Journal of Criminal Justice 33 (2005) 137 – 143
Neighborhood structure differences between homicide victims and non-victims Adam Dobrina,*, Daniel Leeb, Jamie Pricec a
Department of Criminology and Criminal Justice, Florida Atlantic University, 2912 College Ave., Davie, FL 33314, United States b Department of Criminology, Indiana University of Pennsylvania, G-1 McElhaney Hall, Indiana, PA 15705, United States c Department of Criminal Justice, Kentucky Wesleyan College, 3000 Frederica Street, Owensboro, KY 42301, United States
Abstract While there were numerous studies documenting the neighborhood characteristics that led to increased risk of crime victimization, very little was done to compare the neighborhoods of homicide victims to non-victims. The current research used the case-control design to alleviate this gap in the research. A sample of homicide victims and non-victims collected from Prince George’s County, Maryland, in 1993, was used to make these comparisons. Significant differences were noted in the macrolevel measures of education, unemployment, household income, and percentage of female-headed households in the neighborhoods of victims and non-victims. Individual elements, such as age, race, gender, and arrest were also strongly associated with the risk of homicide victimization. Both macro and micro level variables needed to be included when studying factors that increased the risk of homicide victimization. D 2005 Elsevier Ltd. All rights reserved.
Introduction The risk of homicide victimization is not evenly distributed. While there are many factors that may predict risk, two of the most crucial ones are structural characteristics of the neighborhood in which the victim lives and the demographic characteristics of the victim. There were numerous studies that examined impact of micro level demographic characteristics on homicide risk (see Cohen, 1986; Gottfredson, 1984; Hindelang, Gottfredson, & Garofalo, 1978), and while the impact of such characteristics was necessary to include in a study, attention must also be made to neighborhood characteristics (Wilson, 1987). Based on the assumption that official police and human service agency records were a reliable and valid measure of criminal
* Corresponding author. Tel.: +1 954 236 1168; fax: +1 954 236 1065. E-mail address:
[email protected] (A. Dobrin). 0047-2352/$ - see front matter D 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcrimjus.2004.12.005
behavior, available research utilized census data for estimating neighborhood effects on behavior and official police data and agency data for individual outcomes (Reiss & Roth, 1993; Sampson & Lauritsen, 1994). There were many excellent reviews of neighborhood level factors that might increase the probability of homicides (see, for example, Sampson & Lauritsen, 1994). From such reviews, evidence revealed that poverty, economic inequality, female-headed households, high population density, high unemployment, and low education were all related to high rates of homicide (Sampson & Lauritsen, 1994, pp. 48– 49). For example, Block (1979, p. 48) reported that the percent of residents living in poverty in a neighborhood strongly correlated with the homicide rate (.64), as did the percent of female-headed families (.63), while Beasley and Antunes (1974) revealed a negative correlation between median neighborhood income and violent crime of .78. Indeed, numerous studies suggested that neighborhood ecological factors such as poverty, unemployment, cultural heterogeneity, mobility, and family composition helped to
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A. Dobrin et al. / Journal of Criminal Justice 33 (2005) 137–143
account for neighborhood variation in crime (Elliott et al., 1996; Land, McCall, & Cohen, 1990; Taylor & Covington, 1988). What reviews like this could not reveal, however, were the differences between the neighborhoods in which victims lived as opposed to the neighborhoods where similar non-victims lived. The research presented in this study did make such comparisons and predicted that the differences in victimization likelihood were due to variation in the organization (or disorganization) and structure that existed across neighborhoods. Certain theoretical perspectives clarified these predictions. According to social disorganization theory, neighborhood structure influences individual behavior. Shaw and McKay (1942) established the foundation for this theoretical perspective by applying ecological concepts to the study of crime. They argued that urban neighborhoods with higher concentrations of social problems (e.g., poverty, cultural heterogeneity, unemployment, etc.) ultimately suffered from a reduced ability to prevent or control crime. These characteristics also worked to decrease the desirability of a neighborhood and increased the presence of transient residents who never stayed long enough to establish a cohesive sense of community. Since its development, social disorganization received considerable empirical support (see Bursik & Grasmick, 1993; Sampson, 1988; Sampson & Groves, 1989), responded to criticism by evolving into a more elaborate perspective (Bursik, 1988), and went beyond being a theory that offered a flat, descriptive presentation of urban criminality to one that could include dynamic interactive processes (Stark, 1987). Stark (1987) provided an assessment of social disorganization that contributed to the empirical revival of the theory in the 1980s. In his essay, he argued that the structural components of neighborhoods should be assessed independent of the individuals who lived in them. He proposed that crime should be evaluated as a process that was dependent upon certain structural components (i.e., density, poverty, mixed land use, residential mobility, and dilapidation). He used these structural components to present thirty theoretical propositions that explained crime at the neighborhood level. His propositions suggested that the structural components influenced the sentiments and behaviors of individual residents (expressed as moral cynicism, criminal motivation, and surveillance). In the end, neighborhoods became attractive to criminals and criminal behaviors as law-abiding residents moved and social control decayed. Other modern examinations of social disorganization theory continued to embrace this idea of social (or neighborhood-specific) control. Robert Sampson and his colleagues (Sampson, 1997; Sampson & Raudenbush, 1999; Sampson, Raudenbush, & Earls, 1997) assessed different aspects of neighborhood-specific social relationships. One concept that continually emerged from Sampson’s research was collective efficacy. Collective efficacy was explained as the blinkage of mutual trust and the willingness to intervene
