Integrating theories of law obedience: How utility-theoretic factors, legitimacy, and lack of self-control influence decisions to commit low-level crimes

Abstract

We conducted two studies using a sample of students (Experiment 1, N=84) and the general public (Experiment 2, N=412) to assess the relative and unique effects of factors suggested by three major theories of law obedience: a utility-theoretic deterrence theory (Becker, 1968), the general theory of crime (Gottfredson & Hirschi, 1990), and the legitimacy model (Tyler, 1990). Six different types of low-level crime were considered. The probability of breaking the law increases with factors predicted by each of these theories, namely detection probability, expected fine, self-control, and legitimacy. All four factors uniquely contribute to predicting law obedience, effects are mainly additive, and no stable interaction effects are observed. The relative influence of the investigated factors varies between types of low-level crimes. This indicates that an integrative theory of why people obey the law needs to consider factors from various theories and allow for the relative influence of factors to differ among crimes. We observe systematic deviations from a basic utility-theoretic approach to law breaking. Individuals’ tendency to obey the law is much higher than predicted by an approach taking into account detection probability, expected fines, and benefits only. The robust effects of interindividual differences concerning legitimacy and self-control as well as the finding that the tendency to break the law decreases with increasing benefit of the crime also conflict with a basic utility-theoretic approach to law-obedience.

1 Introduction

Understanding the factors that lead individuals to obey the law or to decide to commit a crime is important for policy-making and society. Accordingly, a large body of empirical research exploring the mechanisms underlying compliance with legal rules has been conducted in different academic areas, including psychology (e.g., Martin & Cohn, 2004; Tyler, 1990), criminology (e.g., Nagin, 1998; Nagin & Paternoster, 1993; Pratt & Cullen, 2000; Pratt, Cullen, Blevins, Daigle & Madensen, 2006), and economics (e.g., Becker, 1968). The classic utility-theoretic approach to crime (Becker, 1968, von Neumann & Morgenstern, 1947), in this context commonly termed deterrence theory, assumes that individuals behave rationally and break the law only if it pays to do so according to a comparison of utilities for the different behavioral options. Therefore, the probability of breaking the law should increase with the potential benefit from breaking the law, and it should decrease with increasing detection probability and severity of punishment (i.e., the expected fine or negative utility of incarceration).

The assumption underlying Becker’s approach is that crime is not linked to fixed attitudes or personality structures. Instead, people become criminals “because their benefits and costs differ” (Becker, 1968, p. 176). Despite its merits of being a “straightforward explanation of (and solution to) crime” (Pratt et al., 2006, p. 367), there is mounting evidence pointing at limitations of deterrence theory. On the one hand, behavioral decision research has demonstrated systematic biases in judgment and decision-making in general (e.g., Tversky & Kahneman, 1974). On the other hand, a meta-analysis (Pratt et al., 2006) has shown that the effect sizes of detection probability and particularly of severity of sanctions influencing criminal behavior are medium to small and leave a lot of variance in behavior unexplained (details below).

One of the most influential psychological approaches, which we will refer to as the legitimacy model, spotlights the link between legitimacy and law obedience (Tyler, 1990). According to Tyler, the perceived legitimacy of legal institutions and authorities (including law-enacting authorities, courts, and police) is a crucial predictor of an individual’s law obedience. Legitimacy is assumed to produce a feeling of obligation to follow legal authorities (Sunshine & Tyler, 2003a, 2003b; Tyler, 2006), and recent publications emphasize effectiveness, lawfulness, and both distributive as well as procedural justice as core determinants of legitimacy (Tankebe, 2013). Legitimacy is assumed to generate moral grounds on which authorities act (Beetham, 1991; Tankebe, 2013), and thereby becomes a justification for them to hold power (Bottoms & Tankebe, 2012). People comply with legal authorities and thus obey the law “simply because it is the right thing to do” (Jackson, Bradford, Hough, Myhill, Quinton & Tyler, 2012, p. 3). This implies that legitimacy goes beyond rewards and punishments that are central to deterrence theory. Tyler (2006) acknowledges that it might be possible to govern based on coercive power; however, he argues that legitimacy facilitates ruling and makes it more (cost-)effective.

