STRUCTURAL SOURCES OF VARIATION IN RACE-AGE-SPECIFIC RATES OF OFFENDING ACROSS MAJOR U.S. CITIES

STRUCTURAL SOURCES OF VARIATION

OF OFFENDING ACROSS MAJOR U.S. CITIES*

IN RACE-AGE-SPECIFIC RATES

ROBERT J. SAMPSON University of Illinois

Recently, much attention has been focused on the structural determinants of variations in crime rates across US. cities. Virtually all research in this area has utilized aggregate reported offense rates as the dependent variable. While it provides a good indicator of the total volume of crime, the aggregate crime rate suffers two major disadvantages-it obscures individual- and aggregate-level effects, and it does not allow testing of criminological theory which specifies differential effects of economic variables (for example, poverty, inequality) on offending rates for various population subgroups (for example, black adults, white adults). The present study addresses these issues by examining the economic determinants of age, race, and crime-specij% offending rates for a sample of the nation’s largest cities. The overall results suggest that income inequality has a direct positive effect on black offending rates for serious crime, whereas black poverty has no effect. In contrast, white poverty has positive effects on white violence, while inequality significantly increases white robbery and burglary. The implications of findings for recent theoretical develop- ments of conflict and relative deprivation theory are assessed.

In the past several years a great deal of attention has centered on the social structural determinants of crime in U.S. cities (Blau and Blau, 1982; DeFronzo, 1983; Crutchfield, Geerken, and Gove, 1982; Messner, 1982, 1983a, 1983b; Parker and Smith, 1979; Rosenfeld, 1986; Byrne, 1986; Wil- liams, 1984; Carroll and Jackson, 1983; Jackson, 1984; Bailey, 1984; Samp- son, 1985). Rather than seeking to explain individual involvement in criminal behavior, proponents of the structural perspective have attempted to isolate characteristics of macro social units that lead to high rates of criminal- ity. The general thesis of the social ecological model is that community structure has independent effects on crime that are not strictly disaggregable to the individual level (Byrne and Sampson, 1985).

This is a revised version of a paper presented at the 1984 annual meeting of the American Society of Criminology, Cincinnati, Ohio. Financial support from the National Institute of Justice is gratefully acknowledged. I thank two anonymous reviewers for helpful comments on an earlier draft. This paper has also benefited from the input of Al Blumstein, Jackie Cohen, and Steve Messner.

CRIMINOLOGY VOLUME 23 NUMBER 4 1985 647

648 SAMPSON

Two key issues have arisen in this recent literature on the social ecology of crime. The first concerns the continuing controversy over the validity of official data sources. Conflict theory tends to assume that official crime rates reflect variations in official social control rather than actual involvement in criminal behavior (Bernard, 1981; Chambliss and Seidman, 197 1; Liska and Chamlin, 1984). Traditional criminologists, on the other hand, view official data as imperfect measures of an underlying dimension of criminal offending. For example, Hindelang (1978) acknowledges the pitfalls of police statistics, but nonetheless concludes that UCR arrest data provide relatively unbiased estimates of demographic-specific offending rates. It is not surprising, then, that researchers utilizing aggregate data are divided on whether they are testing propositions concerning official social control (Liska and Chamlin, 1984) or the causes of criminal offending (Blau and Blau, 1982).

A second key issue in the social ecology of crime pertains to the level of analysis at which theory and method are couched. Most ecologically oriented researchers have as a goal the analysis of variations in rates of crime across aggregate units in order to ascertain what macrosociological conditions pro- mote conflict. However, the aggregate offense rate employed in the vast majority of research fails to adequately separate effects arising from individual-level factors from effects arising from macrolevel processes. The con- founding of individual- and aggregate-level effects is a classic issue that has continuously troubled sociologists (Dogan and Rokkan, 1969; Kornhauser, 1978). Indeed, the question of why one area has a higher crime rate than another is difficult to address. The answer may depend on the sociological characteristics of communities, or on the characteristics of individuals selec- tively aggregated into communities (Kornhauser, 1978: 114). Unfortunately, it seems that many recent works have simply assumed that utilization of aggregate crime rates per se guarantees the demonstration of macrosociological effects (Blau and Blau, 1982: 114-115). However, the level at which a causal relation occurs is a complex issue that is not solved simply by the unit of analysis for which variables are measured, since psychological and/or sociological causal factors may underlie relations observed at both the individual and aggregate level of analysis.1

Perhaps the ideal solution to isolating individual- and aggregate-level effects would be to gather individual-level data within different communities and then to add individual- and aggregate-level characteristics as well as an interaction term to a regression equation. The dependent variable would be individual offending. Unfortunately, the data necessary for such a contextual analysis are simply unavailable across a sample of areas varying on important

1. For an excellent general discussion of separating individual and aggregate effects, see Valkonen (1969). For a specific discussion of this issue with regard to race and crime, see Sarnpson (1985).

SOURCES OF VARIATION 649

ecological dimensions. A compromise solution at the macro level is the separate analysis of the effects of community characteristics (for example, racial composition, inequality) on the criminal behavior of blacks and whites. That is, one way of attacking the contextual problem is to disaggregate the crime rate by known individual-level correlates of offending.

This contextual approach was taken by Sampson (1985) in a multivariate analysis of the effects of racial composition and other city characteristics on black and white homicide. Once race of offender was taken into account, percent black had no significant effect on offending rates, which tends to disconfirm a major contextual proposition of the subculture of violence theory (Curtis, 1975). This finding occurred despite the fact that percent black had by far the strongest effect on the aggregate homicide rate. Sampson’s research demonstrates that a test of the aggregate-level or contextual effect of racial composition is possible when individual-level correlates of crime such as race and age are taken into account. The results also suggest that what have passed for macrosocial effects in much previous research may well have been individual effects in disguise.

There are additional theoretical reasons for disaggregating the crime rate. Namely, most criminological theory specifies that structural characteristics will have differential effects on different groups of offenders. Since the aggregate rate lumps together offenses committed by offenders of different age, race, and socioeconomic groups, the propositions of many theories are not directly addressed. For example, the relative deprivation theory advanced in Blau and Blau (1982) proposes that the societal injustices engendered by income inequality lead to a state of disorganization and alienation, which in turn leads to the expression of hostility and criminal behavior. However, the Blaus argue that relative deprivation is most acute among blacks, since in American society race and socioeconomic status are correlated. That is, blacks as a group suffer racial and economic discrimination which conse- quently assigns them a lower position in the economic strata than whites. Therefore, the criminogenic consequences of income inequality, especially racial inequality, are expected to be greater for blacks than whites. Although this is the underlying thesis posited by Blau and Blau (1982), they, in fact, simply tested the effect of inequality on the aggregate offense rate, which includes offenses by whites and blacks, and poor and middle-to-upper-income persons.

