Relative Deprivation,Absolute Deprivation,and Homicide: Testing an Interaction Between Income Inequality and Disadvantage

Abstract

Both relative and absolute deprivation have effects on crime. These two concepts may be complementary, but much scholarship has treated them as separate. The present study assesses whether the effects of relative and absolute deprivation, measured as income inequality and disadvantage, respectively, interact in their effect on known homicide counts in U.S. counties. A multilevel regression model shows that there is a significant interaction between income inequality and disadvantage predicting homicide counts known to police. The plot of this interaction shows that when disadvantage is extremely high, increasing income inequality does not increase known homicides. The less disadvantage there is, the greater the effect of increasing income inequality on homicide counts in U.S. counties. This finding suggests that the effect of relative deprivation on known homicide is contingent on levels of absolute deprivation and vice versa. The implication of this finding is discussed.

Keywords

homicide relative deprivation absolute deprivation inequality disadvantage

Both relative and absolute deprivation have been connected to physical and psychological health, structural and economic stability, access to nourishment, and crime (Block & Block, 1992; Bradshaw & Ellison, 2010; Eberts & Sehwirian, 1968; Hsieh & Pugh, 1993; Ladin, Daniels, & Kawachi, 2009; Land, McCall, & Cohen, 1990; Lhila & Simon, 2010; McCall, Land & Parker, 2010; Saito et al., 2014). This implies that relative and absolute deprivation are multidimensional, explaining not only crime but also other social issues (Crosby, 1976; Rosenfeld, Baumer, & Messner, 2001; Runciman, 1966). Scholars have suggested that relative deprivation has the strongest influence on criminal and social issues in urbanized areas where resources are disproportionately accessible (Ravallion & Lokshin, 2010). An enduring concern in criminology is “what part does absolute deprivation play in the relationship between relative deprivation and crime?” However, the majority of research to date explains the impact each concept has separately, and not collectively, on crime. The present study seeks to partially fill this gap in the literature by examining relative and absolute deprivation as complimentary concepts. To achieve this, the current study tests the effect income inequality and disadvantage have on homicide counts in U.S. counties known to police. In addition, the current study examines whether income inequality and disadvantage interact to explain homicide counts.

Literature Review

Relative Deprivation and Crime

Relative deprivation has been studied at both the micro and macro levels (Chamberlain & Hipp, 2015; Kawachi, Kennedy, & Wilkinson, 1999; Yitzhaki, 1979). The concept of relative deprivation has generally been defined as a feeling of economic deprivation in comparison with one’s peers (Eibner & Evans, 2005), and this feeling can negatively impact individuals in a number of ways. In terms of theory, the importance of relative deprivation for the study of deviance has been framed within the theoretical tradition of anomie/strain (Agnew, 1992; Agnew, Cullen, Burton, Evans, & Dunaway, 1996; Baumer & Gustafson, 2007; Merton, 1938; Messner & Rosenfeld, 2012). This framing makes sense, as unfavorable comparisons of one’s self with others can create feelings of stress (Oshio, Nozaki, & Kobayashi, 2011). When access to resources is not spread evenly within a geographic area that individuals identify with, feelings of strain and frustration can lead to deviancy for those who see themselves as having less than some reference group to which they feel similar (Agnew, 1999; Hipp, 2007). People may feel similarity to others based on macro-level factors, such as residing in the same neighborhood, community, city, county, state, and so on, and derive feelings of inequality based on these factors (Agnew, 1999).

This focus in the literature concerning relative deprivation on comparing one’s conditions with similar others is what sets it apart from other popular macro-level concepts, which tend to focus more on absolute conditions. The focus on relative conditions and comparing one’s self with others relates specifically to homicide because, in the view of anomie/strain theory, having one’s chances at success, wealth, and prestige constrained by society, or being denied the resources and rewards they believe they need, expect, or desire, can result in stress that acts as a motivation for aggression and violence (Messner & Rosenfeld, 1999).