A third commonly used approach, developed and popularized mainly in criminology, is the general theory of crime (Gottfredson & Hirschi, 1990). The finding that people who engage in criminal activities also tend to exhibit activities that are rewarding in the short term, but signify dangerous in the long term (i.e., smoking, gambling), sparked off the idea that inter-individual differences concerning a lack of self-control could be linked to criminal behavior (Pratt & Cullen, 2000). In a nutshell, the general theory of crime postulates that people with low self-control are more likely to be delinquent when they encounter a criminal opportunity because they are unable to consider the potential long-term effects of their behavior (e.g., fines or imprisonment). According to this theory, most crimes are conducted without planning or preparation. In their original work, Gottfredson and Hirschi (1990) claim that self-control is the major (if not only) predictor of delinquency. This strong claim, however, has not been confirmed, and results indicate that “this theory, at best, has identified one mechanism that affects crime” (Grasmick, Tittle, Bursik & Arneklev, 1993, p. 24).

Although all three approaches have received empirical support, only a few studies simultaneously investigated their relative influences, focusing instead on selected bilateral comparisons (for details see below). Knowledge about trilateral dependencies, variation in the relative influence of these factors between types of crimes, as well as dependencies of self-control and legitimacy is largely missing. The current paper addresses this gap in the literature.

2 Previous Findings

2.1 Deterrence Theory

The factors postulated by deterrence theory have been investigated using multiple methodologies, including the recording of responses to virtual scenarios (vignettes), self-reported delinquency, and the analysis of field data. Vignette studies were used particularly often, which will also be the core measurements in the present paper. Individuals report their subjective probability of breaking the law for hypothetical situations, whereby specific factors such as detection probability and/or severity of punishment can be varied between subjects (Nagin & Paternoster, 1993). A meta-analysis (Pratt et al., 2006) summarized more than 200 effect sizes from 40 empirical studies that had investigated the effects of punishment severity and detection probability on the tendency of people to obey the law. The authors identified a medium strength effect of detection probability on law obedience (r _weighted=−0.33, p<.01; n= 107 data sets). This effect was found to be most predictive for white-collar and for mid-range crime. The effect of punishment severity, in contrast, was almost negligible (r _weighted=−0.027, p<.05; n=47 data sets), a finding that is in conflict with a core prediction of deterrence theory.

Furthermore, there is preliminary evidence that the probability of breaking the law increases with the (financial) benefit from crime. Two vignette studies that operationalized benefit in terms of the perceived pleasure from criminal behavior found it to be positively related to the probability of committing a crime (Nagin & Paternoster, 1993; Piquero & Tibbetts, 1996), as predicted by deterrence theory. Overall, deterrence theory and a classic utility-theoretic account seem only partially able to predict individual decisions to break the law.

2.2 Legitimacy Model

Numerous studies have empirically investigated the legitimacy model and established a link between legitimacy and law obedience (or cooperation with legal authorities) in various countries (Bradford, Huq, Jackson & Roberts, 2013; Brubacher, Fondacaro, Brank, Brown & Scott, 2009; Jackson et al., 2012; Huq, Tyler & Schulhofer, 2011; Levi et al., 2009; Nivette & Eisner, 2013; Reisig, Tankebe & Mesko, 2013; Sunshine & Tyler, 2003a, 2003b; Tankebe, 2013; Tyler, 1988, 1990; 2000, 2001, 2003, 2004, 2006; Tyler & Fagan, 2008; Tyler, Schulhofer & Huq, 2010). However, no comprehensive meta-analysis of the effect is available. In his seminal study of 1, 575 Chicago residents, Tyler (1990) established that people’s general and specific obedience of the law, measured in terms of self-reported delinquency regarding six types of low-level criminal activities, is influenced by legitimacy, measured using a 6-item scale. The zero-order correlation of this measure of legitimacy with overall compliance was r=0.22. The effect of legitimacy was found to have a partially independent effect on criminal behavior beyond deterrence factors (i.e., the likelihood to get caught and punished) and further control variables. Yet, the magnitude of the effect of legitimacy on breaking the law was reduced by 70% after controlling for these factors. The analysis did not identify interactions between legitimacy and other factors, and Tyler therefore concluded that the relationship between legitimacy and compliance was at best weakly affected by deterrence and further sociological control factors.

