John M. Eason, L. Ash Smith, Jason Greenberg, Richard D. Abel, and Corey Sparks
INTRODUCTION
In the social sciences, the link between poverty and crime is well established. The relationship has been fortified by decades of sociological research and is fundamentally reciprocal. At the level of both the individual and the neighborhood, poverty exacerbates one’s likelihood of experiencing crime; and encounters with the criminal justice system, in turn, worsen the socioeconomic condition of people and communities. The goal of this chapter is, first and foremost, to explore the ways in which crime influences poverty—and vice versa—in rural America.
It is important to note that the majority of literature exploring the causes and consquences of crime has been conducted within the realm of urban sociology. Many of our empirical crime statistics are collected from urban areas, and many of the theoretical foundations for our understanding of crime also emerge from the urban literature because criminal activity is generally more common in metropolitan areas, which are more heavily populated and policed than are rural regions. This chapter translates what we know about crime and punishment in low-income metropolitan communities to poor rural America.
The chapter begins with a summary of findings from criminologists who have studied poverty, crime, and punishment across the rural–urban continuum in recent years. In addition to laying the groundwork for what is already known, this overview illustrates the gaps in our knowledge and the need for a comprehensive framework linking rural poverty and crime. This framework is provided by adapting Donnermeyer and DeKeseredy’s concept of the square of crime to rural communities. In essence, the square of crime suggests that multiple, interlocking, criminogenic actors (i.e., the public, the state, the offender, and the victim) interact to produce elevated rates of criminal activity in certain neighborhoods. Each corner of the square is supported with relevant theoretical postulations and empirical data on crime in impoverished rural communities. In addition, Sampson’s (2012) theory of the neighborhood effect, which invokes a community’s characteristics, such as its socioeconomic condition and level of residential segregation, is used to better understand the elements of spatial disadvantage and poverty that contribute to crime in rural America.
Having laid the conceptual foundation, the chapter then describes three noteworthy trends regarding the rise in homicide, incarceration, and prison construction in rural regions. Trends in these phenomena have gained considerable attention in recent years, popularizing scholarship in the field of rural criminology. Building on the research of these criminologists, several factors are common to rural areas: (1) certain violent crimes, such as murder, have increased in rural America even as they decrease in urban America; (2) rural towns that are disproportionately poor, black, and rural suffer from targeted policing and higher rates of incarceration; and (3) the prison boom (and the consequent problem of prisoner reentry) has linked the economies of rural places inextricably to the well-being of the local prison. The chapter concludes by describing the implications for the future of the rural poor on the basis of our understanding of crime and punishment in low-income communities in rural America.
CRIME AND PUNISHMENT ACROSS THE RURAL–URBAN CONTINUUM
Much of our knowledge of crime and punishment concerns urban America; for example, in urban areas concentrated disadvantage is a predictor of neighborhood murder rates (Sampson, Raudenbush, and Earls 1997), mass imprisonment (Mauer 2006; Pattillo, Weiman, and Western 2004; Travis, Western, and Redburn 2014; Wacquant 2001), and prisoner reentry (Rose and Clear 1998). These well-established social facts are based on national trends (Mauer 2006; Pattilloet al. 2004; Travis et al. 2014) or findings from select urban neighborhoods in a single city (Sampson et al. 1997; Sampson and Loeffler 2010; Sampson 2012), and they are often assumed to represent urban areas exclusively. As a result, rural poverty and spatial inequality across the rural–urban continuum are often left out of the conversation surrounding crime and punishment.
There is a dearth of comparative scholarship on crime in rural versus urban communities, and it is not known, for instance, whether incarceration rates differ between urban and rural areas. It is known that crime rates, across the rural–urban continuum, are consistently higher in high-poverty counties. Moreover, when studying crime across the continuum, ways in which the criminal activity of urban areas affects that of rural regions must be considered. For instance, criminologists expect that the impetus for rising substance abuse and gang activity in rural regions can be attributed to the trafficking of drugs from urban areas into rural communities. This might explain, in part, why the sale and abuse of methamphetamine is so high in rural communities (Garriott 2011).
Some types of crime are more likely to occur in rural than in urban areas of the United States. For example, organized rings of livestock, grain, and equipment thieves are more common in rural regions (Weisheit, Falcone, and Wells 2005). Many criminal activities related to wildlife (e.g., animal poaching) and agriculture (e.g., crop and timber thefts) are unique to rural regions. Other criminal activities that are more likely to occur in rural regions include violent crimes such as homicide, rape, and assault among acquaintances rather than strangers (Weisheit, Falcone, and Wells 1994).
