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刑事行为的经济学 (2)

Studies of tax cheating based on individual data by Clotfelter (1983),

Slemrod (1985), Witte and Woodbury (1985) and Klepper and Nagin (1989a)

all conclude that both the probability and the severity of punishment have

negative effects upon crime.

 

Many studies of correlation between crime rates and punishment based on

aggregated data appeared in the late 1960s and early 1970s. Using mostly US

data on the state or municipal level these studies indicate a negative association

between the certainty of arrest and the crime rate for different crime categories.

But crime rates are not generally found to vary with the severity of

imprisonment, although in some studies a deterrent effect is obtained for

homicide and a couple of other crime categories.

 

A necessary condition for interpreting the results of these correlation

studies, mostly carried out by sociologists, as estimates of deterrence is, of

course, that there is a one-way causation from punishment to crime, and none

in the opposite direction. The many subsequent cross-section criminometric

studies allowed for a two-way causation by various specifications of the general

model

 

 

where C = crime rate (number of crimes per population), P = probability of

punishment, S = severity of punishment, R = resources per capita of the

Criminal Justice System (CJS), and Zj, Zk, Zl = vectors of socioeconomic factors.

The crime function (3) assumes that the crime rate is a function of the

probability and the severity of punishment; equation (4) assumes that the

probability of punishment is a function of the crime rate and the resources

allocated to the CJS; and equation (5) assumes that the resources allocated to

the CJS is a function of the crime rate. Various socioeconomic factors are

included as explanatory variables in all three equations. In some studies police

resources is included as an explanatory variable in the crime function.

 

Among the first simultaneous regression analyses in this field we find

Ehrlich (1972), Phillips and Votey (1972) and Orsagh (1973). The first major

cross-section study appearing after Becker’s theoretical article was Ehrlich

(1973). He studies seven types of crimes in the US based on data for all states

from 1940, 1950 and 1960. He finds that the probability of imprisonment has

a statistically significant negative effect on all types of crime, and, except for

murder, not less for crimes against the person than for other crimes. The

severity of punishment has a similar effect, but here only about half of the

estimates are statistically significant. Crime is also found to be positively

related to median family income (presumably more assets to steal) and to

income differentials. Ehrlich’s study has been thoroughly scrutinized by several

authors, some of whom have given harsh evaluations of his work. Revisions,

replications and extensions of Ehrlich’s studies by Forst (1976), Vandaele

(1978), and Nagin (1978) resulted in more moderate deterrent effects of the

probability and severity of punishment. Moreover, Forst found that by

introducing variables thought to be correlated with the punishment variables,

such as population migration and population density, the punishment variables

became statistically insignificant. Nagin found that incapacitation could explain

a large part of the apparent deterrent effect. In a fierce attack on Ehrlich’s study

Brier and Fienberg (1980) conclude an empirical investigation of the Ehrlich

type that no deterrence effect of sanctions were found. A response to the

criticism from these and other authors is found in Ehrlich and Mark (1977).

Despite critical remarks by various authors, there is now a long list of studies

similar to the one by Ehrlich.

 

The great majority of correlation studies and cross-section regression

analyses show a clear negative association between punishment variables and

the crime rate. Almost without exception the coefficients of the punishment

variables (which usually are the elasticities of the crime rates with respect to the

punishment variables) are negative, and in most of the cases significantly so.

Furthermore, the estimated elasticities have rather high values. Eide (1994)

summarizes such estimates of 20 cross-section studies based on a variety of

model specifications, types of data and regression techniques (Ehrlich, 1973;

Sjoquist, 1973; Swimmer, 1974; Danziger and Wheeler, 1975; Phillips and

Votey, 1975, 1981; Chapman, 1976; Forst, 1976; Mathieson and Passell, 1976;

Blumstein and Nagin, 1977; Thaler, 1977; Avio and Clark, 1978; Heineke,

1978; Holtman and Yap, 1978; Mathur, 1978; Vandaele, 1978; Wilson and

Boland, 1978; Carr-Hill and Stern, 1979; Myers, 1980, 1982; Furlong and

Mehay, 1981; Sesnowitz and Hexter, 1982; Willis, 1983; Schuller, 1986;

Trumbull, 1989). Eide finds the median value of the 118 estimates of

elasticities of crime rates with respect to various measures of the probability of

punishment to be about !0.7. The median of the somewhat fewer severity

elasticities is about !0.4. The rates of clearance by arrest are usually considered

to be better measures of the certainty of sanction than the rates of conviction

(Andenaes, 1975, p. 347). The median of the elasticities of arrest is found to be

somewhat smaller than the median of the elasticities of conviction, but the

difference is not great.