2.3 General Theory of Crime

A large body of research has investigated predictions of the general theory of crime, mostly using self-reported delinquency and attitudinal self-control measures (Pratt & Cullen, 2000). In the majority of these studies, the unidimensional 24-item scale for self-control by Grasmick et al. (1993) was used, which consists of six dependent subscales. The scale is presumably the “most carefully designed and valid measure of self-control” (Pratt & Cullen, 2000, p. 943), although the assumed factor structure was not consistently confirmed (Vazsonyi, Pickering, Junger & Hessing, 2001). Another study found that this scale by Grasmick et al. was a better predictor of delinquency than other measures for self-control (de Vries & van Gelder, 2013). To examine predictions of the theory, a meta-analysis summarized 126 effect sizes estimated from 21 empirical studies that investigated the influence of self-control on people’s delinquency (Pratt & Cullen, 2000). The estimated effect of self-control on delinquency was of medium size (r _weighted=.223, p<.01; n=82 data sets; only studies with attitudinal measures considered). A meta-analysis of 99 additional studies conducted between 2000 and 2010 found even larger effects (r _{cross-sectional}=.345 and r _longitudinal=0.415, Vazsonyi et al., 2017). Hence, the effect of self-control seems to be larger than the effect of punishment severity, but in most cases smaller than the effect of detection probability. The meta-analysis concluded that, even though self-control was a solid and strong predictor of delinquency, the theory clearly did not cover all aspects that drive criminal acts. A further meta-analysis assessed the effect of self-control on delinquency as published in 102 studies summarizing almost 1,000,000 data points (Engel, 2012). Engel concluded that the effect of self-control on delinquency was stable, but small.

Based on a sample of over 8,000 adolescents from four countries (the US, Switzerland, Hungary, the Netherlands), Vazsonyi et al. (2001) showed that the effect of self-control on the tendency to commit criminal acts generalizes over different nations and cultures. The influence of self-control was independent of age and gender in all countries. However, in line with Engel (2012), (lack of) self-control explained a small part of the variance in delinquency. Other empirical studies have confirmed the effect of lacking self-control on social norm violations (Gailliot, Gitter, Baker & Baumeister, 2012) and music piracy (Hinduja, 2012), and provided additional cross-cultural support for the theory (i.e., among South Koreans, Jo & Zhang, 2013; and among Hispanic youth, Vera & Moon, 2013). Furthermore, the interaction effect between neighborhood characteristics (or, more precisely, their levels of morality) and low self-control in predicting crime, as suggested by the original theory, was investigated and confirmed in a study conducted in Russia and the Ukraine (Zimmerman, Botchkovar, Antonaccio & Hughes, 2012).

2.4 Integrative Investigations and Analysis of Interactions

Similar to the approach by Tyler, described earlier, some studies have jointly investigated two of the three theories under consideration in the current paper. Specifically, there have been attempts to combine deterrence theory with either the legitimacy model (Tyler, 1990; Jackson et al., 2012; Tyran & Feld, 2006), or the general theory of crime (Grasmick et al., 1993; Nagin & Paternoster, 1993; Nagin & Pogarsky, 2001, 2003; Piquero & Tibbetts, 1996; Pogarsky, 2002; Tibbetts & Myers, 1999), but to our knowledge no study has investigated all three approaches simultaneously.

Interactions between self-control and deterrence factors have been occasionally observed in these studies, but results are equivocal. Some studies show that individuals with sufficiently high self-control react more strongly to deterrence factors than people with a lack of self-control (Nagin & Pogarsky, 2001; Piquero & Tibbetts, 1996; Pogarsky, 2002), as suggested by the General Theory of Crime. Other studies, however, found exactly the opposite effect, in that deterrence factors exerted a stronger influence on people who lack self-control (Hirtenlehner, Pauwels & Mesko, 2014; Pogarsky, 2007; Tittle & Botchkovar, 2005; Wright et al. 2004). Differences in the response scale might cause such conflicts (Wagenmakers et al., 2012).

In sum, all three theories have received empirical support, but a simultaneous investigation of multiple factors with the objective of advancing a more comprehensive framework of law obedience is still lacking, even though the importance of linking the existing theories has been highlighted (Pratt et al., 2006). Such an approach seems promising as a starting point to integrate findings from psychology, criminology, and economics into a coherent theory of law-abiding behavior.

3 Hypotheses

We aimed to investigate the impact of factors postulated by deterrence theory, legitimacy, and lack of self-control on the self-reported probability of breaking the law.

Regarding the influence of factors central to these theories, we tested:

H1a_DetPr : The probability of breaking the law decreases with an increasing probability of detection.

H1b_Ben : The probability of breaking the law increases with the financial benefit obtainable from delinquent behavior.

H1c_PunSev : The probability of breaking the law decreases with an increasing severity of the expected punishment in case of detection.