Beyond the types of crimes themselves, crime reporting also varies along the urban–rural continuum. This is due to cultural differences in victimization perspectives. It should be noted that no statistically significant differences in the percentage of violent victimizations reported to police exist for rural versus urban poor areas (Truman and Langton 2015). However, some criminologists theorize that cultural features of rural communities might lead to lower rates of crime reporting in general (violent and nonviolent crimes included). These cultural components include “informal social control among citizens, a mistrust of government, and a reluctance to share internal problems [with outsiders]” (Weisheit et al. 1994). Part of the reluctance to involve outsiders may involve perspectives of personal protection and higher gun ownership among rural residents. Although handgun ownership is only 7 percent higher in rural areas than in urban ones, gun ownership overall in rural communities is nearly triple that of urban areas.
Differences in crime and punishment among rural and urban areas also concern agencies of the state, such as local law enforcement and the courts. The central policing organization in rural areas tends to be the sheriff’s department, whereas in urban areas it is the municipal police (Weisheit, Falcone, and Wells 1995). Police departments in rural areas tend to be underresourced in comparison to those in urban communities, with “lower budgets, less staff, less equipment, and fewer written policies to govern their operations” (Weisheit et al. 1994, 1). In rural regions, nearly half of the local police departments have less than a dozen sworn officers; over 90 percent have fewer than fifty officers (Weisheit et al. 1995). Nonetheless, rural law enforcement officials tend to be more highly respected by the public than police officers in urban regions (Weisheit et al. 1994). Finally, rural courts—in comparison to urban courts—are less formal and more flexible, potentially leading to greater racial differences in criminal sentencing (Weisheit et al. 1995). These facts all speak to how the challenges rural communities face regarding crime and punishment are related to urban challenges yet may differ greatly in type and scope (see Duhart 2000).
WHERE DOES POVERTY FIT?
Though rural and urban regions experience some dissimilarities in crime and punishment, crime and poverty are intricately related in all communities—rural and urban alike. The criminalization of poverty refers to the way in which low-income individuals are drawn into the criminal justice system at higher rates when compared to middle- and high-income individuals. This phenomenon has been documented with respect to the criminalization of extreme poverty and homelessness (Prather 2010; Dolan and Carr 2015); the problem of debtor’s prisons and unaffordable bail fines and court fees, which disproportionately plague indigent criminal defendants (American Bar Association 2016); the criminalization of the welfare system, which injures poor recipients of public assistance (Gustafson 2009); and the issues of the school-to-prison pipeline and probation profiteering, which likewise target low-income individuals and communities (Dolan and Carr 2015).
Individuals in poor households are more likely to suffer violent victimization, irrespective of whether they are located in rural, suburban, or urban areas (figure 13.1). For the rural poor, 38.8 per 1,000 individuals are likely to suffer a violent crime at some point in their lives, versus 43.9 for every 1,000 urban poor individuals. Crime rates are higher both among urban individuals generally and among the urban poor than among rural individuals or the rural poor (Truman and Langton 2015). In rural regions, the difference between rates of violent victimization among low- and high-income individuals is especially pronounced (figure 13.2). Although crime rates in rural counties have generally been lower than those of urban communities, both experience greater rates of crime in high-poverty areas (Weisheit et al. 1994), and the effect of poverty on crime is less pronounced in rural than in urban areas (Weisheit, Falcone, and Wells 2005).
Figure 13.1 Rate of violent victimization, by poverty level and location of residence, 2008–2012.
Source: Reprinted from the Bureau of Justice Statistics, “Special Report: Household Poverty and Nonfatal Violent Victimization, 2008–2012.”
Figure 13.2 Rate of violent victimization, by poverty level and race, Rural counties, 2008–2012.
Note: “!: Interpret with caution; estimate based on 10 or fewer sample cases, or coefficient of variation is greater than 50%” (quoted from p. 5 of the Bureau of Justice Statistics “Special Report”).
Source: Figure reprinted from the Bureau of Justice Statistics, “Special Report: Household Poverty and Nonfatal Violent Victimization, 2008–2012.”
The link between poverty and crime has long been understood as mutually reinforcing (Weisheit et al. 1995), especially in rural areas. For instance, Arthur (1991) found that poverty and measures thereof (e.g., rates of unemployment and use of public aid) were highly correlated with not only property crimes but also violent crimes in rural Georgia. In addition, measures of income inequality are consistently positively correlated with crime rates over time, and a rise in median household income is associated with lower or declining levels of crime over time (Deller and Deller 2011). These examples powerfully illustrate the neighborhood effect, whereby a concensus behavior—or set of characteristics at the neighborhood-level—affects residents’ behaviors at the individual level (Sampson 2012). Thus, if the broader community experiences a drop in median household income or a rise in income inequality, this may influence an individual’s likelihood of becoming a victim (or perpetrator) of a crime in that community. Similarly, elevated rates of crime may contribute to the poverty of the neighborhood through lowered property values, for instance. The relationship between poverty and crime is often bidirectional, and it is difficult to cleanly tease apart which “causes” the other. As such, it is important to note that causation should not be assumed to go only from poverty to crime (or crime to poverty).
