ABSTRACT * * * In a recent paper, Cohen and Felscm argue that changes in our routine activities since World War 11 have contributed to the increase in predatmy crime by creating additional opportuni- ties for it. In particular, they found that a measure of the disper- sal of activities away from the home-the household actiwity ratio-has had a significant effect on crime rate trends since 1947. The aim of the present reseurch is twofold: (1) to determine if this relationship occurs across space as well as over time, and (2) to see i f economic inequality may be an intervening val-iable between the household activity ratio and the crime rate. The sample consists of 93 nonsouthern cities of over 50,000 popula- tion in 1960. Data are drawn from the 1970 Census und the U n i f m Crime Reports. Using path analysis, it is determined that the effects of the household activity ratio cm rates of predatory crime are entirely indirect, and are transmitted by income inequality.

Inequality, Opportunity, and Crime Rates in Central Cities

LEO CARROLL, University of Rho& Island PAMELA IRVING JACKSON, Rhde Island College

Recently, Cohen and Felson (1979) have presented evidence in support of an ecological theory explaining trends in the rates of direct contact predatory crimes. Following Glaser (1971: 4), these crimes are defined as illegal acts involving direct physical contact between an offender and a person or object, in which the offender “definitely and intentionally takes or damages the person or property.” Operationally, these offenses are roughly equivalent to the FBI’s Part I or index offenses. Cohen and Felson (1979: 589) argue that changes in routine activity patterns influ-

AUTHORS’ NOTE: This article is part of a larger research effort to which both authors are equal contributors. It is a revision of a paper presented at the annual meetings ofthe American Sociological Association in August 1981. We m l d like to acknowledge the services provided by the computer centers at the University of Rho& Island and Rho& Island College. CRIMINOI:OGY, V 4 . 2 1 No.2, May 1983 178-194 01983 American Society of Criminology

1 Y8

Carroll, Jackson 1 CRIME RATES IN CENTRAL CITIES 179

ence the rates of these offenses by affecting the convergence in space and time of the minimal elements necessary to their commission: (1) moti- vated offenders, (2) suitable targets, and (3) the absence of capable guardians.

One contemporary change of particular importance for explaining crime is the dispersal of activities away from the home (Cohen and Felson, 1979: 598). The dispersal of the population across more house- holds, particularly non-husband/wife households, as well as the disper- sal of activities away from the household, creates a greater market for movable and portable durables. Moreover, the dispersal of activities away from the home means that homes are more vulnerable to burglary as a greater proportion of the population spends more time outside the home, thus increasing the probability of being victimized. In short, this change in routine activities increases the opportunity for direct contact, predatory crimes.

Cohen and Felson (1979: 600) developed a measure of the dispersal of activities away from the home which they term the household activity ratio, relating changes in i t to changes in the crime rate. The household activity ratio is calculated by adding the number of married, husband- present female labor force participants to the number of non- husband/wife households, and dividing this sum by the total number of households. Holding constant the proportion of the population aged 15-24 and the unemployment rate (indexing the supply of motivated offenders), they found that changes in the household activity ratio had a positive effect on changes in the rates of predatory crime over the period

Cohen and Felson interpret their findings as supporting the view that, to a large extent, the increase in the rate of predatory crime is a function of the increased opportunities for such crime, apart from fac- tors influencing the motivation of offenders to commit them. An alterna- tive interpretation would seem possible, however.

Central to most theories of crime, whether they be functionalist or conflict, is the idea that poverty or inequality is criminogenic (see, for example, Merton, 1957; Turk, 1969). Recent research tends to the con- clusion that it is inequality rather than poverty that is the crucial varia- ble. After an exhaustive review of the literature, Braithwaite (1979: 211) concluded that while the evidence regarding the effect of poverty on crime rates is inconsistent,

1947-1974.

the literature shows fairly uniform support for a positive association between inequality and crime among those studies which, instead of using an unemployment or poverty index which focuses on the bottom tail of the income distribution, use a global index of income dispersion, such as the Gini coefficient.

