Predicting norm enforcement: the individual and joint predictive power of economic preferences, personality, and self-control

Abstract

This paper explores the individual and joint predictive power of concepts from economics, psychology, and criminology for individual norm enforcement behavior. More specifically, we consider economic preferences (patience and attitudes towards risk), personality traits from psychology (Big Five and locus of control), and a self-control scale from criminology. Using survey data, we show that the various concepts complement each other in predicting self-reported norm enforcement behavior. The most significant predictors stem from all three disciplines: stronger risk aversion, conscientiousness and neuroticism as well as higher levels of self-control increase an individual’s willingness to enforce norms. Taking a broader perspective, our results illustrate that integrating concepts from different disciplines may enhance our understanding of heterogeneity in individual behavior.

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Notes

  1. 1.

    We analyze norm enforcement behavior self-reported in the laboratory instead of actual norm enforcement behavior in the laboratory or the field. There is lots of evidence, on risky or intertemporal choice, for example, that self-reports are well aligned with incentivized decisions in experiments and good predictors of actual behavior in the field (e.g., Dohmen et al. 2011; Falk et al. 2016; Vischer et al. 2013). Moreover, findings in experiments about cheating, for instance, are consistent with choices in the field (e.g., Dai et al. 2016; Potters and Stoop 2016).

  2. 2.

    We use the Grasmick et al. (1993) scale as opposed to other also well-established scales for measuring self-control (Tangney et al. 2004 or Rosenbaum 1980) since it is the only scale that specifically conceptualizes self-control as a predictor of norm violations. Already by the year 2000, it had been employed by more than 40 studies (see the meta-analysis by Pratt and Cullen 2000).

  3. 3.

    Borghans et al. (2008) provide a detailed description of research on the development of the Big Five.

  4. 4.

    See, for example, Almlund et al. (2011) for a more extensive description of the Big Five.

  5. 5.

    On the screen, the response categories were always displayed in reversed order, i.e., [5] at the top and [1] at the bottom but without numbers attached to them.

  6. 6.

    In 1999, the mean (standard deviation) for avoiding a fare in public transport is 2.79 (2.21) and 2.74 (2.22) for tax evasion. In 2008, the corresponding numbers are 2.58 (2.10) for fare dodging and 2.28 (1.96) for tax evasion. These numbers are quoted from Douhou et al. (2011) who additionally find similar results for a representative sample of the Dutch adult population in 2008.

  7. 7.

    See Becker et al. (2012) or Burks et al. (2015) as examples for similar empirical approaches.

  8. 8.

    For clarity and conceptual reasons, Table 2 does not display correlations between the different Big Five personality traits. They are assumed to be independent factors by construction and their pairwise Pearson correlations are indeed small (i.e., always below 0.3), in 8 out of 10 cases even below 0.1 and not significant. The exceptions are significant correlations between agreeableness and extraversion (positive) and between neuroticism and extraversion (negative). In contrast, the locus of control is a further personality trait that has originated outside the Big Five taxonomy and is significantly correlated with extraversion (positive) and neuroticism (negative), see Table 2.

  9. 9.

    The explained share of the overall variation may be considered relatively low. However, comparing the Adjusted R2 from our Table 6 to those found in Burks et al. (2015), for example, there is no stark divergence.

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Acknowledgements

Financial support from SFB-TR 15 that did not influence study design, data analysis or interpretation. We gratefully acknowledge helpful comments from Fabian Kosse and two anonymous reviewers.

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Correspondence to Tim Friehe.

Appendices

Appendix 1: More information on data collection

The sequence of each session of our experiment was as follows: introductory task—decision 1—decision 2—survey questionnaire. We used a 2 × 2 design. The introductory task was aimed at inducing ego-depletion or no ego-depletion. Subjects then made one decision in a take game and one in a risky investment task. The order of the two decisions varied across sessions. Further details of the experiment are described in Friehe and Schildberg-Hörisch (2017). In our analysis in the main part of the paper, we use all 180 participants’ responses to the survey questionnaire.

In order to document that the variables we use in our analysis are not affected by treatment variations in the introductory task or by order effects concerning decisions 1 and 2, Table 7 displays results of a Kruskal–Wallis test for each variable.

Table 7 Results of Kruskal–Wallis tests

Results in Table 7 show that we cannot reject the hypothesis that data from the four different experimental treatments are drawn from the same population with p < 0.05 for any variable. For most variables, p values are substantially larger than 0.05. To judge the overall result of Table 7, one should keep in mind that if all 13 variables were statistically independent and each of the four treatment groups was drawn from the same underlying population, we would still expect to reject 5% (about 1) of the hypotheses at the 5% level.

As a further robustness check we run the same regressions as in Tables 3, 4, 5 and 6 with treatment dummies as additional covariates (the omitted baseline category is ego-depletion and risky investment task played before take game). Compared to our baseline results reported in Tables 3, 4, 5 and 6, the results with treatment dummies as additional controls are stable, qualitatively very similar, and only 1 out of 12 treatment dummies is significant. The results are available from the authors upon request.

Appendix 2: Additional tables

Table 8 Reactions to an acquaintance’s norm violation (in percentages, N = 180)

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Friehe, T., Schildberg-Hörisch, H. Predicting norm enforcement: the individual and joint predictive power of economic preferences, personality, and self-control. Eur J Law Econ 45, 127–146 (2018). https://doi.org/10.1007/s10657-017-9556-5

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Keywords

  • Norm enforcement
  • Economic preferences
  • Personality traits
  • Self-control

JEL Classification

  • K42
  • D81
  • D90
  • C21
  • Z02