Assessing Changes in the Effect of Divorce Rates on Homicide Rates Across Large U.S. Cities, 1960-2000: Revisiting the Chicago School

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2010 14: 24 originally published online 15 December 2009Homicide StudiesMark Beaulieu and Steven F. Messner

Across Large U.S. Cities, 1960-2000: Revisiting the Chicago SchoolAssessing Changes in the Effect of Divorce Rates on Homicide Rates

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DOI: 10.1177/1088767909353020http://hs.sagepub.com

Assessing Changes in the Effect of Divorce Rates on Homicide Rates Across Large U.S. Cities, 1960-2000: Revisiting the Chicago School

Mark Beaulieu1 and Steven F. Messner2

Abstract

Researchers commonly include a measure of the level of divorce among the standard covariates in macro-level studies of homicide, justifying this practice with reference to social disorganization theory. We review the underlying logic for a divorce/homicide relationship, distinguishing between a “cultural/normative conflict” variant advanced by the classical Chicago School theorists and a “structural/control” variant associated with the neosocial disorganization perspective. We suggest that the cultural/normative conflict variant implies that the effects of divorce will become attenuated over time, whereas the structural/control variant implies stability in effects. We then assess the degree to which the effects of levels of divorce on homicide rates have changed with panel data for a sample of large U.S. cities during the period 1960-2000. The results of seemingly unrelated regression (SUR) analyses reveal considerable stability in the effects of a measure of div-orce on homicide rates, especially if the divorce measure is combined with a “sibling” measure of family disorganization—the percentage of children not living with two parents. Our analyses suggest that the commonly observed positive effect of measures of divorce on homicide rates over recent decades is most plausibly interpreted with reference to the “structural/control” arguments associated with the neosocial disorganization perspective.

Keywords

divorce, homicide, social disorganization, Chicago School

Introduction

Levels of divorce have emerged as one of the standard predictors of homicide in macro-level criminological inquiry. In multivariate regression models for areal units

1University of Hartford, West Hartford, CT2University at Albany, State University of New York

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Beaulieu and Messner 25

at varying levels of aggregation (e.g., census tracts, cities, metropolitan areas, counties, states), an indicator of divorce is commonly included as a control variable, if not a vari-able of primary interest (Blau & Blau, 1982; Kovandzic, Vieraitis, & Yeisley, 1998; Land, McCall, & Cohen, 1990; Parker, McCall, & Land, 1999; Pratt & Cullen, 2005). The theoretical rationale for including divorce with violent crime is usually grounded in the social disorganization perspective. However, the nature of the processes underly-ing the relationship between these variables is usually referred to fleetingly, if at all.

The purpose of the present analysis is to explicate the mechanisms cited in the social disorganization literature that link divorce and homicide rates and consider the implications of these mechanisms for inferences about stability or change in the effects of divorce. We then assess the impact of divorce rates on homicide rates with panel data for a sample of large cities for the five decennial years from 1960 to 2000. Our orienting research question is whether a measure of divorce exhibits stability in its capacity to explain intercity variation in homicide rates over the latter half of the 20th century or whether any effect of divorce has become attenuated as divorce has become more commonplace and public attitudes more accepting of divorce.

Divorce, Social Disorganization, and HomicideLevels of divorce have been linked with crime rates in two distinct ways from the perspective of social disorganization theory. One variant emphasizes cultural dynam-ics and, in particular, inconsistencies and conflicts among normative standards. The second variant directs attention to the structural implications of divorce for social control. Although both themes can be detected in the arguments of the classical Chi-cago School, the relative importance devoted to the respective processes appears to have shifted over time. Normative conflict plays a central role in the writings of the original Chicago School scholars, whereas processes of supervision, monitoring, and control have moved to the forefront in the work of more recent theorists following in the general social disorganization tradition.

The arguments pertaining to normative conflict are embodied prominently in the writings of Ernest Burgess, who is widely regarded as one of the most influential schol-ars on the family at the time (Amato, 2004, p. 267). According to Burgess, in his monograph coauthored with Harvey Locke, divorce can be regarded as both a sign of social disorganization and a source of social disorganization. On the one hand, the wide-spread prevalence of divorce is a reflection of the breakdown of the traditional institutions, including but not limited to the family, that has accompanied the transition from “an earlier, predominantly rural society, with the imprint of Puritan values, to an urban-industrial culture with its emerging way of life” (Burgess & Locke, 1945, p. 486). With respect specifically to the family on the other hand, Burgess and Locke maintain that new forms of relationships that are not truly “institutionalized” increasingly accom-pany the traditional type of family associated with rural society. The traditional institution of the family is characterized by strict regulation of behaviors by the mores and tradi-tions. This family form is in the process of being transformed, and a new type of family is emerging—what Burgess and Locke refer to as the companionship family.



26 Homicide Studies 14(1)

The respective family types are based on different types of integration. For the traditional form, “a high degree of family integration may be achieved by community pressure, elaborate ritual, rigorous discipline, subordination of other members to the head, and co-operative economic activities” (Burgess & Locke, 1945, p. 333). In the newly emerging form, in contrast, integration “may be obtained by mutual affection, sympathetic understanding, common interests, democratic relationships between hus-band and wife and parents and children” (p. 333). Burgess and Locke observe that remnants of the traditional institutional family coexist with the emerging companion-ship family. This type of situation is “disorganizing” because “familial patterns are in confusion and disorder, for some of the old patterns have been perpetuated and are inconsistent with new ways of acting and also the new patterns are not consistent within themselves” (Burgess & Locke, 1945, p. 518).

Burgess and Locke (1945, p. 714) refer only briefly to crime and delinquency, whereas Shaw and McKay (1942/1972) provide the theoretical framework for a more explicit linkage between divorce and such behaviors. In their well-known formulation, they contrast neighborhoods with low and high delinquency rates, directing attention to the degree of “uniformity, consistency, and universality of conventional values and attitudes with respect to child care, conformity to law, and related matters” (Shaw & McKay, 1942/1972, p. 170). They observe that delinquency is prevalent in neighbor-hoods “characterized by a wide diversity in norms and standards of behavior” (p. 171), and they infer that such normative conflict is problematic because it inhibits effective socialization of youth. In Kornhauser’s (1978) influential explication of Shaw and McKay’s thesis, socialization is rendered ineffective in disorganized neighborhoods because “the messages and methods of the various institutions with which the child has contact are conflicting and uncoordinated” (p. 81).

Burgess and Locke’s arguments about the disorganizing aspects of divorce are con-sistent with conventional applications of the social disorganization perspective to explain macro-level variation in violent crime. However, these scholars also offer insights that have not been fully appreciated in the criminological literature. They not only speculate that divorce is likely to continue to rise in the near term but also caution against the view that disorganization is “pathological,” noting that the disruption of organization per-forms an “equally significant function in mediating reorganization” (Burgess & Locke, 1945, p. 713). Their arguments anticipate the possibility that the new family form—the companionship family—might eventually become “normalized” and institutionalized. This new family form might be compatible with higher levels of divorce than were char-acteristic of prior forms of social organization, but its high prevalence would not necessarily indicate normative conflict; social norms can change.