Also, it is quite likely that many previous analyses have been misspecified since they utilize aggregate income measures when race-specific indicators are called for. Prior research indicates that blacks are disproportionately involved in homicide and other FBI index crimes (Hindelang, 1978, 1981). However, aggregate poverty measures are usually composed of the percentage of families in poverty, and since whites are the majority in almost every city, these measures reflect the poverty of whites to a large extent. Hence, it is

650 SAMPSON

perhaps not surprising that many anomalies have arisen in recent research on the effects of poverty on crime (Messner, 1982, 1983b). Indeed, it is not immediately clear why aggregate poverty, reflecting largely the economic situation of whites, would affect black involvement in crime. In any event, by disaggregating both the crime rate and economic factors by substantively important demographic characteristics, one is then in a position to test the effects of economic characteristics that are specified by current criminological theory.

RESEARCH FRAMEWORK

This study addresses the two issues identified above. First, the contextual/ compositional question is addressed by controlling for the individual characteristics of race and age by which aggregate units differ in their composition. The data base consists of demographic-specific arrest rates constructed from arrest reports provided by the FBI for all cities in the U.S. with population greater than 250,000. These data were merged with census data to produce a data file including estimated demographic-specific offending rates, police characteristics, and the sociodemographic characteristics of cities.

Given that arrest data are being used, one must tackle concerns regarding the validity of official data. While most ecological researchers have used offense rates, very few have examined variations in disaggregated arrest rates across jurisdictions. One exception is a recent contribution by Liska and Chamlin (1984), who take the position that arrest rates reflect variations in official crime control. According to Liska and Chamlin (1984: 383), the conflict perspective “conceptualizes crime control as an instrument used by dominant and powerful groups to control those actions and groups which threaten their interests.” From this viewpoint, arrest data are not seen as reflective of actual involvement in criminal offending. One explanation for the race corre- late at the individual level in official data is thus that blacks are arrested at a higher rate than whites, regardless of actual criminal behavior. As Liska and Chamlin (1984: 384) explain:

Conflict theory assumes that nonwhites have a substantially higher arrest rate than whites, because relative to whites, they are less able to resist arrest and because authorities share common stereotypes linking them with crime.

Another conflict hypothesis at the aggregate level suggests that percent black may actually decrease black arrest rates because of benign neglect on the part of the police with regard to intraracial crime in black ghettoes. According to Liska and Chamlin (1984), the police do not view black-on- black personal crime as serious and therefore crime control efforts in the form of arrests are attenuated in black ghettoes. That is, certain black intraracial crimes are viewed as personal matters undeserving of official intervention.

SOURCES OF VARIATION 65 1

Because percent black increases the ratio of intra- to interracial crimes by blacks (Sampson, 1984), the benign neglect hypothesis predicts a consequent reduction in black arrest rates. Whatever the specific hypothesis, however, the conflict perspective views arrest data as an indicator of official social control, and not as an indicator of offending patterns.

The present study takes an alternative although not antithetical position to the conflict perspective on law enforcement. The framework employed here (Sampson, 1985) assumes that arrest rates are reasonable proxies for involvement in criminal offending for the most serious of the UCR index crimes (that is, murder, rape, robbery, aggravated assault, and burglary). Several empirical facts justify this assumption. First, Hindelang (1978, 198 1) has sys- tematically compared UCR index arrest rates with offending rates estimated from NCS victim surveys and found remarkable agreement. For example, Hindelang (1978) found that 62% of the robbery offenders reported by victims were black, compared to an identical 62% of black robbery arrestees in UCR arrest data for the same year. Because demographic correlates of arrest rates are the same for offending rates measured from a data source independent of the criminal justice system, one can have increased confidence in official data.

Second, a large body of research on police decisions to arrest has found that such criteria as offender demeanor, victim preference for arrest, seriousness of the crime, and victim-offender relationship affect the probability of arrest (Black and Reiss, 1970; Reiss, 1971; Lundman, Sykes, and Clark, 1978; Smith, 1984). However, little evidence has been mustered for the proposition that the police differentially arrest by race. For example, in one of the most thorough investigations of police-citizen encounters to date, Smith (1 984) found that regardless of type of police department, race of suspect did not have an effect on the arrest decision. The most important predictor of arrest is usually the seriousness of the crime (Gottfredson and Gottfredson, 1980), and the present research is limited to the most serious of the seven index crimes, thus reducing the likelihood of bias. Also,it should be noted that the conflict perspective is at odds with a fundamental fact of police work-almost all police response to street crime is reactive (citizen initiated) rather than police initiated (Reiss, 1971). That is, there is little if any evidence showing that the police simply heed the dominant social order by proactively repressing selected groups perceived as a threat. If anything, street crimes such as rape, murder, and robbery pose more of a threat to the inhabitants of poor urban ghettoes than to the economic elite, since it is the former that are disproportionately victimized (Hindelang, 1976).

However, one must still acknowledge the conflict position that official arrest data are contaminated by sources of bias when compared across jurisdictions. But rather than throwing the baby out with the bathwater, the procedure employed here is to explicitly control for the criminal justice system

652 SAMPSON

factors that presumably affect the crime control process. Specifically, three factors emphasized by prior theory are explicitly examined-the size of the police force (Liska, Lawrence, and Benson, 198 l), police effectiveness in clearing arrests (for example, arrest/offense ratio; Blumstein, Cohen, and Nagin, 1978) and police aggressiveness in patrol practices (Wilson and Boland, 1978). In employing multivariate analysis the assumption is that once these criminal justice factors are controlled, the residual variation of demographic-specific arrest rates for serious crimes with economic characteristics of cities represents variations in rates of offending behavior.

DATA, METHOD, AND HYPOTHESES

The issues reviewed above are examined in an analysis of the structural determinants of demographically disaggregated arrest rates for 53 of the 55 largest cities (population > 250,000) in the U.S. in 1970.2 This time period was chosen to allow direct comparison with the many recent contributions that have utilized 1970 crime-specific aggregate offense rates for cities3 and SMSAs (Blau and Blau, 1982; Carroll and Jackson, 1982; Messner, 1982, 1983a, 1983b; Bailey, 1984; Williams, 1984). Unpublished arrest data for individual cities were obtained from the FBI. Arrests for violent crimes (murder, rape, aggravated assault), robbery, and burglary were disaggregated by race of offender in accordance with the UCR data structure, which per- mits estimation of race-specific rates for juveniles (< 18) and adults (> 18).4

New York City did not report race-specific arrest data until 1977, and is thus excluded from analysis. In addition, because of the large nonwhite but nonblack population in Honolulu, this city produced quite anomalous patterns and was also excluded from the analysis. While it is preferable to have a larger sample size than 53, the construction of crime-specific arrest rates disaggregated by population subgroups forces the investigator to limit analysis to large cities that produce enough crime to ensure reliable rates. Because of the relatively small sample size, the .10 and .05 levels of significance are used to protect against type 2 error.

