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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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
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.
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.
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.
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).
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).
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).
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.
Other values held at the mean
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.
References
Angeletti, T. (2019). The differential management of financial illegalisms: Assigning responsibilities in the Libor scandal. Law & Society Review, 53(4), 1233–1265. https://doi.org/10.1111/lasr.12442.
Benson, M. L., & Simpson, S. S. (2015). Understanding white-collar crime: An opportunity perspective. (Routledge, Ed.) (2e ed.). New York.
Berinsky, A. J., Huber, G. A., Lenz, G. S., & Michael Alvarez, R. (2012). Evaluating online labor markets for experimental research: Amazon.com’s mechanical Turk. Political Analysis, 20, 351–368. https://doi.org/10.1093/pan/mpr057.
Braithwaite, J. (1985). White collar crime. Annual Review of Sociology, 11(1), 1–25. https://doi.org/10.1146/annurev.so.11.080185.000245.
Bryant, J., & Oliver, M. B. (Eds.). (2009). Media effects: Advances in theory and research (3e ed.). New York: Routledge https://journals.sagepub.com/doi/pdf/10.1177/02673231100250020505. .
Casey, S., & O’Connell, M. (1999). The influence of consequentiality on perceptions of crime seriousness. Legal and Criminological Psychology, 4(2), 265–271. https://doi.org/10.1348/135532599167897.
Casey, L. S., Chandler, J., Levine, A. S., Proctor, A., & Strolovitch, D. Z. (2017). Intertemporal differences among MTurk workers: Time-based sample variations and implications for online data collection. SAGE Open, 7(2), 215824401771277. https://doi.org/10.1177/2158244017712774.
Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12(1), 53–81. https://doi.org/10.1146/annurev-clinpsy-021815-093623.
Clinard, M. B., & Quinney, R. (1973). Criminal behavior system - a typology (2e ed.). New York: Holt, Rinehart and Winston https://www.ncjrs.gov/app/publications/abstract.aspx?Id=41756. .
Comey, J. B. (2009). Go directly to jail: White collar sentencing after the Sarbanes-Oxley Act. Harvard Law Review, 122(6), 1728–1749 https://www.jstor.org/stable/40379766?seq=1#metadata_info_tab_contents. .
Cox, J., Edens, J. F., Rulseh, A., & Clark, J. W. (2016). Juror perceptions of the interpersonal-affective traits of psychopathy predict sentence severity in a white-collar criminal case. Psychology, Crime & Law, 22(8), 721–740. https://doi.org/10.1080/1068316X.2016.1174864.
Cullen, F. T., Link, B. G., & Polanzi, C. W. (1982). The seriousness of crime revisited: Have attitudes toward white-collar crime changed? Criminology, 20(1), 83–102. https://doi.org/10.1111/j.1745-9125.1982.tb00449.x.
Cullen, F. T., Clark, G. A., Mathers, R. A., & Cullen, J. B. (1983). Public support for punishing white-collar crime: Blaming the victim revisited? Journal of Criminal Justice, 11(6), 481–493. https://doi.org/10.1016/0047-2352(83)90002-8.
Cullen, F. T., Hartman, J. L., Jonson, C. L., Cullen, F. T., Hartman, J. L., & Jonson, C. L. (2009). Bad guys: Why the public supports punishing white-collar offenders. Crime Law Soc Change, 51, 31–44. https://doi.org/10.1007/s10611-008-9143-3.
Daly, K. (1989). Gender and varieties of white-collar crime. Criminology, 27(4), 769–794. https://doi.org/10.1111/j.1745-9125.1989.tb01054.x.
Dearden, T. E. (2017). An assessment of adults’ views on white-collar crime. Journal of Financial Crime, 24(2), 309–321. https://doi.org/10.1108/JFC-05-2016-0040.
Dodge, M., Bosick, S. J., & Van Antwerp, V. (2013). Do men and women perceive white-collar and street crime differently? Exploring gender differences in the perception of seriousness, motives, and punishment. Journal of Contemporary Criminal Justice, 29(3), 399–415. https://doi.org/10.1177/1043986213496378.
Edelhertz, H. (1970). The nature, impact and prosecution of white-collar crime. Washington: Department of Justice.