Historical patterns of divorce and attitudes toward divorce in the United States are suggestive of such normalization. Figure 1 depicts crude divorce rates from 1920 to 2000. The data reveal fairly steady rates from the 1920s through the early 1930s, with increases in the 1940s peaking in the years following World War II, presumably due to the breakup of war-time marriages and war-related stress (Riley, 1991, p. 159). These rates then declined through the late 1950s, to be followed by sharp increases during the




1960s and 1970s. Since the early 1980s, divorce rates have been decreasing, although they remain high relative to those at the beginning of the 20th century.

In addition to the growing frequency of divorce, attitudes toward marital dissolution have changed as well, although the patterns are complex and reveal a certain degree of ambivalence. Thornton and Young-DeMarco (2001, p. 1019) report clear movement toward greater acceptance of divorce as reflected in responses to one item of the Inter-generational Panel Study, that is, the item on whether “parents should stay together even if they do not get along.” Responses on other items in the study, in contrast, show little change into the 1980s. Overall, the survey evidence suggests that the American public continues to express the view that marriages should only be ended for good reasons, but the public overwhelmingly rejects the notion that divorce is never justi-fied. In other words, although marriage and the family continue to occupy an important place in people’s lives, family institutions are seen as “much more voluntary and less obligatory than they were in previous decades” (Thornton & Young-DeMarco, 2001, p. 1031). Riley (1991) reaches a similar conclusion in her historical analysis of divorce from colonial days to the present, arguing that by the late 1980s, divorce had become “a matter to be managed much like life’s other significant milestones—marriage, giving birth, and death” (Riley, 1991, p. 181). She observes that divorce had even become an occasion for humor, citing Hallmark Cards, which in 1988 marketed coffee mugs

0.0

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1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Divorce

Rate

Figure 1. Crude divorce rates—1920-2000Note: Divorce rates per 1,000 population.Source: U.S. Bureau of the Census (1975), (page 4) and National Center for Health Statistics (2007).




bearing the maxim, “‘Tis better to have loved and lost than to be stuck with a real loser for the rest of your entire, miserable existence” (Riley, 1991, p. 181).

The stabilization of divorce rates at historically high levels and the growing accep-tance of divorce as a less-than-ideal but legitimate practice suggest that the companionship family described by Burgess and Locke in the early decades of the 20th century has indeed become normalized and institutionalized. To use their language, the “reorganiza-tion” of the family associated with the transition from a predominantly rural to a predominantly urban society has largely been accomplished. Therefore, to the extent that divorce promotes homicide because it is a sign of, or a cause of, normative con-flict associated with disorganizing forces, it seems reasonable to hypothesize that the effects of divorce rates on homicide rates should become attenuated, if not entirely eliminated, by the latter decades of the 20th century.

The processes allegedly linking divorce and violent crime in the more recent neo-social disorganization literature are slightly different, as reflected most prominently in the work of Sampson and colleagues (Sampson, 1987a, 1987b; Sampson & Groves, 1989). These theorists emphasize the structural rather than the cultural/normative implications of divorce. In initial elaborations of the social disorganization perspec-tive on the potential link between family structure and crime, Sampson (1987a, 1987b) directs attention to consequences of marital and family disruption for two general forms of social control. He cites research indicating that communities with prevalent family disruption are likely to be characterized by meager participation in community organizations, which is likely to inhibit the development of ties between residents and organizations beyond the community and thus weaken formal social controls (Sampson, 1987b, p. 352). He also explicates the consequences of family disruption for levels of informal control. Building on insights from Kornhauser, Sampson (1987a) argues that “pronounced family disorganization” impedes the “‘collective efforts’ of families to link neighborhood youths to the wider society” (p. 106). He further main-tains that “two-parent households provide increased supervision and guardianship not only for their own children and household property . . . but also for public activities in the community” (Sampson, 1987b, p. 353).1 In a subsequent study, Sampson and Groves (1989, p. 781) place primary emphasis on the impact of family disruption on informal controls, specifically linking indicators of family disruption with the monitoring and supervision of street-corner teenage peer groups.2

From the vantage point of the neosocial disorganization perspective, the implica-tions of the growing prevalence and acceptance of divorce for stability or change in the effects of divorce on homicide rates differ from arguments about normative conflict, disorganization, and reorganization in the classic Chicago School. The weakened supervision and monitoring associated with divorce allegedly arise bec-ause, in the absence of two-parent households, there are fewer parents to generate “networks of control” (Sampson, 1987b, p. 353) and because the activity patterns of nonmarried persons provide for less guardianship (Cohen & Felson, 1979). These structural processes associated with informal social control would seem to be largely unaffected by the normative status of marital dissolution. The “structural/control”




variant of the neosocial disorganization perspective implies that the effects of divorce rates should be relatively impervious to movement in the direction of greater institutionalization of divorce and thus should be relatively stable over extended periods of time.

Previous ResearchThe conventional practice of including divorce rates in multivariate regression

models predicting homicide rates can plausibly be traced to the seminal study of “the costs of inequality” by Blau and Blau (1982). In their discussion of the Southern subculture of violence hypothesis, they mention other explanations for comparatively high homicide rates in the South, citing in particular the higher poverty levels and the relatively larger size of the Black population in this region (p. 115). With respect to racial composition, they speculate that subcultural dynamics linked with family stru-cture are at work:

Many children in black ghettos are reared in broken homes, reducing strong identification with societal norms. Oppression and exploitation further under-mine respect for the laws and mores imposed by the alien majority. Normative conflict between subcultures has been traditionally considered a major cause of high crime rates in an area. (pp. 115-116)

When discussing additional covariates of criminal violence, Blau and Blau (1982, p. 119) further imply that the incidence of divorce can be viewed as a “manifestation” of social disorganization.3 In their empirical analyses, they estimate regression models predicting the total violent crime rate and rates of murder, rape, robbery, and assault with data for the 125 largest SMSAs in the United States in 1970. They observe that “the percent divorced exhibits strong positive relationships with all forms of violent crime,” noting that these findings conform to “the conclusions of the ecological studies at the University of Chicago” (Blau & Blau, 1982, p. 124).

A number of subsequent studies that include divorce as a predictor of homicide rates find the theoretically expected positive effect, although the evidence is not entirely consistent. The highly influential work by Land et al. (1990) is particularly noteworthy. The authors provide a comprehensive overview of the existing homicide research, noting many inconsistencies in the literature. They then suggest that these inconsisten-cies can be attributed largely to multicollinearity among predictors, and they propose indexing procedures to deal with this problem. Their analyses have yielded a “baseline model” that has informed much of the subsequent homicide research. With respect to divorce, they report positive effects on homicide rates at the city, SMSA, and state levels in 1960, 1970, and 1980. In contrast, the divorce effect is not significant in 1950 at the city or state level of analysis.