The theory of the square of crime (figure 13.3) is useful in explaining why crime and poverty are so heavily interrelated. Developed by criminologists Joseph Donnermeyer and Walter DeKeseredy, it states that crime in any community involves the mutual interaction of multiple stakeholders. There are four primary actors, each of whom comprises a corner of the square: the victim, the offender, the public, and the state (i.e., law enforcement and the courts). Criminogenic features of a neighborhood therefore include both the area’s structural traits (its police agencies and the attitudes of the public) and individual-level characteristics (the attributes of potential victims and offenders). The former engage in social control, preventing (or exacerbating) the likelihood of criminal activity occurring in a given context. The latter are those directly involved in the criminal act itself (i.e., the perpetrator and the victim). Crime in any context, but especially in rural poor America, cannot be understood in the absence of any one of these four actors.
Figure 13.3 The “square of crime.”
Source: Reprinted from Joseph Donnermeyer and Walter DeKeseredy’s “Toward a Rural Critical Criminology.”
The literature on the police and the public and their respective interactions with crime and punishment help to further explain the social control aspects of crime in rural poor areas. Specifically, predictors of crime in rural regions include the area’s structural features, such as informal social control mechanisms and cultural feelings within the community. Deller and Deller (2011, 126) found that cultural norms in rural regions, such as that crime is a “personal matter,” discourage rural victims from seeking the involvement of “outsiders,” such as law enforcement officials; crime victims may instead prefer to deal with the problem themselves and with other in-group members of their neighborhood (Deller and Deller 2011). The attention to the in-group and the exclusion of the law enforcement out-group may lead to lower rates of crime reporting and increased victim vulnerability.
This perspective is rooted in social disorganization theory, which is the notion that “structural factors influence social networks which in turn influence social control” (Deller and Deller 2011, 123). By this theory, a structural factor, such as the spatial dispersion and geographic isolation of a rural community, may enhance residents’ notions of an in-group versus an out-group. The in-group refers to those individuals viewed as belonging within the community, and the out-group consists of community outsiders. Ideas about who does (and who does not) belong in a community’s social network may in turn influence the ways in which crimes are handled within that community. Thus, criminal activity might be considered local law enforcement’s prerogative if the police are part of the in-group social network. But if the police are viewed as community outsiders, residents may view crime as a personal matter to be handled privately among themselves and without the aid of local law enforcement officials. Illustrating the square of crime, the victim, the public, and the agencies of the state all interact interdependently through social disorganization theory; these groups collectively discourage victims from seeking state assistance due to the public feelings about crime and the cultural beliefs of the community.
In line with social disorganization theory, a lack of resources in rural poor areas—such as underfunded public schools and a lack of possibilities for social mobility—leads to fewer opportunities for “prosocial (i.e., less criminal, less deviant) behavior” among individuals in low-income rural communities (Donnermeyer et al. 2006, 208). This deficiency of opportunity, moreover, occurs for two individual-level actors: the offender and the victim. A lack of resources both feeds antisocial behavior among criminal offenders and enhances the “vulnerability” of potential victims (Mawby and Yarwood 2011, 78). That is, to combat the view that their rural hometown is resource-dearth, some rural residents may seek to “maintain an idyllic representation of rural life” (77). Enacting this “rural idyll,” residents may leave doors unlocked, elevating their risk of becoming a victim of a crime (78). Further, at the public level, residents in rural communities tend to be culturally less fearful of crime compared to residents of urban areas; this, too, may lead to lax security in rural households, aggravating one’s risk for becoming a victim of criminal activity (Weisheit et al. 1995).
Public perceptions such as these are an essential informal form of social control that may reduce (or heighten) one’s risk for experiencing crime in rural communities. Public perceptions that have this controlling influence on crime offer an example of collective efficacy. Collective efficacy is the ability to control the behavior of groups and individuals within a community. This control is a central form of social capital involved in mitigating the predictive power of neighborhood disadvantage and poverty on crime (Sampson et al. 1997). For instance, living in a community where neighbors help each other out tends to motivate new residents to be vigilant against crime, whereas living in a high-crime area where neighbors keep to themselves makes an individual more likely to be a victim of crime. Neighborhoods with higher measures of collective efficacy are better at tamping down crime than neighborhoods with comparable levels of disadvantage and less collective efficacy. With collective efficacy in mind, the square can be extended as a type of neighborhood effect in rural communities.