H2_Legit : The probability of breaking the law decreases with increasing legitimacy.

H3_Self-cont : The probability of breaking the law increases with lack of self-control.

More importantly, we investigate whether all factors make unique and independent contributions to explaining criminal behavior, as would be expected on theoretical grounds. However, as introduced above, research indicates that the effect of legitimacy is largely reduced in a simultaneous analysis including further controls and self-control has been shown to interact with deterrence factors. Therefore, we test the hypothesis:

H4_Indep: Legitimacy, self-control, and deterrence factors explain independent variance in the probability of the law being broken. Thus, the partial correlations for legitimacy and lack of self-control, as well as deterrence factors for predicting the probability of the law being broken, are no lower than the univariate correlations.

Furthermore, we tested for the following interaction effects:

H5a_{IE Legit}: We did not expect two-way interactions between legitimacy, on the one hand, and detection probability and expected fine, on the other hand, in predicting the probability of the law being broken.

H5b_{IE_Self cont}: But we did expect two-way interactions between lack of self-control, on the one hand, and detection probability and expected punishment, on the other, in predicting the probability of the law being broken.

Finally, we use an exploratory approach to investigate whether the relative effects of the proposed factors differ between types of crime and also hold when controlling for further previously investigated factors. Specifically, we use as control factors (1) BIG5-personality, (2) subjectively perceived severity of committing the crime, (3) expected severity of committing the crime by others, and (4) attitudes toward the criminal legal system (ATCLS; Martin & Cohn, 2004).

4 Experiments 1 and 2

We conducted two experiments and an additional pilot¹ using vignettes as the dependent measure for criminal behavior (cf. Nagin & Paternoster, 1993). Experiment 1 involved a student sample and Experiment 2 a larger sample from the general public. The materials and results for both experiments were very similar and therefore only results from an overall analysis will be reported.

Original materials and data of the pilot and the two main studies are also available at https://osf.io/bt9rc. Hypotheses were not pre-registered and power was not estimated, since the studies were conducted in 2011 before we started applying both practices in all our research. A post-hoc sensitivity analysis using G*Power (Faul, Erdfelder, Lang & Buchner, 2007) indicated that we still achieved an excellent power of 1-beta = .999, in the overall analysis for detecting small effects (f ²=.05; α =.05, one sided test, regression with 5 predictors).

4.1 Method

4.1.1 Subjects

For Experiment 1, 84 local residents (mainly students) from Bonn (59.5% female, mean age 24.5 years, SD = 4.75) were recruited from the local subject pool using ORSEE (Greiner, 2004). The first part of the study was run online and the second part was run as part of a larger battery that subjects took 60 to 90 min to complete. Individuals were paid on average 18 Euros (approx. USD 25.20) for their participation. For Experiment 2, subjects were recruited via a commercial online panel of individuals from the German general public, and the study took about 20 minutes. The company also took care of payment for the subjects. Participation was steered by fixed quotas, which were set up according to the distribution of gender and age in Germany’s population. A total of 412 (50.5% females, mean age = 47.4, SD = 15.54) subjects finished the questionnaire and were included in the analysis.² Twenty-four responses (Exp 1: 6, Exp 2: 18) from subjects who expected prison sentences (that were unrealistic for the considered low-level crimes) were excluded from the analysis (including them did not change any of the results).

4.1.2 Design

All subjects worked on six vignettes involving decisions concerning low-level crimes (i.e., free-riding on the train, illegal downloading, speeding, insurance fraud, illegal parking, and littering in a nature reserve). For two of the vignettes (free-riding and insurance fraud) detection probability and financial benefit were independently manipulated between subjects. Hence, we employed a 6 (scenarios for low-level crimes) x 2 (detection probability: high vs. low) x 2 (financial benefit from breaking the law: high vs. low) non-fully crossed mixed design with detection probability and financial benefit as between-subject factors.