Using different units of analysis but following the same basic modeling strategy as that introduced by Land et al. (1990), Baller, Anselin, Messner, Deane, and Hawkins (2001) report significant positive effects of divorce on county-level homicide rates




over the period 1960-1990 in analyses disaggregated by region (South vs. non-South). Treating divorce rates as a control variable in analyses focused on the effects of crack use, Baumer, Lauritsen, Rosenfeld, and Wright (1998) find positive effects on homicide and also robbery. Similar to the findings in Baumer et al. (1998), Stretesky, Schuck, and Hogan (2004) report that divorce has an effect on homicide and robbery as well as rape and assault.

Perhaps the strongest support for the existence of a relationship between divorce and homicide comes from Parker et al. (1999) in their literature review of homicide studies. When percentage divorced is used as an independent variable in research on aggregated homicide rates (in contrast with homicides disaggregated by features of the incidents), 15 of the 16 studies report a significant positive relationship. A study by Simpson (1985) is the only one of these studies reviewed by Parker et al. using aggre-gated homicide statistics that encounters mixed effects. He, Cao, Wells, and Maguire (2003) extend the focus to include another form of lethal violence: suicide. They report that the divorce rate is positively and significantly related to the rate of lethal violence (the number of homicides and suicides per 100,000).

Analyses based on disaggregated homicides reveal more mixed patterns. Kovandzic et al. (1998) discover that divorce exhibits an effect on the total homicide rate, but there is no effect when homicides are disaggregated by victim–offender relationship. A more recent study of female victimization in homicides by Titterington (2006, p. 230) finds that divorce significantly increases female victimization independently of mea-sures of gender inequality. Schwartz (2006a) also examines homicide rates disaggregated by gender but explores the effects of multiple types of family structures, including divorce, as well. In her analyses of county-level data, divorce exhibits no effect on male or female homicide rates. In contrast, other family structure variables, father absence in particular, do yield effects on male and female homicide rates.

In addition to the research on gender disaggregated homicide, studies have looked at the relationship between divorce and a particular type of homicide—domestic homicide. Much of this research focuses on insights by Gillis (1996). Gillis (1996, p. 1274) notes that while divorce can serve as a stimulus for familial violence, it can also serve as a “safety valve,” allowing spouses to escape from dangerous situations. Consistent with the “safety valve” interpretation, Gillis detects a negative association between indicators of separation/divorce and domestic homicides in time-series analyses of France over the period 1852-1908. More recent research similarly suggests that divorce can have differ-ent effects on different types of homicide classified according to the victim–offender relationship. Rosenfeld (1997) finds that marital homicides fell along with decreases in domesticity (decreasing marriage rates and increasing divorce rates) in St. Louis. In a later study, Dugan, Nagin, and Rosenfeld (1999) report that divorce lowered the rate of homicide for married male victims but increased the rate for unmarried female victims. Thus, “the calculation of the net effect of declining marital domesticity on intimate part-ner homicide requires the summation of impacts across marital status” (p. 204).4

Overall, previous empirical studies are generally suggestive of a positive effect of levels of divorce on total (i.e., not disaggregated) rates of homicide. The findings, however, are somewhat mixed. Moreover, researchers have been concerned primarily




with identifying the sign and significance of any divorce effect. Very little attention has been paid to the magnitude of the effect and its stability over time. We assess whether the strength of the relationships between divorce rates and homicide rates at the city level have become attenuated during the latter decades of the 20th century, consistent with a cultural/normative conflict hypothesis or if those relationships have remained relatively unchanged, as might be expected on the basis of the structural/control interpretation.

Data and MethodUnits of Analysis

The social disorganization perspective grew out of a merging of the ecological approach in urban sociology with research on crime and delinquency. The original perspective adopted a dual analytic focus that encompasses both city-level dynamics, as reflected in the work of Park (1936) and Park and Burgess (1921, 1925), and within-city dynam-ics, as reflected in the studies of neighborhoods by Shaw and McKay (1942/1972). In subsequent applications, social disorganization theory has informed research based on a wide spectrum of ecological units, ranging from communities or neighborhoods (see Bursik & Grasmick, 1993; Sampson, Morenoff, & Gannon-Rowley, 2002) to nation states (LaFree, 1999). For purposes of the present analysis, we adopt cities as the units of analysis given the emphasis placed by the Chicago School theorists studying the family on the transition from rural/agrarian to urban/industrial settlements.5

We initially selected 100 U.S. cities with the largest populations for each of the decennial years during the period 1960-2000. Due to changes in population size, the list of cities that meet the “ranking” criterion for each decade is not the same, and thus the total number of cities potentially eligible for analysis is greater than 100.6 A total of 131 cities meet the criterion of having ranked within the 100 largest cities in popu-lation at any point during the period under investigation. After deleting cases with missing data, the final sample consists of a balanced panel of 113 cities.

Dependent VariablesThe dependent variables for the analysis are rates of criminal homicides known to the police as recorded in the FBI’s (various years) Uniform Crime Reports. Follow-ing conventional practices, rates are based on 3-year averages centered on the decennial year to minimize instability in the rates. For example, the homicide rate for 2000 is calculated by adding up the number of homicides for 1999, 2000, and 2001, dividing by the population totals for those years, and multiplying by 100,000.7 Specific sources for all measures are reported in Appendix A. Descriptive statistics are provided in Appendix B.

We have taken natural log transformations of the homicide rates to reduce skew-ness in the distributions. Because a few cities report zero homicides, the value of “1” has been added to all homicide rates to permit log transformations.




Independent and Control Variables

Important conceptual and methodological issues arise when studying the relationship between levels of divorce and homicide rates. Divorce is sometimes conceptualized as a component of the more general constructs of “family disruption” or “family disorga-nization,” and these broader constructs have been operationalized in multiple ways—with indicators of the relative size of divorced persons in the population, indicators of the prevalence of single-parent (or female-headed) households or numbers of children in such households and measures that combine these different dimensions (Pratt & Cullen, 2005; Sampson, 1987b; Schwartz, 2006b). The theoretical arguments reviewed above imply that indicators of both divorce and exposure of children to single- or two-parent households (which we refer to as “household-type measures” for convenience) are relevant to the alleged causal mechanisms. However, as a practical matter, the household-type measures tend to cluster strongly with indicators of deprivation for macro-level units. This clustering creates difficulties in the reliable estimation of net effects, which has prompted researchers to construct composite indexes of “resource deprivation” which typically include household-type measures (Land et al., 1990; Mears & Bhati, 2006; Parker et al., 1999). As explained more fully below, the cluster-ing of a household-type measure with common indicators of deprivation appears for our sample of cities as well.

We thus adopt a dual strategy for model estimation. For one set of analyses, we follow the conventional specification and incorporate our household-type measure into a resource deprivation index. For the second set of analyses, we remove the household-type measure from the resource deprivation index and combine it with the measure of divorce to create a composite “family disorganization” index.

Our models also include the following control variables that are standard in the macro-level research on violent crime: poverty, racial composition, residential stabil-ity, population size, age structure, and region. Social disorganization theory provides a rationale for anticipating that cities with high levels of poverty, high degrees of racial heterogeneity, and large populations will exhibit comparatively weak social control and thus high rates of criminal violence (Bursik & Grasmick, 1993; Land et al., 1990). Young people have relatively high offending rates, and thus a measure of the size of the young population is commonly included in regression models of violent crime rates to account for compositional effects associated with age structure (e.g., Land et al., 1990). Region has long been linked with homicide rates with reference to the thesis of a “Southern culture of violence” (Hawley & Messner, 1989; Nisbett & Cohen, 1996). Whereas prior research has focused on the link between the South and rates of violence, Western states have also exhibited comparatively high homicide rates (Bureau of Justice Statistics, 2007).