 

Almost all criminometric time series studies give additional firm support

to the hypothesis that the probability of punishment has a preventive effect on

crime. The results concerning the effect of the severity of punishment is

somewhat less conclusive. Wahlroos (1981), using Finnish data, finds that the

severity of punishment has a statistically significant deterrent effect on larceny,

but not on robbery. Cloninger and Sartorius (1979), using data from the city of

Houston in the US, obtains a negative, but not statistically significant effect of

the mean sentence length. Wolpin (1978), using a time-series for England and

Wales in the period 1894-1967, finds that the estimates of the effects of the

length of sentences differ among types of crime, and are often not statistically

significant. Schuller (1986) on the other hand, using Swedish data, finds a

negative effect of the average time in prison. In an international comparison of

crime between Japan, England and the US, Wolpin (1980) obtains firm support

for the deterrent effect of the severity of punishment. These diverging results

are not surprising. The theories surveyed above tell us that if there is a

significant proportion of risk lovers in the population, and/or if the income

effect is greater than the substitution effect, and/or the effects of legal activities

are risky, and/or household protection expenditures are inversely related to the

severity of punishment, an increase in the severity of punishment may well

cause crime to increase on the macro level. If, however, in spite of these crime

increasing effects, macro studies show that crime is reduced when punishment

becomes more severe, there is all the more reason to believe in a deterrent

and/or a norm formation effect of punishment.

 

Among the several empirical studies concentrating on just one type of

crime, it is worth noticing that Landes (1978) obtained firm support for the

deterrence hypothesis for hijacking. In a study of draft evasion in the US,

Blumstein and Nagin (1977) avoid four of the main objections against

criminometric studies (see discussion of objections below): draft evaders are

likely to be well informed about possible sanctions; data are relatively error

free; as draft evasion can happen only once, there is no danger of confounding

incapacitation effects with deterrence effects; simultaneity problems caused by

over-taxing of the Criminal Justice System are unlikely because draft evasion

was given priority in the relatively well staffed federal courts. The authors

consider that their results provide an important statistical confirmation of the

existence of a deterrent effect. They find, however, that the severity of the

formal sanction has a modest effect on draft evasion compared to the stigma

effect of being arrested and convicted.

 

The economic model of crime suggests that changes in benefits and costs

of committing a particular type of crime might have effects on other types of

crime. If, for instance, the probability of being convicted for robbery increases,

some robbers might shift to burglary. One crime is substituted for another, just

as people buy more apples instead of oranges when the price of oranges goes

up. Such substitution effects between crimes have been estimated by Heineke

(1978), Holtman and Yap (1978) and Cameron (1987b). A certain number of

statistically significant effects are found, indicating that some crimes are

substitutes whereas others are alternatives.

 

As a whole, criminometric studies clearly indicate a negative association

between crime and the probability and severity of punishment. The result may

be regarded as a rather firm corroboration of the deterrence explanation

obtained from the theory of rational behavior: an increase in the probability or

severity of punishment will decrease the expected utility of criminal acts, and

thereby the level of crime. It should be remembered, however, that in some

studies the effect of an increase in the severity of punishment is not statistically

different from zero, and a statistically significant positive effect has also

occasionally been obtained.

 

6.2 Empirical Effects of Income, Norm and Taste Variables

In accordance with the theoretical models, most criminometric studies contain

income variables representing some of the benefits and costs of legal and/or

illegal activities. Looking first at the benefits of legal activities, the great

variety of proxies applied is striking: median family income, median income,

labor income to manufacturing workers, mean family income, mean income per

tax unit, mean income per capita, and so on. No systematic relationship appears

between the income measures applied and the estimates obtained. Although the

hypothesis that an increase in legal income opportunities decreases crime is not

rejected in most of the studies, others would not reject the inverse hypothesis

that an increase in legal income opportunities would increase crime. This

ambiguity in results might be due to the fact that the income measures used

represent benefits not only of legal activities, but also of illegal ones: Higher

legal incomes (mostly wages) tend to make work more attractive than crime,

but to the extent that higher legal income in a region produces a greater number

of more profitable targets for crime, the same empirical income measure may

be positively correlated with criminal activity. In addition, high legal incomes

also mean high incomes foregone when incarcerated, a cost of crime that will

have a negative effect on crime. If these mechanisms are at work

simultaneously, and their relative strength not universally constant, it is not

surprising that the results of various studies differ. The theory is not necessarily

deficient, but the methods applied do not distinguish between the two

mechanisms. The main problem is that the incomes of legal and illegal

activities are highly correlated, and that it is difficult (or impossible?) to find

empirical measures that with enough precision can distinguish between their

effects. The impact of income is further obfuscated by the fact that private

security measures increase with income, while higher income probably reduces

the marginal utility of each piece of property, and therefore also the measures

taken to protect property. These problems of correlation are not present in

studies where individual data are employed, such as Witte (1980) and Myers

(1980).