Follmer, D. J., Sperling, R. A., & Suen, H. K. (2017). The role of MTurk in education research: Advantages, issues, and future directions. Educational Researcher, 46(6), 329–334. https://doi.org/10.3102/0013189X17725519.
Frank, J., Cullen, F. T., Travis, L. F. I., & Borntrager, J. L. (1989). Sanctioning corporate crime: How do business executives and the public compare? AJCJ, 13(2), 139–169.
Friedrichs, D. O. (2002). Occupational crime, occupational deviance, and workplace crime. Criminal Justice, 2(3), 243–256. https://doi.org/10.1177/17488958020020030101.
Gaddis, S. M. (2017). How black are Lakisha and Jamal? Racial perceptions from names used in correspondence audit studies. Sociological Science, 4, 469–489. https://doi.org/10.15195/v4.a19.
Galvin, M. A. (2019). Substance or semantics? The consequences of definitional ambiguity for white-collar research. Journal of Research in Crime and Delinquency, 002242781988801. https://doi.org/10.1177/0022427819888012.
Geis, G. (1991). White-collar crime: What is it? Current Issues in Criminal Justice, 3(1), 9–24. https://doi.org/10.1080/10345329.1991.12036504.
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of mechanical Turk samples. Journal of Behavioral Decision Making, 26(3), 213–224. https://doi.org/10.1002/bdm.1753.
Grabosky, P. N., Braithwaite, J. B., & Wilson, P. R. (1987). The myth of community tolerance toward white-collar crime. Australian & New Zealand Journal of Criminology, 20(1), 33–44. https://doi.org/10.1177/000486588702000104.
Green, G. S. (1997). Occupational Crime (2e ed.). Chicago: Nelson-Hall. https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=177001. Accessed 12 July 2020.
Hara, K., Adams, A., Milland, K., Savage, S., Callison-Burch, C., & Bigham, J. P. (2018). A data-driven analysis of workers’ earnings on Amazon Mechanical Turk. In Conference on Human Factors in Computing Systems - Proceedings (Vol. 2018-April, pp. 1–14). New York, New York, USA: Association for Computing Machinery. https://doi.org/10.1145/3173574.3174023.
Hauser, D. J., & Schwarz, N. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48(1), 400–407. https://doi.org/10.3758/s13428-015-0578-z.
Heen, M. S. J., Lieberman, J. D., & Miethe, T. D. (2014). A comparison of different online sampling approaches for generating national samples. www.qualtrics.com. Accessed 23 October 2020.
Holtfreter, K., Van Slyke, S., Bratton, J., & Gertz, M. (2008). Public perceptions of white-collar crime and punishment. Journal of Criminal Justice, 36(1), 50–60. https://doi.org/10.1016/j.jcrimjus.2007.12.006.
Hurwitz, J., & Peffley, M. (1997). Public perceptions of race and crime: The role of racial stereotypes. American Journal of Political Science, 41(2), 375. https://doi.org/10.2307/2111769.
Johnson, D. (2006). Crime salience, perceived racial bias, and blacks’ punitive attitudes. Journal of Ethnicity in Criminal Justice, 4(4), 1–18. https://doi.org/10.1300/J222v04n04_01.
Johnson, D. T., & Leo, R. A. (1993). The Yale white-collar crime project: A review and critique. Law & Social Inquiry, 18(1), 63–99. https://doi.org/10.1111/j.1747-4469.1993.tb00647.x.
Katz, J. (1980). The social movement against white-collar crime. In E. Bittner & S. L. Messinger (Eds.), Criminology review yearbook (2nd ed., pp. 161–184). Beverly Hills: Sage.
Leeper Piquero, N., Carmichael, S., & Piquero, A. R. (2008). Research note: Assessing the perceived seriousness of white-collar and street crimes. Crime & Delinquency, 54(2), 291–312. https://doi.org/10.1177/0011128707303623.
Logan, M. W., Morgan, M. A., Benson, M. L., & Cullen, F. T. (2019). Coping with imprisonment: Testing the special sensitivity hypothesis for white-collar offenders. Justice Quarterly, 36(2), 225–254. https://doi.org/10.1080/07418825.2017.1396488.
Maeder, E. M., Yamamoto, S., & McManus, L. A. (2018). Methodology matters: Comparing sample types and data collection methods in a juror decision-making study on the influence of defendant race. Psychology, Crime and Law, 24(7), 687–702. https://doi.org/10.1080/1068316X.2017.1409895.