The specific measures of independent and control variables are as follows. Divorce is measured by the percentage of males above age 14 or 15 (depending on the year) who were divorced.8 Following Land et al. (1990), the indicator of household type is the percentage of children below 18 living with both parents. The polarity of the mea-sure implies that it reflects “traditional” household forms. To create the composite




measure of family disorganization, we subtract this measure from 100, yielding the percentage of children below 18 not living with both parents to be consistent with the polarity of divorce. The composite index is the sum of z scores for divorce and the recoded household-type measure.

The measure of poverty is the natural log of the percentage of families below the poverty line. Residential stability is measured as the percentage of people above age 5 who have not moved in the past 5 years. The measure of racial composition is the natu-ral log of the percentage of the population that is Black.9 The measure of age structure is the percentage of the population between ages 15 and 29. Population size for each city is expressed in natural logarithms. Finally, we use two dummy variables for region based on census designations—South and West.

We initially conducted principal components factor analysis to identify highly inter-correlated variables (Land et al., 1990; Parker et al., 1999). Consistent with prior analyses, the results reveal that three variables cluster together in our data set: percent-age of children below 18 living with both parents (the household-type measure), percentage Black, and percentage below the poverty level. For the analyses based on the conventional specification, we combined these three into a composite index of “resource deprivation” using the loadings from the principal components factor analysis. The factor loadings are reported in Appendix C. For analyses that include the family disorga-nization index, the resource deprivation measure is a composite index based on the sum of the z scores for percentage Black and the percentage below the poverty level.

Analytic Framework and Statistical ProceduresWe begin the analyses by estimating cross-sectional regression models of homicide rates for each of the five time points in the period under investigation: 1960, 1970, 1980, 1990, and 2000. As noted, we consider two specifications: the conventional specification, which allows the resource deprivation factor to “absorb” the household-type measure and the specification which extracts the household-type measure from the disorganization index and includes it in the composite family disorganization index. For both specifications, ordinary least squares (OLS) estimation is inappropri-ate because the disturbance terms are likely to be correlated across equations (Hargens, 1988, p. 71). When discussing OLS, Hargens (1988) states, “OLS estimates are best linear unbiased estimates; but as the correlations between disturbances increase in magnitude, OLS becomes increasingly inefficient compared to procedures that take the correlations into account” (p. 71). We thus use seemingly unrelated regressions (SUR). SUR modeling corrects for correlated disturbance terms and allows for the estimation of all relationships in a single model. Correcting for correlated disturbance terms across equations increases efficiency and, like OLS, still allows for unbiased coefficients. In addition, we can test for the equality of the coefficients for the predic-tor variables over time with SUR and formally assess stability and change. The STEST subcommand in SAS© is used for this purpose.

The bulk of the macro-level research on homicide has employed cross-sectional designs, and thus our primary analyses focus on the effect of levels of predictors on




levels of homicide for each of the respective decades. “Level effects” can of course differ appreciably from “change effects” (Firebaugh, 1980). We accordingly supple-ment the cross-sectional analyses by estimating dynamic models for exploratory purposes. Specifically, we regress homicide rates for a given decade on lagged measures of the predictors (except for the regional dummy variables) and the lagged measure of homi-cide (e.g., 1970 homicide rates on 1960 predictors and the 1960 homicide rate). The coefficients for the predictors in these models indicate the effects of the levels of these predictors on changes in homicide rates over the subsequent decade (e.g., the effect of the levels of divorce rates and controls in 1960 on the change in homicide rates between 1960 and 1970).10 As in the cross-sectional analyses, we use the S test to assess the degree of stability in the effects of divorce and family disorganization on changes in levels of homicide.

FindingsBefore turning to the cross-sectional regression results for our sample of cities, it is instructive to consider national patterns in homicide for the period under investigation. Figure 2 depicts U.S. homicide rates between 1950 and 2000. These rates climbed sharply from the early 1960s to the 1970s, then fluctuated at relatively high levels until the early 1990s, after which rates fell dramatically—the widely heralded “crime drop” (Blumstein & Wallman, 2000). A comparison with trends in divorce rates (Figure 1) reveals some similarities. The rise in divorce rates in the 1960s corresponds fairly closely to the increases in homicide rates, as do the declines in divorces through the 1990s, although divorce rates fell steadily from the peak around 1980 in contrast to the fluctuating pattern for homicide rates. This rough correspondence suggests a relation-ship between these phenomena and supports LaFree’s (1998) arguments that declining legitimacy of the traditional family, followed by institutionalization of alternative family arrangements, helps account for long-term trends in lethal violence.

Turning to the multivariate analyses, Table 1 reports the results of the regression of homicide rates on divorce rates and the control variables for each of the five decennial years, using the conventional specification. As shown in Panel A, the coefficients for the measure of divorce are consistently positive, as expected. The statistical signifi-cance of the divorce effect fluctuates, with the coefficients falling below the .05 level in 1960 and 1990. However, application of S tests indicates that the hypothesis of equality of coefficients cannot be rejected (see Panel B). In other words, in the con-ventional specification, the effects of divorce rates on homicide rates appear to be reasonably stable over the latter decades of the 20th century, although the significance of the effects is not very robust.

With respect to the control variables, the results for the composite index of resource deprivation are striking. This variable exhibits statistically significant positive effects on homicide rates throughout the period. These effects are quite strong relative to other predictors, as reflected in the standardized (beta) coefficients. Population size also exhibits robust and appreciable effects in the expected direction. Not surprisingly, larger cities tend to exhibit higher homicides rates. The expected positive effect of




Southern location emerges for 1980 and 1990, whereas location in the West has incon-sistent effects. The pattern of coefficients for residential stability is also interesting, indicating significant effects in the predicted negative direction during the early years (1960, 1970, and 1980) that become attenuated by the end of the period.11 The mea-sure of age structure exhibits erratic effects on homicide rates.

In Table 2, we report the results for the models with the household-type measure extracted from the resource deprivation index and combined with the measure of divorce. The results indicate significant, positive effects of the family disorganization index in each of the five decades. There is no evidence for an attenuation of the effect, as might be expected on the basis of the cultural/normative conflict variant of the social disorganization perspective. To the contrary, the magnitude of the coefficient for family disorganization tends to increase over the period, and the S tests indicate that the effect in the most recent decade (2000) is significantly stronger than for 1960 and 1980.

The results for the control variables in Table 2 are generally similar to those for the conventional specification. The most noteworthy difference is the reduction in the standardized coefficients for the resource deprivation measure. This is not surprising, given that the explanatory power of the household-type measure has been transferred to the family disorganization index.

