Assessing the validity of white-collar crime definitions using experimental survey data

Abstract

Objectives

We examine the extent to which the characteristics of offenders, the circumstance of offending, and offense characteristics affect public willingness to label an offense a “white-collar” crime.

Methods

We conducted a multidimensional factorial vignette survey hosted onAmazon’s Mechanical Turk. Participants (N = 2696) were randomly assigned to receive information about three of eighteen scenarios that could be considered white-collar crimes. Analyses are conducted at the scenario level with respondent-level fixed effects.

Results

Scenarios in which offenders had high status were rated more highly on a scale of “white-collarness.” Occupational access was also associated with higher ratings for both middle-status and upper-status offenders. Scenarios in which the means and consequences of the crime were financial were more likely to be considered white-collar crime.

Conclusions

In order to maximize generalizability and to support evidence-based policies, white-collar crime research should rely on a definition that incorporates practically relevant dimensions of offender status, occupational access, and financial means.

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Fig. 1
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Data availability

The datasets generated during or analyzed during the current study are not publicly available at this time but may be made available from the corresponding author on a reasonable request.

Notes

  1. 1.

    Authors’ calculation based on 2016 ICPSR files. Total offenders sentenced under Chapter 2B determined using highest guideline computation. Number of offenses sentenced for offenses similar to Enron include violations of 15 U.S.C. §§ 77-78 and 18 U.S.C. §§1348, 1350, 1519, 1520.

  2. 2.

    In general, studies do not prime respondents with a definition of white-collar crime (though see Dearden 2017, for one exception). While this, too, reflects a normative determination by the researcher, it is less likely to result in respondents making use of different conceptions of white-collar crime.

  3. 3.

    The identification of these elements was produced after reviewing more than thirty unique definitions of white-collar crime and adjacent terms (e.g., occupational crime, control fraud) that were identified and cataloged as part of a previous grant project. The identification of common elements of these definitions was an iterative process consisting of discussions between the authors of this research internally as well as conversations with outside experts in the field. These scholars also provided comments and suggestions related to both the elements to be varied and the appropriateness of the crime type scenarios.

  4. 4.

    In some cases, we included two types of crime within a particular combination; respondents were randomized 50–50 into these conditions within the scenario. Respondents were further randomized into conditions that varied the offender’s sex, race, and age within each scenario. Race was implied using socioeconomic-neutral name associations based on Gaddis (2017).

  5. 5.

    This is consistent with Friedrichs (2002), who noted that the concept is “relativistic… illegal and harmful activities may be viewed as more or less purely white-collar crime” (2002) (2002:253).

  6. 6.

    This is between the $1.21 rate suggested at the average time (10 min) and $1.81 suggested for the maximum time (15 min). Note also that this consensus is a significant departure from early research using MTurk, where workers were paid significantly less and may not have taken research seriously (Paolacci et al. 2010).

  7. 7.

    Based on research conducted in 2010, MTurk workers appear to be younger and have lower income relative to the general population (Paolacci et al. 2010). MTurk samples also may have more whites, be made up of individuals with more years of formal education and who are more politically liberal (Berinsky et al. 2012; Mullinix et al. 2015). However, this research is relatively dated. Because we are interested in the role of scenario characteristics, we address individual differences between respondents through the use of fixed effects in our model.

  8. 8.

    Other values held at the mean

  9. 9.

    This is perhaps a glib interpretation of Shapiro’s comments, in which he highlighted the social organization of positions of trust as concentrated among the upper class and argued that it was the characteristics of the offense that protected the offenders from prosecution less than the exercise of class privilege in the justice system (1990: 358-9). We note that our own findings related to the circumstance of offending and the offender’s status as largely consistent with Shapiro’s suggestion that the location of offending and the degree of trust placed in the offender were critical to the concept, rather than pure socioeconomic status.

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Acknowledgments

The authors wish to thank Michael L. Benson and Melissa Rorie for their comments on an early version of the survey instrument.

Funding

This research was funded by a seed grant from the Criminal Justice Research Center at the Pennsylvania State University and faculty research funds from the California State University, San Bernadino.

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Appendix

Appendix

Table 5 Features of white-collar crime for exemplar definitions
Table 6 Coding of crime scenario features
Table 7 Sample means by white-collar definition element combination
Table 8 Random effect regression models with respondent characteristics

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Galvin, M.A., Logan, M. & Snook, D.W. Assessing the validity of white-collar crime definitions using experimental survey data. J Exp Criminol (2021). https://doi.org/10.1007/s11292-020-09455-6

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Keywords

  • White-collar crime
  • Corporate crime
  • Public opinion
  • Survey
  • Vignette