The influence of relative deprivation on crime and deviance has been examined in numerous studies (e.g., Blau & Blau, 1982; Eberts & Sehwirian, 1968; Hagan, 1995; Messner, 1982; Rosenfeld et al., 2001), but an issue that has persisted in this literature is the best way to operationalize the concept relative deprivation. Across studies, relative deprivation has been variously defined as scarcity of health care (Saito et al., 2014), access to electricity (Dugoua & Urpelainen, 2014), perceived social status (Zhang & Chen, 2014), changes in human capital (Bauman & Leech, 2012), and income inequality (Burraston, McCutcheon, & Watts, 2018; Elgar & Aitken, 2011; Krohn, 1976; Messner, 1982; Pridemore, 2008, 2011), with income inequality showing up fairly consistently in this literature.

Although income inequality has consistently been utilized as a measure of relative deprivation, and it has been identified as a reliable predictor of crime at the macro level (e.g., Burraston et al., 2018; Krohn, 1976; Messner, 1982), the strength of this measure’s relationship with crime has been inconsistent (e.g., Hagan, 1995; Messner, Raffalovich, & Shrock, 2002) and can vary in important ways. For example, Krahn, Hartnagel, and Gartrell (1986) found that income inequality demonstrates a stronger effect on homicide rates in more democratic societies. Messner and colleagues (2002) found a remarkably robust relationship between income inequality and homicide in a cross-sectional analysis, but this inequality–homicide relationship in their longitudinal analysis was only significant when they used an income distribution measure of low quality.

As another example, Jarjoura and Triplett (1997) found that poor youths in more affluent communities are more likely to become involved in criminal activity than poor youths living in more impoverished communities. This last finding is perhaps telling, as it means that observing the relationship between relative deprivation and crime while ignoring absolute deprivation may result in models that are misspecified, and this may be the reason that studies looking at income inequality and crime have produced inconsistent results (Pridemore, 2008).

Absolute Deprivation and Its Combination With Relative Deprivation

Since the 1970s, but most especially since the late 1980s, absolute deprivation has been the focus of much macro-level research on the causes and correlates of crime, and this is mostly due to the reemergence of social disorganization theory as one of the most prominent theories in criminology (e.g., Bellair & Browning, 2010; Bursik, 1988; McNulty & Bellair, 2003a, 2003b; Sampson & Groves, 1989; Sampson, Raudenbush, & Earls, 1997). Social disorganization theory dates back to the mid-20th century in Chicago, when Shaw and McKay (1969), borrowing from earlier theories of human ecology, argued that crime at the macro level comes from a community’s inability to exert effective social control over its members. Shaw and McKay (1969) argued in part that economic disadvantage undermined community efforts to achieve common goals and realize common values, resulting in higher crime rates. Over many years of research, absolute deprivation, generally measured as disadvantage, has held up as one of the most consistent, strong, and reliable predictors of violent crime at the macro level (Land et al., 1990; McCall, Land, & Parker, 2010; Pratt & Cullen, 2005; Pridemore, 2002; Shihadeh & Ousey, 1998).

It is not precisely clear how and why higher levels of absolute deprivation increase homicides rates specifically, as absolute deprivation has been tied to homicide through a number of theories, including social disorganization theory, strain theories, and critical theories, but it has been argued that poverty is one important component of the many correlated causes of homicide at the macro level (Messner & Rosenfeld, 1999). In addition, the relationship between poverty as a specific measure of absolute deprivation and homicide has held across many time periods, levels of analysis, types of studies, and various operationalizations of poverty (Pridemore, 2008).