An additional neighborhood-level criminogenic concern for rural poor communities relates to the gendered dynamic of economic displacement. Some rural criminologists, such as Donnermeyer and DeKeseredy (2008), have asserted that a “crisis of masculinity” exists among poor males in the United States today (14). This crisis primes them for committing greater acts of violence in both the private and the public sector. Low-income men in postindustrial rural regions experiencing economic displacement (e.g., through the loss of a family farm or a local economic recession) may no longer view social mobility and the American Dream as realistically attainable. Lacking the ability to claim masculinity through the traditional breadwinner role, some low-income rural men may turn to male-on-female violence and substance abuse. Some scholars attribute the rise in interpersonal crimes in rural America to this gendered phenomenon (Donnermeyer and DeKeseredy 2008). Worse still, male-on-female violence is a structural issue in rural poor regions, invoking the state actors of the square of crime. That is, police in rural regions are less likely to intervene in domestic violence situations than those in urban areas (Weisheit et al. 1995).
Some criminologists have posited that youth experience a similar cultural crisis due to a lack of economic opportunities. Youth may be at a higher risk for substance abuse and criminal activity in impoverished rural communities if they feel “stuck” in their local towns and are unable to find a job to support independence from their parents (Mawby and Yarwood 2011, 78). If the period during which they remain dependent on their families is too prolonged, young people may experience a delayed transition to adulthood and an increased risk of delinquent behavior (Rutherford 2002). This is both a structural concern and an individual-level impetus for the offender to commit crimes in rural regions, again demonstrating the interdependence of actors in the square on crime.
Finally, one last feature of crime in poor rural America concerns patterns of substance addiction and abuse. Rates of substance abuse in rural areas are generally akin to those of small and large metropolitan areas, but certain substances are more regularly abused in rural areas. For instance, binge alcohol use by youths aged twelve to seventeen is more common, as is cigarette smoking and smokeless tobacco use. Other substances are abused at a lower rate in rural than urban areas; these include underage alcohol use, illicit drug use, and illicit drug or alcohol dependence (Substance Abuse and Mental Health Services Administration 2014). In recent years, substance abuse (especially heroin) has been so prevalent in rural areas that it has led to an increase in rates of prison admissions in rural regions. In the early 2000s, individuals in rural, suburban, and urban communities were equally likely to be incarcerated. Due to drug arrests today, individuals in rural counties are “50 percent more likely to go to prison” than those in larger, more urban areas (Keller and Pearce 2016; figure 13.4).
Figure 13.4 A growing divide: people in rural areas are much more likely to go to prison than people in urban areas, a major shift from a decade ago.
Source: Adapted from Keller and Pearce’s “This small Indiana county sends more people to prison than San Francisco and Durham, N.C., combined. Why?”
Rates of incarceration for drug offenders in recent years may be higher in rural areas due to structural features of urban regions. For instance, it has been argued that the prison overcrowding prevalent in urban counties encourages urban judges to offer probationary sentences instead of jail time. Such an extent of prison overcrowding is not present in rural correctional facilities; thus, probationary sentences are employed with less regularity. Similarly, urban areas may be more resource-rich in terms of their ability to combat substance abuse and addiction outside the context of the criminal justice system—a service many rural counties are not equipped to provide (Keller and Pearce 2016). This disparity of resources represents a consequence of spatial inequality; that is, the unequal allocation of resources across spatial scales (or places). In this way, substance abuse is not only an offender-level issue but one concerning state agencies (e.g., a lack of state-run facilities to combat addiction) and structural issues of inequality faced by rural America.
Overall, issues relevant to the criminalization of poverty in rural communities may be summarized using the intersectional “square” of crime. The square of crime suggests that criminal activity involves multiple stakeholders: the offender, the victim, the agencies of the state (e.g., the police), and the public at large (Donnermeyer and DeKeseredy 2008). Several aspects of crime in rural areas are mutually determinative aspects of crime, involving both structure and individual actors: rates of violent victimization, social disorganization theory and the “outsider” problem, the informal social control of organic solidarity, a lack of resources and the rural idyll cultural image, the crisis of masculinity and issue of youth dependence, and substance abuse problems. The criminalization of poverty in rural communities essentially relies on the interdependence and mutual interaction of these varied stakeholders, leading to a prevalence of crime—and lack of resources to successfully combat it—in impoverished rural communities.
