Auto Theft and Restrictive Deterrence

Abstract

Although researchers have examined the attributes that make offenders more or less responsive to sanction threats, far less attention has centered on the manner in which responsiveness can lead to less detectible crime, or perhaps even more overall crime. Restrictive deterrence is the concept that explains this paradox. We explore it here using qualitative interviews with 35 active auto thieves. Findings suggest that auto thieves' restrictively deterrent decision-making strategies fell into three broad categories: discretionary target selection, normalcy illusions, and defiance. Discussion focuses on the data's conceptual implications for restrictive deterrence and offender decision-making.

Keywords:

• restrictive deterrence
• offender decision-making
• motor vehicle theft
• qualitative methods

Acknowledgments

The research reported here was funded by the University of Missouri Research Board (UMRB). Points of view or opinions expressed herein are those of the authors and do not necessarily reflect those of the UMRB. The authors would like to acknowledge the contributions of Richard Wright who was an investigator on the larger project from which this study is based.

Notes

1. In all fairness to Gibbs, his distinction was meant to differentiate restrictive deterrence from its conceptual counterpart, absolute deterrence. The latter form of deterrence happens “where an individual has refrained throughout life from a particular type of criminal act because in whole or in part he or she perceived some risk of someone suffering a punishment as a response to the crime” (Gibbs, 1975, p. 32). The point is that absolute deterrence applies to individuals who have perhaps contemplated but never committed a specific illegal act, whereas restrictive deterrence can only be observed in the behavior of those who have committed a specific illegal act at least once (see also Gibbs, 1986; Paternoster, 1989). Because individuals who have never committed any illegal act at any time are so rare, restrictive deterrence is likely exercised widely.

2. The notion of a probabilistic basis for punishment risk often comes through in offender narratives. One of Letkemann’s (1973, p. 37) incarcerated offenders thus explained that, “The law of averages is against you—I don’t care how good you are—you’ll end up in jail at some time,” while an ex-robber from Cusson and Pinsonneault’s (1986, p. 76) study admonished, “Every time you commit one, you risk being arrested. The law of averages is against you; the prisons are there to prove it.” Admissions such as these imply awareness of a cumulative risk of punishment and support Shover’s (1996, p. 162) contention that “in crime as in other forms of gambling, misfortune looms for those who ‘take the bucket to the well’ repeatedly.”

3. In contrast to burglary and robbery where the bulk of enforcement practices rely on ex post facto investigative capacities of criminal justice functionaries, the visibility of auto theft means that enforcement of the offense is more akin to street-level drug sales whereby detection and apprehension are focused on the point of crime commission and on individuals in possession of stolen contraband. For auto theft, this could be through the use of police decoy vehicles (Sallybanks, 2001), license plate recognition technology, or the wide discretion authorities have over traffic enforcement and vehicle stops (Harris, 1997, p. 558; see also Chambliss, 1994). On this last point in particular, auto theft investigators have long recognized that that the “patrol officer is generally in the most opportune position to look for and come in contact with stolen vehicles” (Brickell & Cole, 1976, p. 46).

4. One respondent, for example, showed up to the interview in a stolen car and offered the second author a lesson on hotwiring (which the second author politely declined).

5. One female interviewee, for example, had stolen only three cars herself, but had served as a lookout during auto thefts committed by her boyfriend. In another instance, the most recent offense of a male respondent fell outside the one-month window, but we included him because in the preceding month he had “been in” cars that were stolen, just not by him.

6. This proportion does not include three arrestees awaiting adjudication at the time of the interview. Excluding them brings the total proportion of those convicted up to 73%—or 11 out of 15, respectively.

7. Some readers may question the ethics of paying respondents. Our response is twofold. First, subject payments were modest and not intended to unduly persuade them. Respondents could have earned much more through crime, and subject payments were designed to defray that opportunity cost. Second, research among active street criminals has found that active criminals rarely do anything for nothing. Appeals to altruism and/or the desire to help advance the cause of research may work, but they are strategies with a low probability of success among street predators (see, e.g. Jacobs, 1999).

8. We could detect no systematic differences in responses between previously-arrested and never-arrested offenders.

9. A consecutive number was given to respondents who provided identical aliases. Duplicates are numbered based on the order in which they were interviewed, not the order in which they appear in the text.

10. Readers should note that flight may be the only instance where defiance “counts” as an act of deterrence, although only because it is restrictively deterrent behavior designed to avoid arrest.

11. Wright and Decker (1994) were among the first to recognize the longitudinal nature of risk perception in the crime commission (see also Wright, 2001). Studying residential burglars, they noted that sanction threats had little influence on the decision to offend (typically called the “involvement” decision; see Cornish & Clarke, 2008) but once offenders were inside the target dwelling, sanction threats influenced where they searched and how long they stayed (i.e. the “event” decision). Jacobs et al. (2000) and Topalli and Wright (2004) also examined the dynamic nature of risk management over the course of offending, although those studies focused more on the “informal” risk of retaliation following drug robbery and carjacking, respectively.

Additional information

Notes on contributors

Bruce A. Jacobs

Bruce Jacobs is a professor of Criminology at the University of Texas, Dallas. He studies offender decision-making.

Michael Cherbonneau

Michael Cherbonneau is a PhD candidate in the Criminology Program at the University of Texas at Dallas. His current research draws heavily from the offender's perspective on street crime and violence with emphasis on situational factors and interactional influences on offending. His previous work appears in the British Journal of Criminology, Journal of Research in Crime and Delinquency and Justice Quarterly.