4.1.3 Materials

Subjects indicated their tendency to break the law in all six vignettes. The vignettes were designed to tempt subjects to disobey the law. In the free-riding and the insurance fraud scenarios, financial benefit and detection probability were manipulated. These two vignettes, including the manipulations, read as follows (translated from German; translations of the other four scenarios can be found in Appendix A):

Scenario 1 (Free-riding)

“Imagine you are at a train station to board a train. A ticket costs [LOW FINANCIAL BENEFIT: 20€] [HIGH FINANCIAL BENEFIT: 100€]. Suddenly, you realize that you have forgotten your wallet at home. Hence, you do not have any money on you to buy a ticket. As you attempt to return home to pick up the wallet, the arrival of your train is announced. You are already running late and the next train is scheduled for 30 minutes later, so you would certainly be late for an important meeting. Instead of picking up the money, you could also try to free-ride on the train. [LOW DETECTION PROBABILITY: There is no conductor nearby, and until now you have never had to show your ticket.] [HIGH DETECTION PROBABILITY: When entering the train station, you walked past the conductor.]”

Scenario 4 (Insurance fraud)

“Imagine someone broke into your home and stole your belongings. A few days before the housebreaking, you had broken your fairly new laptop, which was worth about [LOW FINANCIAL BENEFIT: 750€] [HIGH FINANCIAL BENEFIT: 2, 500€]. According to an expert in an electronics retailer, the laptop cannot be fixed. Consequently, the laptop was worthless when it was stolen. However, since your laptop was sitting in its case on your desk, the burglars were unable to see that it was broken and stole it. You are filling in the damage report to your insurance, on which you have to list the stolen goods and their current value. According to your insurance policy, you have to list the actual value of the goods at the time they were stolen. [LOW DETECTION PROBABILITY: Commonly, with claims of such low value, it is not profitable for the insurance company to check the claims.] [HIGH DETECTION PROBABILITY: Lately, due to more frequent insurance frauds, the insurance company has been conducting many more checks on the claims, even if they are as small as this one.]”

Subjects’ legitimacy was measured using the six-item legitimacy of the law scale (Tyler, 1990) (range: 1–4). Lack of self-control was measured in terms of a scale introduced by Grasmick et al. (1993), consisting of 24 items on 6 subscales, which subjects answered on the same 4-point scale as the items on legitimacy. Note that higher values on this scale indicate lower levels of self-control. Appendix B shows both measures.

As exploratory and validation measure, subjects additionally completed a short scale measuring the general personality based on the standard five-factor model Big 5 consisting of 11 items (range: 1–7; Rammstedt & John, 2007) and a scale measuring attitude toward the criminal legal system (ATCLS; Martin & Cohn, 2004; shorted version in Experiment 2) (Appendix C). The studies included some more control measures (e.g., Exp 1: Raudenbush delinquency scale, Raudenbush, Johnson & Sampson [2003], scales measuring the perceived rule of law based on Carothers [1998], a self-reported delinquency scale by Tyler [1990]), which provided no further insights and are therefore not reported here.

4.1.4 Procedure

Subjects were instructed to imagine themselves as being in the situation of the scenarios. For each scenario, subjects indicated their probability of committing the crime by answering the neutrally phrased question: How likely are you to [show the critical behavior; e.g., enter the train without a ticket]? Answers were given on a scale from 0 (not likely) to 100 (very likely) using a slider, which we transformed into a probability score ranging from 0 to 1 for the analysis. On the next page, subjects indicated a binary decision concerning the critical behavior: Honestly, are you going to [show the critical behavior; e.g., enter the train without a ticket]?

Furthermore, subjects indicated their expected detection probabilities and fines for the criminal act. First, subjects were asked: “Please imagine that 1000 individuals in Germany [show the critical behavior; e.g., enter the train without a ticket]. How many of them get caught?” From the indicated frequency, we calculated the subjective detection probability, which was used in the analyses. Second, we measured the expected severity of punishment in case of detection. Subjects indicated whether they expected a prison sentence or a fine (in all cases fines should be expected according to German law). Then, subjects specified the punishment in terms of duration of incarceration (in days) or fine (in Euros, expected fine).

In Experiment 1, the experiment was split in two parts. The online part contained all sensitive measures for delinquency and breaking the law (i.e., the vignettes) and was completed prior to the lab part at home. A few days later, subjects were invited to the lab and finished the second part of the study, which contained the non-sensitive measures, such as the legitimacy and the lack of self-control scales. Data from both parts were combined using an anonymous code generated by the subjects to assure confidentiality of data. Completion of the first part was conditional for participation in the second part. In Experiment 2, subjects completed the whole study online at once. To reduce the overall duration for the online study, the number of control measures was reduced and the questionnaire was thereby substantially shortened.

