report their sharing economy income, and this result is fully driven by taxpayers whose personal values are incongruent with values promoted by the P2P platform. Our paper contributes to the limited but growing research on the sharing economy and its implications for ethical decisions. It also adds to the moral licensing literature by identifying value congruency as an important moderator for moral licensing effect.

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Change history

  • 07 February 2020

    Table 3 was incomplete in the initial online publication. The original article has been corrected.

Notes

  1. P2P platforms are technology-driven interfaces that allow individuals to interact directly without the need for a third party. Thus, service or asset providers and consumers can engage in transactions directly with each other using those platforms (Kenton 2018).

  2. For example, Mazar and Zhong (2010) demonstrate that participants who chose organic or environmentally friendly products in an initial task cheated more on a subsequent task. Also, Khan and Dhar (2006) demonstrate that participants who imagined helping a foreign student donated less to charity on a subsequent task (for a review, see Mullen and Monin 2016).

  3. Values are defined as the beliefs people possess about appropriate ways in which one should behave across different actions and situations. Once established, individuals tend to feel strongly about their core values and act consistently with them. Values are connected to individuals’ self-identity and are relatively stable over time (Eyal et al. 2009; Feather 1995; Schwartz 1992).

  4. SSI declines to disclose the exact payment made to the participants but informs us that the points rewarded are based on the length of the study and the level of effort required; compensation, in terms of hourly rate, is similar across all studies hosted by SSI.

  5. The four attention check questions are: (1) How does your performance compare to other (EcoShare/ComuuniShare/TravelAlong/MyRyde) service providers in your neighbourhood? (2) How much cash did you earn from (EcoShare/ComuuniShare/TravelAlong/MyRyde)? (3) What is the strength of the platform you just signed up for, compared with the other ride-sharing platform? and (4) Which badge did you earn in the (EcoShare/ComuuniShare/TravelAlong/MyRyde) app? The participants must answer all of them correctly until they can proceed. Among our participants, 32.0% (or 239) of them fail the attention check questions once, 14.1% (or 105) fail them twice, and 6.3% (or 47) fail them more than twice. Our statistical inferences remain unchanged after excluding these 47 participants’ responses.

  6. We direct SSI to distribute our instrument to similar numbers of males and females between 18 and 65 years old. Among the 746 participants, demographic question responses from 59 of them are not recorded due to technical problems with SSI.

  7. All p values reported in this study are two-tailed.

  8. This feature of our design follows the procedure of Khan and Dhar (2006). In their study they also provide participants a choice of prosocial acts and ask them to provide reasons for their choices when attempting to activate moral licensing in an experimental setting.

  9. Before collecting our experimental data, we conducted a validation test to ensure that our manipulation was functioning as intended. Details of the validation test are discussed later in this section.

  10. Using the Environment Value and Community Value scores, we determine participants’ values as such: When participants’ Environment Value is higher than their Community Value and the former is greater than the mid-point of 4, participants are classified as valuing the environment. When participants’ Community Value is higher than their Environment Value and the former is greater than 4, participants are classified as valuing the community. When participants’ Environment Value equals to Community Value, and both are greater than 4, participants are classified as valuing both environment and community. When participants’ Environment Value and Community Value are both less than 4, they are classified as valuing neither environment nor community.

  11. For example, an individual may value environment more than community but they also find the images used by CommuiShare (e.g. the smiling faces or the glowing webs of hexagons) more uplifting and appealing and as a result select that P2P platform that is not congruent with their values.

  12. As a validation check for our dependent measures, we ask the reporting questions using a third-person perspective (i.e. that of their peer, whether a classmate, a colleague, or an acquaintance) because posing sensitive questions using a third-person perspective can help reduce self-reporting bias (Kaplan et al. 2009). We find high correlations between first-person and third-person measures (ρ > 0.60, p < 0.01). Moreover, all our statistical inferences remain unchanged when using third-person measures as the dependent variables with one exceptions: the difference in third-person Reporting $ between Value-Incongruent group and the control condition is not significant (t = 1.42, p = 0.15, two-tailed).

