Download Linking Micro & Macro: Rational Choice Approach to Social Capital & Collective Efficacy and more Study Guides, Projects, Research Criminology in PDF only on Docsity!
CHAPTER 8
RATIONAL CHOICE RESEARCH IN CRIMINOLOGY:
A MULTI-LEVEL FRAMEWORK
Ross L. Matsueda
INTRODUCTION A challenging puzzle for rational choice theory concerns the causes and control of criminal behavior. Crime is a difficult case for rational choice. Compared to market behavior, financial decisions, and corporate crime, in which institutionalized norms frame decision-making in the terms of rationality, street crimes are often characterized as irrational and sub-optimal. Street criminals are commonly portrayed by the media and a few social scientists as impulsive, unthinking, and uneducated, and their behaviors as beyond the reach of formal sanctions (e.g., Gottfredson and Hirschi 1990). Consequently, support of rational choice principles for criminal behavior would provide strong evidence for the perspective (Matsueda, Kreager, and Huizinga 2006). Crime is an important arena for investigating rational choice for another reason: utilitarian principles, and their accompanying psychological assumptions, undergird our legal institution (e.g., Maestro 1973). This connection is rooted in writings of members of the classical school, particularly Jeremy Bentham and Caesare Beccaria. Bentham ([1789] 1948) argued that happiness is a composite of maximum pleasure and minimum pain, and that the utilitarian principle—the greatest happiness for the greatest number—underlies morals and legislation. Punishment by the state constitutes one of four sanctions—political, moral, physical, and religious—that shape pleasures and pains. Influenced by the moral philosophers of the
Enlightenment, Beccaria ([1764] 1963) assumed that criminal laws reflect the terms of a social contract between members of society and the state. Individuals receive protection of their rights to personal welfare and private property in exchange for relinquishing the freedom to violate the rights of others. The rights of individuals are protected by the state through deterrence, threatening potential transgressors with just enough punishment to outweigh the pleasures of crime. With his writings, Beccaria attempted to reform the unjust and brutal legal system of eighteenth-century Europe by developing a rational system in which laws are specified clearly and a priori (so individuals have full information about the consequences of their acts), judicial discretion is eliminated (so all citizens are equal in the eyes of the law), and punishments are made certain, swift, and no more severe then needed to deter the public from crime (Matsueda et al. 2006). Because of the obvious implications for public policy, theory and research on rational choice and crime has focused primarily on the question of deterrence: Does the threat of punishment by the state deter citizens from crime (see Zimring and Hawkins 1973)? Recent research concludes that the threat of formal sanction does deter, but that the effects are modest in size and perhaps conditioned by social context (e.g., Zimring and Hawkings 1973; Nagin 1998). Less research has moved beyond deterrence to examine incentives outside the scope of formal punishment, such as psychic rewards and costs, within a rational choice theory of crime. This modest but growing literature has underscored the importance of rational choice theory for understanding and explaining criminal behavior (e.g., Clarke and Cornish 1985; Cornish and Clarke 1986). At this time, rational choice remains an important, but still minority position in criminology. This is partly because of the historical dominance of sociologists in criminology,
notable exceptions (e.g., Gambetta 1993), most of this research is not explicitly rooted in rational choice perspectives. This chapter uses a multi-level framework to discuss advances in rational choice research on crime. Rather than providing an exhaustive review of pertinent research, I instead organize the discussion around one important theoretical issue, the integration of micro and macro levels of explanation. Thus, the underlying assumption that gives structure to the chapter is that rational choice principles offer a parsimonious micro-foundation for macro-sociological concepts and causal mechanisms. The task then, is to identify how macro-level social contexts condition micro-level processes (individual decisions), and how micro-processes, in turn, produce macro- level outcomes (social organization) (e.g., Coleman 1990). I begin by discussing an individual-level model of rational choice, deterrence, and criminal behavior. A rich and voluminous literature has developed around the question of general deterrence—do threats of formal sanction by the legal system deter the general public from crime? I review the models and different research designs used in empirical studies, and then discuss the individual-level rational addiction model of drug use (Becker and Murphy 1988; Becker 1996). To link individual-level models to macro-sociological models, I review the micro-macro problem in sociology, and the potential utility of using a rational choice model as a micro-foundation for macro-level causal relationships. Here, I summarize Coleman’s (1990) position, which emphasizes the crucial task of identifying micro-to-macro transitions. I then use this multi-level framework to analyze two productive lines of research in criminology: (1) social capital, collective efficacy, and neighborhood controls (Sampson, Raudenbush, and Earls 1997); and (2) the protection racket of organized crime (Gambetta 1993). Theoretically, I treat these processes as examples of what Edwin Sutherland (1947) termed,
“organization against crime” and “organization in favor of crime,” as the defining features of his theory of differential social organization (see Matsueda 2006). In each instance, I stress the utility of rational choice at the individual level, the broader context which conditions individual purposive action, and the micro-to-macro transitions that lead to social organization either against or in favor of crime. The extent to which these lines of research capitalize on a rational choice micro- foundation varies considerably. For example, collective efficacy theory has been treated as a purely macro-level process linking social disorganization, social capital, and informal social control into a macro-structural theory of crime. Therefore, I show how rational choice can provide a micro-foundation for social capital and collective efficacy, which opens new theoretical puzzles and empirical research questions. In contrast, Gambetta’s (1993) analysis of the Sicilian Mafia’s protection racket draws explicitly on a rational choice perspective to explain the origins and functioning of privatized protections. Therefore, I explicate the individual-level rational choice argument and show how it links to a macro-level system of illicit action. In the final section, I discuss avenues for future research within a multi-level framework.