Using cities as units of analysis raises the issue pointed out by Gibbs and Erickson (1976) concerning the effects of city ecological position on crime rates. They argue that the denominator (city population) used in conventional crime rates may be inappropriate since people from outside the city may be victims or offenders. This is not likely to be a problem in the present analysis since arrest rates rather than offense rates are used. Offense rates include the victimizations suffered by suburban residents that occur in the city. However, arrest rates are still accurate even if the victims are noncity residents as long as the offender population resides in the city. The latter is a reasonable assumption, as the evidence clearly indicates that offenders tend to commit crimes in or near their own neighborhoods and almost always in their city of residence (Pyle, 1974; Sampson, 1983).

Although for a variety of technical reasons black arrest rates were constructed using nonwhite counts and are so labeled in the tables, nonwhite arrest rates are in the text called black offending since, with the exclusion of Honolulu, black and nonwhite are essentially synonymous. For example, the correlation of percent black with nonwhite is .99, and inspection of the raw FBI tapes indicated that virtually all nonwhite arrests were of blacks.

2.

3.

4.


Because of the small numbers of arrests disaggregated by both race and age, it was not possible to reliably examine murder and rape separately, and thus a violent crime index was constructed. In conjunction with population estimates from the census, race-age and crime-specific arrest rates were constructed for the years 1969- 197 1. Because of potential year-to-year variations in reporting and recording practices, a three-year average rate was computed to stabilize random fluctuations, a practice followed in previous research (Messner, 1983a; Loftin and Hill, 1974).

The independent variables were selected based on theoretical grounds and previous empirical research. The two variables that have received considerable attention in recent years are income inequality and percent black. As detailed in Blau and Blau (1982), a major question has been whether structural inequality or racial composition is the major determinant of crime rates. According to the Blau and Blau thesis, income inequality should have a direct positive effect on offending rates, regardless of cultural factors which are usually indexed by Southern location and percent black (Messner, 1983a). As noted above, inequality is hypothesized to lead to severe conflicts of interest and hostility, which in turn produces a disproportionate level of crime. Given this argument, the effect of inequality should be stronger and more consistent for blacks than whites because the former suffer greater economic disadvantage than the latter.5 The inequality measure utilized is the Gini Index of income concentration. The effects of racial income inequality, defined as the ratio of white to black median income, are also examined. However, because the latter measure overlaps considerably with black poverty (r = .8) the Gini Index is selected as the main inequality indicator. Although it has been criticized (Allison, 1978), the Gini is the best available measure of the distribution in income.

As outlined by Blau and Blau (1982), the most prominent theoretical model resting on racial composition as a causal variable in offending is the subculture of violence thesis (Wolfgang and Ferracuti, 1967; Curtis, 1975). According to subcultural theory, the unique historical experiences of blacks have led them to adopt a set of values conducive to violence. In particular, criminal acts such as assault and homicide are interpreted as expressions of a subculture that condones and legitimates violence. Life in urban black ghettoes is perceived as tough and fatalistic, and aggressiveness in the form of violence is postulated to be not only necessary but valued as a means of sur- viving and enhancing one’s self-image.

5 . For example, in the city sample under study there is an extremely large differential in poverty by race. In particular, the average of black families with incomes below the poverty line is 25%, compared to only 8% for white families.

SAMPSON

Curtis (1975) has extended Wolfgang and Ferracuti’s original thesis by explicitly considering the consequences of group size for subcultural forma- tion. Curtis (1975: 34) argues that the transmission of violence-related models and the buildup of subcultural values and behavior patterns is critically dependent on the relative size of the black population. In short, subcultural values are thought to flourish when there is a critical mass of black population. Curtis (1975: 34-36) thus hypothesizes that disproportionate homicide and assault by blacks is positively related to percent black. If the contextual proposition of subcultural theory is valid, one should expect a positive effect of percent black-on-black offending rates for violent crimes (homicide, assault, and rape), independent of other structural characteristics such as inequality.

To account for variations in absolute deprivation, measures of poverty were also chosen. Recall that the Blau and Blau (1982) thesis focuses on relative inequalities and not on poverty. In contrast, traditional criminological theorists such as Shaw and McKay (1942) tend to focus on absolute economic level and sheer poverty as criminogenic (see Kornhauser’s 1978 review). It is thus essential to control for the effects of absolute poverty in testing for the effects of income inequality (Blau and Blau, 1982: 116-1 17). However, unlike most past research, the present study employs race-specrj7c measures of poverty. Census data on the percentage of black families and white families with incomes below the poverty line were collected to provide disaggregated poverty measures for the black and white arrest rate equations, respectively. If traditional criminiological theory (Shaw and McKay, 1942; Merton, 1938; Kornhauser, 1978) is correct, one should see a direct positive effect of race- specific poverty on both white and black offending. By disaggregating both the income and crime rate measures, the chance of estimating biased parameters due to misspecification is reduced.

Population size, structural density, and a dummy variable reflecting South- em location were also selected as predictors. Several research efforts have shown aggregate size of the population to have a direct effect on interpersonal crimes (Mayhew and Levinger, 1976; Blau and Blau, 1982). As Mayhew and Levinger (1976) note, increases in size of the population increase multiplica- tively the rate of interpersonal contact and hence the opportunity for criminal contacts. Therefore, population size is specified to have an effect on the interpersonal crimes of robbery and violence (rape, murder, assault). On the other hand, burglary by definition entails dwellings as targets. Sampson (1983) and others (Roncek, 198 1) have hypothesized that structural density (proportion of multiple-dwelling structures) increases the opportunities for crime while decreasing the capacity for surveillance and guardianship activities, thereby increasing the crime rate. Hence, for property crimes structural density (percent of units in structures of five or more units) rather than population size is specified as a predictor. It should be noted that size and density are weakly


related (.22), thus decreasing the likelihood of misspecification error by not including both in the same equation. Both types of density are not simultane- ously included based on the theoretical framework and to save degrees of freedom since the sample size is relatively small. Finally, Southern location is introduced to control for variations in regional orientations to crime and crime control (Messner, 1983a; Liska et al., 1981).