As previously mentioned, relative deprivation has not been as consistent in predicting crime in general (Messner & Rosenfeld, 1999; Messner, Rosenfeld, & Baumer, 2004; Pridemore, 2011), or homicide more specifically (Pridemore, 2008). It was during the rise of the importance of absolute deprivation that income inequality was introduced as another potential variable to explain crime at the macro level (Blau & Blau, 1982; Messner, 1982). Income inequality and its theoretical connections to anomie/strain theories (Agnew, 1992, 2006; Baumer & Gustafson, 2007; Messner & Rosenfeld, 2012) created a new way to understand the relationship between structural disadvantage and crime. But one thing that has been generally lacking in the research literature is studies that assess both relative and absolute deprivation simultaneously (Pridemore, 2008). Although much cross-national research focused on relative deprivation and homicide, a good deal of research in the United States focused on absolute deprivation and homicide, with few instances where both concepts and their relationship to homicide at the macro level were assessed (for a key exception, see Pridemore, 2008).

Scholars have indeed long recognized the importance of both relative and absolute deprivation as important predictors of crime at the macro level, in particular homicide, but not enough studies have been conducted that look into their joint relationship with crime at the macro level (Land et al., 1990; Messner & Tardiff, 1986; Pridemore, 2008). This is surprising, given that the concepts of relative and absolute deprivation have similar theoretical roots in the anomie/strain and social disorganization literatures. In isolation, relative and absolute deprivation both have the potential to increase violent crime, as previously noted. But what if they are combined? Might the combination of their criminogenic effects push violence rates even higher, or might the effects of one cancel out the effects of the other?

Research that has examined the joint relationship between relative and absolute deprivation and their relationship to violence includes a study by Messner and colleagues (2002) that found that measures of relative deprivation are only significantly related to homicide when measures of absolute deprivation lack validity or reliability. In another study, conducted using cross-national data, Pridemore (2011) found that relative deprivation’s impact on homicide disappears once absolute deprivation is controlled for. Finally, a cross-national study by Pare and Felson (2014) found that income inequality failed to reach significance in predicting a number of crime types once poverty was utilized as a control variable. Findings such as these suggest that the inequality–crime relationship must be reassessed (Pridemore, 2008).

The studies cited above clearly point to the possibility that measures of relative and absolute deprivation may be somehow related in their effect on crime at the macro level, perhaps by magnifying or weakening each other’s effects. But little research to date has attempted to observe this potential association between the two concepts, often relying on simply looking at the effect of relative deprivation in the presence of absolute deprivation and vice versa, but not their relationship with each other as it relates to crime at the macro level. In one example that clearly illustrates how the two concepts might work together, Bernburg, Thorlindsson, and Sigfusdottir (2009) found in a sample of Icelandic youth that the effect of individual-level economic disadvantage on anger and delinquency is weak in a community context where most people are not well off, but the effect is significantly stronger in a community context where there is a mix of poor and well-off families and individuals. This finding suggests that relative and absolute deprivation may be complementary in their relationship with crime, that is, they may moderate each other. In another study suggestive of this possibility, Sigfusdottir, Kristjansson, and Agnew (2012) found in a sample drawn from five low inequality cities in Europe that absolute deprivation had a very weak effect on individual-level delinquency. This unexpected result led the authors to suggest that absolute deprivation may only reveal its effects on crime in a context of high inequality.

Given these findings, the relationship between relative and absolute deprivation in their effect on crime at the macro level certainly warrants a greater focus and more research. Considering the conflicting results of past research, and that past research has not often looked at the relationship between relative and absolute deprivation in their impact on crime, the current study seeks to expand this literature by testing the interaction between relative and absolute deprivation in their effect on homicide specifically. This focus on homicide is partly driven by the disconnect between cross-national research on homicide and its general focus on relative deprivation and research specially looking at homicide in the United States and its general focus on absolute deprivation. This study is one of only a handful to date to look at relative and absolute deprivation simultaneously in how they relate to homicide, and the first to test whether there is an interaction between relative and absolute deprivation in their effect on homicide at the macro level.

Method

Data from U.S. counties were utilized in the present analysis. We combined the Uniform Crime Reporting Program (UCRP): County-Level Detailed Arrest and Offense Data for 2010, 2011, and 2012 (U.S. Department of Justice, Office of Justice Programs, Federal Bureau of Investigation, 2010, 2011, 2012) with 5-year average American Community Survey (ACS) 2010 Census data (U.S. Census Bureau, 2010). Of the 3,143 U.S. counties with data in the 2010 5-year average ACS, there are 3,131 counties (99.6%) from the UCRP with violent crime data from 2010 through 2012. For a detailed description of the UCRP data collection and missing data imputations, see U.S. Department of Justice, Office of Justice Programs, Federal Bureau of Investigation (2010, 2011, 2012). All of the variables discussed below are described in Table 1.