This section has shown the importance of the neighborhood effect and context for understanding how broader structural factors may affect crime at the level of the individual. Some aspects of context (e.g., poverty and inequality) affect crime in similar ways across the rural–urban continuum, but other aspects are unique to rural America. These issues of context that affect crime and punishment concern three nascent crime trends in rural areas: rising murder rates, mass imprisonment, and the prison boom. The following sections explore these phenomena, as well their implications for the future of crime and punishment in rural America.
TRADING PLACES?: MURDER RATES ACROSS THE RURAL–URBAN CONTINUUM
The first trend concerns homicide rates across the rural–urban continuum. For the first time in decades, the United States witnessed a decline in murder as part of the crime drop of the 1990s (figure 13.5). Despite the broad policy and theoretical implications of murder for all communities, the debate over the “great” crime drop of the 1990s has been centered on deterrence in cities—specifically, on the role of active and intentional criminal justice policy in reducing urban violent crime. For example, many argue that improved policing, mass incarceration, gun control, and reduction of open-air drug markets were key determinants in reducing the murder rate (Blumstein and Wallman 2006; Zimring 2011). Tactics believed to cut crime include increasing police officers, data-driven patrols based on geographic information system (GIS) analysis, stop-and-frisk, zero-tolerance, and community policing. Economic growth and shifting demographics (e.g., aging baby boomers) were also believed to be important factors in reducing murder (Blumstein and Wallman 2006).
Figure 13.5 For the first time in decades, the United States witnessed a decline in murder as part of the crime drop of the 1990s.
Source: Levitt (2004), based on FBI Uniform Crime Report data.
Zero-tolerance and other police practices of locking up citizens for small offenses such as panhandling or drug possession grew out of the War on Drugs and have contributed to mass imprisonment and, ultimately, the prison boom (Martin and Price 2016). Despite stop-and-frisk resulting in more people being locked up for petty offenses for longer periods, from the zero-tolerance policies perspective, aggressive and over policing was a huge success in reducing crime in urban areas. Because these policies were widely heralded as making cities safe (Zimring 2011), many rural communities imported these practices with very little question (McQuade 2016). However, the effect of zero-tolerance policing policies on rural crime may be negligible (McQuade 2016), highlighting the importance of context for understanding crime trends. If the policy prescriptions credited for the crime drop were taken at face value, we would not only continue aggressive policing but also expand zero-tolerance policing tactics like stop-and-frisk that many argue (Gelman, Kiss, and Fagan 2005; Meares 2014) are thinly veiled forms of racial profiling. Scholars (Gelman et al. 2005; Meares 2014) also find that these policies lock up people for longer sentences on increasingly petty offenses. In fact, aggressive and over policing may contribute to rising incarceration in rural communities, fueling the construction of new jails (Pragacz 2016). Martin and Price (2016) assert that these policies ultimately contribute to rising levels of imprisonment nationwide. As it stands, the United States is the world leader in imprisonment, with well over 2 million Americans presently incarcerated.
Though debates on the causes and consequences of the “great” crime drop continue, the universality of this trend is rarely disputed. Levitt (2004) is not alone in claiming that “the drop of crime in the 1990s affected all geographic areas and demographic groups” (167). But was the crime bust really universal? There are several important reasons to explore this question. First, some areas may not have experienced any decline in murder rates. Second, some explanations for the crime drop may have substantial boundary conditions. That is to say, the context of murder in urban northern places could vary drastically from that in rural southern places. It is important to explore whether the precipitous decline of murder during the 1990s was universal across all U.S. census places. According to the U.S. Department of Commerce, a U.S. census-designated “place” is defined as “the statistical counterparts of incorporated places [that] are delineated to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located.” Recent empirical advances in the study of rural U.S. census places enables a better understanding of how context shapes crime and punishment across rural and urban communities. Although homicides declined in the largest U.S. cities, the crime drop of that period was not universal.
In general, victimization rates for violent crime are higher in urban (22.2 per 1,000 people) than in rural (18.3 per 1,000 people) counties; incidences of murder/manslaughter are likewise greater in urban (4.7 per 100,000 people) than in rural (3.0 per 100,000 people) counties (Office for Victims of Crime 2016). Nonetheless, Weisheit, Falcone, and Wells (2005) demonstrate that seventeen of the thirty counties with the highest murder rate were rural in 2000. This means that the majority of high murder rate counties are not anchored by a large urban city. Many of the highest murder rates during this period were in smaller, less densely populated communities.