for the common goodQ (Sampson et al., 1997, p. 919). Essentially, their investigations focused on the antithesis of social disorganization (i.e., social cohesion). This collective efficacy was negatively associated with violence and could mediate the impact of other detrimental neighborhood characteristics (Sampson et al., 1997). That is, despite the presence of the social problems typically associated with social disorganization, a neighborhood whose members were socially bound together would have a lower likelihood of crime. The theoretical basis for a neighborhood’s contribution to criminal activity (and victimization risk) should also be credited to Newman’s (1972) concept of defensible space. Newman argued that a neighborhood’s measure of territoriality, surveillance, image, and milieu combined to induce or control criminal events. As neighborhoods increased their ability to define and monitor strangers or inappropriate behaviors and established the collective sentiment that these people and their behaviors were not tolerated, crime became less likely. These concepts were similar to those included in another related perspective, crime prevention through environmental design (see Jeffery, 1971). Still other theoretical perspectives could offer insight into how neighborhood characteristics were associated with criminal behavior and victimization risk. The routine activities/lifestyles perspective (see Cohen & Felson, 1979; Hindelang et al., 1978) argued that crime and victimization occurred when the ordinary and routine activities of a potential victim converged with certain offender and place characteristics. Messner and Tardiff (1985) tested the principles of this perspective and found that the sociodemographic characteristics of the victim were associated with homicide victimization. Specifically, they found that being male, older, and employed were positively associated with homicide location (coded ordinally as at home, within ten blocks of home, or further than ten blocks from home). Given that they determined that nearly half of the homicides that they analyzed (44.3 percent) occurred in or within ten blocks of the victim’s home address, it was reasonable to assume that the characteristics of a victim’s neighborhood were associated with the likelihood of victimization. The current exploratory research examined the impact that neighborhood characteristics had on the risk of homicide victimization by comparing the neighborhoods where homicide victims lived with those where non-victims lived. Obviously, homicide victims lived in neighborhoods with many non-victims, and non-victims might have had victims living in their neighborhoods as well. If there were similarity in the neighborhoods of victims and non-victims, then there would be no differences between the characteristics where victims lived as compared to non-victims. Based on the theoretical tradition discussed above, the following research attempted to determine the macro-social factors that increased the risk of homicide victimization. The analysis incorporated data related to the structural features
A. Dobrin et al. / Journal of Criminal Justice 33 (2005) 137–143
of the neighborhood, as well as data related to individual victims and non-victims.
Methods When examining data in which the outcome variable was known (in this case, whether a person was a homicide victim), the central question was which factors increased or decreased the likelihood of the outcome variable (homicide victimization). To answer this question, a comparison needed to be made between the two groups, the one with the outcome and the one without. In the analysis, the occurrence of suspected factors such as education level, poverty, unemployment, median household income, and number of households headed by females that could influence the outcome were compared between the two groups. This comparison was best managed by employing a case-control design (Armenian & Lillienfeld, 1994, p. 1; Schlesselman, 1982, p. 14). The other distinctive feature of this design was that it proceeded from a known effect backwards toward potential causes. A more complete description of the design follows. The case-control was an efficient design that was commonly used as a public health/epidemiological study design in order to locate causes of rare diseases (Schlesselman, 1982, p. 25), and was ideally suited for the study of homicide. Due to the rarity of homicide events, it was more economical to study a small sample in which the outcomes were known, rather than gathering large enough samples that would include an adequate prevalence rate for study (Goodman, Mercy, Layde, & Thacker, 1988, p. 74; Loftin & McDowall, 1988, p. 89). In other research designs, such as a cohort study, data concerning a tremendous number of people would have to be collected in order for enough homicides to occur that would allow adequate comparisons between victims and non-victims for causal analysis. For example, 100,000 randomly selected residents of the county from which the data for the current study was selected, Prince George’s County, Maryland, would have to be sampled in order to produce less than seventeen murder victims in 1992 (Federal Bureau of Investigation, 1993, p. 173; U.S. Bureau of the Census, 1994, p. 256). By making use of known homicide events and working backwards, this design allowed for a rare individual level analysis of homicide victimization that could also consider neighborhood-specific information. Most studies of this topic solely used aggregate level data. The current study incorporated both aggregate neighborhood characteristics and individual level data to offer a more complete analysis.
