Abstract
We examine the interplay between unethical behaviour and competition with a lab experiment. Subjects play the role of firms in monopoly, weak competition (Bertrand–Edgeworth duopoly) or strong competition (Bertrand duopoly). Costs are determined either by a computer draw or a self-reported die roll, and pricing decisions are made with knowledge of one’s own costs and—in duopoly—the rival firm’s costs. Under self-reporting, lying is profitable and undetectable except statistically. We find that competition and lying are mutually reinforcing. We observe strong evidence that (behavioural) competition in both duopoly treatments is more intense when lying is possible: prices are significantly lower than when lying is impossible, even controlling for differences in costs. We also observe more lying under duopoly than monopoly—despite the greater monetary incentives to lie in the monopoly case—though these differences are not always significant.
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Notes
Note that we are using the word “competition” in two different ways: “structural” competition as determined by the numbers of buyers and sellers in the market, and “behavioural” competition as determined by how sellers’ prices relate to equilibrium behaviour (in the previous and current paragraphs, respectively).
Dufwenberg and Gneezy (2000) find that pricing is much less competitive in duopoly than the Bertrand prediction, though the prediction fares better as the number of oligopolists increases. See also Ledyard (1995) for examples in the public-good literature, and Holt (1995) for some from the oligopoly literature.
There are also experimental literatures examining other kinds of unethical behaviour, such as sabotage (Carpenter et al. 2010) and corruption (Serra and Wantchekon 2012), as well as an old literature on deception in bargaining: unstructured bargaining with cheap talk (e.g., Roth and Keith Murnighan 1982), or structured bargaining with incomplete information, where deception involves opportunistic pooling instead of outright lying (e.g., Mitzkewitz and Nagel 1993).
By contrast, when lying affects someone who can be thought of as an innocent bystander (i.e., someone with whom there is no interaction other than the effect of the lie), an accomplice (lying is mutually beneficial, or requires coordination to be effective), or someone with a positive outside relationship (e.g., a friend or relative), behaviour is usually consistent with pro-social preferences. See Gneezy (2005) and Wiltermuth (2011) for examples of the first type, Weisel and Shalvi (2015), Barr and Michailidou (2016) for examples of the second type, and Houser et al. (2016) for an example of the third type.
Rigdon and D’Esterre’s (2015) design is similar to several of these other papers, and quite different from ours. Subjects perform a task individually for a piece rate in one treatment, and the same task in competition (against one or more other subjects) in another treatment. The major difference is that Rigdon and D’Esterre keep the same piece rate under both individualistic and competitive treatments, rather than multiplying it by the number of competitors. Thus, in contrast to other studies that maintain the expected piece rate and therefore raise the monetary incentives to lying, Rigdon and D’Esterre halve the expected piece rate, thus lowering (though not quite halving) the monetary incentives to lie.
These studies used psychology experiment methods, including deception and post-session debriefing. Subjects were told that they would perform a real-effort task, with performance determining their chance of winning a raffle, and that the “prize” meant being given an intrinsically rewarding task instead of a tedious task. In fact, there was no “other subject” and no task, and all subjects were given equal chances of winning the raffle.
The reason for feedback in the second block is to allow a more direct comparison with our duopoly treatments, in which subjects not only compete, but also observe the reported cost of another subject in each round. Thus, our treatments in the second block hold constant the information subjects receive about others’ reported costs, and hence the inferences subjects can make about others’ lying. There is mixed evidence that observation of others’ lying can affect honesty; see our literature review for details. See López-Pérez (2012) for a theoretical treatment of honesty norms, where the cost of lying is increasing in the fraction of other people observing the honesty norm, so that observing others lying can increase one’s own propensity to lie via changes in beliefs regarding how much the norm is followed.
See the “Online Appendix” for sample instructions and screen-shots, including those for the questionnaire. Other experimental materials including the raw data are available from the corresponding author upon request.
At the time of the experiment, one AUD was worth roughly 0.70 USD.
There are a few cases (when sellers’ costs are equal, at 0.20, 0.30, 0.80, 0.90, 1.00, 1.10 or 1.20) where multiple equilibria exist. In these cases, the payoffs in Table 1 are calculated by taking the simple average of each equilibrium payoff; however, the equilibria are “nearly payoff equivalent” in the sense that order relationships across outcomes within any column are unaffected by choice of any particular one of the multiple equilibria. For example, if Firm 2’s cost is 0.30, then Firm 1’s payoff given a cost of 0.30 ($5.18), and based on the figure, is less than that given a cost of 0.20 but higher than that given any higher cost. The two equilibria when both costs are 0.30 give Firm 1 payoffs of $5.14 and $5.22 respectively, so using either individual equilibrium payoff instead of their average does not affect any of those order relationships. The same is true for Table 2 below.
The increasing marginal cost is by assumption. The decreasing marginal benefit is due to the increasing likelihood of the minimum-cost constraint of 20 cents becoming binding as under-stating becomes more severe.
In (1), this would be represented by \(\alpha\) decreasing.
An alternative experimental procedure, which would have eliminated the need for such controls, would have the self-roll treatment conducted first, then used the observed distribution of reported die rolls for the computer-roll treatment instead of fair die rolls.