180 CRIMINOLOGY M A Y 198s

In his own analysis of the average annual crime rates between 1967 and 1973 in 193 American cities, Braithwaite (1979: 212-216) found that poverty, defined in either absolute or relative terms, does not in general increase the explained variance in index offenses beyond that explained by population size and region, but that income inequality significantly adds to the explained variance for all seven offenses. Similar results have been reported more recently by Jacobs (1981) and by Blau and Blau (1982). In the first, Jacobs found that inequality, but not poverty, was predictive of the rates of burglary and larceny in 195 SMSAs in 1970. Blau and Blau (1982), on the other hand, found that inequality, but not poverty, had a direct positive effect on rates of murder and assault in the 125 largest SMSAs in 1970.

Given the composition of Cohen and Felson’s household activity ratio, i t would be likely for it to have a positive effect on family income inequality. As is well known, and as they observe, the percentage of husband-present, married women who are in the labor force has increased dramatically over the time period considered in their study (Cohen and Felson, 1979 599). However, this increase occurred dispro- portionately among white women rather than black women, whose labor force participation has remained relatively constant a t about 48% over the past 25 years (Burstein, 1979: 387). Thus, the increase in labor force participation of husband-present, married women as indexed by one component of Cohen and Felson’s ratio should increase racial income inequality and, through it, overall income inequality.

The second component, the number of non-husband/wife households, should have a similar effect, though for different reasons. A major factor in the increase of these households is the increase in female-headed families: The percentage of all families headed by females increased by 30% between 1960 and 1976 (US. Bureau of the Census, 1976: 40). In 1976, approximately one-third of the female-headed families had incomes below the poverty line, compared to only 5% of those with male heads (U.S. Bureau of the Census, 1976: 418). Thus it would seem that the increase in female-headed families should also increase family income inequality.

Considering these likely effects on inequality of the dispersal of rou- tine activities away from the home leads to an alternative specification of the Cohen-Felson model. The direct effect on predatory crime rates, which they attribute to the household activity ratio, may in fact be indirect, operating through an unmeasured intervening variable- income inequality (see Figure 1). If this were found to be the case, it would cast some doubt on their interpretation that increases in preda- tory crime rates are due to increases in the opportunities for such crimes, apart from factors affecting the motivation of offenders. The absence of a direct effect would seem to require a different interpretation: that the

Carroll, Jackson 1 CRIME RATES IN CENTRAL CITIES 181

b. HOUSEACT- I NEQUAL I TY - CR I HE

Figure 1: Alternative Specifications of the Household Activity Model

dispersal of routine activities away from the home is itself a process influencing the structural factors that motivate offenders to commit direct contact predatory crimes. Our purpose in this article is to test these alternative specifications and interpretations.

DATA AND METHODS

The sample for this study consisted of 93 nonsouthern cities of over 50,000 population in 1970. These cities are part of the “Permanent Community Sample”(PCS) established by the National Opinion Research Center at the University of Chicago and were included in a school desegregation study conducted during 1968-1969 (confer Kirby et al., 1973). The exclusion of southern cities is a distinct limitation to the present research, but the sampling design used to select these cities precluded merely adding a random sample of southern cities. As we shall argue in the conclusion, we do not think our major findings would be altered by the inclusion of southern cities.’ However, this limitation means that our findings can only be regarded as suggestive.

The data for the study were drawn from two principal sources: the US . Census of the Population, 1970, and the Uniform Crime Reports, 1971.

We have attempted to develop a comprehensive yet parsimonious model of “direct contact predatory crimes” that includes not only mea . sures of the variables of primary interest but also variables suggested to be significant by previous research.

Crime. We estimated separate models for burglary (BURG), robbery (ROBBERY), and crimes against the person (PERCRIME). This last category includes homicide, forcible rape, and aggravated assault. Lar-

182 CRIMINOLOGY f MA Y 1983

ceny and auto theft were not included with burglary because they were excluded by Cohen and Felson (1979: 601) for technical reasons having to do with definitional changes over the time period they observed and problems with multicollinearity. Robbery was kept separate because it is a unique offense containing elements of both property loss and vio- lence. The year of observation was 1971, and the variables were mea- sured in terms of rates per 100,000 population.