utility function is concave, and C will have a greater effect than pc. Finally, if a person is risk- neutral, the utility function is linear, and pc and C will have identical effects. If we ignore the role of legitimate opportunities, that is, assume the expected utility from non-crime is zero, E(UN )= 0, we can specify that a crime will occur when E(UC ) > E(UN ) = 0 , so that from equation (1), a crime will occur when the following holds: U(R) > pc U( C) (3) That is, when the returns to crime exceed the punishment, weighted by the probability of detection, an individual will commit a crime. The policy implication here is that by increasing the certainty and severity of punishment, the probability of crime will be reduced. Crime can also be reduced by lowering the rewards to crime—by defending public spaces through increasing surveillance, employing security guards, and using technological advances in metal detection, alarms, locks, fences, and the like. Historically, following Becker’s (1968) work, most microeconomic research on crime has focused on the policy implications of increasing the certainty and severity of punishment. Of course, legitimate opportunities are important for criminal decisions, as most members of society obtain some utility from non-criminal activities.^1 Bueno de Mesquita and Cohen (1995) present a simple model that considers legitimate opportunities by specifying a utility function for non-criminal activity: E(UN ) = pi U(I) + (1 - pi ) U(W) (4) where I is income (returns to conventional activity), pi is the probability of obtaining I (through having high social status, resources, or talent), and W is welfare or the social safety net for those who cannot obtain I (i.e., p (^) i = 0 ). Then, the utility function for criminal behavior becomes:
(^1) With his control theory, Hirschi (1969) specified that people who are committed to non-criminal activities are less likely to deviate for fear of jeopardizing their investment.
E(UC ) = (1 – pc ) [U(R) + p (^) i U(I) + (1 - pi ) U(W)]+ pc U(R + W – C) (5) In other words, the utility from crime is a function of the returns to crime plus income from conventional activity (each weighted by the probability of getting away with crime), plus the returns to crime and conventional activity minus the punishment for crime (each weighted by the probability of getting caught and punished). This assumes that the criminal’s booty from crime is not confiscated upon arrest. (Becker 1968). Note that when the probability of getting caught is zero (p (^) c = 0), the utility from crime is equal to the returns to crime plus the returns to non-crime. When the probability of getting caught is 1.0 (pc = 0) , the utility from crime is the returns to crime, plus welfare, minus the penalty. A crime will be committed when E(UC ) > E(UN ) ; therefore, from (4) and (5), a crime will occur when the following holds: (1 – pc ) [U(R) + p (^) i U(I) + (1 – pi ) U(W)]+ pc U(R + W – C) > pi U(I) + (1 – pi ) U(W) (6) Or, equivalently, stated in terms of the risk of punishment, crime will occur when pc < U(R) / U(C)+ pi U(I – W) (7) That is, crime occurs when the probability of detection is less than the ratio of the reward to the sum of the punishment plus the returns to noncriminal activity weighted by the probability of realizing those returns. From a policy point of view, the probability of crime can be altered not only through criminal justice policies that increase the certainty and severity of punishments or that change defensible space (and thereby reduce opportunities for crime), but also through policies that increase conventional alternatives to crime. For example, job training, higher education, and other programs to enhance human and social capital may reduce the attractiveness of crime by increasing pi , the probability of obtaining a desired income from legitimate activities. Returns to conventional activity include not only income but also social status and prestige, self-
the subjective probability distribution is assumed to fall on the value of the objective probability (Nagin 1998). Empirical research from a subjective expected utility framework uses survey methods to measure perceived risk of punishment directly from respondents, rather than inferring it from behavior through the method of revealed preferences (e.g., Kahneman, Wakker, and Sarin 1997). Early empirical research by sociologists used cross-sectional data and found small deterrent effects for certainty of punishment but not for severity (e.g., Williams and Hawkins 1986). Respondents who perceive a high probability of arrest for minor offenses (like marijuana use and petty theft) report fewer acts of delinquency. Such research has been criticized for using cross-sectional data in which past delinquency is regressed on present perceived risk, resulting in the causal ordering of the variables contradicting their temporal order of measurement. To address this criticism, sociologists have turned to two-wave panel models and found, for minor offenses, little evidence for deterrence (perceived risk had little effect on future crime) and strong evidence for an experiential effect (prior delinquency reduced future perceived risk) (see Williams and Hawkins 1986; Paternoster 1987). Piliavin et al. (1986) specify