We use different incentive schemes to study truth-telling in a die-roll task when people are asked to reveal the number rolled privately. We find no significant evidence of cheating when there are no financial incentives associated with the reports, but do find evidence of such when the reports determine financial gains or losses (in different treatments). We find no evidence of loss aversion in the standard case in which subjects receive their earnings in a sealed envelope at the end of the session. When subjects manipulate the possible earnings, we find evidence of less cheating, particularly in the loss setting; in fact, there is no significant difference in behavior between the non-incentivized case and the loss setting with money manipulation. We interpret our findings in terms of the moral cost of cheating and differences in the perceived trust and beliefs in the gain and the loss frames.
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Studies by, e.g., Greene and Paxton (2009), Shalvi et al. (2011), Conrads et al. (2013), Gravert (2013), Jiang (2013), Ploner and Regner (2013) and Hao and Houser (2017) have also used the die-rolling task. See Abeler et al. (2016) for a recent meta-study. Other studies have used the sender-receiver game where cheating is strategic (i.e., the sender needs to send a message to the receiver about the real state of the world and the receiver may believe it or not). This includes, among others, Gneezy (2005), Sutter (2009), Lundquist et al. (2009), Erat and Gneezy (2012), Erat (2013) and Vanberg (2017). Other studies in which subjects can misreport their private information or send cheap-talk messages include Charness (2000a), Croson, Boles and Murnighan (2003), and Charness and Dufwenberg (2006). For recent revisions of the literature on cheating see Rosenbaum et al. (2014) and Jacobsen, Fosgaard and Pascual-Ezama (2017).
Garbarino et al. (2016) derive a prediction that people will lie more frequently when the probability of a (the) bad outcome is lower, since the higher expected payoff means that the “loss” avoided by lying compared to reference point is greater. They find support from an analysis of studies in the literature as well as new experiments. See also Abeler et al. (2016) and Gneezy et al. (2018) for other experiments that vary the probability of a (the) bad outcome.
On the other hand, Kajackaite (2016) finds the opposite. It is also possible that people cheat more in the die-task because they can attribute a bad outcome to bad luck; it is much more difficult to do so with a real-effort task. Thus, cheating with a die roll may well have a lower moral cost than with a real-effort task. Serra-Garcia et al. (2013) is another study that compares how people cheat about what they observe and what they do. In their setting, two subjects play a public-goods game with asymmetric information. In one of the conditions, the informed subject knows the return from contributing and can lie about it, while in another treatment the informed subject can lie about his contribution to the public good. Although there should be no difference across these two conditions (messages are cheap talk and do not affect the payoffs), Serra-Garcia et al. (2013) find that subjects lie more about what they observe than about what they did.
Schindler and Pfattheicher (2017) consider a second study in mTurk, where subjects self-report the outcome of tossing a coin (Bucciol and Piovesan 2011). In this task, where cheating is a binary decision, Schindler and Pfattheicher (2017) find that cheating occurs in both the gain and the loss frame, with more cheating being observed in the later.
This procedure is frequently used in the literature due to the short nature of the task and the opportunity to avoid paying two separate show-up fees. See Section B1 in Appendix B (ESM) for further details about the previous experiments.
We use a 10-sided die to increase the number of possible outcomes and have more variability in our data. A translated version of the instructions can be found in Appendix A (ESM).
When giving them the initial endowment, we observe that roughly 1/3 of the subjects decided to leave the money on the table, while the rest put it in their pockets. We cannot determine if these subjects behave differently but we tried to minimize this problem by asking participants in the Loss-MM to put their initial endowment on the table before rolling the die.
Consistent with previous theoretical models and the preponderance of the observed evidence, we assume subjects never report an outcome R < T in our model.
We are not aware of any other paper that directly tests for cheating behavior in the absence of economic incentives.
