Abstract
Objectives
Scholars have long emphasized that communicating, or “advertising”, information about legal sanction risk is necessary for the success of deterrence-based crime policies. However, scant research has evaluated whether direct communications about legal risk can cause sanction perception updating, the updating of ambiguity in sanction perceptions, or changes in persons’ willingness to offend. No prior studies have evaluated sanction perception updating for white-collar crimes.
Methods
To address this research void, the current study analyzes data from an experiment embedded in a recent national survey (N = 878). Multivariate regression models estimate the effect of providing participants with information about the “objective” arrest risk for white-collar offenses on their sanction perceptions.
Results
The findings provide the first evidence that such information, when it is inconsistent with individuals’ prior beliefs, causes them to update: (1) their perceptions of the certainty of arrest; (2) their ambiguity about arrest risk; and, indirectly, (3) their willingness to commit white-collar crimes.
Conclusions
The results imply that individuals are willing to incorporate relevant information into their subjective beliefs about sanction risks. Importantly, however, they also make meaningful distinctions about the value of new information for understanding criminal risks.
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Notes
Beccaria (1872 [1764]: 26) argued, for example, that the deterrent value of punishments would increase proportionally “as the code of laws is more universally read, and understood.”
This is in contrast to the more traditional economic concept of perfect information, whereby individuals have all the necessary and relevant information with which to make a decision.
Ideally, we would have achieved a higher response rate. Future studies should consider providing monetary incentives to respondents, which have been shown to increase response rates in both traditional and online surveys (Tourangeau et al. 2013). At the same time, it bears emphasizing that studies consistently find that, across modes of data collection, response rates are a bad indicator of non-response bias (Curtin et al. 2000; Holbrook et al. 2008; Keeter et al. 2000, 2006). Meta-analyses confirm this point (Groves 2006; Groves and Peytcheva 2008). As Krosnick et al. (2015: 6) explain in their recent report on survey research to the National Science Foundation, “nonresponse bias is rarely notably related to [the] nonresponse rate.”
Specifically, our study used a posttest-only experimental design (Campbell and Stanley 1963: 25). We avoided using pretests—measuring risk perceptions before, as well as after, the information treatment—to reduce the risk of testing effects. Our key assumption is that, because of random assignment, the subjective priors of the treatment and control groups were equal prior to the information treatment, and, thus, any observed differences between the groups will reflect the causal effect of the information on the treatment groups’ sanction perceptions.
We included the clearance rates for property crimes to help reduce non-substantive anchoring. Specifically, if respondents are provided with only a few clearance rates, which are all similar, they may unconsciously anchor their risk perceptions on those rates, regardless of the relevance of the rates (see Kahneman 2011).
As Cook (1980: 241, emphasis in original) has explained, “at best, [the clearance rate] can be viewed as a measure of the average probability of punishment for crimes committed.”
Although most citizens overestimate arrest risk (Tittle 1980), it is possible that a minority of respondents may have been surprised that the clearance rates were not lower. Theoretically, such respondents should increase their risk estimates in response to the information treatment. Thus, if these respondents classified themselves as being surprised, the findings would underestimate the differences in the effect of the treatment on those who were surprised vs. not surprised, whereas the opposite would occur if they classified themselves as being not surprised.
It is possible that, because the scenarios did not include descriptive details about all situational factors that could potentially be relevant to the crimes described therein, respondents may have differentially imputed the circumstances of the crimes, and, thus, that their estimates of arrest risk may not be comparable in every instance. If this occurred, it would result in random measurement error that biases our study toward null results.
To preserve the sample size, missing values (N = 56) on Income were imputed based on the values of the other variables in the analyses. This did not appreciably alter the results.
The parallel lines assumption is met in all of the models for the effect of the experiment on the measures of ambiguity. We also estimated supplementary models using OLS regression with the six-point measures of ambiguity. Regardless of our treatment of the dependent variable, the substantive conclusions were identical.
It is possible that the differential effect of the treatment on ambiguity across the three white-collar offenses may reflect differences in respondents’ familiarity with the respective behaviors. For example, respondents may have had an especially low level of familiarity with the stock market.
We estimated a series of supplementary models including power polynomials—quadratic, cubic, and quartic—for the measures of perceived arrest risk. We did not observe consistent evidence of a non-linear relationship between perceived arrest risk and intentions to offend. The quadratic and quartic terms were significant in the models for insurance fraud, but these effects did not emerge for the other two offenses.
When negative binomial regression models are estimated with continuous versions of the offending variables, the coefficients for the effects of perceived arrest risk and ambiguity on tax fraud offending (Model 2) are not significant. The results for the other two white-collar offenses remain unchanged.
In our study, for all three white-collar offenses, there is not a significant total effect of the treatment on intentions to offend. However, despite common wisdom, this fact is not informative about whether an indirect effect of the treatment exists. Hayes (2009: 413) stresses this point in his seminal discussion of mediation analysis: “it is easy to show that the claim that X can’t affect Y in the absence of a detectable total effect is false.”
Although structural equation modeling (SEM) can be used in lieu of OLS or logistic regression for estimating indirect effects, “doing so is neither necessary nor better” (Hayes 2013: 159).
We used the Stata command “binary_mediation” to perform the mediation analyses.
There is no definitive reason why risk perceptions should directly converge to objective rates. Individuals exercise bounded rather than perfect rationality (Clarke and Cornish 2001). Additionally, perceptual errors in estimating arrest risk are not random, in which case they would cancel each other out at the aggregate level, but, instead, tend to be biased toward overestimating arrest risk. The convergence of levels of perceived and objective arrest risk is also not a requisite assumption for the standard economic model of crime, as individual-specific detection probabilities can vary due to multiple factors, including different levels of skill and experience, offense mix, and presence of self-serving bias in self-evaluation. This is why models of Bayesian updating, which are harmonious with rational choice models of offending, allow for each individual to have their own mean.
By definition, the mean of an offender’s subjective probability distribution necessarily provides a point estimate of generalized arrest risk, not of situational arrest risk, because the subjective distribution contains information amassed over time from his or her full set of relevant personal, vicarious, and mass-mediated experiences. This point estimate must then be adjusted based on the situational context.
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This work was supported by funding from the University at Albany Faculty Research Awards Program (FRAP) Category A and from the Hindelang Criminal Justice Research Center at the University at Albany, SUNY.
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Pickett, J.T., Loughran, T.A. & Bushway, S. Consequences of legal risk communication for sanction perception updating and white-collar criminality. J Exp Criminol 12, 75–104 (2016). https://doi.org/10.1007/s11292-016-9254-2
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DOI: https://doi.org/10.1007/s11292-016-9254-2