Household Activity (HOUSEACT). This variable is defined in the same way as by Cohen and Felson (1979: 600-601). The data are from Tables 84 and 85 in Volume 1 of the 1970 Census. Again, our principal concern is to determine if the effect of this variable on predatory crimes is direct, net of inequality, or indirect, operating through income inequality.2

Inequality. We employed two measures of income inequality, both drawn from the 1970 Census. The first is the dollar difference between the median white and median black family income, divided by the total median (BWDIFF), while the second is the standard index of income concentration (GINI).3

As discussed above, we expected the household activity ratio to have a positive effect on both racial and total income inequality. As racial inequality increases, so should total inequality; thus, the effect of BWDIFF on GIN1 was expected to be positive.

In line with previous research, we hypothesized a direct, positive effect of income inequality on all three categories of crime, although we expected it to be stronger for burglary than for crimes against the person, with perhaps an intermediate effect on robbery. Despite some speculation regarding the effects of racial inequality on crime (confer National Advisory Commission, 1973: 116), the question has not been extensively researched. Braithwaite (1979: 281-219) found that, net of income inequality, income differences between blacks and whites are negatively related to crime rates, though statistically significant only for rape and robbery. He speculates that these results may reflect an increase in racial tensions with increased racial inequality. However, in their recent analysis, Blau and Blau (1982) found that rates of violent crime increase with increased racial inequality. They attribute this effect to increased aggression resulting from the perceived illegitimacy of ascriptive inequality. In light of these contrary findings, our expecta- tions concerning the effects of racial inequality remain ambiguous.

Unemployment and Percentage Male, 16-24. Various researchers have found crime rates to be affected by the age structure of the population,

Carroll, Jackson / CRIME RATES IN CENTRAL CITIES 183

especially the proportion in the high crime ages (Fox, 1978). Cohen and Felson (1979: 600) employed the percentage of the population aged 15-24 to measure the supply of motivated offenders. We departed somewhat from them by employing a more refined measure: the percentage male aged 16-24 (PCTMALE), as arrest statistics suggest that 85%or more of the offenses considered here are comitted by males (FBI, 1976: 183-184).

The unemployment rate (UNEMPLOY) is another measure of the supply of motivated offenders frequently reported to have a positive effect on crime rates (Danziger, 1976). In line with Cohen and Felson (1979: 601), we used the average unemployment rate for each city in 1970.

We expected both unemployment and the percentage of the popula- tion that is male and 16-24 to have positive effects on crime rates. Moreover, we hypothesized a substantial indirect effect for unemploy- ment, operating through its effects on inequality.

Percentage Black. Another variable commonly linked to crime, espe- cially violent crime, in ecological analyses is the percentage of the popu- lation that is black (PCTBLACK), though the precise reason for this effect is the subject of some debate (Gastil, 1971; Loftin and Hill, 1974). We have thus included this measure and hypothesize a direct positive effect, especially for robbery and crimes against the person. We also expected the percentage black to have substantial indirect effects, as previous research has found this variable to have major influence on the extent of racial inequality (Frisbie and Neidert, 1977).

Population Size, Density, and Region. Last, our model includes three control variables. These variables are population size (SIZE), population per square mile (DENSITY), and region (NOREAST)! Common wis- dom to the contrary, there is little theoretical or empirical support for a direct effect of size or density on crime rates (Spector, 1975; Choldin, 1978). Nonetheless, these variables, together with region, are correlated with many other characteristics of cities, and we thus included them in our model primarily for the purpose of achieving statistical control of possible unmeasured influences.

Techniqu OfEstimatiOn, The full model is presented diagrammatically in Figure 2. It was first estimated for each type of crime by ordinary least squares regression, following the technique suggested by Alwin and Hauser (1975). The estimated coefficients for the full models are presented in Table 2. Causal paths not approaching significance at the .05 level were dropped and each model was reestimated. The fit of each equation in the restricted model was tested by means of an analysis of variance using hierarchical decomposition (Nie et. al., 1975: 336-340).

184 CRIMINOLOGY / MAY 1989

DENS I TY

NOREAST

PCTEIALE

PCTBLACK

UNEMPLOY

HOUSEACT 1 Figure 2: Schematic Representation of the Full Model

Where it was found that including the omitted set of variables would significantly increase (.05 level) the explained variance, the omitted set was added to the equation stepwise to the point where further increases would not be statistically significant.