Arrest probability as measured by the index arrest/offense ratio and police per capita are introduced to control for potential criminal justice system (CJS) effects on the arrest rate.6 Based on Wilson and Boland’s (1978) theoretical model of policing, an attempt is made to control for police aggressiveness in patrol practices. Wilson and Boland (1978) hypothesize that in legalistic-style police departments (Wilson, 1968), the police adopt an aggressive strategy, by which they mean a strategy that maximizes the number of observations and interventions in the community. Aggressive police patrols tend to stop motor vehicles to issue citations and question or arrest suspicious and disorderly persons at a high rate (Wilson and Boland, 1978: 370). An aggressive patrol strategy affects the crime control process in the following manner (1978: 373):

By stopping, questioning, and otherwise closely observing citizens, especially suspicious ones, the police are more likely to find fugitives, detect contraband (such as stolen property or concealed weapons), and appre- hend persons fleeing from the scene of a crime.

Direct measures of police aggressiveness are unavailable across a large sample of cities, so Wilson and Boland selected a proxy: the number of citations for moving traffic violations issued per sworn officer. In the present study a similar proxy measure is used: the number of arrests per police officer for suspicion, vagrancy, disorderly conduct, drunkenness, and driving under the influence. All these offenses fall under the category of “social control” offenses that legalistic-style departments tend to vigorously enforce (Wilson, 1968). Measures of police aggressiveness in arresting for these offenses are developed for each population subgroup. Thus, for example, in estimating economic determinants of black adult robbery, the ratio of black adult arrests for the social control offenses (disorderly conduct, suspicion, and so on) per police officer is controlled. An overall police aggressiveness measure is also examined, but it is highly correlated with the race-specific measures and did not produce materially different results. In short, the introduction of arrest probability, police per capita, and police aggressiveness as control variables

6. Deterrence research suggests that there is a simultaneous relationship between crime rates and CIS factors such as arrest certainty and police size (Blumstein et al., 1978). However, the present study examines disaggregated arrest rates-not aggregate reported offense rates. Moreover, the police factors are introduced as controls; this study is primar- ily interested in the effects of economic variables. Therefore, simultaneity is not considered a theoretical or empirical problem.

SAMPSON

should serve to partition the effect of economic factors from police and CJS influences.

The correlation matrix of structural variables is presented in Appendix A. All correlations between variables that are included in the same equation are less than .60, with the exception of black poverty and Southern location (.67). By econometric standards (Kennedy, 1979: 13), the overall magnitude of correlations does not impose a serious multicollinearity problem, although the coefficients in the black offending models may suffer some inefficiency. To assess this issue, each regression equation was estimated twice by varying the model specification. Also, for each model the variance-covariance matrix. of parameter estimates and the diagonal elements of (X’X)-l were examined. The latter elements, defined as (1 - R2,)-’, have been termed variance inflation factors (Fisher and Mason, 1981: 109) because they measure the amount that the variance in OLS parameter estimates are inflated in the presence of multicollinearities. Commonly accepted practice regards factors above 4 (for example, tolerance < 2 5 ) as an indicator that the coefficient estimates are highly inefficient. Values below 4 are not considered severe (Fisher and Mason, 1981: 109). Variance inflation factors (VIF) were examined for each variable. No predictor shared more than 75% of its variance with other predictors in the same model, and the largest VIF was less than three. Conse- quently, multicollinearity does not appear to be a major problem.

The relatively small number of cities under investigation requires caution regarding the issue of influential observations. Recently emerging literature (Cook and Weisberg, 1980; Weisberg, 1980) advocates case analysis to detect the importance of influential observations in estimating regression parameters. In the present study, for example, it is possible that one particular city may disproportionately influence the results, especially since there are only slightly more than 50 cities in the model. To protect against this possibility, all regressions were subjected to a case analysis. Specifically, Cook‘s D and Studentized residuals (see Weisberg, 1980) were inspected for each city. A case is defined as influential if its deletion from the model results in a substan- tial change in the estimate of the parameter vector.

Preliminary analysis revealed that Washington, D.C., and Baltimore were disproportionately influential in most of the white and nonwhite equations, with values of Cook‘s D several standard deviations above the mean. One reason appeared to be Washington’s anomalous value for police per capita, which probably stems from the counting of both local and federal officers. For example, the mean police size for all cities is 222 per 100,OOO (SD = lo&), while Washington has 668 officers per 1OO,OOO, a full four standard deviations above the mean. Washington and Baltimore were thus excluded


from analysis which, given earlier restrictions (see footnote 3), yields an effec- tive sample size of 51 cities for most regressions.’ Reestimating models increases confidence that the results are generalizable and not due to deviant patterns or extreme values evidenced in one or two cities.

Demographic-specific arrest rates were transformed to logarithmic scale, resulting in a series of semi-log models. Preliminary analysis revealed some heterogeneity of variances for black arrest rates, and in addition almost all rates were skewed. Through log transformation, homoskedasticity of variances was induced and model fitting was generally improved.

RESULTS Analysis begins in Table 1 with an examination of juvenile race-specific

arrest rates for the violent crimes of murder, rape, and aggravated assault. Panel A reports the results when police per capita and arrest probability are controlled. The race-specific social control data were unavailable for Detroit, so, rather than eliminate the nation’s fifth largest city, in all analyses police aggressiveness was introduced in a second model in Panel B. This model differs in that Detroit is not included and predictors yielding small or insignificant parameter estimates in Panel A were eliminated. Thus, the panel B models provide an examination of the effects of economic sources of variation in arrest rates once the model has been refined and police aggressiveness is introduced as a control. This procedure provides an added check on the effects of multicollinearity with regard to the stability of regression coefficients.

The equations for both white and black juvenile violence are highly significant, with more than 30% of the variance explained in each. For both white and black offenders, Southern location is a significant predictor of violent offending. Note that the coefficient is negative, indicating that the Northeast- ern, Midwestern, and Western cities have a higher average juvenile violence rate than southern cities. While the negative effect for South runs counter to some state and SMSA offense rate results (Messner, 1983a), it is consistent with recent research using city index offense rates (Byrne, 1986).

In terms of the economic variables, white poverty has a strong positive effect on white juvenile violence, while overall income inequality has no independent influence. For black juveniles, on the other hand, absolute deprivation in the form of poverty does not have a significant effect on violent

7. A few cities were influential observations for particular crime types and population subgroups. Specifically, San Jose was an outlier for black juvenile burglary, while El Paso was an outlier for black juvenile robbery and burglary. For example, San Jose has few blacks and reported only 4 black juvenile robbery arrests, resulting in a highly unreliable rate. San Jose along with Miami also produced an anomalous white adult burglary rate. The n is thus 49 for black juvenile and white adult burglary and 50 for black juvenile robbery.