Lee, Maume, and Ousey (2003) found that homicide rates are consistently higher in U.S. counties with greater socioeconomic disadvantage and concentrated poverty.1 However, they note that concentrated poverty is a significant predictor of homicide only in urban areas; for rural counties, socioeconomic disadvantage is the key determinant (Lee et al. 2003). Further, Barnett and Mencken (2002) examine the impact of poverty and a lack of resources on crime in rural areas. Defining resource-deficient counties as those with high unemployment, poverty, income inequality, or a large number of female-headed households, they find that “resource disadvantage” is positively associated with both property crimes and violent crimes (e.g., homicide). Murder also is concentrated in high-poverty, rural counties (Lee and Ousey 2001; Light and Harris 2012), but little is known about the specific concentration of crime in rural towns and neighborhoods. To be more precise, there are few empirical tests of neighborhood effects as a predictor of murder across the rural–urban continuum—a system differentiating nonmetropolitan counties by proximity to metro areas and degree of urbanization from metropolitan counties by the size of their metro area. In this section, novel data is used to investigate incidents of murder between 1970 and 2000 across rural, suburban, and urban U.S. census places. Ultimately, these data show that the context of rural communities matters in determining national murder trends.
Although homicides declined in the largest U.S. cities between 1980 and 2000, the trend was far from universal across spaces and demographics. In contrast to the precipitous decrease in urban murders during the great crime decline, suburban and rural communities witnessed a marked increase in murder. Although concentrated disadvantage cannot be definitely stated as the cause, there is a strong link between rising crime/violence and deprivation. Thinking about the violence across the rural–urban continuum unearths the acute, compounded, and chronic effects of disadvantage and allows the mapping of new geographies of poverty.
Before mapping murder trends, let’s consider the data and methods used to measure murder rates across time and space. The analysis spans 1980 to 2000 using data from the FBI’s Uniform Crime Report, U.S. decennial census data, and county- and state-level demographics from the University of Kentucky Center for Poverty Research. How context influences crime in rural places is broadly examined, but the analysis deals specifically with the role of racial stigma and concentrated disadvantage in increasing murder rates among rural communities.2
The analysis (see table 13.1) reveals that murder rates in urban areas declined overall between 1980 and 2000, but the homicide rate increased across U.S. census places by region, state, nonmetropolitan U.S. census place, as well as across key demographic dynamics (e.g., poor blacks) representative of concentrated disadvantage. In comparing nonmetropolitan areas, rural areas have experienced even greater increases in their homicide rates than suburban places. By delving deeper into these trends, huge variations are seen within the rural, suburban, and urban contexts. Figure 13.6 shows the maximum (upward bound) and minimum (lower bound) murder rates, and the graph line is the regression fit mean of the murder rate from 1980 to 2000 in rural U.S. census places. The figure demonstrates that, over this period, the average murder rate rose across all rural U.S. census places.
Table 13.1 Fixed Effect Negative Binomial Regression Predicting Murders 1980–2000
| Variables |
(IRR/z-Ratio) |
(IRR/z-Ratio) |
| Year |
.995 (−7.76) |
.995 (−7.74) |
| Year*rural (Beale code) |
1.00 (9.17) |
|
| Year*micropolitan |
|
1.00 (8.63) |
| Controls |
Yes |
Yes |
| Wald Chi-square |
2,488.27*** |
2,480.74 |
| N |
106,196 (5,635) |
106,196 (5,635) |
Source: Federal Bureau of Investigations Uniform Crime Report, 1980–2000.
Note: Exposure set to LN (population); controls include: % black, % Hispanic, % female-headed households, poverty rate, unemployment rate, % population living in same household 5+ yrs, % owner occupied housing, % vacant housing.
* p<0.05, ** p<0.01, ***p<.001.
Figure 13.6 Homicide rates for rural U.S. census places, 1980–2000.
Source: Federal Bureau of Investigations, Uniform Crime Report, 1980–2000.
In investigating the relationship between rural poverty and murder, disadvantage is a stronger predictor of murder outside of urban areas over time. To clarify further, in contrast to the common belief that the decrease in violent crimes such as murder has been a universal trend since the 1990s, the murder rates in rural communities with higher levels of concentrated disadvantage have been rising. The maximum and minimum murder rates are shown in figure 13.7, and the graph line shows the regression fit mean of the murder rate from 1980 to 2000 in rural U.S. census places that are at least 50 percent African American.
Figure 13.7 Homicide rates for rural U.S. census places 1980–2000, majority Black.
Source: Federal Bureau of Investigations, Uniform Crime Report, 1980–2000.
Some areas may not have experienced any decline in murder, but murder is a commonplace outcome in U.S. census places with higher levels of concentrated disadvantage. A central theoretical implication of these findings is that the relationship of spatial inequality to crime in rural communities must be reconsidered. For this reason, further examination of rural crime studies is needed, but the neighborhood effect across and within places also needs to be extended because violent crime is one of the most important outcomes of racial and economic stratification (e.g., Sampson 2012; Peterson and Krivo 2010).
