The Effect of Pledges on Lying: an Online Experiment

Transcription

1 The Effect of Pledges on Lying: an Online Experiment Franziska Heinicke Stephanie Rosenkranz Utz Weitzel Abstract: People lie for their own benefit but in many situations honesty increases overall welfare and society has an interest in discouraging lying. A few studies have found that lying can be significantly reduced by simple truth pledges in which people promise not to lie. In the present study we extend the understanding of truth pledges by testing a truth pledge in a potentially less effective experimental context. We increase the anonymity of the experiment by conducting it online and we apply truth pledges in a gain and in a loss frame. Anonymity and loss framing have both been shown to mitigate honesty preferences and with this study we analyse whether the greater willingness to lie in these settings interferes with the effectiveness of truth pledges. For this purpose, we adopt the well-established coin-toss design to an online setting where the availability of a coin is not certain. The adopted task instructs participants on how to generate a random number and rewards a high payoff for this number being even or odd. In our 2x2 experimental design participants can either get an additional payment by lying or can avoid giving up part of their initial budget by lying. Our results show that a simple truth pledge administered at the beginning of the experiment reduces lying significantly and that this effect does not differ between the two frames. We further observe opposite gender differences in the two frames with women reporting more high payments in the gain frame but less in the loss frame. The results show potential for truth pledges to be applied to a range of settings. Keywords: Lying, truth pledge, loss aversion, online experiment

2 Introduction In conventional economic theory people lie whenever they can gain an advantage from lying and people are honest only in situation where honesty rewards the highest payoff (Becker, 1968). Recently this notion has gained increasing attention in the literature of experimental economics. The overwhelmingly robust result in this literature is that people lie to increase their payoff but not in a profit maximizing way (for a review see Rosenbaum et al., 2014). While people strive to increase their payoff, there seems to be strong preference for honesty which keeps people from fully exploiting situations by lying. A growing number of studies is providing insights into the determinants of honesty preferences and the drivers of individual differences in these preferences. It has been shown that women tend to lie less than men (Conrads et al., 2013; Friesen and Gangadharan, 2013) and that honesty preferences increase with age (Conrads et al., 2013; Childs, 2012). Next to these demographics, honesty preferences are also influenced by professional identities as bankers or criminals who both lie more when reminded of their professional identity (Cohn et al, 2014a, 2014b) and as nuns who actually lied to their disadvantage (Utikal and Fischbacher, 2013). While the above mentioned literature holds many answers on who is lying, it is fairly silent on how to discourage people from lying. In everyday life there are many situations where honesty increases overall welfare and where society has an interest in discouraging lying altogether. Examples include but are not limited to witnesses in court, doctor patient relationships, insurance companies and loss statements. Often the chosen tool to increase honesty is to straight-forwardly tell people not to lie or to make them promise to stick to the truth. In oath taking in the Anglo-Saxon legal system, in academic honor codes and in the Dutch bankers oath people make a direct statement in which they pledge to tell the truth or to act honestly in the respective situation. 1 The possible discouraging effect on lying of such direct truth pledges has received little attention in the economic literature, even though a better understanding of the effectiveness of such tools could improve and extend their application in real world situations. Mazar et al. (2008) and Bucciol and Piovesan (2011) are the only two studies to our knowledge which study this direct intervention in a controlled experimental setting. Both studies find truth pledges or explicit requests for truth telling to significantly reduce dishonest behavior. However, the setting of both studies was very similar with an experimenter being present at the moment of the decision and with lying leading to higher experiment earnings. In this study we study the effectiveness of truth pledges in a different setting to get a better understanding on the robustness of the decrease in dishonest behavior and the sensitivity of this effect to the particular setting. Specifically, we make two changes to the setting that have been shown to affect lying behavior itself but have not been related to truth pledges. First, we increase the anonymity of the decision by conducting an online experiment. Conrads and Lotz (2015) show that the increased anonymity of an online experiment leads participants to lie more. Second, we apply a truth pledge under a gain and a loss frame. Grolleau et al. (2016) analyze the effect of framing on lying behavior and 1 The nature of a truth pledge is related to a promise. The effect of promises on behavior in multi-player situations has been widely studied (e.g. Charness and Dufwenberg, 2006; Kataria and Winter, 2013). However, the keeping of promises has been found to be mainly driven by social preferences (Vanberg, 2008). Truth pledges discussed here differ considerably from promises because they are not related to other people but only affect the own outcome.