Table 1.

Descriptive Statistics for U.S. Counties (N = 3,131).

Variable	M	SD	Minimum	Maximum
Dependent variable
Homicide count (2010-2012)	13.92	68.01	0	1,785
Independent variables
Disadvantage	0	1	−2.83	4.88
Income inequality	0.43	0.04	0.21	0.65
Controls
Population	98,596.74	313,445.04	82	9,818,605
Population (logged)	10.28	1.46	4.41	16.10
Racial/ethnic heterogeneity	0.28	0.19	0	0.82
Residential mobility	13.87	4.54	0	39.80
Rural^a	0.42	0.49	0	1
Northeast	0.07	0.25	0	1
Midwest	0.34	0.47	0	1
West	0.14	0.35	0	1
South	0.45	0.50	0	1

Note. MSA = metropolitan statistical area; MicroSA = micropolitan statistical area.

1,166 U.S. counties that are part of a MSA, 641 counties that are part of a MicroSA, and 1,324 rural counties.

Measures

Dependent variable

Homicide

The dependent variable is homicide counts at the county-level for all counties in the United States known to police from January 2010 through December 2012 that had UCRP violent crime data (99.6% of counties). These data represent homicides known to the police. We used the 3-year average of the homicides to smooth year-to-year fluctuations. The county-level homicide data were extracted from the Inter-university Consortium for Political and Social Research (ICPSR) data library.

Independent variables

Income inequality

Research concerning relative deprivation uses various measures to tap this concept (Agnew et al., 1996; Baron, 2003; Baumer & Gustafson, 2007). In the current study, the GINI coefficient for each county from the U.S. Census 2010 is used as the measure of inequality. The GINI coefficient ranges from 0 (complete income equality) to 1 (complete income inequality), and is one of the most commonly used measures of inequality (Kaplan, Pamuk, Lynch, Cohen, & Balfour, 1996; Lynch et al., 1998).¹ We used the z scores of the GINI coefficient.

Disadvantage

The most common measures of disadvantage are percent in poverty, percent unemployed, and percent female-headed households (McNulty & Bellair, 2003a; Morenoff, Sampson, & Raudenbush, 2001; Sampson & Wilson, 1995; Wilson, 1987, 1997). In addition, percent of population aged 25 years or older without a high school degree or general educational development (GED) and median family income (reversed) are also common measures of the construct disadvantage (Krivo, Peterson, & Kuhl, 2009). Our measure of disadvantage is an index of the average z scores of five U.S. Census items measured at the county level: percent in poverty, percent unemployed, percent female-headed households with a child below 18 years, median family income (reversed), and percent of population aged 25 years or older without a high school degree or GED (reliability α = .85).

Interaction variable (moderator)

The interaction variable is the product of the GINI coefficient (z score) and disadvantage (z score). Using standardized scores (z score) to create the interaction variable centers the data and reduces multicollinearity between the main effects and the interaction variable.

Control variables

Population

We used the natural log of the population from the U.S. Census 2010.

Racial/ethnic heterogeneity

Data on race and ethnicity were gathered from the U.S. Census to control for heterogeneity (Vandeviver, Van Daele, & Vander Beken, 2014). This measure of racial and ethnic heterogeneity was developed by Blau (1977). The heterogeneity measure is calculated by taking one minus the squared proportions of the population in each racial and ethnic group producing a range from 0 to 1. The racial groups included non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian/Alaska Native, non-Hispanic Native Hawaiian/Other Pacific Islander, and non-Hispanic Other racial groups. The ethnic group is Hispanic, any race.