Although roughly fifteen years have passed since the release of these data from the Uniform Crime Report, national crime and murder rates remain low. However, incarceration rates continued to rise until they leveled off after 2010. Sociologist Bruce Western (2006) shows that this rise in incarceration took place despite a decrease in crime. This is evidenced by the way in which incarceration continually rises, until it peaks at 2.4 million annually after 2010. The crime drop also did not have much to do with the number of citizens under state supervision, and the number of U.S. citizens on parole, probation, and other forms of supervision now totals more than 7 million annually.
The ripple effects of misguided policing strategies such as stop-and-frisk travel beyond mass imprisonment as they give way to increased prisoner reentry (Wodahl 2006; Eason, Zucker, and Wildeman 2017) and the construction of new prison facilities—the majority of which are being constructed in rural communities (Eason 2010, 2017). Continuing these policies and practices means more for disadvantaged rural communities than just building more prisons. Rural communities are experiencing not only increased murder rates but also an expansion of the criminal justice system through aggressive policing (McQuade 2016) and the construction of new jails (Pragacz 2016). The reach of punishment into rural communities also occurs along color lines, and research shows that the disproportionate rates of imprisonment for African Americans are higher in rural communities (Eason et al. 2017). This pattern of higher rates of minority contact with the criminal justice system in rural communities makes sense given that rural communities often have more traditional values and may be more culturally repressive. In fact, some would argue that differences in the public’s perception and tolerance for crime explain the different homicide rates across rural and urban America. The next section considers how spatial inequality and context are linked to incarceration rates.
IMPRISONMENT ACROSS THE RURAL–URBAN CONTINUUM
The oversight of rural communities in studies of crime and punishment occurs in part because mass imprisonment is conflated with the prison boom. Mass imprisonment refers to the warehousing of 2 million U.S. citizens, whereas the prison boom describes the massive expansion of prison construction—from 511 prisons to 1,663 in just over thirty years. Though explorations of the relationship between imprisonment and prison building are still young, it is clear that the marked increase in prisoner reentry is a direct result of mass imprisonment. Like murder and prisoner reentry, mass imprisonment is often characterized almost exclusively as an urban phenomenon. Many studies continue to link crime and punishment to the importance of urban neighborhoods (Sampson 2012), thus expanding our knowledge on the spatial and racial contours of urban communities and punishment (Pattillo et al. 2004; Wacquant 2001; Western 2006). We know far less about the link between spatial inequality and crime in rural neighborhoods. The northern, urban-centered view of crime and punishment is so dominant that it renders rural communities virtually invisible and is a key reason rural communities remain underinvestigated.
But the relevance of the prison boom to rural communities cannot be overstated. Today the majority of prisons are constructed in nonmetropolitan U.S. census places, whereas less than a third of prisons had been built in rural areas before the 1980s (Huling 2002). More recently, some rural counties, especially those with high rates of unemployment, “actively lobby” for the construction of new prisons in their area to achieve economic development (Weisheite et al. 1995, 86). Though there is some debate over whether the prison boom has led to economic gains in rural regions (see, e.g., Huling 2002), the preponderance of recent scholarship suggests that it has not. For instance, King, Mauer, and Huling (2003) analyzed seven rural counties over a twenty-five-year period. They found no statistically significant differences in the economic trends for rural counties with prisons versus those without them. Prisons are hailed as a potential fix for economically depressed rural communities because of the jobs they have the potential to create, but local residents are not necessarily qualified for these positions. As such, rates of unemployment and per capita income are roughly the same for rural communities with a correctional facility and those without one (King et al. 2003). Thorpe (2014) found that building new prisons in economically disadvantaged rural areas has not effectively lowered poverty in those counties.
In fact, these so-called development projects (Huling 2002) not only fail to produce economic growth in a locality but may actively hurt the community. Thorpe (2014) argues that they uphold racial systems of domination by “link[ing] the perceived economic stability and political power of lower-class, rural whites to the continued penal confinement of poor, urban blacks” who are incarcerated in rural facilities in majority-white areas (17). This is especially important insofar as prisoner reentry is concerned. John Eason (2012, 2017) has studied rural prison towns—that is, nonmetropolitan municipalities that have secured and constructed a federal, state, or private prison. Eason finds that they both receive prisoners (through building new facilities and taking in new inmates from elsewhere) and produce them (via prisoner reentry). Thus, rural prison towns may be hurt by the vast prison construction and export of urban criminals to rural communities.