3 conclude that loss aversion induced by the loss frame increases lying. While anonymity and framing have been shown to be determinants of lying behavior, we aim to answer whether they also have to be taken into account when considering truth pledges. We test a truth pledge treatment in a 2x2 treatment design in an online experiment with 484 participants. The treatments include a base treatment in a gain and a loss frame and a truth pledge intervention in the same two frames. This allows us to test whether truth pledges are effective in an anonymous online setting where no experimenter is present and whether the effectiveness of truth pledges is affected by loss aversion and the framing of the task. Further, we introduce a new task to elicit honesty preferences which does not require additional tools like the well-established coin-toss or roll-adie tasks (Fischbacher and Föllmi-Heusi, 2013). Because in an online experiment it cannot be guaranteed that participants have such tools readily available and actually use them, using tool-dependent task might invite participants to randomize in their head. Human randomization is known to be biased against repetition and to impose high cognitive load (Wangenaar, 1972; Figurska et al., 2008; Jokar and Mikaili, 2012). These drawbacks of human randomization become increasingly problematic when randomization is done over several periods. By introducing the Even or Odd task we aim to circumvent the issues while staying as close as possible to the structure of established tasks. The task instructs participants on how to generate a random number and rewards a high payoff for this number being even or odd. We apply this task in four between-subject treatments over 10 rounds. Our results show that participants claim to have earned a high payoff significantly more frequently than expected under truth-telling. A simple truth pledge administered at the beginning of the experiment reduces the probability of claiming a high payoff by 5% in both frames. This decrease is significant but high payoffs are still claimed significantly more frequently than in 50% of the cases which means that the pledge reduces lying reliably but does not eliminate it completely. We find no significant effect of loss framing on lying behavior which stands opposite to Grolleau et al. (2016) who find participants to lie significantly more to avoid losses. We discuss possible explanations for this discrepancy in the concluding section of this paper by reflecting on the differences between the two task designs. Another interesting finding from the data reported in this study is the difference in honesty between genders which strongly depends on the frame of the task. As compared to men, women are found to lie less in the loss frame but more in the gain frame. We discuss directions for future research to further explore this phenomenon. The remainder of this paper is organized as follows. In the next section we will discuss the theoretical effect of truth pledges and existing evidence on their effectiveness. This is followed by a detailed description of the experimental design which introduces a new task to elicit honesty preferences. Finally, we will report results and discuss the implications of our findings. Truth pledges The central and robust finding of literature on honesty preferences is that people lie partially and it has explained this observation in two ways. The first theory assumes that people have a strong inherent preference for honesty (Sánchez-Pagés and Vorsatz, 2007; Gneezy et al., 2013; Kartik et al., 2014; Abeler

4 et al., 2014). Lying is contrary to this preference and thus, people constantly have to balance possible benefits from lying against the costs involved with acting against their preference for honesty. One interpretation for the costs from lying is that seeing oneself as an honest person contributes to a positive self-image and that lying is costly because the image of an honest person does not hold up (Mazar et al., 2008). The other explanation raised in honesty literature does not assume a lie itself to lead to disutility but that disutility arises from the effect that a lie has on one s reputation (Fischbacher and Föllmi-Heusi, 2013; Mazar et al., 2008; Utikal and Fischbacher, 2013; Dana et al., 2006; Shalvi et al., 2012). According to this argument lying is motivated by a desire to appear honest and morally superior in front of others (Utikal and Fischbacher, 2013). Therefore, lying costs are not constant but depend on how visible the lie is to others (Mazar et al., 2008)and on whether the lie can be justified (Shalvi et al., 2012) because a justifiable lie is less damaging to the reputation. Recently, some studies have started arguing for a combination of both explanations. Abeler et al. (2016) conduct a series of experiments to disentangle different possible explanations for lying behavior and conclude that a combination of lying costs and reputation provides the best fit for all observed behavior. This is further supported by evidence from Conrads and Lutz (2015) who elicit honesty preferences under varying degrees of anonymity and find that lying increases with higher anonymity. However, even in a remote online treatment under perfect anonymity participants lie not to the full extend. This indicates that disutility from lying is in part but not entirely driven by the wish to appear honest before others. Truth pledges might hinder lies in several ways and under both explanation of honesty preferences provided by the literature. A truth pledge defines wrong-doing explicitly because it makes people aware of what they should not do (McCabe and Trevino, 1993, 1997). By defining what moral behavior should be, truth pledges make acting against the moral behavior more difficult and generate stronger guilt. Thus, truth pledges could increase lying costs resulting from appearing dishonest to oneself (Bucciol and Piovesan, 2011). The explicit definition of wrong-doing also every act of lying a conscious violation of the pledge that cannot be justified (Mazar et al., 2008). Finally, truth pledges convey to people that the person who presented them with the pledge is aware that people might lie and is looking out for such wrong-doing. This might give the impression that lies are more visible to others. Truth pledges could potentially also lead to an increase in lying. Asking people not to lie emphasizes the fact that lying is possible and people who had not considered lying as an option or were uncertain about the chance to be found out might be more tempted to lie. Indeed, Fosgaard et al. (2013) found in a cointoss experiment that making lying a more salient option increases lying, especially among female participants, which indicates that a certain share of participants in the control group were unaware that lying was possible. However, experiments with explicit truth pledges indicate that the arguments for the effectiveness outweigh the effect of increased awareness for the possibility of lying. Mazar et al. (2008) presented participants with a real effort task in which participants had to find a pair of numbers in a matrix that adds up to 10. They compared a treatment in which the task was graded by the experimenter with a treatment in which participants had to grade the task themselves and could cheat on the final outcome