RESULTS

The zero-order correlations among the variables in our model are presented in Table 1. Looking a t the correlations of principal concern (those among the household activity ratio, inequality, and crime), we found all twelve correlations to be in the direction predicted, and eight statistically significant (p. < 05). The strongest correlation was that between the household activity ratio and the Gini index (r = .56). The correlations of each of these two variables with the three types of crime were at the same moderately strong level (r = .24 to .43). The weakest correlations were those between racial inequality and crime, only one of which was statistically significant, and between the household activity ratio and racial inequality (r = .02).

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INEQUALITY AND CRIME

From the full model presented in Table 2 and the restricted models- presented diagrammatically in Figures 3-5-it is apparent that inequal- ity has strong causal effects on crime rates.

As expected, total family income inequality had a strong positive direct effect on the rate of burglary (Pa9 = .46). The net effect of the difference between black and white median family income on the rate of burglary was negligible (.02). However, this net effect resulted from two paths, the effects of which were in opposed directions. The direct effect was negative (Pa8 = -.23), but there was a slightly greater positive indi- rect effect transmitted through total family income inequality (Pg8 Pa, = .25). Racial inequality, then, appears to have contrasting effectson the rate of burglary. In cities where blacks are closer to whites in terms of family income, the rates of burglary are higher, other things being equal. But cities with more racial equality also tend toward amore equal income distribution and, as a result, have lower rates of burglary. On balance, then, income inequality has a strong positive effect on rates of burglary. This results solely from the effect of Gini, which has a strong direct effect and which mediates a moderately strong indirect effect of racial inequality.

As hypothesized, the effect of inequality on crimes against the person was not as pronounced as the effect on burglary. The difference between

Carroll, Jackson j CRIME RATES IN CENTRAL CITIES 187

s I ZE\

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white and black median income had a negligible (-.09) zero-order corre- lation with crime against the person, while that of the Gini coefficient was moderately positive (.30). When these associations are decomposed in terms of our specifications, we find that approximately 83% of the association of Gini with the violent crime rate was causal ( P b g = .25) and that the causal effects of racial inequality were entirely indirect, trans- mitted by income inequality (Pgspbg = .14).

In the case of robbery, as with burglary, the effects of specifically racial and general income inequality were in opposed directions. The difference between white and black median incomes had a significant negative direct effect on the rate of robbery (Pc8 = -.29) and a smaller positive indirect effect transmitted by total income inequality (PcgPg8 = .lo). Thus, the net effect of racial inequality was negative. The positive effect of income inequality on robbery rates was less than its effect on either burglary or crimes against the person (Peg = .19). On balance, then, the effect of inequality, both total and racial, on the rate of robbery is negligible.

The causal effects of inequality on crime seem quite complex, varying not only by the type of inequality, but also by the type of crime. Particu- larly puzzling are the negative effects of racial inequality. One possible explanation, suggested by Braithwaite (1979220), is that increased racial equality in relatively poor cities generates an atmosphere of racial antagonism, resulting in more extensive victimization of both

188 CRIMINOLOGY / MAY 1988

blacks and whites by members of the opposite group. If this were so, however, we would expect racial equality to be most strongly predictive of crimes against the person, when in fact we found that it had a greater effect on property crimes.

Another explanation might be that cities with greater racial equality are cities that have experienced the flight of affluent whites to the suburbs. That is, the greater equality may result more from a lower white income level than from a higher black income level. We attempted to control for this possibility by making our measure of racial inequality scale invariant with respect to the total median income. As an additional check, however, we added a measure of elite suburbanization6 and entered it into our equations. If cities with high crime rates have higher percentages of elites in the suburbs, and if racial equality results from this, then the negative effect of racial inequality on crime should disap- pear with this variable controlled. This was not found to be the case; instead, the coefficients remained substantially unchanged from those shown in Table 2.

Still a third possible explanation, and consistent with the observed effects, is that the relatively small differences in racial income inequality across cities act not to lessen but to increase feelings of deprivation on the part of blacks, as whites become a more significant reference group for them (compare Merton, 1957: 285-387). Blacks in cities where the differ- ence between black and white median incomes is 30%of the total median may feel more deprived than in cities where it is 50% of the median. If this is true, then a wider range of inequality may show the true relation- ship to be curvilinear. Further investigation of the effect of racial equal- ity on crime is clearly necessary. It does, however, extend beyond the scope of the present article.