Gneezy et al. (2018) argue that the utility function should incorporate a term that accounts for the fact that people value honest behavior or being perceived as honest; see also Garbarino et al. (2016) or Abeler et al. (2016). In our setting, it seems reasonable to assume that people will be perceived as being more honest when they report truthfully in treatments with money manipulation, which would lead to less cheating in these treatments, as a result.
We also note that there is no significant gender difference in any of the five treatments. The overall average number reported by males (females) was 5.849 (5.633). The overall proportion of zeroes reported by males (females) was 0.050 (0.048), while the proportion of nines reported by males (females) was 0.171 (0.172). See, among others, Cappelen et al. (2013), Childs (2012), Ezquerra et al. (2018), Gylfason et al. (2013) and Pascual-Ezama et al. (2013) for other studies showing no gender differences in cheating behavior.
Throughout the paper, we round all p-values to three decimal places. The interested reader on the comparison between the reported outcomes in each treatment and expected actual outcomes using the Wilcoxon rank-sum test or the Kolmogorov–Smirnov test of cumulative distributions can consult Appendix B in ESM (Table B1). This includes information on the fraction of subjects who cheat to avoid the worst possible outcome using the estimation method in Garbarino et al. (2016).
We note that ordinary least squares regressions provide qualitatively the same results and similar levels of significance. See Appendix B in ESM (Tables B3, B4) for further analysis and robustness checks.
Although participants in our experiment were not informed about their earnings in the prior experiments, one may argue that they could had formed some beliefs to be used as a reference point. The correlation between the previous earnings and the reports in each of the treatment is negative but never significant (p > 0.165). Appendix B in ESM presents further details about the previous experiments and show that the reports are independent of the previous task or the role of subjects in these experiments (see Sects. B1, B2).
Note that the effect of the money manipulation in the Gain frames becomes significant when we control for the previous experiment.
In fact, Harinck et al. (2007) find evidence of reversed loss aversion in a series of experiments where subjects are asked to rate how (un)pleasant would be finding (losing) small amounts of money. They argue that negative feelings associated with small losses may be outweighed by positive feelings associated with equivalent small gains. A similar argument applies to our setting; the unpleasant feelings may refer to moral costs of being dishonest, while the positive feelings are associated to being honest and reporting the actual outcome.
To our surprise, however, our null result does not appear to have been driven by a lack of a sense of “ownership” of the funds, since we find no evidence of loss aversion in the money-manipulation treatments.
In a sense, this resembles the idea of omission-commission in Spranca et al. (1991). However, participants in our experiment are always asked to enter the number they have obtained in the computer screen, thus cheating requires acts of commission even in the loss condition (Cameron and Miller 2009).
Some people even seem to care about reporting a higher number in the Baseline treatment, since more high numbers (5–9) are reported than low numbers in this case—more than 60% of the reports are high numbers.
In the loss treatments (Loss-No and Loss-MM) subjects knew about or received the 5-Euros when they entered the room. In both settings, we induced the reference point at the beginning, so it should be the manipulation of money (not the point at which earnings were announced) that drives the results.
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We are thankful to the Co-editor Charles Noussair and three anonymous referees for their very valuable feedback and suggestions that really helped to improve the quality of the manuscript. We have also benefitted from discussion with participants at SEET Malta 2016, RES Brighton 2016, IMEBESS Barcelona 2017, CNEE Workshop Copenhagen 2017, Johannes Abeler, Alexander Cappelen, Hubert Janos Kiss, Martin Kocher, Praveen Kujal, Matteo Ploner, Giovanni Ponti and Marie Claire Villeval. We also thank the Laboratory for Research in Experimental Economics (LINEEX) for conducting our experiments. Ismael Rodriguez-Lara acknowledges financial support from FEDER and the Ministerio de Economia y Competitividad under the Project CO2017-87245-R.
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Charness, G., Blanco-Jimenez, C., Ezquerra, L. et al. Cheating, incentives, and money manipulation. Exp Econ 22, 155–177 (2019). https://doi.org/10.1007/s10683-018-9584-1
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