658 SAMPSON

Table 1. Regression of Juvenile (< 18) Race-Specific Arrest Rates For Violent Crimes (Murder, Rape, Aggravated Assault) on Structural Characteristics of U.S. Cities with Population Greater than 250,000

A. White Nonwhite

Structural Characteristics

Juvenile Juvenile

b Beta t-ratio b Beta t-ratio - - - ~ - -

Population Size .18 .16 1.07 -.03 -.03 -.18 Percent Black - .78 -.16 -.84 -1.65* -.33 -1.82 South -.77** -.56 -2.87 -.60** -.44 -1.98 Income Inequality 5.54 .24 1.36 10.61** .43 2.48 Black Poverty - NI - - .01 -.06 -.35 White Poverty .09** .36 2.40 - NI - Police per Capita .OO .07 .36 .002 .28 1.38 Arrest Probability -.15 -.02 -.14 1.54 .18 1.38

R* = .35, p <.01 RZ = .32, p <.01

B. White Juvenile Social

Black Juvenile Social Control - .05 -.08 -.64 - NI -

Control - NI - -a02 -.01 -.01 White Poverty .11** .40 3.05 - NI - South -.72** -.53 -4.00 -.89** -.65 -3.89 Income Inequality - NI - 10.05 * * .41 2.36 Percent Black - NI - - .62 -.12 -.78

R* = .30, p <.01 Rz = .27, p <.01 NI = Not included in model specification. *p <.lo **p <.05

offending, whereas income inequality has a very strong positive influence. The results in Panel B for the most part replicate those in A. Inequality continues to have a positive effect for black juveniles, and poverty for white juveniles.

Racial composition has a small (p = .08) negative effect on black juvenile violence, but this effect is rendered insignificant in Panel B. While black violent offending rates are much higher than those of whites, once the compositional effect is controlled the relative size of the black population exerts either a negative contextual effect or no effect on black juvenile offending. The direction of this relationship thus tends to disconfirm the subculture of violence thesis (Curtis, 1975), while providing only weak support for the benign neglect hypothesis (Liska and Chamlin, 1984) since the effect of racial composition is small and inconsistent with regard to variations in model specification.


With regard to criminal justice factors, police aggressiveness does not have a significant effect on violent offending for either race. In conjunction with the Panel A results where neither arrest apprehension probability nor police size have significant effects on violent offending rates, the data in Table 1 indicate that neither whites nor blacks are affected by variations in arrest practices and the size of the crime-control bureaucracy.

Table 2. Regression of Adult (> 18) Race-Specific Arrest Rates for Violent Crimes (Murder, Rape, Aggravated Assault) on Structural Characteristics of U.S. Cities with Population Greater than 250,000

A.


Population Size Percent Black South Income Inequality Black Poverty White Poverty Police per Capita Arrest Probability

B.

White Adult Social Control

Nonwhite Adult Social Control

White Poverty Population Arrest Probability Police per Capita Income Inequality South Percent Black

White Adult

b Beta t-ratio

.08 .07 .43 -.27 -.05 -.27 -.12 -.09 -.41

- - -

3.92 .17 .88 - NI .11** .42 2.57

.59 .07 .51

-

- .oo -.03 -.15

R* = .22, p = .12

.02** .27 2.14

- - NI .lo** .39 3.05 - NI - NI - - NI - - NI - NI - - NI -

-

-

Nonwhite Adult

b Beta t-ratio - - - -.22 -.24 -1.46

-1.40* -.35 -1.80 - .28 -.26 -1.08 6.66* .36 1.89 .oo .07 .34 - NI - .oo2 .34 1.62 1.48 .22 1.55

R2 = .22, p = .13

.02 .I6 .83 - NI -

- .24 -.25 - 1.62 1.49 .21 1.48 .oo3* .41 1.84 6.53* .36 1.87 - .26 -.24 -1.13 - 1.74* -.43 -1.90

R2 = .26, p <.01 R2 = .21, p <.15 NI = Not included in model specification. *p <.lo **p <.05

The pattern for adult arrest rates for violent crimes (Table 2) is somewhat different from the juvenile patterns, and in addition the models provide a poorer fit to the data. For white adults the only significant predictor of involvement in violence is white poverty, which has a positive effect as

660 SAMPSON

expected. Income inequality has a positive effect on black adult violence, and percent black a negative effect as in the juvenile model. The major difference from the juvenile results is that regional location does not have an effect on adult violence for either racial group.

The models in Panel B suggest that police aggressiveness influences white adult violence, while police per capita has an effect on black adult violence. Because of suspected multicollinearity problems in the Panel A black model (for example, relatively large betas but small t-ratios for police per capita and South), only black poverty (beta = .07) was eliminated in Panel B. However, the fit of the model is still poor and no variable is significant at the .05 level. Thus, the black offender equations in both Panels A and B of Table 2 should be viewed with considerable caution. Apparently, black adult offending in violent crimes is not much affected by variations in structural characteristics across cities. On the other hand, the respecified white model in Panel B is acceptable, suggesting again that white adult violence is determined mainly by variations in white poverty. As a further test of the black models, racial income inequality was added to the black juvenile and adult equations. No improvement in fit resulted as racial inequality proved to be insignificant in predicting black violence (data not shown).

Table 3 shifts the focus to the interpersonal crime of robbery by juvenile offenders. Robbery arrests are considered to be some of the most accurate and unbiased measures in official data (Hindelang, 1978), and thus probably provide the most valid estimates of offending patterns. Interestingly, the results are very similar to the patterns shown above for juvenile violence. Regional location has the strongest effect on both black and white robbery, with nonsouthern cities experiencing on average higher rates than Southern cities. Income inequality is a strong predictor of black juvenile robbery, as is racial composition and police per capita. In fact, fully 57% of the variance in black juvenile robbery is explained by the theoretical model.

For white juvenile robbery, income inequality is significant at the .05 level, while police per capita is also marginally significant (p = .l 1). Unlike white juvenile violence, however, white poverty does not have any effect on robbery offending. Apparently the effects of income inequality are quite pervasive and not limited to any particular offender subgroup. One notes that inequality has a slightly greater effect on black juvenile robbery than on white juvenile robbery. However, a t-test for the difference of regression coefficients (Kleinbaum and Kupper, 1978: 100) revealed that this difference is insignificant. The results in Panel B support the patterns in Panel A with no shifts in patterns. One notes, though, that black juvenile social control has a slight effect on black juvenile robbery.