HOUSEHOLD ACTIVITY RATIO AND CRIME

Our primary concern is with the relations among the household activ- ity ratio, income inequality, and rates of crime. Having seen that inequality has rather strong effects on crime, we turn now to examine whether the effects of the household activity ratio are direct or, as we suggested they may be, mediated by the effects of inequality.

As can be seen in Table 2 and Figures 3-5, our predictions concerning the effect of the household activity ratio on income inequality were borne out. Its effect on the difference between white and black median income was positive and significant. Additionally, it had a strong direct effect on the Gini index (P9,= .39; p < .001) and a positive indirect effect mediated by racial inequality (P,,P,, = .13). Thus it appears that the dispersal of activities away from the home has a strong impact on the degree of inequality in a city.

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UNEMPLOY’ I

Figure 5 : Schematic Representation of Restricted Model for Robbery

Of the total association of the household activity ratio with burglary (r = .36), approximately 50% was noncausal, and none of the causal effect was direct. Most of the indirect effect was mediated by total income inequality (Pag Pg7 = .18), with smaller effects being transmitted by racial inequality (Pas P87 = -.06) and by both measures of inequality jointly (P, P98P87 = .06).

The zero-order correlation between the household activity ratio and the rate of crimes against the person was .24. Only 16% (.04) of this association was causal. Again, none of the causal effect was direct. Nearly 75% of the indirect effect was transmitted by income inequality, with the remainder mediated jointly by racial and total income inequality.

The same was found to be true with respect to robbery. A negative indirect effect of the household activity ratio on robbery was mediated by racial inequality (Pc8P87 = -.07). This effect was balanced by a positive indirect effect of the same magnitude transmitted by the Gini coefficient (PC9Pg7 = .07). In addition, there was a slight effect transmitted jointly by both dimensions of inequality (Pc$g8Ps, = .03).

As the household activity ratio had only indirect effects on the three types of crime, our analysis supports the conceptualization that inequality operates as an intervening variable between this measure and crime. A further analysis of the relation between the two variables was made by examining the unique contribution of both the household activity ratio and the Gini index to the explained variance for each typeof crime. This was accomplished by forcing each measure to enter last in the three

Carroll, Jackson 1 CRIME RATES IN CENTRAL CITIES 191

equations. With the household activity ratio in the equations, the Gini index added 9.2% to the explained variance for burglary, 4.6% for crimes against the person, and 1.4% for robbery. With the Gini index in the equations, the unique contributions of the household activity ratio were only 0.5%, O.O%, and 0.2%, respectively. This analysis thus suggests rather strongly that inequality operates as a n intervening variable.

OTHER EFFECTS

With the exception of the regional variable, the predetermined varia- bles all had effects on crime. For the most part, these effects were indirect, and the paths through racial inequality and total income inequality were in opposed directions and of nearly equal strength, so that the net effects approximated zero. Two variables were exceptions to this pattern: the percentage black and the rate of unemployment.

The percentage of the population which is black had significant direct effects on each type of crime. These direct effects were, as expected, considerably stronger for robbery and violent crime than for burglary. This accords with much previous research, as well as with arrest statis- tics showing that about 60% of those arrested for robbery, 45% of those arrested for violent crimes, but only about 30% of those arrested for burglary are black (FBI, 1976: 185). As has been endlessly debated, this may represent the diffusion out of the South of a regional subculture of violence, or an effect of hard-core urban poverty(Gastil,1971; Loftin and Hill, 1974). That the effect was entirely direct, independent of and not mediated by either measure of inequality, suggests to us that it had little if anything to do with feelings of deprivation.

Despite common wisdom, research results on the relation of unem- ployment rates to crime rates are contradictory and inconsistent, although a careful review shows effects to be more likely for property crime and more likely to be found by the more recent research (Braith- waite, 1979: 209-211). Our findings are partially consistent with this general conclusion in that unemployment is found to have its strongest effect on burglary. I t also, however, is found to have a moderately positive effect on the rate of personal crime but virtually no effects on the rate of robbery.

CONCLUSION

As noted above, we believe that our finding, that the effects of the household activity ratio are indirect, may require a revision of Cohen and Felson’s (1979) explanation that increased opportunities for preda- tory crime are a major factor in its increase, apart from factors motivat- ing offenders. If their explanation were true, the effects of the household

192 CRIMINOLOGY I M A Y 1983

activity ratio should be direct, unmediated by other factors influencing motivation. In fact, though, the household activity ratio has strong effects in the direction of increasing income inequality, and its effect on crime we find to be entirely transmitted by inequality.