Michel, C. (2016). Violent street crime versus harmful white-collar crime: A comparison of perceived seriousness and punitiveness. Critical Criminology, 24(1), 127–143. https://doi.org/10.1007/s10612-015-9295-2.
Michel, C. (2017). Examining the influence of increased knowledge about white-collar crime on attitudes toward it in the undergraduate classroom. Journal of Criminal Justice Education, 28(1), 52–73. https://doi.org/10.1080/10511253.2016.1165854.
Michel, C., Heide, K. M., & Cochran, J. K. (2015). Sociodemographic correlates of knowledge about elite deviance. American Journal of Criminal Justice, 40(3), 639–660. https://doi.org/10.1007/s12103-014-9276-0.
Michel, C., Cochran, J. K., & Heide, K. M. (2016a). Public knowledge about white-collar crime: An exploratory study. Crime, Law and Social Change, 65(1–2), 67–91. https://doi.org/10.1007/s10611-015-9598-y.
Michel, C., Heide, K. M., & Cochran, J. K. (2016b). The consequences of knowledge about elite deviance. American Journal of Criminal Justice, 41(2), 359–382. https://doi.org/10.1007/s12103-014-9285-z.
Mullinix, K. J., Leeper, T. J., Druckman, J. N., & Freese, J. (2015). The generalizability of survey experiments. Journal of Experimental Political Science, 2(2), 109–138. https://doi.org/10.1017/XPS.2015.19.
Newman, D. J. (1956). Public attitudes toward a form of white collar crime. Social Problems, 4 https://heinonline.org/HOL/Page?handle=hein.journals/socprob4&id=230&div=35&collection=journals. .
O’Connell, M., & Whelan, A. (1996). Taking wrongs seriously: Public perceptions of crime seriousness. British Journal of Criminology, 36(2), 299–318. https://doi.org/10.1093/oxfordjournals.bjc.a014087.
Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184–188. https://doi.org/10.1177/0963721414531598.
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision making, 5(5), 411–419 https://psycnet.apa.org/record/2010-18204-008. .
Peer, E., Vosgerau, J., & Acquisti, A. (2014). Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior Research Methods, 46(4), 1023–1031. https://doi.org/10.3758/s13428-013-0434-y.
Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163. https://doi.org/10.1016/j.jesp.2017.01.006.
Perse, E. M., & Lambe, J. L. (Eds.). (2016). Media Effects and Society (2e ed.). New York: Routledge. https://books.google.com/books?hl=en&lr=&id=W8bLDAAAQBAJ&oi=fnd&pg=PP1&dq=perse+lambe+2016+media+perceptions+crime+2009&ots=GY-JnPJeIC&sig=BSPW0J0jr8gUXvFc3fL-31qGIMA#v=onepage&q=perse lambe 2016 media perceptions crime 2009&f=false. .
Pontell, H. N. (2016). Theoretical, empirical, and policy implications of alternative definitions of “white-collar crime”: “Trivializing the lunatic crime rate. In S. Van Slyke, M. L. Benson, & F. T. Cullen (Eds.), The Oxford Handbook of White-Collar Crime (pp. 39–56). New York: Oxford University Press.
Quinney, E. R. (1964). The study of white collar crime: Toward a reorientation in theory and research. Journal of Criminal Law, Criminology and Police Science, 55 https://heinonline.org/HOL/Page?handle=hein.journals/jclc55&id=218&div=32&collection=journals. .
Reed, J. P., & Reed, R. S. (1974). “Doctor, lawyer, Indian chief”: Old rhymes and new on white collar crime. Australian & New Zealand Journal of Criminology, 7(3), 145–156.
Reiss, A. J., & Biderman, A. D. (1980). Data sources on white-collar law-breaking. MD: Rockville https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=72651. .
Rorie, M., Alper, M., Schell-Busey, N., & Simpson, S. S. (2018). Using meta-analysis under conditions of definitional ambiguity: The case of corporate crime. Criminal Justice Studies, 31(1), 38–61. https://doi.org/10.1080/1478601X.2017.1412960.
Rosenmerkel, S. P. (2001). Wrongfulness and harmfulness as components of seriousness of white-collar offenses. Journal of Contemporary Criminal Justice, 17(4), 308–327. https://doi.org/10.1177/1043986201017004002.