 

The estimates of the effects of gains to crime underscore the problem of

finding good empirical measures for theoretical variables. Whereas Ehrlich

uses median family income as a measure of gains to crime, other authors use

the same measure to represent legal income opportunities. A variety of other

measures of gains have been used, with diverse estimated effects on crime.

 

A large income differential may indicate that crime is a comparatively

rewarding activity for the very low income group (that may find a lot to steal

from the very rich). Estimates of the effect on crime of income differentials also

vary across studies. It is interesting to note, however, that a study which

includes variables of both legal and illegal income opportunities in addition to

one of income differential (Holtman and Yap, 1978), obtains significant

estimates of the expected signs for all three variables. Also Freeman (1995)

finds that wages from legitimate work and measures of inequality have the

expected effects on crime.

 

Unemployment is usually included in criminometric studies as a proxy for

(lack of) legal income opportunities. Unemployment will make crime more

attractive if the alternative is a life in poverty. The estimates of the effect of

unemployment on crime, however, are positive in some studies, and negative

in others. A comprehensive survey by Chiricos (1987) demonstrates that

unemployment in most studies seems to increase crime. He has reviewed 63

aggregate studies published in major journals of economics, sociology and

criminology containing 288 estimates of the relationship between

unemployment and crime. He finds that 31 percent of the estimates were

positive and statistically significant, whereas only 2 percent were negative and

statistically significant. Most of the non-significant estimates were positive. A

similar conclusion is obtained in a survey by Freeman (1995). Chiricos finds

little support for the hypothesis that unemployment decreases the opportunity

for criminal activity because of fewer and better protected criminal targets, an

hypothesis that has been launched in order to explain why in some studies a

negative relationship is obtained. Another explanation of such a negative

association, suggested by Carr-Hill and Stern (1973), is that unemployed

fathers stay at home and keep an eye on their delinquent sons. Furthermore,

differences in results might be the variability in unemployment insurance

schemes. In some places unemployment insurance is only slightly below

ordinary legal income, and in addition, some of the formally unemployed

receive income from short term jobs. According to economic models of crime,

the number of offenses will then not increase when unemployment increases.

A decrease may even occur. But if unemployment hits people without such

income opportunities, crime will increase.

 

According to criminal statistics the well-to-do are less likely to commit

crimes than the poor. Lott (1990c) provides a survey of various explanations of

this fact. In an empirical study of ex-convicts Lott (1992a) finds that the

reduction in income from conviction is extremely progressive, a result that

corroborates the hypothesis that an increase in the costs of committing crimes

has a negative effect on the amount of crime.

 

6.3 Effects of Norm and Taste Variables

In most studies various sociodemographic variables have been included.

Unfortunately, the reasons for including many of these variables are often not

thoroughly discussed. For instance, an explanation of how differences in

preferences (tastes) for legal and illegal activities may vary between groups of

people are often lacking. The various choices of empirical measures probably

reflect the availability of data. The estimated coefficients on the proportion of

non-whites in the population are usually found to be positive. It is difficult to

decide whether this result reflects differences in norms, in tastes, in abilities,

or in income opportunities. The high proportion of non-whites might also be

the result of a tendency among the police to concentrate search for offenders to

this group.

 

The predominance of young people among those arrested and convicted

suggest that age would be a very important factor in explaining crime. In many

studies such an effect is not found. One reason might be that there is not

enough variability in the proportion of youth between statistical units to

produce precise estimates. Also possible is that crime among young people is

not a consequence of their preferences (lack of socialization, and so on), but of

their meager legal income opportunities that possibly is adequately represented

by other variables. Young people are perhaps not different, just poorer.

Population density is in most cases found to be a statistically significant

explanatory variable. Population density may reflect various phenomena, such

as differences in social control, psychic diseases, and so on. The studies

reviewed are hardly suitable for a discussion of which of these mechanisms may

be at work.