  13. For the first question, the possible responses were (1) an app that matches drivers with riders (correct answer), and (2) a website to find vacation rentals. For the second question, the possible responses were: (1) Airbnb, (2) Google (correct answer), (3) Uber, and (4) Lyft.

  14. The results on Honesty Scale mirrors the results on Reporting $, in terms of both statistical inferences and significance levels. For parsimony, we exclude Honesty Scale in the following sections and only report results on one measure of honesty magnitude.

  15. As shown in the classification table in Table 4, Panel B the logistic regression model is much more accurate in predicting honest taxpayers’ behaviour (accuracy rate of 76.3%) than in predicting dishonest taxpayers’ (37.0%).

  16. To determine whether knowledge about tax laws influences our results, we conduct our main analyses with a subsample including only participants who usually complete their own tax returns (N = 419). Our results are directionally similar when we consider only this portion of our participants.

  17. Since Guilt for Misreporting is measured after the tax reporting decisions, one may argue that participants may indicate lower (higher) level of guilt after they have made less (more) honest reporting decisions. To address this issue, we collect additional data where we measure Guilt for Misreporting before we measure reporting honesty (N = 340). Planned comparisons suggest that, consistent with our main study, individuals in the Value-Incongruent group on average feel less guilt than those in the control condition (t = 2.66, p < 0.01) and those in the Value-Congruent group (t = 3.78, p < 0.01). There is no significant difference in the mean guilt level between the Value-Congruent group and the control condition (t = 1.07, p = 0.29).

  18. For the path coefficients, the Bayesian approach to path analysis does not generate the typical statistical values for significance tests (such as t values and p values); rather, it generates distributions of the coefficient estimates. We report in the paper the means (or unstandardized) and standard deviations of the path coefficients as well as the standardized coefficients.

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Acknowledgements

We gratefully acknowledge the helpful comments by Joanna Andrejkow, Jonathan Farrar, Darren Henderson, Kelsey Kirbyson, Chima Mbagwu, Bruce McConomy, Sara Wick, Chris Wong, Michael Wynes, and Zhuoyi Zhao. We thank Zhuoyi Zhao for her excellent research assistance. We gratefully acknowledge funding provided by the CPA-CAAA Research Grant, the CPA Ontario Centre for Capital Markets and Behavioral Decision Making, the KPMG Accounting Fellowship Fund and the Grant Thornton Fellowship Fund for this study.

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Correspondence to Leslie Berger.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the university’s research committee and with the 1964 Helsinki declaration and its later amendments. Informed consent was obtained from all individual participants included in the study.

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The original version of this article was revised: Table 3 was incomplete in the initial online publication. The original article has been corrected.

Appendices

Appendix 1: Screenshots of P2P Platforms

Participants view screenshots of both P2P platforms’ websites before choosing one.

(1) Prosocial condition: EcoShare

figure a

(2) Prosocial condition: CommuniShare

figure b

(3) Control condition: MyRyde

figure c

(4) Control condition: TravelAlong

figure d

Appendix 2: Screenshots of Cell Phone Applications from P2P Platforms

Participants see one of the screenshots depending on the platform chosen.

(1) Prosocial condition

figure e

(2) Control condition

figure f

Appendix 3: Dependent Variables—Tax Reporting Honesty

Reporting $

How much of the $3000 earned from ride-sharing would you decide to report on your tax return? Please slide the bar to indicate the amount you decide to report.

figure g

Honesty Scale (reverse coded)


Please indicate your agreement with the following statements (1 = strongly disagree and 7 = strongly agree):

1. I would be tempted not to report all my extra earnings on my tax return.

2. Under these circumstances, I might not report all of my extra earnings on my tax return.

3. I am unlikely to report all my extra earnings to the CRA.

4. I will not report all the extra earnings to the CRA.

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Berger, L., Guo, L. & King, T. Selfish Sharing? The Impact of the Sharing Economy on Tax Reporting Honesty. J Bus Ethics 167, 181–205 (2020). https://doi.org/10.1007/s10551-019-04409-z

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