Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-25T08:21:12.709Z Has data issue: false hasContentIssue false

Willingness to Pay to Reduce White-Collar and Corporate Crime

Published online by Cambridge University Press:  29 July 2015

Mark A. Cohen*
Affiliation:
Justin Potter Professor of American Competitive Enterprise and Professor of Law, Vanderbilt University, USA University Fellow, Resources for the Future, 401 21st Avenue South, Nashville, TN 37203, USA, Phone: +1 615 322 0533, Fax +1 615 343 7177, e-mail: [email protected]

Abstract

Consumer protection and financial regulatory agencies such as the Federal Trade Commission (FTC), the Securities and Exchange Commission (SEC), and the Consumer Financial Protection Bureau (CFPB) regulate various types of consumer, investor and financial frauds. Whether required or not, rulemaking proceedings oftentimes include some form of benefit-cost analysis. Thus, the benefits of proposed regulations – whether fully quantified or not – are an increasingly important component of rulemaking decisions. Anecdotal evidence suggests that the impact on victims in some cases includes significant time and financial hardships and even pain, suffering, and reduced quality of life. Further, the existence of these offenses causes nonvictims to take costly precautionary behavior and might even inhibit legitimate business activities. Yet, little is known about the true costs of consumer and financial crimes other than the out-of-pocket monetary losses incurred by victims. To the extent society wishes to optimally deter such crimes, without better data on nonmonetary costs, any benefit-cost analyses of criminal justice or prevention programs designed to reduce these crimes will inevitably underestimate program benefits. This paper provides an initial framework and empirical estimates of the willingness to pay (WTP) to reduce four types of white-collar and corporate offenses – consumer fraud, financial fraud, corporate crime, and corporate financial crime. Utilizing a contingent valuation survey approach that has been used to estimate the cost of street crimes, the average WTP for a 10% reduction in each of these four offenses is estimated to range between $35 and $85 per household. In the case of consumer fraud and financial fraud, where estimates of prevalence are available, this translates into a WTP of $1200 per consumer fraud and $12,000 for financial fraud. In contrast, the out-of-pocket costs to victims of consumer fraud have been estimated to average about $100, and about $200 to $250 for various types of financial frauds. These figures also compare favorably to the WTP for a reduced household burglary of $19,000.