4.2 Results

4.2.1 Descriptive Statistics

The assessed probability of breaking the law correlated highly with the binary decision to break the law (r = .82). All subsequent analyses are based on the continuous measure. There was substantial variation in the tendency to break the law among scenarios and subsamples (i.e., students vs. general public), and from our manipulations of benefit and detection probability in two of the scenarios (Figure 1). Insurance fraud was most likely to be committed, whereas littering in a national reserve was least likely. Students showed a substantially higher tendency to break the law in the considered low level crimes than persons from the general public. The manipulation of detection probability descriptively tended to decrease the probability of breaking the law as expected (Figure 1, middle and right panel). Surprisingly, our manipulation to increase the (sure) benefit from crime lead to less law breaking, contrary to a utility theoretic account.

Figure 1: The probability of breaking the law by scenarios. Error bars indicate standard errors.

The perceived probabilities of being caught were generally low (Md ≤ 11%), except for train free-riding and illegal parking (Appendix D, Table D1, column 2). Expected median fines ranged from 40 € (train free-riding) to 500 € (insurance fraud) (Appendix D, Table D1, column 2). The median expected value of the fines calculated from these values ranged from 0.40 € (littering) to 56 € (high benefit insurance fraud) (Appendix D, Table D1, column 3). Considering these expected values, deterrence theory predicts a crime rate of 94% in the scenarios with sure monetary gains (free-riding: gains of 20€ or 100€; insurance fraud: gains of 750€ or 2500€) but the rate is much lower (Figure 1) indicating effects of further factors.

The own perceived severity of crimes was highest for littering and medium to low for the other scenarios (Appendix D, Table D2, column 1). Severity perceptions were considerably higher in the general public sample as compared to the student sample. The ratings concerning how severe others would perceive committing the crime were lowest for downloading and highest for speeding (Appendix D, Table D2, column 2).

4.2.2 Hypotheses tests

To test our hypotheses we used multivariate random effects tobit regression analyses for the scenarios including manipulations (Table 1, column 1) and over all scenarios (Table 1, column 2) as well as univariate analyses (Table 1, column 3). The deterrence factors detection probability (measured and manipulated) and expected fine influenced the probability of breaking the law as predicted by deterrence theory. The tendency to commit a crime decreased with increasing detection probability (H1a_DetPr) and expected fine (H1c_SevPu). The effect of benefit ran significantly counter to the prediction of a utility theoretic account in that people’s tendency to break the law decreases with increasing sure benefits of the low-level crime, hence H1b_Ben had to be rejected. The coefficient is essentially unchanged (from −.08) when simultaneously controlling for potentially higher expected fines and detection probabilities for higher benefits (b[benefit] = −0.07, z = 1.97, p = .049, from Table 1, column 1).

Table 1: Regression models and univariate coefficients predicting probability to break the law in Experiments 1 and 2

Note. z statistics in parentheses. Results are from tobit mixed-effect regressions with random effect intercepts for subjects. Scenario dummies in model 2 also include controls for manipulations of scenarios. Constant omitted. Variables “det. prob high” and “benefit high” are centered to avoid correlations with their interaction term. det. prob h = high detection probability; ben, h. = high benefit.

* p < .05

** p < .01

*** p < .001.

In line with the predictions derived from the legitimacy model and the general theory of crime, legitimacy and lack of self-control predicted the probability to break the law beyond utility theoretic factors. Individuals with higher legitimacy and self-control³ were less likely to break the law supporting H2_Legit and H3_{Self_con}, respectively. The analyses (Table 1, columns 1–3) show that the effects of self-control, legitimacy, detection probability, expected fine and benefit are relatively independent. Effects do not change significantly when comparing coefficients from the univariate and multivariate analyses with all other factors, supporting H4_Indep (all Chi²(1) < 1.3, all p >.25), and the effects remain significant.

When adding the interactions of detection probability and expected fine with legitimacy or self-control to the regression model (column 2 + two-way interactions), none of the interactions reached conventional significance levels (all z < 1.70, all p > .089). This is in line with our hypothesis concerning legitimacy H5a_{IE_Leg}, but fails to provide support for the theoretically expected interactions with self-control (H5b_{IE_SelfC}). Hence, a lack of self-control does not lead to a weaker influence of deterrence factors contrary to a central reasoning of the general theory of crime.

4.2.3 Exploratory analyses for testing robustness and differences between crimes

In further exploratory analyses, we investigated whether (a) the effects of the factors influencing law obedience are robust to the inclusion of further control measures and (b) whether effects of the various influence factors are roughly equal for all crimes and therefore independent of contexts.