Besides the sessions shown in the figure, there were two pilot sessions, conducted before the first “real” session, to test the program and ensure the instructions were understandable. Also, a real session had to be cancelled while in progress due to a hardware fault. The data from the pilots and the cancelled session are not included in any of our analysis, and we do not discuss them further.
A normal approximation yields a z-score of roughly –38.5.
Since subjects did not interact, nor receive any feedback about others, in block 1, it is appropriate to treat each individual subject as independent. See Siegel et al. (1988) for descriptions of the non-parametric tests used in this paper, and Feltovich (2005) for critical values of the robust rank-order test used here and later. We refer to probabilities in our statistical tests with q (e.g., “q-value”) to avoid confusing probabilities with prices, which continue to be denoted by “p”.
Indeed, nearly half (45 percent) of subjects in the self-roll treatment report average costs over rounds 1-20 that are lower than the lowest average cost of any subject in the computer-roll treatment. However, only 6 out of the 184 subjects in the self-roll treatment lie maximally (reporting the minimum possible cost of $0.20 in every round), and only 14 (7.6 percent) do so in the last five rounds.
This regression, and the corresponding one for the self-roll treatment, have average price choice over rounds 1-20 as the dependent variable, and a constant and the average cost over rounds 1-20 on the right-hand side. Other specifications, including Tobits replacing OLS, dropping one or more outliers, using only late rounds instead of all rounds from block 1, and using the fraction of optimal $1.50 choices as the dependent variable, yield similar results. We note that a positive relationship in the computer-roll treatment could be explained by a uniform random component in price choices (keeping in mind that price is constrained to be between the cost and $1.50) or by a “cost-plus” pricing heuristic.
Another potential explanation is “moral cleansing” (Sachdeva et al. 2009): after unethical behaviour, people subsequently behave more morally or altruistically to atone. In our setting, subjects could be atoning for lying by reducing their prices. This is arguably not altruistic since our use of automated buyers means that no-one benefits from these lower prices, though one cannot rule out the possibility subjects have altruistic feelings toward the experimenter and incorrectly believe lower prices benefit him. In any case, harming oneself to atone for past sins - irrespective of any benefit to others - is certainly consistent with traditional religious forms of moral cleansing.
Due to the interaction among subjects within sessions in block 2, we use session-level averages for non-parametric tests involving these data. Because of this, and because of the larger number of experimental cells than in block 1, significance results will generally be weaker here than in the block-1 results.
Similarly, it should be unsurprising that within both the self-roll and the computer-roll treatment, prices are significantly lower under strong competition than under weak competition (robust rank-order test, two-sided, session-level data, \(q\approx 0.008\) for self-roll and \(q=0.10\) for computer-roll; note for the latter that no lower two-sided q-value is possible for a 3-session-to-3-session comparison), and in the self-roll treatment, they are significantly lower under either than under monopoly with feedback (\(q\approx 0.008\)).
Additional Tobits, not reported here, included the complete set of demographic variables collected in the experiment. None of the additional variables were significant, nor did their inclusion affect the signs or significance levels of the variables shown in Table 4. Our results are also not substantially affected by using linear panel regressions (instead of Tobits) with clustering by individual subjects, clustering by session or bootstrapped standard errors. The same is true for the results reported in Table 5 below, and for still other regressions based on those models: including an equilibrium-price-squared variable to allow for a non-linear effect of the equilibrium price, or including the previous opponent’s reported cost (as with Model 3 in Table 4).
The strong- and weak-competition indicators’ marginal effects just miss being significantly different from each other (\(q\approx 0.101\) in Model 3).
Note that this statement is about different responses to the treatment, not about overall propensity to lie. The significant positive effect of the “native born” variable in the Model 1 results suggests that overall, lying by native-born subjects is significantly less prevalent than by subjects born elsewhere, while the corresponding effect for females also suggests less lying than by males, though not significantly so. A coherent, though speculative, explanation for these results would be that males are more intrinsically prone to unethical behaviour than females, leaving less room for more intense competition to further increase dishonesty (thus showing less of a treatment effect than females), and similarly for foreign- versus native-born subjects.
Accordingly, we do not see evidence for the alternative conjecture discussed in Sect. 3.3 that subjects behave more cooperatively to reward anticipated truth telling across the board (which would imply a positive marginal effect). We also do not observe evidence for a positive interaction between the self-roll dummy and the rival cost, as would be implied by a strategy of conditional cooperation. Specifically, in [5], the estimated coefficient of the interaction term between self-roll and rival cost is not significantly different from zero (and indeed is negative rather than positive), and in both [5] and [6], the marginal effect of the rival cost is not significantly different between the self-roll and computer-roll treatments. We have found alternative specifications where this difference becomes significant, but we view these as inappropriate (e.g., [6] but with the equilibrium price removed as an explanatory variable).
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Financial support from Monash University is gratefully acknowledged. We thank Lana Friesen, Lata Gangadharan, Philip J. Grossman, Mehmet Y. Gürdal, Andreas Ortmann, Maroš Servátka, an associate editor, and two anonymous referees for helpful suggestions and comments.
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Feltovich, N. The interaction between competition and unethical behaviour. Exp Econ 22, 101–130 (2019). https://doi.org/10.1007/s10683-018-9578-z
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DOI: https://doi.org/10.1007/s10683-018-9578-z