Offender Decision Making and Behavioral Economics Research Paper

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This research paper discusses potential applications of Behavioral Economic principles in Criminology. First it highlights aspects of the deterrence and economic approaches to offender decision making. Next, it introduces concepts and seminal findings from Behavioral Economics research outside of Criminology. Finally, potential areas of interesection between Behavioral Economics and Crime decision making are addressed.

Fundamentals

In Criminology, offender decision-making typically comes within the sub-field of deterrence. This approach shares much with economic models of decision-making and behavior (Nagin, forthcoming). First and foremost, under both perspectives, crime is sometimes a voluntary choice (Becker 1968; Bushway and Reuter 2008; McCarthy 2002). Offenders are not fully impelled toward crime by external circumstances, such as poor upbringing, financial disadvantage, or psychological infirmity, though such factors can contribute (sometimes greatly) to the likelihood of offending. Incentives relating to the potential consequences from crime impact offending decisions. Someone might refrain from crime to avoid an official sanction, such as incarceration or probation. Or, he or she might commit a crime believing the likelihood of detection is acceptably low. Each of these possibilities also pertains to various potential non-legal punishments, such as social stigma or compromised employment prospects (Grasmick and Bursik 1990; Pogarsky 2002).

Both the deterrence and economic perspectives recognize the paramountcy of perceptions. Risks and/or benefits only influence offending decisions insofar as they are perceived by potential criminal actors (Andeneas 1974; Apel and Nagin 2010). Such perceptions should reflect reality (though perhaps imperfectly) in at least two ways. First, an individual’s past experiences committing crime and either experiencing or avoiding punishment should influence their sanction risk perceptions in what economic theory terms a Bayesian updating process (Anwar and Loughran 2011). As a corollary, when an individual becomes aware that someone else has committed a crime and either experienced or avoided punishment, this too should affect that individual’s perceptions of the potential consequences from crime (Stafford and Warr 1993). Second, the sanctioning climate for an individual, reflected for example by law enforcement presence or arrest rates, also influences sanction risk perceptions (but compare Apel, Pogarsky, and Bates, 2009; Kleck et al. 2005; Lochner 2007). Finally, under both perspectives an individual’s perceptions of the potential consequences to them from crime influence the likelihood that individual will offend (Matsueda et al. 2006).

Economic models have influenced scholarship on a range of human activities, including health, finance, public policy, and even reproduction and marriage (Becker et al. 1977). And there is little doubt about the historic and continuing influence of economic perspectives on criminological thought. Beginning in the 1970s, however, scholars have documented either systematic departures from economic predictions, or behavioral regularities that could not be accounted for by economic principles. These findings, which are not routinely applied in criminology, evolved into contemporary Behavioral Economics, the topic of this research paper (e.g. Kahneman and Tversky 1979).

Some early Behavioral Economic findings involve, for example, the role of risk in decisionmaking. Consider the “lives saved-lives lost problem” of Tversky and Kahneman (1981: 453):

Imagine that the USA is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows:

If Program A is adopted, 200 people will be saved.
If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and 2/3 probability that no people will be saved. On average, each program is expected to save the same number of people: 1/3*600 + 2/3*0 = 200. But option B is considerably riskier, since one-third of the time, no one will be saved. In fact 72 % of individuals tend to choose option A, the less risky option that will save 200 people for sure. Next consider these reworded options:
If Program C is adopted, 400 people will die.
If Program D is adopted, there is one-third probability that nobody will die, and two-third probability that 600 people will die.

Notice that options C and D are functionally identical to options A and B. However, C and D are reframed in terms of how many will die (a loss) rather than how many will live (a gain). The second set of options produces a “preference reversal.” That is, 78% tend to prefer option D, the riskier option, over option C. Thus, individuals are far more comfortable taking a risk to avoid a loss than they are taking the nominally equivalent risk for gains. This finding is a profound challenge to economic theory, under which preference for risk should be invariant to the framing of choices.

Another seminal Behavioral Economic finding involves the “Endowment Effect,” which was illustrated by Kahneman et al. (1990). Half of a group of students were randomly given a coffee mug from the school bookstore. Individuals with a mug were asked to report their minimum selling price, whereas the remaining participants (who could see and touch the mugs) were asked to report their maximum buying price. The mean buying price was $2.25 while the mean selling price was $4.75. In economic terms, the researchers elicited functionally equivalent information from both groups: the dollar value of a mug. Yet those who received a mug only moments earlier valued it twice as much as those who did not. As before, under economic theory, the value of the mug should not depend on the manner of valuation. But it does.