a full rational choice model of crime, including rewards to crime as well as risks, and find, for serious offenders, that rewards exert strong effects on crime, but perceived risk do not. Recent longitudinal survey research has used more sophisticated measures of risk, better- specified models, and better statistical methods. Matsueda et al. (2006) specify two models based on rational choice. First is a Bayesian learning model of perceived risk, in which individuals begin with a baseline estimate of risk, then update the estimate based on new information, such as personal experiences with crime and punishment or experiences of friends. Second is a rational choice model of crime, in which crime is determined by prior risk of arrest, perceived opportunity, and perceived rewards to crime, such as excitement, kicks, and being seen
as cool by peers (see also McCarthy 1995; Hagan and McCarthy 1998). Using longitudinal data from the Denver Youth Survey, Matsueda et al (2006) find support for both hypotheses: perceived risk conforms to a Bayesian updating process (see also Pogarsky et al. 2004; Anwar 2011), and delinquency is determined by perceived risk of arrest, rewards to crime, perceived opportunities, and opportunity costs (see also Pogarsky and Piquero 2003). Similarly, Lochner (2007) uses two national longitudinal datasets and finds support for an updating model of “beliefs about the criminal justice system” and a deterrent effect of perceived risk. Sherman (1990) has observed that the deterrent effect of interventions, such as police crackdowns or passage of more punitive legislation, often has an initial deterrent effect that diminishes with time. A simple explanation of this decay in deterrent effect is that criminals initially overestimate the effect of the policy change on certainty of getting caught, and consequently through Bayesian updating, adjust their risk perceptions downward (Nagin 1998). A second explanation of initial decay in deterrence derives from decision theorists’ concept of “ambiguity aversion.” In contrast to risk aversion, which refers to an event in which a probability can be assigned to every outcome, ambiguity aversion refers to an event in which the probabilities of outcome are unknown (Epstein 1999). A new intervention may increase the uncertainty of the risk perceptions of potential offenders, which will create a deterrent effect if offenders find uncertainty or ambiguity aversive. Over time, this ambiguity over risk may diminish, as offenders adapt to the new policy and sharpen their estimates of true risk. The important point here is that, even if the policy did not change the true certainty of punishment or the mean values of offenders’ subjective perceptions of risk, it may change the variance of risk perceptions, which will deter crime if offenders are risk averse (Nagin 1998). Sherman suggested that a policy of varying police crackdowns over time and space may increase
a small and inconsistent effect. Consistent with rational choice, returns to crime—particularly psychic returns, such as excitement and high status among peers—and opportunity costs are both important predictors of future criminality. Note that models of deterrence and crime are essentially depicting a two-person game between the criminal and the criminal justice system. Most research on deterrence, however, treats individual criminal behavior as endogenous with respect to the actions of the criminal justice system, which are assumed exogenous (that is the endogeneity of legal actors is treated as a nuisance to be overcome). Nagin (1998) and Swaray, Bowles, and Pradiptyo (2005) review economic research on the effects of interventions on the criminal justice system—in which the intervention is truly exogenous. A more complete treatment would model the legal system and the criminal as interdependent actors, using game theory—the use of mathematical models to tease out interdependent decision-making. McCarthy (2001) reviews applications of game theory, particularly two-person games, to the relationship between criminals and the legal system (see also Bueno De Mesquita, and Cohen 1995). McAdams (2009) reviews the relevance of game theory beyond the prisoner’s dilemma for law and legal analysis. By extending the equations used earlier, I can give an illustrative example, based on research by Bueno de Mesquita and Cohen (1995), of the utility of game theory in theorizing about criminal behavior, and drawing links between macro-structures and social interactions. Bueno de Mesquita and Cohen (1995) show how an unjust social structure—containing selective barriers to human and social capital that undermine job attainment and wages—can change the incentive structure for criminal decisions. For individuals, there is uncertainty about fairness or justice in the social system. Therefore, we can define pj as a measure of individual perceptions of the probability of justice or fairness in social institutions, and (1 – pj ) as a measure