5 to increase their payoff. In the cheating treatment participants reported significantly more solved matrices. In a third treatment participants also had the option to cheat but were first asked to sign an honor statement. This honor statement referred to the honor system of the participants university which asks student to be honest. The results reported by this treatment group were not significantly different from the control group without cheating. Bucciol and Piovesan (2011) conducted a coin toss experiment with children. Their experimental design does not include an explicit truth pledge but children in the treatment group were told not to lie. This request to answer truthfully significantly reduced the number of successful trials reported but the effect was more prevalent for girls. With this study we extend the understanding of the effectiveness of truth pledges by analyzing how sensitives they are towards the setting they are conducted in. First, we conduct the experimental study online which is a more anonymous setting than the laboratory study of Mazar et al. (2008) and Bucciol and Piovesan (2011). In online experiments participants are not facing an experimenter or the other participants. Therefore, the concern for the own reputation in the eyes of others is diminished. This also serves as an explanation to the increased lying in online experiments when compared to a laboratory experiment (Conrads and Lutz, 2015). With regard to truth pledges, the anonymous online setting enables us to determine whether the effect of truth pledges is fully mitigated by the wish to appear honest in front of others. If this is the case a truth pledge will be ineffective in the anonymous online setting. If on the other hand, a truth pledge affects lying costs or makes intrinsic justification more difficult, it will also effectively reduce lying in an online experiment. Second, we apply truth pledges to a gain frame, under which participants can lie to get a higher payoff, and to a loss frame, under which participants can lie to keep a larger share of their payoff. Grolleau et al. (2016) find that participants are more likely to cheat in a self-grading experiment if the payoff is presented in a loss frame than in a gain frame and interpret this as a consequence of loss aversion where the disutility from losing part of the payoff outweighs the costs of cheating. The result is supported by Schindler and Pfattheicher (2017) who apply gain and loss frames to the coin-toss task and the roll-a-die task. While the effect of loss aversion on lying behavior seems to be in line with the idea that the desire of avoiding losses makes not lying too costly, it is unclear how truth pledges are affected by loss framing. If a loss frame only causes a higher disutility from honesty (if honesty leads to a loss), the effect of truth pledges does not necessarily differ across the gain and the loss frame. In this case it is more likely that it reduces lying in both frames similarly but the decrease starts from a higher level of lying in the loss frame. However, Camerer et al. (2008) argue that unethical behavior is more common under a loss frame because loss framing induces a feeling of entitlement. Being put in a situation where they might lose something, people feel entitled not to lose and thus, they are better able to justify their unethical behavior. If loss aversion makes it easier to justify one s actions, this could offset the increased need of justification under a truth pledges. In this case truth pledges are less effective under a loss frame.