To test the robustness of our findings, we extended the overall model from Table 1, column 2 by including various control measures. Specifically, we included the personally perceived severity of the offense as well as the assumed severity perceived by others, a score for Attitude Towards the Criminal Legal System (ATCLS score; Martin & Cohn, 2004) and the five general personality factors extraversion, conscientiousness, neuroticism, agreeableness, and openness for experience (measured by the short 11-item scale; Rammstedt & John, 2007). All previously observed effects were little changed and remained significant, except for fine, which remained in the same direction but did not reach significance (analysis not reported). From the additional factors, only severity and severity of others had significant additional effects (details below in the analysis per scenario).

To analyze potential differences between crimes, we calculated separate univariate correlation and multivariate (tobit) regression analyses for each scenario (Table 2). The results concerning the main variables (expected fine, lack of self-control) were similar in univariate and multivariate analyses (but some differences and reversals concerning the control variables were observed).

Table 2: Univariate correlations and multivariate analyses by crime scenario

Note. Univariate results are Pearson product-moment correlations, multivariate coefficients are raw coefficients from tobit regressions including all listed factors simultaneously (constant not reported). The predictor ‘expected value of fine’ is not included in the tobit regression since it is calculated from the already included factors expected fine and detection probability. ATCLS refers to a score from the questionnaire Attitude Towards the Criminal Legal System.

* p < .05

** p < .01

*** p < .001.

Although the direction of the effects remained mainly in the same direction between crime scenarios, we found substantial variation in how strong the various factors influence the probability to break the law. These differences were also confirmed by respective significant interactions in an overall regression (comparing against the grand mean). Detection probability was particularly important for the decision to commit insurance fraud (p = .030). The effect of legitimacy was reduced for speeding (p = .002) and littering (p = .008) but increased for illegal downloading (p = .04). Furthermore, in the parking scenario the effect of (lack of) self-control was reduced (p = .03).

The observed pattern of variation is plausible and could be explained post-hoc, although these explanations necessarily have to remain speculative since they are based on exploratory analyses. In some cases, breaking the law might not be considered a serious violation of a legal rule (speeding), or legitimacy effects might be overwritten by context specific social norms (e.g., “protect the environment and do not litter”; see Feldman & Harel, 2008), which may be why the effect of legitimacy was reduced for some scenarios. In the insurance case, the subjective feeling of shame and social disapproval in case of detection might have led to a stronger effect of detection probability on criminal decision-making (cf. Feldman, 2009). Illegal downloading seems to be more a matter of principle than deterrence as indicated by the higher effect of legitimacy and perceived severity.

In all scenarios, perceived severity of the offense was the strongest univariate predictor of committing the crime. It is noteworthy, that in a multivariate regression the effect for perceived severity of others reverses. Hence, after controlling for the internalized feeling of wrongness (measured by severity), increased severity perceptions by others even tend to increase crimes. Overall, the results from these exploratory analyses suggest that the influence of deterrence factors, of perceived legitimacy, and of lacking self-control differs between different types of crime.

5 Discussion

Theories from various disciplines aim to explain law obedience and delinquency. Arguably, among the most prominent approaches in economics, psychology, and criminology, respectively, are a utility-theoretic deterrence theory (Becker, 1968), the general theory of crime (Gottfredson & Hirschi, 1990), and the legitimacy model (Tyler, 1990). In two studies employing German samples drawn from a student pool and the general public, we investigated the extent to which the factors postulated by these theories uniquely contribute to explaining law obedience, and whether there are substantial interaction effects. Moreover, we investigated the generalizability of the theories across crime types.

Three main conclusions can be drawn from the studies. First, although we partially confirm predictions of a utility-based deterrence theory, we also find clearly conflicting evidence. Second, the person factors legitimacy and lacking self-control influenced law obedience independent of deterrence factors and from each other. Effects of deterrence factors were not reduced for people with lacking self-control as suggested by the general theory of crime. Third, none of the theories can readily be generalized across types of crimes. Rather, we found that the effects of the factors varied considerably among the scenarios.