The results of the dynamic models are reported in Tables 3 and 4. Considering the conventional specification first (Table 3), the effect of divorce is consistently positive,

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1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

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Figure 2. Homicide rates for the United States, 1950-2000Note: Homicides per 100,000 population.Source: Bureau of Justice Statistics. (2007). Homicide trends in the United States. Retrieved October, 17 2009 http://www.ojp.usdoj.gov/bjs/homicide/tables/totalstab.htm (page 25).




but the coefficient attains statistical significance only in 1980. The S tests indicate, more-over, that there are no statistically significant differences in the coefficients across decades.12 As in the cross-sectional analyses, the resource deprivation index emerges as the most powerful predictor of changes in homicide rates (other than the lagged measure of homicide) in the expected direction. Cities with more extreme disadvantage experi-enced the greatest increases in homicide rates over the course of the subsequent decade.

As shown in Table 4, the effects of the family disorganization index in the dynamic models are much more impressive than those of the divorce measure alone. The coef-ficients are significantly positive for each decade other than the prediction of the 1970 homicide rates. The S tests indicate that there are no statistically significant differ-ences in the coefficients across decades, but the overall pattern of results for the lagged model is generally consistent with the contemporaneous models, suggesting positive and stable effects of the composite measure of family disorganization.13

Table 1. Seemingly Unrelated Regressions of Homicide Rates on Predictors with the Conventional Specification—Cross-sectional Design (N = 113)

Panel A: Regression Coefficients

Independent variable 1960 1970 1980 1990 2000

Divorce 0.036 (0.054) 0.060* (0.108) 0.050** (0.123) 0.029 (0.069) 0.053** (0.152)Resource 0.432** (0.642) 0.467** (0.721) 0.457** (0.727) 0.522** (0.700) 0.572** (0.834) deprivationResidential -0.028** (-0.379) -0.023** (-0.267) -0.017** (-0.221) 0.002 (0.018) -0.004 (-0.029) stabilityPercentage of -0.014 (-0.056) -0.024* (-0.127) -0.026** (-0.131) 0.020 (0.089) 0.012 (0.056) population 15-29Population 0.145** (0.218) 0.101* (0.131) 0.201** (0.236) 0.286** (0.277) 0.107* (0.116)South 0.085 (0.062) -0.036 (-0.027) 0.158* (0.122) 0.440** (0.286) 0.136 (0.096)West -0.210 (-0.140) -0.244* (-0.169) -0.082 (-0.058) 0.281* (0.169) 0.220* (0.144)System 0.577 weighted R2

Note: Standardized coefficients are included in parentheses.*p < .05. **p < .01.

Panel B: Comparisons of the Strength of Relationship Between Divorce and Homicide Over Time (S Tests)

1970 1980 1990 2000

1960 0 0 0 01970 0 0 01980 0 01990 0

Note: A zero indicates no difference in the strength of relationship between years; an * indicates the




We have focused our analyses on homicide rates because the official statistics for these offenses are less likely to be susceptible to the reporting/recording limitations associated with other UCR offenses and are thus likely to be the most valid for comparisons across cities and over time. See O’Brien (1985) and Schneider and Wiersema (1990). In prelimi-nary analyses, we also estimated models for robbery rates. The results of these analyses are less consistent than those for homicide rates. There is evidence for attenuation of the effects of divorce and the family disorganization index on robbery rates in the cross-sectional analyses. The effects of the family measures exhibit no significant differences across decades in the dynamic models. The discrepant results across offenses could conceivably be a product of measurement error, but if not, they call for further theorizing about the

Table 2. Seemingly Unrelated Regressions of Homicide Rates on Predictors With the Divorce/Household Type Composite Index—Cross-Sectional Design (N = 113)


Independent variable 1960 1970 1980 1990 2000

Family 0.067* (0.150) 0.098** (0.226) 0.092** (0.209) 0.130** (0.247) 0.160** (0.370) disorganization index

Resource 0.214** (0.568) 0.204** (0.587) 0.206** (0.600) 0.210** (0.486) 0.235** (0.572) deprivation

Residential -0.027** (-0.369) -0.023** (-0.266) -0.016** (-0.211) 0.007 (0.066) -0.000 (-0.002)stability

Percentage of -0.009 (-0.035) -0.023* (-0.121) -0.026** (-0.129) 0.030* (0.134) 0.021 (0.097)population 15-29

Population 0.134** (0.202) 0.093* (0.120) 0.193** (0.226) 0.293** (0.284) 0.112* (0.122)South 0.082 (0.059) -0.050 (-0.038) 0.136* (0.105) 0.399** (0.260) 0.103 (0.073)West -0.179 (-0.119) -0.256* (-0.178) -0.079 (-0.056) 0.242 (0.145) 0.201* (0.131)System

weighted R2 0.576

Note: Standardized coefficients are included in parentheses.*p < .05. **p < .01.

Panel B: Comparisons of the Strength of Relationship Between the Family Disorganization Index and Homicide Over Time (S Tests)

1970 1980 1990 2000

1960 0 0 0 *1970 0 0 01980 0 *1990 0

Note: A zero indicates no difference in the strength of relationship between years; an * indicates the relationships are significantly different by decade.




effects of family arrangements on levels of violent crime. The logic of both classical social disorganization theory and more recent variants would seem to apply to crime in general, and thus there is no obvious rationale for anticipating offense-specific patterns.

Summary and ConclusionsWe have argued that different variants of social disorganization theory direct attention to distinctive mechanisms that could conceivably link divorce rates with homicide rates and that these mechanisms imply different implications about the stability of the effects

Table 3. Seemingly Unrelated Regressions of Homicide Rates on Lagged Predictors With the Conventional Specification—Panel Design (N = 113)


Independent variablea 1970 1980 1990 2000

Divorce 0.041 (0.064) 0.086** (0.159) 0.037 (0.076) 0.027 (0.069)Resource 0.279** (0.431) 0.210** (0.335) 0.226** (0.303) 0.213** (0.310)

deprivationResidential 0.000 (0.003) -0.009 (-0.106) 0.007 (0.080) 0.003 (0.036)

stabilityPercentage of -0.034* (-0.137) -0.021* (-0.113) 0.009 (0.039) -0.004 (-0.018)

population 15-29Population 0.095* (0.149) 0.099** (0.132) 0.104* (0.103) -0.108* (-0.114)South -0.109 (-0.082) -0.039 (-0.030) 0.268** (0.174) -0.110 (-0.078)West -0.116 (-0.080) -0.133 (-0.094) 0.147 (0.088) -0.059 (-0.039)Homicide (lagged) 0.379** (0.393) 0.443** (0.456) 0.590** (0.498) 0.603** (0.656)System

weighted R2 0.791

Note: Standardized coefficients are included in parentheses.a. With the exception of the regional dummy variables, the independent variables are lagged. Thus, in the 1970 column, 1970 homicide rates are regressed on 1960 values of the time-varying independent variables, along with 1960 homicide rates.*p < .05. **p < .01.