6 Design For this study we elicit honesty preferences of participants in ten decision rounds. While a one-shot experiment gives an indication on the prevalence of lying in the sample, an experiment with multiple rounds additionally allows us to identify extreme and partial liars (Conrads and Lotz, 2015). The economic literature on lying mainly includes two established tasks to elicit honesty preferences in isolation without other interfering preferences. In coin toss experiments participants are asked to flip a coin and to report the side that landed on top (e.g. Cohn et al., 2014a, 2014b; Houser et al., 2012). The payoff of participants depends on the side of the coin that participants reported to lie on top and as the toss is done privately, participants can misreport the outcome. A similar approach is taken in die roll tasks where the random outcome is generated by a privately rolled die and the payoff of participants is determined by the number the die is showing (Fischbacher and Föllmi-Heusi, 2013). Both methods provide certain challenges when conducting them online. As participants are all by themselves at the time of the experiment and not under supervision of an experimenter, it cannot be ensured that they indeed make use of the randomization tool. If no coin or die is easily available, participants could resort to deciding on a possible outcome in their mind. In a one-shot decision this is less problematic but over ten rounds the attempt of participants to randomize by themselves can lead to considerable distortions. First, people are known to be biased randomizers who tend to report a deficit of repeats of the same number (Wangenaar, 1972; Figurska et al., 2008; Jokar and Mikaili, 2012; Bains, 2008). Second, randomizing in the head also imposes considerable cognitive load on participants because when generating a random sequence humans try to make the sequence appear random and thus, recall all prior draws before making another one (Jokar and Mikaili, 2012; Bains, 2008). Imposing cognitive load over the course of the whole experiment has been shown to influence lying behavior (Shalvi et al., 2012). Other proposed task to elicit honesty preferences are mind-games, in which participants are ask to make a decision in their mind which will determine their payoff (Jiang, 2013), and self-grading tasks which give participants the option to cheat on the outcome of a real-effort task by letting them grade their own work in private (Mazar et al., 2008). While the former bears the same challenges concerning randomization as explained above, the latter does not elicit honesty preferences in isolation but in connection to the own effort. Houser et al. (2012) show that frustration in a preceding task increases lying in a subsequent lying experiment. In order to avoid the challenges presented by established tasks we design a new elicitation method which requires no additional tools or randomization by participants. In the Even or Odd -task participants first receive instructions to think of familiar numbers. The familiar numbers include birthday and age of relatives and friends, house numbers, digits of telephone numbers or the number of letters in a word. Second, participants are asked to sum the numbers they just thought of. This generating process is intended to give participants as little as possible control over the final number that is calculated and to avoid a personal connection participants might have to a certain number. Even if the originally thought of number (e.g. a birthday) holds a strong personal connection, the final sum will be disconnected from this. Similarly, even if participants think about the familiar numbers with the payoff in mind, it is more

7 demanding to recall familiar numbers that also add up to a desired number. At the end of the generating process participants are asked to keep in mind whether the generated number was even or odd. An example for the number generating process is shown in Figure 1. The particular familiar number that has to be recalled is different for all the ten rounds. Figure 1: Number generation in "Even or Odd" -task On a new screen participants are informed on whether an even or odd number leads to a higher payoff in this round. Which of the two result in a higher payoff is determined randomly for every round. On the same screen, participants then report whether their number was even or odd. For this study we applied the Even or Odd -task to a 2x2 experimental design which included a loss frame treatment with (PLEDGE_loss) and without (BASE_loss) a truth pledge and a gain frame with (PLEDGE_gain) and without (BASE_gain) a truth pledge over ten rounds of which one was paid out. In the loss frame, participants were informed in each round that they have an initial budget of 3 which they could keep if their number corresponded to the requirements of a high payment. Otherwise, 1.50 were deducted from their initial budget. The gain frame had an initial budget of In case of a winning number participants received 1.50 extra. The truth pledge was implemented at the very beginning of the experiment via an extra screen with the following text: By continuing from this page with the Next -button, I agree to tell the truth during this experiment. In addition to highlighting the importance of truth, this screen also linked the statement of telling the truth to a particular action, namely pressing the Next -button. After completing the ten rounds, participants filled out a questionnaire on demographics and decided whether they wanted to receive their payment via a bank transfer or via the online payment provider Skrill. The payment was made within two days after the experiment. In addition to this questionnaire participants also answered related questions from the World Value Survey. Specifically, questions V24 and V56 on trust and questions V198-V202 on minor offenses were asked. The experimental study was conducted with students recruited from the subject base of the online experimental platform GlobaleXperimentalPlatform (GXP) and implemented with the open-source and online software otree (Chen et al., 2016). The participants received an invitation to the experiment