Rossi, P. H., Waite, E., Bose, C. E., & Berk, R. E. (1974). The seriousness of crimes: Normative structure and individual differences. American Sociological Review, 39(2), 224. https://doi.org/10.2307/2094234.
Savelsberg, J. J., & Brühl, P. (1994). Constructing white-collar crime: Rationalities, communication, power. Philadelphia: University of Pennsylvania Press.
Schrager, L. S., & Short, J. F. (1980). How serious a crime? Perceptions of organizational and common crimes. In G. Geis & E. Stotland (Eds.), White collar crime - theory and research. Thousand Oaks, CA: Sage Publications https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=65756. .
Sellin, T., & Wolfgang, M. E. (1964). The measurement of delinquency. Wiley https://psycnet.apa.org/record/1964-35003-000. .
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton, Mifflin and Company https://psycnet.apa.org/record/2002-17373-000. .
Shapiro, S. P. (1980). Thinking about white collar crime - matters of conceptualization and research. DC: Washington https://www.ncjrs.gov/App/abstractdb/AbstractDBDetails.aspx?id=71090. .
Shapiro, S. P. (1990). Collaring the crime, not the criminal: Reconsidering the concept of white-collar crime. American Sociological Review, 55(3), 346. https://doi.org/10.2307/2095761.
Shover, N., & Cullen, F. T. (2008). Studying and teaching white-collar crime: Populist and patrician perspectives. Journal of Criminal Justice Education, 19(2), 155–174. https://doi.org/10.1080/10511250802137200.
Simpson, S. S. (2013). White-collar crime: A review of recent developments and promising directions for future research. Annual Review of Sociology, 39(1), 309–331. https://doi.org/10.1146/annurev-soc-071811-145546.
Simpson, S. S. (2019). Reimagining Sutherland 80 years after white-collar crime*. Criminology, 57(2), 189–207. https://doi.org/10.1111/1745-9125.12206.
Stylianou, S. (2003). Measuring crime seriousness perceptions: What have we learned and what else do we want to know. Journal of Criminal Justice, 31(1), 37–56. https://doi.org/10.1016/S0047-2352(02)00198-8.
Sutherland, E. H. (1940). White-collar criminality. American Sociological Review, 5(1), 1. https://doi.org/10.2307/2083937.
Sutherland, E. H. (1983). White collar crime: The uncut version. New Haven: Yale University Press.
Tajfel, H., & Turner, J. C. (2004). The social identity theory of intergroup behavior. In J. T. Jost & J. Sidanius (Eds.), Political psychology: Key readings (pp. 276–293). Psychology Press. https://doi.org/10.4324/9780203505984-16.
Unnever, J. D., & Cullen, F. T. (2009). Empathetic identification and punitiveness. Theoretical Criminology, 13(3), 283–312. https://doi.org/10.1177/1362480609336495.
Unnever, J. D., Benson, M. L., & Cullen, F. T. (2008). Public support for getting tough on corporate crime racial and political divides. https://doi.org/10.1177/0022427807313707.
Ward, L. M. (2003). Understanding the role of entertainment media in the sexual socialization of American youth: A review of empirical research. Developmental Review, 23(3), 347–388. https://doi.org/10.1016/S0273-2297(03)00013-3.
Weisburd, D., Wheeler, S., Waring, E., & Bode, N. (1991). Crimes of the middle classes: White collar offenders in the federal courts. New Haven: Yale University Press.
Wheeler, S., & Rothman, M. L. (1982). The organization as weapon in white-collar crime. Michigan Law Review, 80(7), 1403. https://doi.org/10.2307/1288554.
Williamson, V. (2016). On the ethics of crowdsourced research. PS - Political Science and Politics, 49(1), 77–81. https://doi.org/10.1017/S104909651500116X.
Wright, J. P., Cullen, F. T., & Blankenship, M. B. (1995). The social construction of corporate Violence: Media Coverage of the Imperial Food Products Fire. Crime & Delinquency. https://doi.org/10.1177/0011128795041001002.
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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Galvin, M.A., Logan, M. & Snook, D.W. Assessing the validity of white-collar crime definitions using experimental survey data. J Exp Criminol 18, 665–693 (2022). https://doi.org/10.1007/s11292-020-09455-6
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DOI: https://doi.org/10.1007/s11292-020-09455-6