 

Some studies use police expenditures or the number of police officers as

possible deterrent variables instead of measures of probability and/or severity

of punishment. Many of these studies show that police activity has a negligible,

and sometimes positive, effect on crime. On the other hand, Buck et al. (1983),

including both police presence and arrest rates as explanatory variables, find

that the former rather than the latter has a deterrent effect. The studies

concluding that police activities have a minor effect have tempted some authors

to dismiss deterrence as an efficient means against crime. It must be kept in

mind, however, that in the theoretical models the deterrence variables are the

probability and the severity of punishment, and not the police. There are at least

two interpretations of the minor effect on crime of expenditures on the police.

Either these expenditures do not have a noticeable effect on the probability of

punishment, or such expenditures result in a higher proportion recorded of

crimes, a fact that decreases the probability of punishment registered in the data

used.

 

In some studies routine activity and situational opportunity are included as

main explanations of crime (compare Cohen, Felson and Land, 1980).

Chapman (1976), for instance, finds that the female participation rate in the

labor market, a proxy for the proportion of unguarded homes, has a significant

positive effect.

 

It has been argued that the rational choice framework might be relevant for

certain property crimes, but not for violent crimes that are considered to be

‘expressive’ and not ‘instrumental’. Undoubtedly, the degree of

‘expressiveness’ differs among crimes. Many empirical studies may be

interpreted as support for the view that threat of punishment also has a

preventive effect on expressive crime. At least substantial elements of

rationality are revealed in a study of mugging by Lejeune (1977), in a study of

rape and homicide by Athens (1980), and in a study of spouse abuse by Dobash

and Dobash (1984). Although the effect of punishment may differ among types

of crime, evidence so far indicates that the rational choice framework is

relevant for all types of crime, and that analyses rejecting a priori that some

particular types of crime are deterrable are inadequate.

 

7. Methodological Problems and Criticism

 

Objections to economic studies of criminal behavior have been many and

occasionally fierce, see for example, Blumstein, Cohen and Nagin (1978),

Orsagh (1979), Brier and Fienberg (1980), Prisching (1982), and Cameron

(1988). In particular, studies based on aggregated data have been criticized. In

addition to attacks on the assumption of rational behavior, the main criticism

relates to interpretations of empirical results, to statistical identification of

equations and unobserved heterogeneity, to measurement errors, and to

operationalization of theoretical variables.

 

7.1 Interpretation of Empirical Results

It has been argued that many studies do not take into consideration that more

certain or more severe punishment may prevent crime by two different

mechanisms: either directly as a cost, or indirectly through norm formation. A

type of crime that is cleared up more and more seldom, or sanctioned more and

more leniently, will easily be considered as not very serious by the population.

Individual norms may adjust accordingly, people’s crime aversion decrease,

and consequently the level of crime increases. It seems true that in most

empirical studies no effort is made to distinguish between this mechanism and

the more direct deterrence effect of an increase in punishment. Results are often

interpreted as a deterrent effect, and not as a general prevention effect where

also the indirect norm formation mechanism is included.

 

Can criminometric studies possibly distinguish between the two

mechanisms? In cross-section studies one can imagine that people living in

regions where the clear-up probability is low tend to consider crime as less

serious than do people in other regions. If such differences in norm formation

exist, they are probably more predominant the longer the distance between the

regions that are compared, for instance in international comparisons, or in

studies of states in the US. It is not probable that norm formation differ among

the districts within a rather small region, especially if news about punishment

can be assumed to be more or less the same, and mobility of people is high. The

effect on crime of variation in the severity of punishment found in studies using

data from rather small areas within a region can therefore hardly be explained

by a norm formation mechanism. Where one obtains a negative relationship

between the crime rate and the clear-up probability when data representing

counties of only one state (Chapman, 1976; Avio and Clark, 1978; Trumbull,

1989), or of police districts in a metropolitan area (Mathieson and Passell,

1976; Thaler, 1977; Furlong and Mehay, 1981), one will have reason to believe

that the norm formation mechanism must be of minor importance. The same

holds true for some studies of substitution of crime which show that an increase

in punishment of one type of property crime will have a statistically significant

effect on the number of other property crimes. It is not probable that a higher

probability of being punished for burglary has any effect on the norms

regarding robbery. It is more reasonable to think that robbery is substituted for

burglary because of a change in relative costs.