Panel B: Comparison of the Strength of Relationship Between Divorce (Lagged) and Homicide Over Time (S Tests)a

1970 1980 1990

1960 0 0 01970 0 01980 0

Note: A zero indicates no difference in the strength of relationship between years; an * indicates the relationships are significantly different by decade.a. The years in Panel B represent the lagged year for divorce. Thus, the 1960-1970 cell indicates that the effect of divorce in 1960 on homicide in 1970 is equal to the effect of divorce in 1970 on homicide in 1980 controlling for the lagged measure of homicide.




of divorce in the explanation of cross-sectional variation in homicide rates. The variant associated with the classical Chicago School highlights the role of normative conflict associated with high levels of divorce as a disorganizing force. Insofar as divorce has become normatively approved over recent decades, the effect of divorce rates on

Table 4. Seemingly Unrelated Regressions of Homicide Rates on Lagged Predictors With the Divorce/Household Type Composite Index—Panel Design (N = 113)


Independent variablea 1970 1980 1990 2000

Family 0.016 (0.037) 0.089** (0.211) 0.076* (0.145) 0.067* (0.139) disorganization index

Resource 0.184** (0.507) 0.083** (0.246) 0.076* (0.186) 0.078* (0.197) deprivation

Residential -0.002 (-0.031) -0.009 (-0.114) 0.008 (0.092) 0.005 (0.053)stability

Percentage of -0.033* (-0.135) -0.022* (-0.122) 0.011 (0.045) 0.000 (0.001)population 15-29

Population 0.099* (0.155) 0.103** (0.137) 0.100* (0.099) -0.112* (-0.119)South -0.141 (-0.106) -0.040 (-0.031) 0.263** (0.171) -0.123 (-0.087)West -0.035 (-0.024) -0.118 (-0.084) 0.131 (0.079) -0.064 (-0.042)Homicide 0.344** (0.357) 0.418** (0.431) 0.607** (0.511) 0.622** (0.676)

(lagged)System

weighted R2 0.791

Note: Standardized coefficients are included in parentheses.a. With the exception of the regional dummy variables, the independent variables are lagged. Thus, in the 1970 column, 1970 homicide rates are regressed on 1960 values of the time-varying independent variables, along with 1960 homicide rates.*p < .05. **p < .01.

Panel B: Comparisons of the Strength of Relationship Between the Family Disorganization Index (Lagged) and Homicide Over Time (S Tests)a

1970 1980 1990

1960 0 0 01970 0 01980 0

Note: A zero indicates no difference in the strength of relationship between years; an * indicates the relationships are significantly different by decade.a. The years in Panel B represent the lagged year for the family disorganization index. Thus, the 1960-1970 cell indicates that the effect of the family disorganization index in 1960 on homicide in 1970 is equal to the effect of the family disorganization index in 1970 on homicide in 1980 controlling for the lagged measure of homicide.




homicide rates might be expected to wane because divorce no longer entails a high degree of conflict over normative standards. Neosocial disorganization theory, in con-trast, emphasizes structural dynamics associated with a high prevalence of divorce, especially diminished informal and formal social control. These structural processes would seem to be unaffected by the cultural meanings of divorce. Accordingly, we have proposed that stable, positive effects of divorce rates on homicide rates would be con-sistent with the “structural/control” variant of social disorganization theory.

The results of our analyses provide no evidence for a steady attenuation of the effect of divorce rates on homicide rates over the latter half of the 20th century. In both contemporaneous and lagged models, the differences between coefficients for the effects of divorce across decades are not significant. The null hypothesis of stable divorce effects thus cannot be rejected.

The theoretical interpretation of the stability of the divorce effect, however, proves to be rather ambiguous in some respects given the overall patterns of results. For the con-temporaneous models, three of the five coefficients are statistically significant when considered alone. This would be consistent with the structural thesis that divorce affects homicide rates primarily through diminished social controls, which presumably are not dependent on the normative status of divorce and are likely to have remained fairly con-stant. However, given that these positive coefficients do not differ significantly from the null effects observed in two of the decades, it might also be argued that the results are equally consistent with a conclusion of no overall impact of divorce when the entire period is considered. The results for divorce in the lagged models are even more sugges-tive of “null effects.” These findings are contrary to the “structural/control” thesis.

Indeed, the results for the divorce measure could conceivably be interpreted as sup-portive of the “cultural/normative” thesis, if one assumes that the fundamental shift in norms about marital dissolution changed prior to the initial observation in our data set—1960. Perhaps the sharp increase in the divorce rate around 1960 shown in Figure 1 signaled the “normalization” of divorce. Recall that the Chicago School theorists con-ceptualized social life as being highly dynamic, with disorganization setting into motion processes that could ultimately lead to reorganization. If the companionship family had already become institutionalized in American society by the 1960s, the reorganization of family institutions may have already taken place and divorce would no longer generate nor signify normative conflict. The attenuation of the effect of divorce rates implied by the “cultural/normative” thesis might have thus occurred during years that are censored from our analyses. Future research with data for earlier periods is needed to assess this speculative interpretation.

The theoretical interpretation of the results for the composite family disorganiza-tion index, which combines divorce rates with a measure of children not living with two parents, is more clear-cut. In the contemporaneous models, the effects of the com-posite index are significantly positive in every decade, and there are no significant differences between coefficients. In the lagged models, the coefficients for the family disorganization index also do not differ significantly across decades, and the coefficients reach statistical significance in four of the five models. These results for the family disorganization index, especially in the contemporaneous models, are supportive of




the structural/control thesis. The stability of the positive effects of family disorganiza-tion on homicide rates is consistent with the presumed underlying causal dynamics.

We caution that our analyses only allow for indirect inferences about the specific processes linking levels of divorce with levels of homicide. Similar to other studies that attempt to explain intercity variation in homicide rates, our arsenal of independent variables is limited to those that can be measured with census data. We are thus unable to introduce into the models direct indicators of the hypothesized intervening mecha-nisms. Nevertheless, we suggest that the overall pattern of results lends further credibility to the claims of the neosocial disorganization perspective about the pro-cesses that are likely to connect levels of lethal violence and family disorganization, insofar as family disorganization is conceptualized broadly to encompass not only divorce but also disrupted family relationships more generally.

Given the theoretical grounding of our research in social disorganization theory, which has historically been characterized by an urban focus, we have restricted the analyses to large cities. Recent research suggests that the social disorganization per-spective is useful for explaining variation in levels of crime in nonurban areas as well, although the processes sometimes differ (Bouffard & Muftic, 2006; Osgood & Chambers, 2000). Moreover, there is evidence to suggest that attitudes toward divorce differ between rural and urban communities, although much of this appears to be due to educational differences (Gore, 1990, p. 215). In addition, appreciable racial differ-ences in indicators of family disruption and levels of criminal violence have been well documented in the literature, and evidence indicates that changes in patterns of domes-ticity over recent decades have affected homicides differently for different racial groups (Dugan et al., 1999; Dugan, Nagin, & Rosenfeld, 2003; Rosenfeld, 1997). It would thus be useful to replicate our analyses with samples based on other types of areal units and with racially disaggregated data.