Finally consider Ellsburg’s (1961) “two color” problem which explored ambiguity and choice. Respondents select one of two urns from which they will draw one ball. If the ball is red, they win a cash prize. Urn 1 contains 50 red balls and 50 black balls. Urn 2 also contains 100 red or black balls, but in unknown proportions. Although the probability of selecting a red ball and winning is identical across urns (in urn 2, the number of red balls is uniformly distributed from 0 to 100, hence, E[x] = 50), individuals routinely prefer Urn 1. This finding has been termed “ambiguity aversion,” which cannot be accounted for under the straightforward application of economic principles.

These and other findings evolved into a body of scholarship known as judgment and decision-making research or Behavioral Economics. Among the core themes of such research is that individuals often deviate from the norms of rational choice economics in predictable ways. Thaler (1996: 12) has described Behavioral Economics as “economics with a higher R2.” For his pioneering scholarship on Behavioral Economics, Daniel Kahneman, a psychologist, received the 2002 Nobel Prize in Economic Science.

Just as Behavioral Economics has provided insights into other forms of human decision-making, it has great potential to elaborate crime decision-making. For example, Pogarsky and Piquero (2003) found some evidence for a “resetting effect” to explain why, contrary to deterrence and economic theory, individuals might reduce their estimate of the future certainty of punishment after getting caught and punished. This explanation clearly foregoes the “rational actor” assumption of deterrence and economic theory and instead relies on the Behavioral Economic principle of a gambler’s fallacy in the interpretation of chance events (Gilovich 1983). Card players sometimes increase their bets after losing several consecutive hands because they feel they are due to win. Lottery participants decrease the amount wagered on a particular combination of numbers after that sequence has “hit” (Clotfelter and Cook 1993). The gambler’s fallacy stems from a desire for and resultant belief in consistency. Laboring under this expectation, individuals impute an interdependence among chance events where none exists. Most offenders believe getting caught is a somewhat rare occurrence. Thus when they do get caught, under the resetting explanation, they may reduce their future estimated certainty of punishment believing they would have to be exceedingly unlucky to be caught again. If this occurs in some individuals, it is a profound challenge to both the deterrence and economic perspectives on offending decisions under which individuals should elevate their perception of the risk of punishment after getting caught.

Earlier, this research paper addressed Behavioral Economic research relating to ambiguity and choice. Loughran et al. (2011) examined the interrelationship between ambiguity, sanction risks, and offending in a survey to college students with several hypothetical choice problems and data from the Pathways to Desistance study, a longitudinal investigation of serious adolescent offenders transitioning from adolescence to young adulthood. There was some evidence that individuals are “ambiguity-averse” for decisions involving losses such as criminal punishments. This means that a more ambiguous perceived certainty of punishment is a greater deterrent of some crimes than a nominally equivalent but less ambiguous one.

This finding suggests a modification of traditional deterrence and economic theory which, by omission, has presumed the irrelevance of ambiguity in perceived risk. Applying behavioral economic insights, then, generates potential policy implications not discernible from the straightforward application of deterrence and economic principles to the crime decision. In general the risk of detection for less serious crimes, such as breaking-in, stealing, theft and vandalism is quite low (e.g., Lochner 2007). Sherman (1990: 7) argued that law enforcement policies ought to exploit ambiguity aversion for these types of crimes by offering offenders “low certainty about whether the risk of punishment is high or low at any given time and place”. That is, police could rotate their enforcement across both offenses and places so that the risk of punishment is far more unpredictable than it has been in the past. With the same amount of resources, an alteration of police policy by manipulating the ambiguity of the certainty of arrest – would enable them to enhance the deterrent effect they have. Thus it might be worthwhile for law enforcement to exploit any vagueness in perceptions of the risk of punishment.

This research paper was intended to expose criminologists to the historical relationship between Deterrence, Economics, and Behavioral Economics. Already the few applications of Behavioral Economic principles to offender decision making have furthered our understanding of crime. These applications have helped identify policy issues not raised by the traditional approach to studying crime decisions, which is largely based on deterrence and economic principles. Further application of Behavioral Economic principles in studying crime decisions, thus, appears likely to bear even more fruit.

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