 

Even if the importance of each mechanism is regarded as uncertain, the

estimates obtained in various studies are still of interest. Not only from a

political point of view, but also from a scholarly one, it may be useful to know

that the probability of punishment has a certain negative effect on crime,

notwithstanding the mechanism(s) involved.

 

Another possible uncertainty concerning the evaluation of results is that

there might exist an underlying phenomenon, unknown and/or not studied, a

phenomenon that at the same time produces a low crime rate and a high

probability of punishment. Individual norms may create such a relationship. If

people in one region appreciate each others’ welfare more than on average, they

will both have a relatively strong aversion against criminal infringements

against others, and a high interest in clearing up crimes in order to decrease

crime in general. If such differences in norms exist, they must be rooted in

cultural differences of some kind. Possibly, such differences can develop if

regions are situated far from each other, or if distance in time is substantial. For

the smaller regions, such differences seem less realistic.

 

Theories of criminal behavior show that a whole series of ‘causes’ may be

involved, and that recorded differences in crime between regions, gender, races,

drug abuse, and so on might be related to more fundamental explanations of

crime, involving norms, wants, opportunities and circumstances. The intricacy

of relationships shows the difficulty in interpreting the estimates of the effects

on crime of such variables.

 

7.2 Identification and Unobserved Heterogeneity

If, in an empirical study, one finds that crime rates and probabilities of

punishment are negatively correlated, one cannot easily distinguish between the

hypothesis that higher probabilities of punishment cause lower crime rates

(equation (3)), or the hypothesis that higher crime rates cause lower

probabilities of punishment (because of police overloading, equation (4)). If

such a simultaeneity exists it is not acceptable to use the method of ordinary

least squares (OLS) to estimate each equation. Using the Hausman test Layson

(1985) and Trumbull (1989) have for homicide found that simultaneity was not

a problem in their data, and OLS could be applied. If simultaneity is present,

the standard procedure to identify the first relation, the crime function, consists

of introducing exogenous variables that have an effect on the probability of

punishment, but not on the crime rate. In an excellent discussion of the

(im)possibility of identifying the crime function in macro studies, Fisher and

Nagin (1978, p. 379) declare that they know of no such variables. The

consequence of this view is that all attempts of identification in empirical

macro studies are illusory. The equations may be technically identified, but by

false assumptions. Using panel data for police districts, Aasness, Eide and

Skjerpen (1994) claim to have solved this problem. In studies based on

individual data, the question of identification is much less serious, see above.

It is interesting to note that in the cross-section studies reviewed by Eide

(1994) the method of ordinary least squares tend to give smaller estimates of

the elasticities of crime with respect to the probability and severity of sanctions

than do the methods of 2 stages least squares, full information maximum

likelihood, and other more advanced methods. This is what might be expected

if a simultaneous equation bias is present. The difference in estimates is,

however, not great.

 

Cornwell and Trumbull (1994) point to the fact that aggregate cross-section

econometric techniques do not control for unobserved heterogeneity.

Addressing this problem by use of a panel dataset of North Carolina counties,

they obtain more modest deterrent effects of the arrest and conviction rates than

those obtained from cross-section estimation.

 

7.3 Measurement Errors

Since a substantial part of all crimes is not registered by the police, one may

have serious doubts about the results of empirical studies based on official

statistics. However, the problem of underreporting is not damaging to empirical

research if the rate at which actual crimes are reported is constant across

regions (in cross-section studies) or over the years (in time-series studies). This

seems to be an implicit assumption in most studies. Blumstein, Cohen and

Nagin (1978) explain how differences in ‘dark numbers’ between observational

units create a spurious negative association between the recorded crime rate and

the probability of clearance. Aasness, Eide and Skjerpen (1994) introduce, in

addition to the recorded crime rate, a latent variable for the real crime rate, and

relates the latter to the former by a linear function and a stochastic term. By

this procedure measurement errors are given an explicit stochastic treatment,

that allows for a distribution of ‘dark numbers’ among police districts.

 

The existence of a substantial dark number of crime, has fostered a certain

interest in using victimization studies to obtain more reliable data. These

studies give more or less similar results as those based on recorded crimes. A

prominent example is Goldberg and Nold (1980) who find that the reporting

rate, and thus the probability of clearance, has a great impact on the amount of

burglaries. Another comprehensive study is Myers (1982) who obtains almost

the same estimates of the effects of sanction variables by correcting crime rates

by victimization data.