Residential mobility

The variable Residential Mobility comes from the ACS and is the percent of the county population who lived in a different house the previous year.

Rural counties

A county was classified as rural if it was not part of either a metropolitan statistical area (MSA) or a micropolitan statistical area (MicroSA). The MSA and MicroSA population classifications come from the ACS, and the rural-urban continuum codes from the U.S. Department of Agriculture (USDA).

Census region

We included dummy coded variables from the U.S. Census 2010 to denote census region. The four census regions are Northeast, Midwest, West, and South, with South as the reference category in the negative binomial regression models. For example, the dummy variable Northeast was coded one for all counties in the Northeast and zero for all other counties. The dummy variables Midwest and West were coded in the same manor. The South was the references group and was assigned the value of zero for every region’s dummy variable.

Analytic Strategy

Prior to running the final models, we first performed a variance inflation factor (VIF) analysis to be certain that there was no significant multicollinearity among the independent variables; all VIFs were under 2.00. Next, we tested for significant nesting at the state and/or MSA levels. One of the challenges with these data is that counties are nested within states and some counties are also nested within MSAs. If significant, each level of nesting can influence the results of a regression equation. Therefore, we used multilevel mixed-effects negative binomial modeling in STATA (Rabe-Hesketh & Skrondal, 2008) to examine the extent of nesting (clustering) of counties by state and MSA. For the county-level analysis, we found significant nesting at both the state and MSA levels; therefore, we used a three-level mixed-effects negative binomial modeling model (3LMENBM) with a state level, MSA level, and county level. Finally, we performed a post hoc analysis to test for undue influences from extreme cases and found none; therefore, we did not exclude any counties from our final models.

Results

The results of the 3LMENBM are shown in Table 2. Model 1 of Table 2 includes all the study variables except for the interaction variable, which is introduced in Model 2 of Table 2. The interaction between income inequality and disadvantage is significant; therefore, Model 2 is superior to Model 1, and the relationship between income inequality and homicide counts in U.S. counties varies by levels of disadvantage and vice versa. Figure 1 is a graph of the main effects only model (Model 1) which depicts the relationship between income inequality predicting known homicide counts at levels of disadvantage, plotted at the mean levels of the control variables and for nonrural counties. Figure 2 is a graph of the interaction between income inequality and disadvantage predicting homicide counts (Model 2), plotted at the mean levels of the control variables and for nonrural counties.²

Table 2.

Three-Level Mixed-Effects Negative Binomial Models of County Homicide Counts.

	Model 1		Model 2
	Coefficient (SE)	IRR	Coefficient (SE)	IRR
Disadvantage (z score)	0.351*** (0.022)	1.420***	0.379*** (0.023)	1.461***
Income inequality (z score)	0.102*** (0.019)	1.107***	0.108*** (0.019)	1.114***
Interaction (Disadvantage × Inequality)	—		−0.045*** (0.014)	0.956***
Racial/ethnic heterogeneity	0.243*** (0.023)	1.275***	0.237*** (0.023)	1.267***
Residential mobility	0.002 (0.018)	1.002	−0.004 (.018)	0.996
Rural	−0.041 (0.046)	0.960	−0.049 (0.046)	0.952
Northeast^a	−0.333** (0.112)	0.717**	−0.339** (0.111)	0.712**
Midwest^a	−0.191* (0.091)	0.826*	−0.187* (0.090)	0.829*
West^a	−0.207* (0.094)	0.813*	−0.205* (0.093)	0.815
Population (logged)	1.158*** (0.018)	3.184***	1.159*** (0.018)	3.187***
Constant	−11.067*** (0.208)		−11.049*** (0.207)
Natural log alpha	−1.508*** (0.075)		−1.515*** (0.075)
Random effects
Within state variance	0.041 (0.012)		0.039 (0.011)
Within MSA variance	0.034 (0.012)		0.033 (0.012)
Model fit statistics
Log likelihood	−6,457.98		−6,452.60
AIC	12,941.97		12,933.19
BIC	13,020.61		13,017.88

Note. IRR = incidence rate ratio; MSA = metropolitan statistical area; AIC = Akaike information criterion; BIC = Bayesian information criterion.