8 which they could open via their personal profile on GXP. Each session was available for a period of several hours to give participants the opportunity to participate at a time that was convenient to them. The software assigned participants alternatingly to each treatment. In total 484 students participated in the experiment of which 88% finished all ten rounds which took approximately 10 to 15 minutes. The sample included 70% Dutch students and 59% female students with an average age of 21.3 years. The students came from a range of study programs but in a majority from an economics or business program (34%) or a social sciences program (30%). Table 1 provides summary statistics for all four treatments. The decreasing numbers of observations indicate participants dropping out after the main task and before the questionnaire on demographics. Number of high payoffs claimed BASE_loss PLEDGE_loss BASE_gain PLEDGE_gain Mean Obs Mean Obs Mean Obs Mean Obs (2.0211) (1.7732) (1.9126) (1.7560) Demographics: age (3.4380) (4.2085) (2.1999) (4.0307) female economics social Results Table 1: Summary statistics with standard errors in parentheses The Even or Odd -task revealed significant lying across all treatments. Figure 2 shows the share of high payoffs that were claimed in each treatment. This includes all 10 decisions of all participants who finished the 10 rounds of the experiment. If participants answered honestly, we would expect a high payoff in 50% of the cases because the number generating process leads to even and odd numbers with equal probability. The shares of high payoffs displayed in Figure 2 are all significantly higher than 0.5 (pvalues<0.0001, Binomial test). The data further shows that the truth pledge treatment reduced lying in both frames and in the whole sample. For the total sample the share of high payoffs drops significantly from 66.5% to 62.9% (p-value=0.0158, two sided score test). The decrease in high payoffs from the truth pledge is significant at a 10% level and of similar magnitude in both frames (loss frame: p-value=0.0789, gain frame: p-value=0.0976, two sided score test). However, the results from this experiment reveal no significant difference between lying behavior in the loss and gain frame. The share of high payoffs is neither in the base treatments (p-value=0.6711, two sided score test) nor in in the truth pledge treatments (p-value=0.6637, two sided score test) significant different the gain and loss frames.

9 Percentage of high payoffs claimed 75% 70% 65% 60% 55% 50% 45% BASE PLEDGE BASE_loss PLEDGE_loss BASE_gain PLEDGE_gain Figure 2: Share of high payoffs by treatment with exact binomial proportion confidence intervals. Following the procedure of Houser et al. (2012) we can also calculated the share of misreported answers. Under the assumption that participants only misreport to their advantage, the number of high payoffs h is given by: h = m 1 + (1 m) 0.5 where m is the ratio of misreported answers which means that participants observed a number leading to a low payoff but claimed a high payoff instead. Thus, the share of misreported answers is the share of lying: If people lie they report the high payoff with probability 1 and if they are truthful they get the high payoff with probability 0.5. Solving for m, we find 33% of misreported answers in the base treatment (32% in the loss frame and 34% in the gain frame) and 26% of misreported answers under the truth pledge (25% in the loss frame and 27% in the gain frame). We further study the effect of the truth pledge in a regression analysis. Table 1 shows the results of a Probit model on the probability that a high payoff was claimed. The models presented in Table 1 include all 10 decision points of the participants who finished the ten rounds. The first model includes the full sample and shows a significant negative effect the pledge. Among the control variables, only economics students show to have a significant higher probability to reach a high payoff. This is in line with earlier literature that found economics students to lie more than students of other programs (López-Pérez and Spiegelman, 2012). We also control for variables of the Even and Odd -task. While the reporting of high payoffs is stable over rounds, we find a significantly negative effect of the variable odd which captures whether the odd number leads to a high payoff. This negative effect means that participants were less likely to report a high payoff if the requirement for a high payoff was an odd number. In the following two models we split the sample along the loss and the gain frame. In both frames the truth-pledge reduces the probability of reporting a high payoff significantly. The effects of studying an economics program and of the high payoff being awarded for an odd number are similar across frames.

