What does a young cheater look like? An innovative approach

Personality traits matter. We could summarize with these simple words a vast\r\nliterature in social sciences dedicated to explaining the behavior of individuals,\r\nacting alone or within societies. Examples abound in several economic contexts spanning from the intention to become a social entrepreneur (Nga and\r\nShamuganathan, 2010), to management of household finances (Brown and\r\nTaylor, 2014), to labor market outcomes (Fletcher, 2013). Similarly vast is the\r\nliterature on cheating, justified by the profound economic and social consequences of such behavior.\r\nIn this chapter, we link these two growing strands of literature by studying\r\nthe relation between experimental measures of cheating behavior among adolescents and personality measures obtained through a questionnaire.\r\nThe first consistent finding from the experimental literature on cheating is\r\nthat some (not all) individuals are dishonest, i.e., when facing the opportunity to\r\nlie in order to extract a gain (with the lie typically concerning the result of a dice\r\nroll, or a fair coin toss), a sizable share of individual do so: that is, the proportion of individuals reporting a win usually exceeds the objective probability of\r\na win, while still being smaller (often considerably smaller) than one (Abeler\r\net al., 2018). This general result overshadows, however, a huge heterogeneity in\r\nthe observed individual cheating behavior. Most individuals are willing to cheat\r\nonly a little (Shalvi et al., 2011), some entirely refrain from lying, while others lie to the maximum possible extent (Fischbacher and Föllmi-Heusi, 2013).\r\nThis observed heterogeneity, coupled with the fact that individuals who cheat\r\nin the lab tend to cheat also in the field (Cohn and Maréchal, 2016), raises the\r\nquestion of which characteristics of an individual’s personality influence her\r\ndecision to lie.\r\nAs a precondition for any discussion, we all know that people care about\r\ntheir self-image and struggle to preserve it (Mazar et al., 2008). This struggle\r\nimposes a cost, of psychological nature, to the cheater, which changes according\r\nto the context. As a matter of example, we know that the decision to lie implies\r\na different psychological cost when people have to report their immoral intentions before acting (Jiang, 2013), when acting dishonestly hurts (or benefit) others (e.g., Fischbacher and Föllmi-Heusi, 2013), when temporally distancing the\r\ndecision task from the payment of the reward (Ruffle and Tobol, 2014), when\r\nindividuals are under scrutiny (Ostermaier and Uhl, 2017; Pierce et al., 2015),\r\nwhen they act alone or in groups (Kocher et al., 2018), and when they have a\r\npotential accomplice (Barr and Michailidou, 2017).\r\nOnly recently some papers have considered the importance of personality\r\ntraits in cheating behavior. In a recent contribution, Pfattheicher et al. (2018)\r\nuse economically incentivized cheating paradigms (a dice-rolling paradigm and\r\na coin-toss paradigm) to show that, in line with previous literature (Hilbig and\r\nZettler, 2015; Kleinlogel et al., 2018), the basic personality trait of HonestyHumility from the HEXACO personality model (Ashton and Lee, 2007) is\r\nnegatively related to cheating behavior. That is, they identify a relation between\r\ncheating and personality which goes beyond the dark personality traits of narcissism, Machiavellianism, psychopathy, and sadism already studied by Jones\r\nand Paulhus (2017)—no effect is found instead when a third-party scrutiny is\r\nsimulated by presenting the subjects with stylized watching eyes. Interestingly,\r\npersonality traits have different effects on different types of lies. Jonason et al.\r\n(2014) show that while Machiavellianism is related to white lies, narcissism is\r\nrelated to lying for self-gain, whereas psychopathy is related to telling lies for\r\nno reason. In a companion paper, Baughman et al. (2014) show that psychopathy predicts scholastic cheating. In another recently published paper, Heck et al.\r\n(2018) address the question of power and sample size by exploiting the richness\r\nof 16 studies (N= 5002), assessing dishonest behavior in an incentivized, oneshot, cheating paradigm. While confirming the negative correlation between\r\ncheating and Honesty-Humility, which was independent of other personality,\r\nsituational, or demographic variables, they found that one other trait only from\r\nthe “Big Five” (Agreeableness) was (negatively) associated with unethical\r\ndecision-making, although the strength of the relation is much lower than with\r\nthe Honesty-Humility trait.\r\nAlthough cheating, lying, and deception are diffused behaviors both in the\r\nadult and in the young population, there is relatively limited evidence of the\r\ndeterminants of cheating among children and adolescents in the economic literature. Some relevant exceptions are Bucciol and Piovesan (2011), GlätzleRützler and Lergetporer (2015), Maggian and Villeval (2016), Korbel (2017),\r\nBattiston et al. (2018), Cadsby et al. (2019); see Heyman et al. (2019) for a\r\nrecent review of this stream of research.\r\nIn this chapter we use data gathered from an experiment conducted with scout\r\ngroups from Trentino-Alto Adige, a region in Northern Italy, during their summer camps in August 2017. The experiment, employing a revised version of the\r\nfair coin toss paradigm proposed by Bucciol and Piovesan (2011), was followed\r\nby a rich questionnaire including, beyond standard demographic questions, a\r\ndetailed self-assessment of risk aversion, “Big Five” personality traits, level of\r\ntrust in other people and propensity to break the rules. In our empirical analysis,\r\nwe employ a principal component analysis (PCA, henceforth), a dimensionality\r\nreduction technique aimed at capturing common moments in the data, to gauge\r\nthe extent to which different personality traits might influence the propensity to\r\ncheat. We then reanalyze the decision to cheat by using decision tree classifiers,\r\na very popular technique in the machine learning literature, which also achieve\r\nthe aim of dimensionality reduction, but focusing on the interaction between\r\nvariables rather than on (linear) common moments across them.\r\nOur results suggest that, while risk propensity is not a strong overall predictor of cheating behavior, self-confidence is irrespectively of the beneficiary of\r\nthe payment being the individual or the patrol. The use of decision tree classifiers confirms these results, supports the validity of the PCA approach, and\r\nfurther suggests that, among less self-confident subjects, risk propensity does\r\nexplain a larger propensity to cheat.\r\nThe remainder of this chapter is organized as follows: Section 2 presents the\r\ndata gathered from the experiment, Section 3 presents the empirical analysis,\r\nwhile Section 4 concludes.