According to a utility theoretic deterrence theory, people’s decisions to break the law result from a rational comparison of expected costs and benefits. On the most general level, the observed crime rates were much lower than predicted from a utility theoretic account taking into account (monetary) costs and benefits only. Also, no effects of legitimacy and self-control would be expected from a standard utility theoretic perspective, which is clearly contradicted by the data. Still, in line with such an approach and previous findings (see Pratt et al., 2006, for an overview; further see Korobkin & Ulen, 2000; Levitt & Miles, 2006; Marvell & Moody, 1996), detection probability had a significant impact on the probability to break the law. We also found an effect of self-reported expected punishment, although the effect was smaller, which resembled findings in previous empirical studies (Pratt et al., 2006). The effect of financial benefit was even in the opposite direction to the predictions of deterrence theory. Subjects were less inclined to break the law when they could have earned more money from it (controlling for detection probability and expected fine). One potential explanation might be that people are willing to cheat only a little bit and avoid cheating by large amounts to keep up a positive self-view (Mazar, Amir & Ariely, 2008). As a potential limitation to this finding, however, it also seems plausible that the effect of benefits on law obedience might be non-linear (i.e., U-shaped) and that our manipulations might have missed the relevant range that effectively changes behavior (e.g., extremely high benefits that constitute a sufficiently strong immoral offer; Zamir & Medina, 2008).

The differential analyses per scenario showed that the relative importance of the considered influence factors varies with the specific context of the (low-level) crime. In some situations such as in parking offenses the tendency to break the law seems to be a result of weighing costs and benefits. Lack of self-control and legitimacy seem to be less important. In situations such as illegal downloading, obeying the law is driven mainly by legitimacy and hence general principles whether breaking the law is ever acceptable or not. For situations such as littering in a natural reserve lack of self-control and the subjective feeling of severity of doing so seem to be the driving factor and legitimacy and deterrence are less important.

5.1 Limitations

A number of limitations need to be pointed out. First, although we aimed at following a broad and integrative approach, our study clearly cannot be considered fully comprehensive. Other factors have been linked with delinquency as well, though not all of these could be considered here. Among these are the dark-triad personality trait psychopathy (e.g., Asscher et al., 2011; Chabrol et al., 2009), education (Lochner & Moretti, 2004), sensation-seeking (Wilson & Daly, 2006), morality (Stams, Brugman, Dekovic, van Rosmalen, van der Laan & Gibbs, 2006), peer influence and social control (Meldrum, Miller & Flexon, 2013), gender (Moon, McCluskey, McCluskey & Lee, 2012), religiosity (Reisig, Wolfe & Pratt, 2012), belief in a just world, authoritarianism (Cohn & Modecki, 2007), and social identity (Bradford, Hohl, Jackson & MacQueen, 2015). Future research could attempt to test and integrate more of these factors. Also our analysis concerned only low-level crimes and some of these further factors (e.g., psychopathy) might be particularly relevant to predict more severe crimes.

Second, the scenario method, as used in our study, has often been criticized for measuring only intention, but not actual behavior, and a meta-analysis recently found that lack of self-control was more strongly related to imagined than to actual behavior (de Ridder, Lensvelt-Mulders, Finkenauer, Stok & Baumeister, 2012). Although we cannot rule out this objection entirely, we were able to validate our scenario measures using self-reported delinquency scales in the pilot study, indicating that our scenario measures captured the relevant factors at least partially.

Third, although we also employed a sample of the general public to test the robustness of our effects across a wide range of demographic backgrounds, our online survey method restricted subjects to users of the World Wide Web. Furthermore, using the instruction manipulation check, we excluded people who had not properly read the instructions. Both factors might have led to a selection bias in our sample, which might have excluded people who are prone to conduct more severe crimes. Nonetheless, the sample in Experiment 2 was at least representative of the German population according to age and gender. Furthermore, the comparison to the student samples proves the generality of the basic findings reported.

5.2 Summary and Conclusion

Our findings show that a standard utility-theoretic perspective to crime taking into account monetary costs and benefits remains incomplete and cannot fully account for decisions to break the law. The results highlight the additional role of individual differences in explaining delinquency with both legitimacy (Tyler, 1990) and lack of self-control (Gottfredson & Hirschi, 1990). They contribute to predicting the probability of the law being broken. Both factors were equally strong in univariate and multivariate analyses, all indicating small to medium effects. Most importantly, we show that both effects work independently of each other and also independently of utility-theoretic deterrence factors. Hence, a complete model that aims to describe why people obey the law should include factors from all three theories.

The finding that the tendency to commit a crime declines with an increasing sure benefit from the crime, the small effects of expected fines and the observed variation of the relative importance of these factors dependent on the specific crimes, however, indicate that further refinements are necessary to generate a comprehensive theory of crime.