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and controls were selected on the presence or absence of the dependent variable (homicide victimization), statistical procedures that assumed a normally distributed dependent variable, such as Ordinary Least Squares (OLS) regression, were inappropriate because they presented biased results (see Loftin & McDowall, 1988, p. 89). The accepted standard multivariate statistical procedure that was used to estimate binary dependent variables (as all case-control designs included) was logistic regression. The resulting coefficient was then exponentiated to create the odds ratio. The odds ratio in the sample approximated the relative risk of the occurrence of the outcome in persons exposed to the risk factor from the population (Goodman et al., 1988, p. 76; Schlesselman, 1982, p. 33) and approached the relative risk estimates for the population as the event became increasingly rare (Loftin & McDowall, 1988, p. 92; Schlesselman, p. 33). The interpretation of a logistic regression depended on the structure of the individual independent variables. When a binary independent variable was used, the odds ratio was interpreted as the odds that the case exhibited the risk factor relative to the odds that control exhibited it. For example, if the odds ratio was 2.0 in an equation that had a binary variable of smoking (0 = never smoked, 1 = has smoked) with a dependent variable of occurrence of cancer, this could be interpreted as the odds that a smoker would exhibit cancer were twice the odds that a nonsmoker would (or that those who had cancer had twice the odds of having smoked than never smoked). When the independent variable was continuous instead of dichotomous, however, the interpretation of the odds ratio was slightly different. For every increase in the independent variable, the risk of the outcome increased by the amount of the odds ratio. In an example that used a ten-year unit of age for the independent variable regressed on appearance of chronic heart disease, the odds ratio was calculated to be 3.03 (Hosmer & Lemeshow, 1989, p. 57). For every ten-year increase in a person’s age, then, the risk of chronic heart disease increased 3.03 times. When an odds ratio less than 1 was produced with either a dichotomous or continuous independent variable, the inverse of it was calculated, and became the odds of reducing the outcome.
Data The data for the current analysis were selected from different sources of information concerning the residents of Prince George’s (PG) County, Maryland, in 1993.1 Homicide victims
Statistical analysis of the case-control design In case-control studies, the exposure to a risk factor within the case group was compared to the exposure to the same risk factor within the control group. Since the cases
The name, address, date of birth, age, race, and gender of the subjects were collected from the records of the Prince George’s County Police Department (PGPD) Homicide Bureau. For this study, only victims who were identified
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A. Dobrin et al. / Journal of Criminal Justice 33 (2005) 137–143
by name, had known addresses in Prince George’s County, and were sixteen years of age or older were selected for the sample for the current study. The reasons for these selection criteria were to allow for comparisons to non-victims, whose selection procedures are described below. Due to these restrictions, 105 of the 150 cases were selected for the 1993 sample. Of the 45 cases that were dropped from the sample, 35 were removed because they either did not live in Maryland or had no address recorded in the homicide files, and the rest were discarded because the subjects were under the age of sixteen. Maryland arrest records were also collected for each victim. Homicide non-victims The sample for the 105 non-victims was selected from Maryland’s 1993 state Motor Vehicle Administration (MVA) records. Licensed drivers in Maryland must be over the age of sixteen, which necessarily forced the non-victim sample to have a minimum age of sixteen. For this reason, only victims over sixteen were selected for comparison to the non-victim sample. Additionally, the non-victim sample was compared to the victim sample to ensure that none of the non-victims were in fact victims in 1993. None were. While 65 percent of the total PG population (including those too young to have a license) had a drivers’ license, Table 1 reveals that the MVA data closely approximated the population distributions of PG County. Data limitations prevented analyses to determine if there were biases introduced by nonrandom differences between those who possessed licenses and those who did not. Maryland arrest records were also collected for each non-victim. Neighborhood characteristics Addresses were collected for each victim and nonvictim. These were then geo-coded and linked to census block-group data. Information concerning the percentage of high school graduates, percentage of unemployed residents, percentage of households headed by females, percent living Table 1 Percentages of race and gender characteristics in the MVA sample and PG population MVA data for PG 1993
Census data for PG 1990
Race Black Non-Black
50.1 49.9
51.8 48.2
Gender Male Female Total population
49.7 50.3 474,000
48.5 51.5 729,000
From the Maryland MVA records and the County and City Data Book: 1994 (U.S. Bureau of the Census, 1994, pp. 256–257).