 

7.4 Wrong Beliefs

If people have wrong beliefs, one may also question the validity of estimates of

the effects of punishment variables and various socio-economic factors.

Presumably, the true risk of sanction is not known to the individual. Empirical

studies suggest that people tend to overestimate the average risk, while at the

same time believing that the risk they themselves run is lower than average.

Offenders, however, seem to be better informed. Wilson and Herrnstein (1985,

p. 392) refer to a study where over two thousand inmates of jails and prisons

in California, Michigan and Texas were interviewed about their criminal

careers. The study revealed a close correspondence between the actual and

perceived risk of imprisonment in Michigan and Texas, whereas a somewhat

weaker correspondence was found in California. The study further corroborated

the theoretical result that an increase in the probability of imprisonment will

decrease crime.

 

Even if beliefs to some extent are wrong, macro studies might still be of

some value. It may well be that some persons do not observe a given change,

and also that they have been mistaken in their beliefs. But the gradual change

from very lenient to very harsh punishment will certainly be registered by at

least a part of the population, and behavior will change, more or less, as already

explained.

 

7.5 Various Operationalizations

Many studies give weak arguments for the choice of theoretical variables (for

example, of variables of punishment, benefits and costs), and of their empirical

measures. Orsagh (1979) argues that the great diversity of variables in

empirical criminology shows that no good theory exists, and that macro studies

of the usual kind have little interest. The objection is certainly relevant, but the

consequence is not necessarily that such analyses should be avoided. Problems

of operationalization do not make a theory irrelevant. Better than to drop such

studies is to continue the theoretical discussion about determinants of crime,

and produce more empirical studies, in order to improve the foundation for

choosing acceptable measures of theoretical constructs. If various

operationalizations produce similar results, there is reason to believe that the

theory is robust to such differences. Then, one might even conclude that the

theory is quite good, despite the fact that each and every formal test of

significance is of limited value.

 

The studies reviewed above reveal quite consistent results as far as the sign

of effects of the punishment variables is concerned. The insensitivity of these

results to various operationalizations is comforting. The effects of income

variables are less consistent, a result that might either imply that economic

factors do not have a uniform effect on crime or that some, or all, of the

operationalizations tried so far are unacceptable.

 

Several measures of punishment variables have been employed. When only

one type of sanctions is included, one would expect that the effect assigned to

this variable really includes effects of punishment variables correlated with the

one included. A better alternative is to use several sanctions simultaneously, as

proposed and employed by Witte (1980) and others.

 

8. Future Research

 

The reasons why people are more or less law-abiding are manifold. The

norm-guided rational choice framework seems to provide a suitable framework

for discussing various theories of crime, including characteristics of individuals

and circumstances (Cornish and Clarke, 1986, p. 10). The framework allows

for a simultaneous consideration of many possible determinants of crime. The

abstract model is a means of gaining insight into the elements of rational

behavior, and it permits filling bits of information into a broader context. In

criminometric studies it might be useful to distinguish between norm variables

(representing desires for various courses of action), want (or taste) variables

(representing preferences for various outcomes), ability variables (representing

intellectual, psychic and physical characteristics), punishment variables

(representing the probability and severity of punishment), individual economic

variables (representing legal and illegal income opportunities) and

environmental variables (other than punishment and economic variables). A

survey of variables used in various empirical studies of crime organized

according to this typology is given in Eide (1994). Variations in crime among

individuals are traditionally related to gender, age, race, and so on. A deeper

understanding must be sought in variations in norms and wants, in abilities,

and in the opportunities, rewards and costs determined by the environment.

Variations in crime among individuals may be caused by differences in all these

elements of the rational choice framework. Certain individuals may have more

crime-prone (or less crime-averse) norms than others. The special norm

structure may be a result of genetic, biological or psychological characteristics,

an effect of lack of socialization, or a consequence of cultural conflict, cultural

deviance, or anomie. Inherited or acquired abilities may restrict legal activities

more than illegal ones. In an empirical study of college students Nagin and

Paternoster (1993) found that both individual differences (poor self-control) and

the costs and benefits of crime were significantly related to crime. The

formidable task for the future may be found in a proposition for social science

research by the Nobel Prize Winner Niko Tinbergen (who should not be

confused with J. Tinbergen who has won the Nobel Memorial Prize in

economics) that four levels of analysis should be put together: the biological

(genetical), the developmental (how an individual is socialized), the situational

(how the environment influences behavior), and the adaptive (how a person

responds to the benefits and costs of alternative courses of action).




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