Table 4 presents race-specific robbery results for adult offenders. One difference between the two models is that the level of explained variance is higher for the black model than for the white model (Panel A). This is fairly


Table 3. Regression of Juvenile (< 18) Race-Specific Arrest Rates for Robbery on Structural Characteristics of U.S. Cities with Population Greater Than 250,000

A.


Population Size Percent Black South Income Inequality Black Poverty White Poverty Police per Capita Arrest Probability

White Juvenile

b Beta t-ratio _ _ _ - -

-.04 -.03 -.21 -1.09 -.21 -1.15 --.78** -.55 -2.86 8.24** .34 1.99 - NI -

.02 .08 .56

.002 .33 1.64

.35 .04 .32 R2 = .38, p <.01

Nonwhite Juvenile

b

-.19 -2.22** - 1.09**

11.91** .01

.m** 1.34

R2

Beta -

-.14 - .38 - .68 .44 .06 NI .42 .13

= .57, p

t-ratio

- 1.15 -2.56 -3.70

3.11 .41

2.62 1.27

-

< .01

B. White Juvenile Social

Nonwhite Juvenile Control - .07 -.11 -.78 - NI -

Social Control - NI - .38* .21 1.77

Income Inequality 7.45. .31 1.98 12.09** .45 3.31 Police per Capita ,001 .13 .86 .m** .43 2.8 1

South -.90** -.63 -3.61 -1.04** -.64 -4.17

Percent Black - NI - -2.83.. -.46 -2.86 R2 = 46, p <.01 R2 = .57, p <.01

NI = Not included in model specification. *p <. lo **p <.05

inconsequential since R2 is a sample-specific statistic and not a structural parameter. In contrast, the estimates of structural parameters are similar. Namely, the two strongest predictors of adult robbery for both races are Southern location and income inequality.8 A t-test indicates no difference between races in the effect of these two variables.

The major difference in parameter estimates between models is the fact that percent black and arrest probability are marginally significant (p < .lo)

8. Detailed analysis reveals an apparent suppression effect with regard to Southern location and inequality. Namely, while the zero-order correlation between inequality and black offending is weak and sometimes negative, it is positive in both the South and non- South. For example, black adult robbery is significantly correlated with inequality in both the South (.32) and nonSouth (.42), but unrelated in the overall sample (-.16). The sup- pressor effect arises because South has opposite effects on offending and inequality.

662 SAMPSON

Table 4. Regression of Adult (> 18) Race-Specific Arrest Rates For Robbery on Structural Characteristics of U.S. Cities with Population Greater than 250,000

A. White Nonwhite Adult Adult

Structural Characteristics b Beta t-ratio b Beta t-ratio - - - ~ _ _ _ _

Population Size .13 .14 .88 -.04 -.04 -.32 Percent Black -.24 -.06 -.30 -1.19* -.29 -1.81 South -.61** -.56 -2.57 -.76** -.68 -3.46 Income Inequality 8.26* .44 2.27 7.16** .38 2.41 Black Poverty - NI - .oo .01 .08 White Poverty .03 .16 .96 - NI - Police per Capita - .oo -.19 -.86 .oo1 .14 .99 Arrest Probability .65 .09 .68 1.56* .22 1.93

R2 = .22, p = .13 R2 = .46, p <.01

B.

White Adult Social

Nonwhite Adult Control .04** .48 3.01 - NI -

Social Control - NI - - .02 -.18 -1.24 South -.81** -.73 -4.05 -.77** -.68 -4.57 Income Inequality 7.48** .40 2.51 7.69** .41 2.70 Arrest Probability - NI - - 1.59' .22 1.98 Percent Black - NI - - .65 - .15 -1.18

R2 = .30, p <.01 R2 = .47, p <.01 NI = Not included in model specification. *p < . l o ** p <.05

predictors of black but not white adult robbery. However, the effect of percent black is insignificant in Panel B. The significant effect of police per capita remains. While police aggressiveness toward black adults is insignificant, white adult social control has a strong effect on white adult robbery arrests. Indeed, the white model in Panel B explains 8% more of the variance than Panel A and is significant at .01. Evidently, then, metropolitan police factors have an effect on both white and black robbery, but the specific form of this effect varies by race of offender.

In brief, the main result in Tables 3 and 4 with regard to economic variables is the positive effect of relative but not absolute economic deprivation on robbery offending. Unlike violence, income inequality serves to increase the offending of both blacks and whites. It appears that the criminogenic effects of inequality are more general with regard to robbery offending than hypothesized by Blau and Blau (1982). As a further test of this notion, equations 1


and 2 reestimate the Panel B black offender regressions in Tables 3 and 4 by substituting racial income inequality for black poverty (SEs in parentheses).

(1 ) Black juvenile robbery = 1.02 - .078*RACIAL INEQ + 12.37.INEQ (.545) (3.82)

-2.96*BLACK - l.Ol*SOUTH + .406*POLAGG + .004*POLCAP C94) (.27) (.203) ( . o w

(2) Black adult robbery = 3.07 + .129*RACIAL INEQ + 7.44*INEQ (.369) (2.93)

--.609*BLACK - .791*SOUTH - .025*POLAGG + 1.56*ARR RATIO (.508) (.179) (.018) (.78)

As is evident, the specific hypothesis of Blau and Blau (1982) is simply not supported. Racial inequality in income has no effect on black offending, whereas income inequality continues to have a strong positive effect. These findings are not surprising because, as noted earlier, racial inequality is highly correlated with black poverty, which also had no effect on black robbery or violence. It appears, therefore, that the effect of inequality on black crime stems from overall inequalities in the distribution of income rather than inequality between races.

In Table 5 juvenile offending rates are analyzed for the most serious index crime against property-burglary. Not surprisingly, the results indicate a shift in patterns from the personal crimes analyzed earlier. First, note that the arrest/offense ratio has a strong positive effect on burglary offending for both black and white juveniles. All else equal, cities in which a relatively large proportion of reported offenses culminate in arrest tend to have higher juvenile arrest rates than cities with a low arrest probability. In comparison with earlier findings, these results suggest that burglary arrests are more contaminated with official crime control processes than are arrests for robbery and violence.

In terms of other structural characteristics, Table 5 indicates that economic characteristics have a decidedly weak influence on juvenile burglary. Surpris- ingly, absolute poverty has no effect on burglary by either race group, while inequality has only a weak effect on white juvenile burglary. Unlike robbery and criminal violence, then, juvenile burglars do not seem much affected by the influences of either absolute poverty or income inequality.