of perceived probability that society is unfair. The likelihood that an individual will be treated fairly by social institutions will affect the probability of returns to conventional activity. A fair society will allow individuals to gain income from conventional sources (I) based on pi , the probability of getting a good job, which is based on ability, human capital, and social capital. An unfair society will prevent some qualified individuals from getting good jobs, which implies that those individuals will receive zero income from conventional jobs (I = 0) , making total benefits equal to welfare, W. Therefore, if we incorporate fairness into our earlier equation (4), the utility from non-crime becomes: E(UN ) = pj [ p (^) i U(I) + (1 – pi ) U(W)] + (1 – pj ) U(W) (8) In a completely fair society, in which all members perceive fairness, pj = 1, utility from non- crime is pi U(I) + (1 – pi ) U(W), as above. But in a completely unfair society, in which all members perceive unfairness, pj = 0, utility from non-crime is reduced to welfare, U(W). Then, modifying equation (5), the utility from crime, allowing fairness to vary, is: E(UC ) = (1 – pc ) {U(R) + pj [pi U(I) + (1 – pi ) U(W)]+ (1 – p (^) j ) U(W)} + p (^) c U(R + W – C) (9) A crime will be committed when E(UC ) > E(UN ) ; therefore, from (8) and (9), a crime will occur when the following holds: (1 – pc ) {U(R) + pj [ p (^) i U(I) + (1 – pi ) U(W)] + (1 – pj ) U(W)}+ p (^) _c U(R + W – C)
pj [ p_ (^) i U(I) + (1 – pi ) U(W)] + (1 – p (^) j ) U(W) (10) Stated in terms of perceived probability of injustice, crime will occur when pj < U(R) – pc U(C) / pc pi U(I - W) (11) and it follows that p (^) j pc pi U(I - W) < U(R) – pc U(C) (12)
by raising the marginal utility of current drug use. Second is a forward looking model, in which current consumption is a function of anticipated future utility: an individual expecting to consume drugs in the next period will consider the utility from that future drug use when maximizing utility of current drug consumption. Individuals recognize that consumption of beneficial goods (e.g., sex) increases future utility, whereas consumption of harmful goods (e.g., illicit drugs) reduces future utility. Thus, in making current decisions, rational actors trade off the present utility of drug consumption with the future utility of drug addiction. The model implies strong inter-temporal complementarity for drug consumption: consuming drugs at time one will be highly correlated with drug consumption at time two. A myopic (or backward looking) model is a special case in which individuals fail to consider utility of future behavior on current choices. Empirical research on rational addiction models of drug use models the relationship between drug prices and drug use over time (e.g., Becker, Grossman, and Murphy 1994; Grossman and Chaloupka 1998). Drug use at time t is specified as a function of price at time t, drug use at time t - 1 (backward-looking), and drug use at time t + 1 (forward-looking).
Ct Ct 1 Ct 1 1 Pt 2 t 3 t 1
where Ct is present consumption, Ct-1 is past consumption, Ct+1 is future consumption, θ is a parameter reflecting addiction, β is a time discount factor ( 1/[1 + r] ) assumed to be less than one, θ 1 is a coefficient for price Pt , and
Ct 1 Ct 2 Ct 1 Pt 1 2 t 1 3 t
Ct 1 Ct Ct 2 1 Pt 1 2 t 3 t 2
To address the obvious endogeneity problem, price at time t – 1 is used as an instrument for drug use at time t – 1 , price at time t is used as an instrument for drug use at time t , and price at time t
+ 1 is used as an instrument for drug use at t + 1. Identification is achieved by the perhaps plausible assumption that price at t – 1 and price at t +1 have no effects on drug use at time t , net of price and time t. Such models have the weakness of assuming perfect foresight, although partial foresight models are tractable here. Using data from the national Monitoring the Future dataset as well as data on marijuana prices (from Drug Enforcement Agents’ attempts to purchase marijuana in 19 cities for 1982- 1992), Pacula et al. (2000) estimates price elasticity of demand, estimating that a one percent increase in price reduces demand by about 30 percent. They find, however, that peer effects and attitudes are the strongest predictors of marijuana use. Using the same data, Chaloupka et al. (1999) find that youth living in decriminalized states were more likely to use marijuana than in other states, and that youths’ consumption patterns were responsive to median fines for possession of marijuana. In contrast, Farrelly et al. (1999), using fixed-effects models on the National Household Survey on Drug abuse, find no relationship between fines and marijuana use. This line of research assumes that youth are aware of the objective costs of marijuana use, and use those costs in their decision-making. It has been criticized for assuming that youth are able to anticipate future prices of marijuana accurately. On this point, with respect to cigarettes, Gruber and Köszegi (2001) argue that a more reasonable assumption is that individuals are able to anticipate future changes in excise taxes because they tend to be publicized, whereas increases in cigarette prices are rarely announced in advance. Using data on excise taxes, Gruber and Köszegi (2001) find support for a forward-looking model of rational addiction for cigarette smoking. The theory of rational addiction is an audacious attempt to explain addictive behavior— an act that is almost always deemed irrational—within a conventional rational choice framework.