This study should not, of course, be regarded as a refutation of their thesis. As we employed a cross-sectional design, while Cohen and Fel- son’s was a time-series analysis, the studies are hardly comparable. Nonetheless, we believe that over time inequality may also intervene between the household activity ratio and crime. Danziger and Wheeler (1975) examined the relationship between various measures of inequal- ity and crime for the period 1949-1970. Controlling for several other variables, including unemployment and the percentage of the popula- tion in the high-crime ages, they found that during periods when the gap between rich and poor increases, crime rates also increase. A clear matter for further research, then, is a time-series analysis including both the household activity ratio and a measure of inequality.

Another limitation of the current analysis is its exclusion of southern cities. As noted earlier, we do not believe that this is a serious limitation, given our results. Crime in southern cities is distinct from that in others, mainly in terms of the higher level of violent crime. Including southern cities would, it seems to us, most likely affect our conclusions regarding crimes against the person, where the effects of inequality were weakest, and would probably not alter substantively our conclusions regarding burglary and robbery.

In the final analysis, however, our findings are mainly suggestive of the need for further research. In addition to the time-series analysis suggested above, the complex ways in which racial inequality affects crime rates are both theoretically provocative and fraught with policy implications. Moreover, the limited research to date comes to quite inconsistent conclusions. Braithwaite (1979), as noted previously, found the effects to be negative, though significant only for rape and robbery. We found a negative effect for robbery and also burglary, but the effect on crimes of violence we found to be negligible and insignificant. In a recently published article on violent crime, however, Blau and Blau (1982) found positive effects of racial inequalityon murder, robbery, and assault. The theoretical and practical importance of the question and the inconsistency of the research mark this as a crucial issue in need of a more detailed examination than has been done to date. Indeed, the whole question of the impact of income inequality on crime seems to be a central one that for some unknown reason has been largely overlooked by criminologists.

NOTES 1. Gibbs and Erikson have questioned whether central cities are an approp-

riate unit of analysis for the study of crime rates. The crime rates of cities may be

Curroll, Juckson / CRIME RATES I N CENTRAL CITIES 199

inflated due to the fact that many of those involved in crimes, either as offenders or victims, are drawn from the urban community, but beyond thecity limits, and thus are not included in the denominator of the conventional rate. If this is so, then one would expect a positive correlation between a measure indexing communi- ty/city size and crime rates. Gibbs and Erikson (1976) found such an association for singular cities, those that are the only central city in an urbanized area of SMSA, butnotforthetotal sampleofcities. Given thisfinding,and the factthatfewer than one-half of the cities were singular cities, we have conducted our analysis without introducing a correction for the size of the SMSA.

2. The Household Activity Ratio and the two measures of inequality have been multiplied by 100 to facilitate the presentation of regression coefficients.

3. The Gini index is derived from a Lorenz curve obtained by plotting the cumulative percentage of aggregate income earned by units (in this case, fami- lies) against the cumulative percentage of units. If all units had exactly the same relative income, say, each 10% of the families earned 10% of the income, the resulting ordinate would be a straight diagonal line-the line of equality. Lorenz curves drawn to actual data invariably fall below the line of equality, and the Gini index measures the extent of the departure. It ranges from 0.0 in the case of equality to 1.0 in a situation of complete inequality (confer Bronfenbrenner, 1971; U.S. Bureau of the Census, 1967).

4. The variable for region (NOREAST) was coded as follows: 1-New England or Mid-Atlantic; 2-Elsewhere.

5. Number of Families<$50,000 in city/Number of Families < $50,000 in SMSA.

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Leo Carroll is Professor of Sociology at the University of Rhode Island. He is the author of a book and numerous articles dealing with race relations and racial biases within the criminal justice system. His current research is a time-series analysis of the relations among inequality, crime rates, and rates of incarceration.

Pamela Irving Jackson is an Associate Professor of Sociolow at Rhode Island College. Her research focuses on regional differences in patterns of criminal activity, race relatiuns, and law enforcement. She is currently engaged in a comparison of cross-sectional and multistage panel models of municipal criminal justice activity.