With respect to other factors, structural density has a significant positive effect on both white and black offending, although the effect is significantly stronger for black offenders. In fact, structural density is the second most important determinant of variations in black juvenile burglary. Apparently,

SAMPSON

Table 5. Regression of Juvenile (< 18) Race-Specific Arrest Rates for Burglary on Structural Characteristics of U.S. Cities with Population Greater than 250,000

A. White Nonwhite Juvenile Juvenile

Structural Characteristics b Beta t-ratio b Beta t-ratio - - - _ _ _ _ - -

Structural Density Percent Black South Income Inequality Black Poverty White Poverty Police per Capita Arrest Probability

.01* -.81 -.33 5.49*

.o 1 -.001 2.10**

-

R2

.26 - .22 - .32

.32 NI .04

- .32 .33

= .28, p

1.65 -1.10 - 1.54

1.68

.27

2.42

-

- 1.51

< .05

B.

White Juvenile Social

Nonwhite Juvenile Control .02 .04 .24

Social Control - NI - South -.39** -.38 -1.96 Structural Density .o 1 .23 1.47 Income Inequality 4.93 .29 1.62 Arrest Probability 1.93** .30 2.22 Police per Capita -.OO** -.43 -2.32 Percent Black - NI -

R2 = .25, p <.05 NI = Not included in model specification. *p < . lo ** p <.05

.Ol** .38 2.56 -1.15** -.33 -2.02 -.34* -.37 -1.75 1.45 .09 .56 .02 .23 1.30 - NI -

-.OO1 -.22 -1.27 3.24** .56 4.63

R2 = .46, p <.01

.01 .01 .08

.01** .3 1 2.29 NI

3.24** .56 4.26 - NI -

-1.38** -.41 -3.05

- .03 -.03 -.25

- -

R2 = .41, p <.Ol

cities with a high proportion of multiple-dwelling unit structures offer a set- ting conducive to the commission of household burglary. This general finding lends support to the linkage of defensible space theory with the opportunity model of criminal victimization (Roncek, 198 1; Sampson, 1983).

The models in Panel B essentially replicate those in Panel A. Specifically, the economic predictors of burglary show weak and insignificant effects, thereby increasing confidence in the earlier results. Note also that police aggressiveness is insignificant regardless of race; hence, a convincing pattern of differential social control by race is absent.

Table 6 presents results of the regression of black and white adult burglary. Unlike for juvenile burglary, income inequality has a consistent and strong positive effect on adult burglary. Indeed, in all four regressions inequality significantly increases adult burglary net of the effect of other variables. Note


Table 6. Regression of Adult (> 18) Race-Specific Arrest Rates for Burglary on Structural Characteristics of U.S. Cities with Population Greater than 250,000

A. White Nonwhite Adult Adult

Structural Characteristics b Beta t-ratio b Beta t-ratio - - - - - _ _

Structural Density Percent Black South Income Inequality Black Poverty White Poverty Police per Capita Arrest Probability

.003 .I0 .67 -.21 -.05 -.30 -.48** -.45 -2.38 11.61** .6 1 3.66 - NI - .07** .3 1 2.22

--.m -.I1 -3 1.38* .2 1 1.67

R2 = .42, p <.01

.002 - .27 -.59** 5.42** .01

- .Ooo -

1.26** R2

.09 .60 - . lo -.56 -.78 -3.72

.42 2.45

.I7 .96 NI -

-.08 -.46 .26 2.10

= .38, p <.01

B.

White Adult Social

Nonwhite Adult Control .01 .22 1.47 - NI -

Social Control - NI - - .02 -.17 -1.22 South - .51** -.49 -3.10 -.44** -.60 -3.88 Income Inequality 10.10** .53 3.53 5.84** .45 2.96

Arrest Probability .93 .14 1.17 1.28** .27 2.27 R2 = .42, p <.01

NI = Not included in model specification. *p <.I0 **p <.05

White Poverty .06** .29 2.36 - NI -

R2 = .36, p <.01

also that white poverty has a positive effect on white adult burglary, in contrast to the insignificant effect of black poverty on black adult burglary. The pattern of effects for regional location, percent black, and arrest probability are equivalent between the races. Structural density has no effect on either black or white adult burglary.

Finally, the black offender equations in Tables 5B and 6B were reestimated by including the effects of racial income inequality:

(3) Black juvenile burglary = 5.21 + .207*RACIAL INEQ + .OOS*DEN (.383) (.ow

-.11*SOUTH - 1.45*BLACK + 3.14*ARR RATIO + .047*POLAGG (-16) (.46) (.78) (. 144)

666 SAMPSON

(4) Black adult burglary = 3.56 + .380*RACIAL INEQ + 5.237*INEQ (.266) (2.019)

- .534*SOUTH + 1.25*ARR RATIO - .015*POLAGG (.129) (.56) (.013)

As with robbery and criminal violence, racial income inequality has no effect on black burglary offending.

DISCUSSION In analyzing and interpreting arrest data, criminologists have taken two

divergent perspectives. One group of criminologists argues that arrest data are relatively unbiased and represent valid indicators of involvement in criminal offending (Hindelang, 1978, 1981). Conflict theorists, on the other hand, argue that arrest rates reflect variations in official social control, as the police are seen as instruments used by elites to control subordinate groups (Cham- bliss and Seidman, 1971; Liska and Chamlin, 1984). The present paper attempts an analytical compromise between these two viewpoints with reference to the analysis of arrest rates across jurisdictions. Some sort of compromise is essential because, as noted earlier, aggregate offense rates used in past research fail to address crucial theoretical questions concerning the effects of racial composition and economic factors (for example, poverty, inequality) on black and white offending rates.

To accomplish these goals age-, race-, and crime-specific arrest data were collected and analyzed in a multivariate causal framework in conjunction with sociodemographic and police data for large cities. To control for variations in the law enforcement process across jurisdictions, the following criminal justice factors were explicitly taken into account-police size, arrest certainty, and police aggressiveness. Using multivariate procedures, the compromise advanced was that after partitioning out the effects of these factors, the residual variation of arrest rates with etiological variables (for example, poverty, inequality) is reflective of variations in criminal offending for the most serious of the FBI’s seven index crimes.

The major set of implications and focus of this research concern the economic sources of serious criminal offending among blacks and whites. A considerable amount of recent research has generated conflicting findings regarding the effects of poverty and inequality on crime (see reviews in Mess- ner, 1982, 1983b; Williams, 1984; Bailey, 1984). In the present study both crime rates and economic factors were disaggregated by race to further examine this issue. Overall, the data support the notion that structural economic factors are important in predicting offending patterns. Specifically, a general pattern emerged in the data whereby income inequality had consistent and relatively strong effects on black offending. Indeed, in 10 out of 12 regression models, income inequality had a significant positive effect on black


criminal offending, net of the influence of region, racial composition, poverty, and structural opportunity. Importantly, however, in not one equation did the absolute level of black poverty influence the black crime rate.