Table 2 Comparison of sociodemographic characteristics
Percent high school graduate (or higher) Percent over sixteen unemployed Percent femaleheaded households Percent living in poverty Median household income
PG County
Victims’ census block mean
Non-victims’ census block mean
82.3
78.2
85.2
4.3
5.9
3.8
23.0
31.5
19.7
5.8
7.7
6.2
43,127
39,311
47,946
in poverty, and the natural log of the median household income was gathered for the block-group where each subject lived. Block groups were used as proxy measures of neighborhoods. Table 2 illustrates the amounts of these items for the entire county, as well as for the aggregated averages of the block groups of the victim and non-victim samples.
Analysis and results Comparisons were made between homicide victims and non-victims. Bivariate comparisons were reported initially, followed by multivariate analyses. In all models, the bivariate variable of homicide victimization (victim or non-victim) was the dependent variable. All of the bivariate models (Table 3) showed a significant difference between the neighborhood characteristics where victims and non-victims lived, except for the neighborhood poverty levels. The first bivariate analysis showed that for every 1 percent decrease in high school graduates, the odds of being a homicide victim increased by 1.1 (or conversely, for every 1 percent increase in high school graduates, the odds of becoming a homicide victim decreased by .91). Model 2 indicated that for every 1 percent increase in unemployment, the odds of becoming a homicide victim increased by 1.4. The third model revealed that for every 1 percent increase in poverty in the census block group, there would be an increase in the odds of getting killed of 1.03. Again, this was not a significant increase in the odds. The next analysis showed that neighborhoods in which homicide victims resided were significantly more likely to have female-headed households than neighborhoods in which non-victims lived. For every 1 percent increase in female-headed households, the odds of getting killed increased by 1.07. Finally, for each one unit
A. Dobrin et al. / Journal of Criminal Justice 33 (2005) 137–143 Table 3 Bivariate analyses Model
1
2 3 4
5
Variable
Sig.
Percent high school graduates Percent unemployed Percent in poverty Percent of households headed by female Median household income (natural log)
0.000
Exp(B) [Odds ratio]
95 Percent C.I.
0.9103
0.877–0.945
0.000
1.4069
1.224–1.617
0.119
1.0358
0.991–1.083
0.000
1.0651
1.041–1.090
0.000
0.0961
0.033–0.277
1/Exp(B)
10.34
Table 4 Multivariate model of neighborhood characteristics
Percent high school graduates Percent unemployed Percent in poverty Percent of households headed by female Median household income (natural log)
Sig.
of
Exp(B) [Odds ratio]
95 Percent C.I.
1/Exp(B)
0.054
0.9544
0.910–1.001
1.05
0.035
1.2063
1.013–1.437
0.014
0.811
0.796–0.975
0.037
1.041
1.002–1.081
0.207
0.2386
0.026–2.211
1.23
4.19
neighborhood
characteristics
with
Variable
Sig.
Exp(B) [Odds ratio]