We close with a final comment concerning the practice of incorporating measures of household type within composite indexes of resource deprivation. As noted above, this practice has become commonplace in the literature as homicide researchers have drawn on the work of Land et al. (1990) to justify baseline regression models. Simpli-fying the covariance matrix by creating factor indexes certainly makes a good deal of sense for purposes of avoiding potential multicollinearity among control variables. Our findings indicate, however, that more robust results are obtained when the mea-sures of divorce and household type are combined in a family disorganization index than when the divorce measure is considered alone. In addition, the composite index has significant and appreciable effects net of standard measures of deprivation. Our results in this regard are similar to those of Schwartz (2006a, 2006b) in her analyses of gender-disaggregated homicide rates with data for U.S. counties. These findings imply that the estimated effects of divorce on homicide rates in research that adopts the Land et al. baseline model are likely to underestimate the importance of the more general construct of family disorganization. We thus echo Schwartz’s (2006b, p. 271) caution that future homicide researchers should consider carefully the appropriateness of adopting the conventional Land et al. baseline model if family arrangements are an integral theoretical component of the study.




Appendix A

Variable

Homicide

Divorce

% children under 18 living with both parents

Source

Uniform Crime Reports





Census: Characteristics of the Population



Census: General Population Characteristics

Census 2000 Summary File 3 (SF 3)




Year

1960

1970

1980

1990

2000

1960

1970

1980

1990

2000

1960

1970

1980

Table number and name

Table 38. Number of Offenses Known to Police, 1960, Cities and Towns 25,000 and Over in Population

Table 60. Number of Offenses Known to the Police, 1970, Cities and Towns 25,000 and Over in Population

Table 6. Number of Offenses Known to the Police, Cities and Towns 10,000 and Over in Population, 1980

Table 6. Number of Offenses Known to the Police, Cities and Towns 10,000 and Over in Population, 1990

Table 8. Offenses Known to Law Enforcement by City 10,000 and Over in Population, 2000

Table 21. Characteristics of the Population for Standard Metropolitan Areas, Urbanized Areas and Urban Places and Selected Townships of 10,000 or More: 1960

Table 30. Characteristics of the Population, For Standard Metropolitan Areas, Urbanized Areas and Urban Places and Selected Townships of 10,000 or More: 1970

Table 29. Type of Family and Marital Status by Race and Spanish Origin, For Areas and Places: 1980

Table 64. Household and Family Characteristics by Race and Hispanic Origin: 1990

QT-P18. Marital Status by Sex, Unmarried-Partner Households, and Grandparents as Caregivers: 2000

Table 72. Social Characteristics of the Population, for Standard Metropolitan Statistical Areas, Urbanized Areas, and Urban Places of 10,000 or More: 1960

Table 84. Education, Fertility, and Family Composition, for Areas and Places: 1970

Table 28. Household Relationship for Selected Age Groups by Race and Spanish Origin: 1980

(continued)




Variable

% of people over age 5 who have not moved in the past 5 years

% families below the poverty line

% of the Population aged 15–29

Source

Census 1990 Summary Tape File 3 (STF 3)




Census of Population: General Social and Economic Characteristics

Census of the Population: Social and Economic Characteristics


County and City Date Book 1962







Year

1990

2000

1960

1970

1980

1990

2000

1960

1970

1980

1990

2000

1960

1970


P023. Family Type and Age of Children—Universe: Own children under 18 years

P16. Own Children Under 18 Years by Family Type by Age [26]—Universe: Own children under 18 years

Table 72. Social Characteristics of the Population, for Standard Metropolitan Statistical Areas, Urbanized Areas, and Urban Places of 10,000 or More: 1960

Table 82. Mobility, Commuting, and Veteran Status, for Areas and Places: 1970

Table 118. Geographical Mobility and Commuting for Areas and Places: 1980

Table 172. Geographic Mobility, Commuting, and Veteran Status: 1990

P25. Residence in 1995 for the Population 5 Years and Over—MSA/PMSA LEVEL [35]—Universe: Population 5 years and over

Table 6. Cities, Income in 1959 of Families, 1960: Under $3,000

Table 4. Cities, Families With Money Income in 1969: Percent Below Poverty Level

Table C. Police Officers, Education, Money Income, and Housing: Percent Below Poverty Level, 1979

Table C. Cities

QT-P35. Poverty Status in 1999 of Families and Nonfamily Householders: 2000

Table 20. Age by Color and Sex for Standard Metropolitan Statistical Areas, Urbanized Areas, and Urban Places of 10,000 or More: 1960

Table 24. Age by Sex and Race for Areas and Places: 1970

(continued)

Appendix A (continued)




Population100 largest cities were selected based on the following sources:

Variable

% Black

Source


Census 1990 Summary Tape File 3 (STF 3)


County and City Data Book 1962



County and City Extra Annual Metro, City and County Book 1998


Year

1980

1990

2000

1960

1970

1980

1990

2000


Table 25. Age by Race, Spanish Origin, and Sex for Areas and Places 1980

P013. Age—Universe: Persons

P8. Sex by Age [79]—Universe: Total population

Table 6. Cities, Income in 1959 of Families, 1960: Under $3,000

Table 4. Cities, Families With Money Income in 1969: Percent Below Poverty Level

Table C. Cities

Table C. Cities

P6. Race [8]—Universe: Total Population

Appendix A (continued)

US Census Bureau

http://proximityone.com/

1960-1990

2000

Gibson, Cambell, 1998—Population of the 100 Largest Cities and Other Urban Places in the United States: 1790 TO 1990 Population Division Working Paper No. 27

The 100 Largest U.S. Cities—Based on Census 2000—Based on Population Change from 1990 to 2000 Population http://proximityone.com/plc100.htm

Appendix BUnivariate Statistics for Independent and Dependent Variables

Minimum Maximum M SD

Population1960 10,026 7,781,984 375,704 788,5631970 36,228 7,867,760 402,809 798,5971980 87,700 7,035,348 404,002 727,9941990 95,732 7,322,564 431,873 767,4362000 83,466 7,746,511 470,148 817,299

(continued)




Minimum Maximum M SD

Divorce1960 1.14 5.42 2.91 1.001970 1.29 6.63 3.75 1.171980 3.41 10.37 7.20 1.541990 4.79 12.82 9.06 1.792000 4.32 14.01 9.83 1.96

Residential stability (% of people who have not moved in the last 5 years)1960 11.60 59.00 43.67 9.181970 23.80 63.80 48.11 7.611980 26.30 68.18 49.22 8.391990 35.53 67.23 48.38 7.112000 35.64 62.95 48.88 5.75

% of children under 18 living in married couple families1960 67.78 94.03 83.83 5.371970 59.20 91.60 77.36 6.581980 39.70 81.90 64.17 9.531990 35.76 86.18 66.81 10.872000 31.75 86.25 62.07 12.08

% of population 15-291960 12.02 29.43 20.30 2.631970 15.79 43.60 25.74 3.431980 20.09 41.31 29.70 3.151990 12.52 35.40 25.40 3.372000 12.83 33.83 23.57 3.17

Poverty (% of households below the poverty line)1960 7.40 37.90 18.35 6.651970 3.10 21.60 10.61 3.961980 3.70 29.90 11.91 4.841990 3.00 29.00 14.00 5.682000 3.40 28.22 13.88 5.41

% Black1960 0.00 54.00 14.24 12.141990 0.10 71.10 16.93 14.311980 0.18 70.84 20.93 17.211990 0.39 80.53 22.89 19.382000 0.81 84.03 24.22 20.11

Homicide rate1960 0.00 18.97 6.10 4.191970 1.27 43.10 12.83 8.561980 2.92 54.65 17.26 11.651990 0.61 76.73 18.17 14.162000 0.64 68.83 12.75 10.69

Note: South and West are excluded since they are dummy variables.