10 However, the gender effect is positive and significant for the gain frame but not for the loss frame which means that female participants reported significantly more high answers than male participants in the gain frame. 2 Dependent variable: high payoff = 1 Full sample Loss Gain odd *** *** ** (0.0148) (0.0198) (0.0223) round (0.0023) (0.0032) (0.0034) pledge *** ** ** treat_loss (0.0177) (0.0236) (0.0265) (0.0176) female ** (0.0206) (0.0272) (0.0305) age (0.0022) (0.0029) (0.0034) economics 0.102*** 0.142*** ** (0.0221) (0.0307) (0.0314) social (0.0230) (0.0301) (0.0348) Observations Wald Χ Prob > Χ *p<0.10, **p<0.05, ***p<0.01 Average marginal effects of Probit model with robust standard errors corrected for clustering on subject level Table 2: Probit regression of the probability the high payoff = 1 The gender difference in the two frames becomes even clearer when comparing female and male participants in a two-sided score test for proportions. In the overall sample female participants report 64.4% of high payoffs without the pledge and 62.8% with the pledge treatment. For male participants the shares are 65.2% and 63.1% respectively. Neither in the base treatment nor in the pledge treatment the gender difference is significant (p-value=0.603 and p-value=0.883 respectively, two-sided score test). However, when we consider gender by frame we see that gender differences strongly depend on the frame. Figure 3 summarizes the share of high answers for female and male participants in the two frames but across the base and the pledge treatment. The Figure shows that female participants reported less high payoffs than men in the loss frame but more in the gain frame. Both of these gender differences are significant (p-value=0.01 and p-value= respectively, two-sided score test). 2 The results reported in Table 2 are robust when including the items of the World Value Survey. However, as the questionnaire for these items was conducted right after the main task we cannot exclude the possibility they were affected by how the participants answered the main task. Therefore, no causal relation should be concluded from any correlations.

11 Percentage of high payoffs claimed 72% 68% 64% 60% 56% 52% female_loss female_gain male_loss male_gain Figure 3: Share of high payoffs by frame and gender with exact binomial proportion confidence intervals Besides the share of high payoffs, we consider the total number of high answers for each participant. Table 2 summarizes the regression results which support the earlier findings. The truth pledge significantly reduces the number of high answers and being in an economics study program significantly increases the number of high payoffs. The truth pledge reduces the number of high answers by half a high answer on average but being in an economics program increases the number of high answers by about one full answer on average. The size relation of these two effects is comparable to the earlier results of Table 1. Different from the analysis on the answer level, the sum of rounds in which the odd number was rewarded with a high payoff has no significant effect on the total number of high answers. Finally, we ask who is affected by truth pledges. Is lying reduced among all participants or are particular types of liars more affected by the pledge than others? Figure 4a shows the distribution of the number of high payoffs for the base and the pledge treatment and in comparison to a binomial distribution which would be expected if participants answered truthfully. The base treatment is clearly shifted towards higher number of high payoffs when compared to the binomial distribution. Five or less high answers appear less frequently than expected while six or more high payoffs appear more frequently and 10.7% of participants report ten high answers. The high share of ten high payoffs is caused by extreme liars that always report a high payoff. The high shares of payoffs between six and nine are made up by participants who lie but not optimally. The distribution in the pledge treatment shows a similar shift towards higher payoffs. When comparing it to the base treatment, we see a decrease in the share of participants with nine or ten high payoffs but an increase in the share of participants reporting seven or eight high payoffs. This shows that the pledge reduces lying especially among extreme liars who report a high payoff for almost all rounds. Figures 4b and 4c show that this pattern is the same across both frames.

12 Percentage Dependent variable: number of high payoffs Full sample Loss Gain sum_odd * (0.0573) (0.0768) (0.0946) pledge ** ** * (0.178) (0.2396) (0.2708) loss frame (0.177) female ** (0.206) (0.2704) (0.3054) age (0.0224) (0.0285) ( ) economics 1.005** *** 0.608* (0.219) (0.3015) (0.3134) social (0.239) (0.3255) (0.355) _cons 6.740** ** ** (0.603) (0.8184) (0.8619) Observations R *p<0.10, **p<0.05, ***p<0.01 Results of OLS regression with robust standard errors Table 3: Regression analysis of total number of high answers Figure 4a: Distribution of the number of high answers for base and pledge treatment; comparison to binomial distribution 30% 25% 20% 15% 10% 5% BASE PLEDGE binomial 0% Number of high payoffs