95 Percent C.I.
1/Exp(B)
Percent high school graduates Percent unemployed Percent in poverty Percent of households headed by female Median household income (natural log) Age Race Sex
0.010
0.9319
0.886–0.984
1.07
0.380
1.0811
0.905–1.291
0.054
0.9081
0.823–1.002
0.873
1.0035
0.962–1.047
0.173
0.1761
0.014–2.145
5.68
0.000 0.016 0.048
0.9461 2.7258 2.1242
0.921–0.972 1.199–6.195 1.005–4.491
1.06
1.10
increase in the natural log of the median household income, the odds of getting killed were reduced by .09, or for every one-unit decrease in the natural log of the median household income, residents were 10.34 times more likely to get killed. Table 3 consistently exhibited evidence that the neighborhood characteristics were significantly different between the places homicide victims and non-victims lived. Poverty of the area inexplicably did not follow the predictions. When all of the neighborhood level variables were combined to form a single multivariate model, the results were similar to the previous results (see Table 4). People were more likely to become homicide victims in neighborhoods with fewer high school graduates, more unemployed, more households headed by females, and a lower median household income. Poverty became significant, but
Variable
Table 5 Multivariate model demographics
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1.10
not in the direction predicted, and while the odds ratio for household income remained large, it lost statistical significance. Table 5 revealed that both individual level variables, such as age, race, and gender, as well as the economic and social characteristics of the neighborhood, were important Table 6 Multivariate model of neighborhood characteristics with demographics and arrest Variable
Sig.
Exp(B) [Odds ratio]
95 Percent C.I.
1/Exp(B)
Percent high school graduates Percent unemployed Percent in poverty Percent of households headed by female Median household income (natural log) Age Race Sex Any previous arrests
0.036
0.9421
0.891–0.996
1.06
0.356
1.0899
0.908–1.309
0.056
0.9051
0.817–1.003
0.583
1.0125
0.968–1.059
0.220
0.2042
0.016–2.597
4.90
0.002 0.088 0.132 0.008
0.9493 2.0944 1.1851 4.5815
0.923–0.976 0.894–4.904 0.836–3.943 1.476–14.222
1.05
1.10
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predictors of risk. Controlling for all of the other variables in the model, every 1 percent decrease in the number of high school graduates in an area increased the risk of becoming a homicide victim 1.07 times. Unemployment also increased the risk, but it was not significant. Poverty, in the model, was inversely related to homicide risk, which was counterintuitive to most theoretical predictions. As the natural log of the median household income increased by one unit, the risk of homicide victimization increased by 5.7 times. While this was a very large increase in the odds of becoming a victim, it was not statistically significant. Individual level measures such as age, race, and gender were all significant. Controlling for all the other variables in the model, for every one year younger a person was, the odds of getting killed increased by 1.06, Blacks had 2.7 times the probability of becoming a homicide victim than Whites, and males had 2.1 times the odds than females. From this multivariate model, it seemed that individual level variables had a stronger influence on the risk of homicide victimization than did macro-social variables. The results from Table 6 presented similar findings to those of Table 5, with the addition of the individual measure of arrest. Any arrest history increased the chances of homicide victimization by 4.6 times. While this was not quite as large as the odds ratio for median household income, arrest was significant, while income was not.
As was the case with many studies, this analysis relied on proxy measures of social cohesion and disorganization, as well as individual social characteristics. This made it difficult to make sense of them theoretically, except to say that both individual and community characteristics were important in influencing risk of homicide on the individual. If actual measures of community levels of cohesion, for example, were to be added to the models, it was likely that the direct effects of the social indicators would diminish (as was already most likely happening with the measure of poverty). Should measures of individual characteristics such as daily activities, lifestyles, and relationships similarly be included, one would expect that the proxy variables would become less important. This study illustrated the need for multi-level modeling, and future research must gather measures of the important theoretical constructs at each level, in order to best understand the interaction effects between neighborhood and individual characteristics on the risk of homicide victimization.
Note 1. While the sample was from Prince George’s County, the point of the analysis was not to make population estimates, but rather to test a causal model. Supporting conclusions should not be interpreted as evidence that this relationship would only occur in Prince George’s County.
Conclusions The results from the current research supported previous conclusions and confirmed this study’s premise concerning the relationship between neighborhood level characteristics and the associated risk of homicide. Macro-social measures of education, income, and female-headed households all were related to homicides occurring. In addition to macrolevel variables, the current study also allowed for the inclusion of individual-level variables. These individuallevel variables seemed to be as strong as the macro variables in their relationship to the risk of homicide victimization. Studying homicide at exclusively macro or micro levels appeared to be myopic; both characteristics needed to be incorporated simultaneously in order to accurately determine the strength of independent variables. While there were numerous studies examining the importance of neighborhood or individual characteristics on homicide, no distinction was made between the areas where victims lived as compared to similar non-victims, linking micro and macro characteristics in this manner. The case-control design allowed for this comparison to be made, filling a gap in the literature. By comparing the neighborhoods in which victims lived to those of non-victims, it was now possible to determine the actual risk each macro or micro characteristic would have on an individual becoming a homicide victim. This should point future researchers towards important micro/macro issues of homicide risk.
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