THE MICRO-MACRO PROBLEM IN SOCIOLOGY
Sociologists have long attempted to overcome the bifurcation of the discipline into separate sub- disciplines of social psychology and social organization by identifying specific linkages between micro- and macro-levels of explanation (e.g., Hechter 1983; Alexander, Giesen, Muench, and Smelser 1987; Huber 1991). Such linkages would presumably help overcome criticisms lodged at myopic theorizing and research operating at single levels. For example, structural theories— and the macro-level research they stimulate—typically explain system outcomes based on causal mechanisms operating at the macro-level, thus ignoring the role of individual actors. Such theories have been criticized for being crudely functionalist (a system outcome is explained by a system characteristic defined by its function), obviously teleological (a system outcome is explained by a system level purpose), and unlikely to identify effective interventions to bring about positive social change (e.g., Coleman 1990). Individual-level theories of purposive action—and the micro-level research they stimulate—explain individual outcomes based on causal mechanisms operating at the individual level, with macro outcomes assumed to be mere aggregations of such processes. These theories have been criticized for trivializing the role of social organization and oversimplifying the micro-macro problem. 3 Among the many proffered solutions to the micro-macro problem (e.g., Sawyer 2001), perhaps the most distinctive approach, outlined in a series of papers and chapters by Coleman (1983, 1986, 1990), specifies that macro-level relationships are brought about by micro-level processes, and vice-versa, through a series of micro-macro transitions. Figure 1 illustrates these relationships. Macro-social theories focus on link 4 between a macro-level context (e.g., social
(^3) Economists have attempted to model “social interaction effects,” such as peer effects, using standard economic approaches, including using the method of revealed preference to capture utility maximization processes, andinstrumental variables to identify social interaction effects, which are unmeasured peer effects disentangled from contextual effects, selection effects, and correlated individual effects (see Manski 1995; Brock and Durlaf 2001).
structure) and a macro-level outcome (e.g., rates of crime). Micro-individual theories focus on link 2 between a micro-level predictor (e.g., human capital investment) and a micro-level outcome (e.g., earnings). These two levels are connected by two cross-level linkages. Link 1, commonly investigated in sociological studies of individual behavior, shows how macro-context (e.g., social class) conditions individual attributes (e.g., human capital investments), which in turn produce micro-level outcomes (e.g., earnings) through a micro-level theory (e.g., micro- economic theory). The other cross-level relationship, link 3, is less studied and more complicated. Here, individual outcomes combine to produce macro-level outcomes (e.g., social organization). Stated differently, the question becomes, “How are interdependencies formed among individual actors to organize action?” Here, Coleman uses the concept of emergence to show how “collective phenomena are collaboratively created by individuals yet are not reducible to individual action” (Sawyer 2001). For Coleman (1990, p. 5), emergence is tied to purpose in interaction: “The interaction among individuals is seen to result in emergent phenomena at the system level, that is, phenomena that were neither intended nor predicted by the individuals.” This allows for more complexity than the simple assumption, made by reductionists and some economists, that collective phenomena are merely the aggregations of individual actions. The ways in which individual purposive actions combine to create macro-level outcomes vary by the complexity of the social organization being constituted and reconstituted. In the simplest case of bilateral exchange between two actors, an agreement or contract governing the exchange is the macro-level outcome. In this case, the macro outcome is intended by the individuals. Bilateral exchange between two parties can also result in externalities, which are costs or benefits to third party stakeholders—usually in the form of a public good—for which compensation is neither collected nor paid. Thus, parties to the exchange do not necessarily reap