Type
Articles
Copyright
© Society for Benefit-Cost Analysis 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, Keith B. (2007). Consumer Fraud in the United States: The Second FTC Survey. Federal Trade Commission.Google Scholar
Anderson, Keith B. (2013). Consumer Fraud in the United States: The Third FTC Survey. Federal Trade Commission.Google Scholar
Aos, Steve & Drake, Elizabeth(2010). WSIPP’s Benefit-Cost Tool for States: Examining Policy Options in Sentencing and Corrections. Washington State Institute for Public Policy.Google Scholar
Arrow, Kenneth et al. (1993). Report of the NOAA Panel on Contingent Valuation. Federal Register, 58, 46014614.Google Scholar
Barnett, Cynthia(undated). The Measurement of White-Collar Crime Using Uniform Crime Reporting (UCR) Data. http://www.fbi.gov/about-us/cjis/ucr/nibrs/nibrs_wcc.pdf.Google Scholar
Belfield, Clive R., Nores, Milagros, Barnett, Steve & Schweinhart, Lawrence (2006). The High/Scope Perry Preschool Program: Cost–Benefit Analysis Using Data from the Age-40 Followup. Journal of Human Resources, 41(1), 162190.CrossRefGoogle Scholar
Blomquist, Glenn C., Newsome, Michael A. & Brad Stone, D. (2004). Public Preferences for Program Tradeoffs: Community Values for Budget Priorities. Public Budgeting & Finance, 24(1), 5071.CrossRefGoogle Scholar
Bureau of Justice Statistics(2000). Criminal Victimization in the United States, 1999.Google Scholar
Bureau of Justice Statistics(2013). Criminal Victimization in the United States, 2012.Google Scholar
Cohen, Mark A. The Costs of White-Collar Crime. In Van Slyke, Shanna, Cullen, Francis & Benson, Michael (Eds.), Oxford Handbook of White-Collar Crime. Oxford University Press, (forthcoming).Google Scholar
Cohen, Mark A. (2010). Valuing Crime Control Benefits Using Stated Preference Approaches. In Roman, John K., Dunworth, Terence & Marsh, Kevin (Eds.), Cost-Benefit Analysis and Crime Control. Washington, DC: Urban Institute Press.Google Scholar
Cohen, Mark A. (2005). The Costs of Crime and Justice. New York, NY: Routledge.Google Scholar
Cohen, Mark A. (1989). Corporate Crime and Punishment: A Study of Social Harm and Sentencing Practice in the Federal Courts, 1984–1987. American Criminal Law Review, 26(3), 605660.Google Scholar
Cohen, Mark A., Rust, Roland T. & Steen, Sara (2006). Prevention, Crime Control or Cash? Public Preferences towards Criminal Justice Spending Priorities. Justice Quarterly, 23(3), 317335.CrossRefGoogle Scholar
Cohen, Mark A., Rust, Roland, Steen, Sara & Tidd, Simon (2004). Willingness-to-Pay for Crime Control Programs. Criminology, 42(1), 86106.Google Scholar
Cropper, Maureen, Hammitt, James K. & Robinson, Lisa A. (2011). Valuing Mortality Risk Reductions: Progress and Challenges. The Annual Review of Resource Economics, 3, 313336.CrossRefGoogle Scholar
Federal Bureau of Investigation (2000). Crime in the United States, 1999.Google Scholar
Federal Bureau of Investigation (2011). Financial Crimes Report to the Public, Fiscal Years 2010–11.Google Scholar
Federal Bureau of Investigation (2013). Crime in the United States, 2012.Google Scholar
Federal Trade Commission (2003). Identity Theft Survey Report, prepared by Synovate. http://www.ftc.gov/os/2003/09/synovatereport.pdf.Google Scholar
Federal Trade Commission (2007). 2006 Identity Theft Survey Report, prepared by Synovate. http://www.ftc.gov/os/2007/11/SynovateFinalReportIDTheft2006.pdf.Google Scholar
Ganzini, Linda, McFarland, Bentson & Bloom, Joseph (1990). Victims of Fraud: Comparing Victims of White Collar and Violent Crime. Bulletin of the American Academy of Psychiatry and Law, 18, 5563.Google Scholar
Huff, Rodney, Desilets, Christian & Kane, John (2010). The 2010 National Public Survey on White Collar Crime. National White Collar Crime Center.Google Scholar
Johansson-Stenman, Olof (2008). Mad Cows, Terrorism and Junk Food: Should Public Policy Reflect Subjective or Objective Risks? Journal of Health Economics, 27(2), 234248.Google Scholar
Kling, Catherine L., Phaneuf, Daniel J. & Zhao, Jinhua (2012). From Exxon to BP: Has Some Number Become Better than No Number? Journal of Economic Perspectives, 26(4), 326.Google Scholar
Koford, Brandon C. (2010). Public Budget Choices and Willingness to Pay. Public Budgeting & Finance, 30(2), 4768.Google Scholar
Levitt, Steven D. (1997). Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime. American Economic Review, 87(3), 270290.Google Scholar
Meier, Robert F. & Short, James F. Jr. (1995). The Consequences of White-Collar Crime. In Geis, Gilbert, Meier, Robert F. & Salinger, Lawrence M. (Eds.), White-Collar Crime: Classic and Contemporary Views (3rd ed.) (pp. 80104). New York: Free Press.Google Scholar
Owens, Emily G. (2009). More Time, Less Crime? Estimating the Incapacitative Effect of Sentence Enhancements. Journal of Law and Economics, 52(3), 551579.CrossRefGoogle Scholar
Piquero, Nicole Leeper, Cohen, Mark A. & Piquero, Alex R. (2010). How Much is the Public Willing to Pay to be Protected from Identity Theft? Justice Quarterly, 28(3), 437458.Google Scholar
Sharp, Tracy, Shreve-Neiger, Andrea, Fremouw, William, Kane, John & Hutton, Shawn (2004). Exploring the Psychological and Somatic Impact of Identity Theft. Journal of Forensic Science, 49, 131136.Google Scholar
Shover, Neal, Litton Fox, Greer & Mills, Michael (1994). Long-term Consequences of Victimization of White-Collar Crime. Justice Quarterly, 11, 7598.Google Scholar
U.S. Department of JusticePrison Rape Elimination Act, Regulatory Impact Assessment, May 17, 2012. http://ojp.gov/programs/pdfs/prea_ria.pdf.Google Scholar
Viscusi, W. Kip & Aldy, Joseph E. (2003). The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World. Journal of Risk and Uncertainty, 27(1), 576.Google Scholar
Vossler, Christian A. & Poe, Gregory L. (2011). Consequentiality and Contingent Values: An Emerging Paradigm. In Bennett, Jeff (Ed.), The International Handbook on Non-Market Environmental Valuation. Northhampton, MA: Edward Elgar; Chapter 7.Google Scholar
Welsh, Brandon C., Farrington, David P. & Sherman, Lawrence W. (2000). Costs and Benefits of Preventing Crime. Westview Press.Google Scholar