13 Percentage Percentage Figure 4b: Distribution of the number of high answers for base and pledge treatment in loss frame; comparison to binomial distribution 30% 25% 20% 15% 10% BASE_loss PLEDGE_loss binomial 5% Figure 4c: Distribution of the number of high answers for base and pledge treatment in gain frame; comparison to binomial distribution BASE_gain PLEDGE_gain binomial 30% 25% 20% 15% 10% 5% 0% % Number of high payoffs Number of high payoffs This graphic analysis is confirmed by a quantile regression of the effect of the pledge on the total number of high answers per participants. The quantile regression model as introduced by Koenker and Bassett (1978) defines the θth regression quantile as the solution to the following minimization problem: min b R K[ t {t:y t x t b} θ y t x t b + (1 θ) y t x t b t {t:y t <x t b} ]. In this formalization the dependent variable is denoted by y t, the independent variables by x t and b is the vector of parameters to be estimated. For this analysis we estimated the quantile regression of Model 2 in Table 2 for θ {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}. The graph in Figure 5 plots the estimated b coefficient for the independent variable pledge in all ten deciles. As can be seen, the truth pledge significantly reduces the upper three deciles.

14 Figure 5: Effect of pledge on number of high answers in repeated quantile regression with bootstrap confidence interval Conclusion In this study we analyse the effect of truth pledges on lying behaviour in an online experiment with 484 participants. Our results show that truth pledges significantly reduce lying in an anonymous online setting and that this effect is independent of the frame in which the task is presented. The fact that we find no significant differences in the effectiveness of the truth pledge between the loss and the gain frame seems to indicate that loss aversion has no consequence for the perception of truth pledges. These results illustrate a great potential for truth pledges to be applied to a range of settings in which lying is to be discouraged. However, our results raise important questions for future research. In the analysis on the decision level we find a robust effect of the task variable odd which indicates whether an odd number led to a higher payoff. This strong negative effect is to be interpreted in the way that the probability of reporting a high payoff is lower when the odd number was the winning number. We think that for the results presented here this effect can be neglected because all results found on the decision level are confirmed by the analysis on the participant level where this variable has no effect. Nonetheless, the strong significance of the effect calls for further replications of the task to determine whether the effect is unique to this study or is reoccurring. We apply a truth pledge to a loss and gain frame because Grolleau et al. (2016) find the frame to be an important driver of cheating behaviour. On the contrary, we find no difference in lying behaviour across frames and therefore, no effect of loss aversion on lying behaviour. While we have no immediate explanation for this difference, future research will benefit from exploring the differences between the two studies to get a better understanding on the type of situations under which loss aversions leads to a decrease in honesty. The two studies differ in the type of the task as Grolleau et al. (2016) use a self-

15 graded effort task while this study depends on a randomization process in which participants have to be lucky to honestly receive a high payoff. Further, the stakes in Grolleau et al. (2016) were considerably higher than in the present study and thus, might have amplified the effect of the loss frame. Lastly, the two studies were conducted in a laboratory and online respectively. The different behaviour in the loss frame might also have been driven by the different degrees in anonymity. Finally, we find opposite gender effects on lying in the two frames. Women seem to lie less in the loss frame but more in the gain frame. This result is somewhat puzzling as earlier literature is reporting a weakly negative effect for women (Conrads et al., 2013; Friesen and Gangadharan, 2013) or no gender effect at all (Childs, 2012; Muehlheusser, 2015). While the loss frame is line with these results as women are lying less in this treatment, the gain frame is at odds with those earlier results as no other study that we are aware of reports more lying for women. The only exception are white lies which are more frequently told by women (Ward and Beck, 1990). However, the nature of white lies is very different from lying in our task because white lies do not create an advantage for oneself but benefit someone else. Grolleau et al. (2016) find women to cheat less in both frames but their female participants are less sensitive to the framing which results in a lower gender difference in the gain frame. As such, our results point in the same direction but are more extreme than the results of Grolleau et al. (2016). A possible explanation for our observation might be the online setting in which the experiment was conducted. Conrads and Lotz (2015) find no positive effect of women on lying in general but when comparing the results of a laboratory setting to the results of an identical online experiment, they find women to more frequently report the highest possible payoff. Among women in the online setting there are significantly more extreme liars than among men. Because this is not true for partial liars, the overall effect for women is not significantly positive. However, taken together with the results of this study, this indicates the need to analyse gender differences in lying behaviour in varying settings and frames.

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