Appendix B (continued)




Authors’ Note

An earlier version of this article was presented at the Fifty-Ninth Annual Meeting of the American Society of Criminology, Atlanta, GA, November 14-17.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding

The authors declared no financial support for the research and/or authorship of this article.

Notes

1. Sampson (1987b, p. 352) also notes that family disruption might be related to crime via a “compositional effect” associated with any broken homes/delinquency relationship, but the structural effect is given primary emphasis in this study as well as in later research.

2. The neosocial disorganization perspective incorporates processes extending beyond mon-itoring, supervision, and control. These include deficiencies in socialization associated with the lack of adult (especially male) role models and the relative lack of social capital in communities with widespread family disruption (Schwartz, 2006b, p. 255; see also Schwartz, 2008). The arguments pertaining to the consequences of high levels of divorce for the socialization of youths overlap to some extent with those of the classical Chicago School—normative conflict impedes effective socialization. We highlight the “control” element in drawing a contrast between neosocial disorganization theory and the classical Chicago School, given the distinct emphasis on the dynamics of control in much of the recent literature.

3. Blau and Blau also refer to divorce as an indicator of “anomie,” citing Durkheim; yet their interpretation of the results for divorce follow more closely in the tradition of social disor-ganization theory.

Appendix CFactor Loading Scores for the Variables Comprising the Resource Deprivation Factor

Factor loadings

Variable name 1960 1970 1980 1990 2000

Percentage of children under 18 living -0.830 -0.869 -0.883 -0.887 -0.907with both parents

Percentage Black 0.899 0.906 0.899 0.824 0.870Percentage below the poverty level 0.815 0.842 0.873 0.826 0.773




4. In a follow-up study, Dugan et al. (2003) examine the effects of domestic violence preven-tion resources on intimate partner homicides. The results reveal mixed effects, with some resources increasing and other resources decreasing the level of these homicides. Another line of research has suggested that the effects of divorce might vary by type of violent of-fense. See MacDonald (2002) and Greenberg (2001).

5. The selection of the unit of analysis for macro-level research on homicide has been the subject of much debate (e.g., Bailey, 1984; Land et al., 1990; Messner, 1982). Parker et al. (1999, pp. 118, 119) adopt a rather strong position, positing that “the unit of analysis is not [italics in original] a relevant concern for studies of homicide” and concluding that “any general theory of homicide should generate empirical invariant findings regardless of the level of analysis examined.” We adopt an agnostic position on whether the effect of divorce on homicide rates will necessarily be robust across all aggregate units and simply proceed on the assumption that it is legitimate and important to assess the utility of measures of divorce for explaining intercity variation in homicide.

6. As a result of changes in population size, some cities that meet the criterion of being “large” at one decennial year were actually rather small at other points in time. The number of such observations is relatively small. Of the 565 observations in the data set (113 × 5), only 33 or 5.8% are cities with populations below 100,000 at any stage of the panel. We thus refer to “large cities” when characterizing the sample for linguistic convenience. Increases in population can result not only from population growth but also from changes in city bound-aries. We caution that such changes could conceivably affect the analysis by incorporating suburban areas with lower homicide rates and different levels of family disruption.

7. The homicide totals for 2001 initially included the 9/11 deaths. These were removed prior to the calculation of the 3-year average for New York City.

8. We employ male divorce rates following the influential research by Land et al. (1990). For our sample of cities, the correlations between male and female divorce rates are above .9 for each decade, and thus not surprisingly, results are highly similar if female divorce rates are used. In particular, the pattern in the strength of relationships is similar across models. Although there is ample precedent for the use of our measure of divorce, we recognize that it is limited in important respects. It measures the number of males who are divorced rather than the event of divorce and thus is “static” rather than “dynamic.” In contrast with a refined divorce rate, which is expressed per 1,000 married women, it is insensitive to the specific population at risk. Changes in marital patterns can thus affect measured levels of divorce independently of marital dissolution. Crude divorce rates also fail to adjust for relevant differences in age structure, in contrast with an age-standardized divorce rate. See England and Kunz (1975) for an evaluation and critique of different divorce measures.

9. Scottsdale, Arizona had zero African Americans in 1960. One was added to the percentage Black for every city in 1960 prior to the log transformations.

10. There are various possible dynamic models that could be estimated with the panel data, such as estimating the effects of the levels of predictor variables on changes in homicide rates, estimating the effects of changes in predictor variables on changes in homicide rates, and estimating the effects of both levels and changes in predictor variables on changes in homicide rates. Neither classical nor “neo-” social disorganization theory offers any guidance as to the




most appropriate dynamic model for linking divorce rates and homicide rates. We estimate and present results for models estimating the effects of lagged predictors on changes in homicide rates because these models are parsimonious and readily interpretable.

11. S tests for residential stability reveal that the differences in magnitudes of the coefficients for residential stability for 1960, 1970, and 1980 in comparison with 1990 are statistically significant. S tests for residential stability reveal that the differences in magnitudes of the coefficients for residential stability for 1960 and 1970 in comparison with 2000 are statisti-cally significant.

12. The results for the lagged models must be interpreted cautiously because the modeled tem-poral process reflects data availability rather than theoretical knowledge. Specifically, the coefficients reflect the effect of the independent variables on changes in homicide rates over the subsequent decades. It is of course possible that the various predictors have lagged effects that operate over different time spans.

13. We also estimated equations with untransformed homicide rates. The substantive results are highly similar to those reported for the log transformed rates. The contemporaneous effects of divorce rates on homicide rates are significantly positive in 3 of the 5 equations, whereas none of the S tests is significant. In the lagged models, a single significant difference across decades emerges for divorce rates. The lagged effect predicting the 1980 homicide rate is significantly stronger than the lagged effect predicting the 1970 homicide rate. The positive effects for the family disorganization index are robust across models for both the untransformed and trans-formed homicide rates. We present only the results for the log transformed homicide rates because the distributions for homicide rates in the original metric are highly skewed.

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Bios

Mark Beaulieu is an assistant professor at the University of Hartford. He is particularly inter-ested in issues surrounding segregation, concentrated disadvantage, and violent crime. He is currently working on examining the relationship between segregation and White advantage.

Steven F. Messner is distinguished teaching professor of sociology at the University at Albany, State University of New York. His research focuses on social institutions and crime, understand-ing spatial distributions and trends in crime, and crime and social control in China. In addition to his publications in professional journals, he is coauthor of Crime and the American Dream and Perspectives on Crime and Deviance and coeditor of Theoretical Integration in the Study of Deviance and Crime and Crime and Social Control in a Changing China.



Documents

Assessing Changes in the Effect of Divorce Rates on Homicide Rates Across Large U.S. Cities, 1960-2000: Revisiting the Chicago School