South is the reference region.

p < .05. **p < .01. ***p < .001.

Figure 1.

Model 1: Main effects only income inequality, at levels of disadvantage, predicting homicide counts for nonrural counties in the United States.

Figure 2.

Model 2: Moderating relationship between income inequality, disadvantage, and homicide counts for nonrural counties in the United States.

The coefficients for moderating relationships (the interaction and two main effects) cannot be interpreted on their own because the relationship between income inequality and homicide count depends on the level of disadvantage. Comparing Figure 2 (the moderating model) with Figure 1 is the easiest way to depict the impact of the interaction effect (b = –.045, p < .001) in Model 2, which represents the moderating relationship between income inequality and disadvantage on homicide counts. In both Figures 1 and 2, extreme disadvantage is 2.5 SDs above the mean of disadvantage (representing 85 counties), very high disadvantage is 2 SDs above the mean of disadvantage (representing 178 counties), high disadvantage is 1 SD above the mean of disadvantage (representing 631 counties), mean disadvantage is plotted at the mean of disadvantage (representing 1,233 counties), low disadvantage is 1 SD below the mean of disadvantage (representing 920 counties), and very low disadvantage is 2 SDs below the mean of disadvantage (representing 114 counties). Each line is plotted within the range of the data.³

Of note in Figure 2, as disadvantage increases, the y-intercept for income inequality increases dramatically, but as disadvantage increases, the slope for income inequality decreases. At the county level in the United States, income inequality’s relationship with homicide counts is at its strongest at very low levels of disadvantage; extreme levels of disadvantage homicide counts are at their highest but income inequality is not related to homicide counts.

With the exception of rural and residential mobility, all of the control variables in Model 2 were significantly related to county-level homicide counts. A 1 SD increase in racial heterogeneity results in a 1.26 times increase in number of homicides (incidence rate ratio [IRR] = 1.26, p < .001). The Northeast region (IRR = 0.71, p < .01) had 29% fewer homicides than the South, while the Midwest (IRR = 0.83, p < .05) and West (IRR = 0.81, p < .05) had 17% and 19% fewer homicides than the South, respectively. For each unit increase in the natural log of the population, homicides increased by 3.19 times (IRR = 3.19, p < .001).⁴

Discussion

This study tested whether income inequality and disadvantage interact to shape homicide counts in U.S. counties that are known to police. Prior research shows that both relative and absolute deprivation matter for macro-level crime rates, but there were inconsistent findings when measures of both relative and absolute deprivation were considered simultaneously (e.g., Blau & Blau, 1982; Messner, 1982; Pare & Felson, 2014; Pridemore, 2008, 2011). The data and analysis in the current study show that income inequality and disadvantage interact significantly in their effect on homicide counts in U.S. counties; that is, they have a moderating relationship and the effect of income inequality on homicide counts depends on levels of disadvantage and vice versa. This suggests that the relationship between relative and absolute deprivation and their effects on crime should be examined as an interactive one to avoid model misspecification, which may have been an issue in past studies. The current findings produce two main takeaways.

First, more absolute deprivation (disadvantage) means more homicides, regardless of levels of relative deprivation (income inequality). This finding is consistent with prior research, which has shown that absolute deprivation, measured in a number of ways, is one of the strongest and most consistent predictors of crime at the macro level (Land et al., 1990; McCall et al., 2010; Pratt & Cullen, 2005; Pridemore, 2002, 2008). It is important to note that the concepts of relative and absolute disadvantage hold much foundational relevance to social disorganization theory. The current study is not a true test of social disorganization theory, but instead an examination of concepts that are often utilized in the studies that test the impact of social disorganization on crime. That being said, the above findings support these concepts that are associated with social disorganization. Furthermore, the results show how these measures may relate and provide a bridge between concepts from other theoretical paradigms, including strain and social disorganization (Pratt & Cullen, 2005).