For whites, a somewhat different pattern emerged as the level of white poverty had significant positive effects on white violence and burglary. The only crime types where income inequality consistently increased white offending were robbery and adult burglary. Thus, while inequality is important, the absolute poverty of the white population also surfaces as a salient criminogenic factor. This finding accords well with traditional criminological theory stressing sheer economic status (Shaw and McKay, 1942; Kornhauser, 1978).

In short, the results suggest that relative inequality is a more important factor in explaining black crime than absolute poverty. This finding supports the general thrust of the structural inequality thesis proposed by Blau and Blau (1982). Since blacks as a group suffer discrimination and economic deprivation, and are therefore expected to experience injustice and hostility, the logic of the theory predicted a strong effect of income inequality on black crime. Interestingly, however, the inequality effect was stronger for blacks than whites only for violence.

It is also important to note that the Gini Index refers to inequality in the overall distribution of income. Racial inequality, which shares over 60% of its variance with black poverty, had no effect on black violence, robbery, or burglary. Hence, while the results support the Blaus’ main emphasis on structural inequality, their specific hypothesis regarding racial income inequality is not supported when the crime rate is disaggregated across cities.9 Instead, the results indicate that for blacks, overall inequality is more criminogenic than poverty. Perhaps more importantly, this pattern holds for white robbery and burglary as well.

In any event, these race-specific results underscore the importance of disaggregating both the crime rate and economic indicators, because aggregate measures would have masked racial differences in the economic sources of criminal offending and led to biased parameter estimates. For example, the

9. It is important to note that Blau and Blau (1982) used SMSAs as units of analysis. Bailey (1984: 534-535) has criticized the use of SMSAs because they are generally less homogeneous than central cities with regard to economic variables of theoretical interest. SMSAs usually contain relatively well-to-do suburbs and poor central cities, with the latter contributing disproportionately to the crime measures. Also, as Bailey (1984: 535) argues, the relevant frames of reference in assessment of economic well-being are usually daily associations and other personal contacts within relatively confined areas. The use of inequality measures for entire counties, which assumes inner city residents know about and are in contact with suburban wealth, may be an inappropriate theoretical assumption. In short, the Blaus’ findings regarding racial income inequality may be artifactually tied to their units of analysis rather than to an underlying causal process (for further discussion see Bailey, 1984).

SAMPSON

significant positive effect of white poverty on white violence in conjunction with the corresponding null influence of black poverty is likely to lead to an attenuation of the effect of overall poverty on the offense rate. In light of this it is perhaps not surprising that numerous discrepancies have arisen in previous investigations of the economic determinants of crime (Messner, 1982, 1983b).

While racial differences were evident, there were very few differences in patterns between juvenile and adult offending with regard to economic influences. The only real difference occurred for burglary, where economic factors had a significant effect for both black and white adults but not for juveniles. This suggests that adult burglars are influenced more by poverty and inequality than juveniles, perhaps because the former have more economic con- straints placed on them. It is conceivable that juvenile burglary is driven more by group and situation factors (for example, peer influence) than by motivational factors. However, this is only speculative and needs to be veri- fied by other research focusing on age and race interactions with structural economic factors.

As for the other noneconomic characteristics, the results tended to disconfirm the contextual hypothesis arising from the subculture of violence theory (Wolfgang and Ferracuti, 1967; Curtis, 1975). The insignificant or negative contextual effect of racial composition on black involvement in violent crimes invalidates the subcultural proposition that the relative size of the black population serves to solidify subcultural values and hence increase black offending. Because of the tremendous black-white differences in prevalence of offending, however, the theory may still be accurate at the individual level of analysis.

Finally, the results yielded only weak support for structural opportunity theories of crime. Population size did not have a significant effect on interpersonal offending for any population subgroup. However, the determinants of burglary revealed some support for opportunity theory. In particular, structural density of the physical environment was a strong predictor of black juvenile burglary, and a positive although weaker predictor of white juvenile burglary. These findings lend some support to the contentions of Roncek (1981) and Sampson (1983), who argue that high building density decreases the defensible space and guardianship potential of an area while increasing the actual and perceived opportunities for household crimes.

Although the goal of the present study was to analyze offending patterns, the results of analysis also have implications for conflict perspectives on the law enforcement process. After all, arrest data are not simply indicators of offending, but also contain the influences of official social control. In this vein, the results yielded some support for the benign neglect hypothesis of Liska and Chamlin (1984). Independent of the effects of other exogenous factors, percent black exerted a negative effect on black crime rates for the


interpersonal crimes of robbery and violence. The effects were rather weak, but the overall pattern was in the direction predicted by the benign neglect framework. All else equal, it may be as argued by Liska and Chamlin (1984) that the police discount black victims and hence underarrest black offenders in black areas. Perhaps the most efficient method of further examining this issue would be to study police-citizen encounters across areas varying in racial composition (Smith, 1984). As for the effects of police size, arrest effectiveness, and patrol aggressiveness, no consistent patterns emerged in the data.

CONCLUSION

The analysis of the structural determinants of demographically disaggregated arrest rates across U.S. cities appears to have important implications for future research. Almost all previous ecological research has used aggregate crime rates. While useful in providing an indicator of the total volume of crime, the aggregate crime rate precludes the posing of many interesting and crucial theoretical questions. Indeed, the contextual/compositional question and the interaction of individual-level characteristics with macrosocial factors in predicting offending are important areas of future research. This study has shown that patterns of offending are rather complex, with the effect of economic variables contingent upon race and age of offender and type of crime. A resulting implication is that aggregate offense rates may at best be too crude to inform the testing of criminological theory, and at worst lead to seriously misspecified models. More finely grained analyses are clearly needed to test the contextual propositions of macro-criminological theory. 10

It is hoped that the conceptualization and methodology presented in this study for estimating disaggregated offending rates will further such efforts.

10. For example, one area of future research would be to explore the effects of family structure (for example, percent female-headed families, divorce) on black and white offending. There was insufficient independent variation in percent black and female-headed families (r = .8) in the present sample to adequately separate race and family structure effects. Limited analysis, however, revealed that introduction of family structure did not alter the major results with regard to income inequality.

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Robert J. Sampson is Assistant Professor of Sociology at the University of Illinois, Urbana-Champaign. His major research project involves an examination of the effects of official sanctions and other forms of community social control on disaggregated offending rates. He has recently coedited The Social Ecology of Crime.

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STRUCTURAL SOURCES OF VARIATION IN RACE-AGE-SPECIFIC RATES OF OFFENDING ACROSS MAJOR U.S. CITIES