Second, relative deprivation (income inequality) matters for homicide counts, but this effect tapers off and eventually disappears as absolute deprivation (disadvantage) increases. Essentially, the current findings show that relative deprivation matters the most in a context of very low disadvantage, whereas in a context of extremely high absolute disadvantage, relative deprivation is not meaningful for homicide counts. This finding potentially addresses the inconsistent results of previous studies that have simultaneously considered, but failed to account for the relationship between relative and absolute deprivation. These studies were misspecified in that they considered relative and absolute deprivation to be separate, rather than complementary, concepts.

These findings have important implications for theories of relative and absolute deprivation and macro-level crime rates, namely, anomie/strain theories and social disorganization theory. Whether the level of analysis is the nation, state, county, or neighborhood, these theoretical schools of thought must come together to propose a joint theory of how relative and absolute deprivation come together to influence crime rates at the macro level. The most important questions to consider, based on the current study’s findings, are the following: Why does the effect of relative deprivation on crime vary by levels of absolute deprivation and vice versa? and What is the mechanism for this macro-level finding?

Although this study makes an important contribution to the literatures on relative deprivation, absolute deprivation, and homicide, a few key limitations can be noted. First, the current findings used a very specific unit of analysis, U.S. counties. Future research should look to replicate the current findings in similar models, where the interaction between relative and absolute deprivation is the focus, at other levels of aggregation. These studies could look at states or nations, but another unit of analysis that is commonly considered in social disorganization theory, the neighborhood or community would also be ideal. It is also important to note that these relationships may differ by type of crime. For instance, economically motivated crimes may impact these effects, although economics can be a motivator for homicides as well. An approximation of neighborhoods or communities that is often utilized in social disorganization research as the unit of analysis is the census block group. Analyses at levels of aggregation like the census block group could allow dynamics present in social disorganization theory, like collective efficacy, to be more closely examined in regard to how they may shape the interaction between relative and absolute deprivation on crime. Gathering the necessary data at the census block group level nationally could be a challenge, and an alternative option to looking at a national sample could be looking at data from one or more cities.

Second, the current study focused on homicide, but other specific crimes should be considered to see whether the pattern of results concerning the interaction of relative and absolute deprivation holds for these crimes. Finally, it should be noted that our findings are marginal and could be period-specific because we only derive data from the 2010 U.S. Census and the 2010-2012 UCRP, and other time periods of study could yield different results concerning the interaction between relative and absolute disadvantage and its effect on homicide.

In conclusion, more theorizing and research are needed on the relationship between relative deprivation and absolute deprivation, and how they jointly shape crime. These findings, although marginal, demonstrate the relationship between income inequality and disadvantage as predictors to homicide and need continued exploration. Theories of anomie/strain and social disorganization theory need to be integrated more to explain the relationship between these two concepts, and future research should account for these concepts with various measures, at various levels of aggregation, and for various crime types.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Bert Burraston is an assistant professor in the Department of Criminology and Criminal Justice at the University of Memphis. He is a sociologist whose specialty is criminology, family studies, advanced statistics, and prevention science. He is currently examining the complex relationship between inequality, disadvantage, and crime. In addition, his research focuses on the prevention of antisocial and criminal behavior, and he has conducted a variety of studies using new technologies to improve services within the juvenile justice system.

Stephen J. Watts is an assistant professor in the Department of Criminology and Criminal Justice at the University of Memphis. His research focuses on the victimization–offending overlap, the testing of criminological theories, and the integration of these theories into a biosocial framework for explaining antisocial behavior.

James C. McCutcheon is an assistant professor in the Department of Criminology and Criminal Justice at the University of Memphis. His main areas of interest are environmental criminology, structural and economic predictors of violent crime, and drug misuse and abuse.

Karli Province holds a Masters of Arts in Criminal Justice and graduate certificate in Geographic Information Systems from the University of Memphis. She has recently accepted a position as graduate research assistant at the University of Cincinnati Ph.D program. Her resarch interest includes biosocial criminology and homicide.

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