Ultimately, many (Eason 2010; Gilmore 2007; Schlosser 1998; Lawrence and Travis 2004; Weisheit et al. 1995) agree that there are strong connections between socioeconomic disadvantage in rural communities and the prison boom. There is less consensus on whether the prison boom is the result of disadvantage or a cause of disadvantage. The prevailing view is that the prison boom is primarily a function of northern, urban, mass imprisonment (Schlosser 1998). However, one of the authors of this chapter, penologist John Eason (2017), demonstrates that rural communities build prisons not simply for jobs but to manage their poor reputations. In fact, southern rural spatial inequality (concentrated disadvantage) at the U.S. census place is a key driver of the prison boom (Eason 2010, 2017). Eason (2012) also argues that if rural concentrated disadvantage is measured at the appropriate spatial scale—that is, on the level of the town and the neighborhood—it can explain a host of other crime and punishment phenomena, including criminal activity and mass imprisonment.
The link between crime and mass imprisonment remains the subject of debate. Some scholars, such as Bruce Western (2006), argue that mass imprisonment is unrelated to rising crime rates. Others, such as Steven Levitt (2004), argue that imprisonment deters crime. Even so, mass imprisonment and reentry remain grossly underinvestigated topics in rural communities. How does spatial inequality shape the contours of ex-convicts reentering life in rural America? What is the link between concentrated disadvantage, crime, and criminal justice in rural America? To answer these questions, the theoretical rationale behind mass imprisonment must be explored.
Imprisonment, like all punishment, is a form of social control. In an effort to explain the U.S. imprisonment rate, Blumstein and Cohen (1973) posit the theory of the stability of punishment. They argue that crime is not only functional and natural in society but that it will “be maintained at a specific level” and remain constant over time (198). This thesis builds on sociologist Émile Durkheim’s (1984) assertion that crime is “normal” and “part of all healthy societies.” From this perspective, punishment is a form of boundary maintenance. Based on this assertion, Blumstein and Cohen (1973) propose a behavior-punishment distribution in considering the imprisonment rate as a measure of punishment from 1930 to 1970. Their analysis provides evidence that imprisonment rates in the United States during this period support the stability-of-punishment thesis. Although they later retracted this position in the face of increasing rates of U.S. imprisonment, the central question posed in this line of inquiry lingers: If societies accept crime as natural and functional, should there be a natural and functional level of punishment? Since Blumstein and Cohen’s time, scholars have argued that the primary reasons for increased imprisonment are conflicts of race and class.
According to conflict theory, mass imprisonment is a strategy to manage surplus labor. From a racial threat perspective, on the other hand, mass imprisonment is a means of social control for black and Latino populations. Mass imprisonment is not only considered a backlash to a specific form of racial threat—the civil rights movement—but a product of the racially biased War on Drugs (Alexander 2010; Mauer 2006; Tonry 2011; Western 2006). To many, the War on Drugs is the most important driver of mass imprisonment and the fivefold increase in the U.S. prison population since 1970 (Alexander 2010; Mauer 2006; Tonry 2011; Western 2006). Although these theories provide plausible explanations for past trends in imprisonment, they do not tell the entire story.
Hawkins (1987) raises questions about the utility of racial threat and conflict perspectives in explaining racial differences in punishment. He argues that the conflict and racial threat perspectives oversimplify the nexus of race and punishment and must be reworked. Along this same line, one can question whether mass imprisonment is simply a backlash to the civil rights movement or whether mass imprisonment can be reduced to a by-product of the War on Drugs. These factors play an important role in determining mass imprisonment, but the spatial and temporal differences in these two factors alone cannot account for increased punishment given the magnitude and longevity of this phenomenon. For instance, the civil rights movement took place primarily in the urban South. Aside from the dubious ability and desire of government actors to sustain a backlash against the civil rights movement over time, if mass imprisonment is indeed a response to the movement, then there should be increased racial disparities in imprisonment in the South. However, Hawkins and Hardy (1989) establish that, since 1970, the largest increases in the disparate rate of incarceration for African Americans versus whites took place outside of the South. Moreover, Western (2006) found that during the era of mass imprisonment the disproportionate rate of imprisonment between black and white males that has existed since the convict-leasing system decreased slightly during the late 1990s. Although racial disparities still abound in the criminal justice system, this empirical evidence suggests that race cannot be the only factor in mass imprisonment. Therefore, like other incarnations of racial threat and the conflict theory, the civil rights backlash and the War on Drugs hypotheses are oversimplified explanations for mass imprisonment.
Rather than attempt to theorize mass imprisonment, the more important question raised here is whether this empirical evidence sheds light on differences in mass imprisonment across rural and urban places. Convicted criminals are not likely to be incarcerated near the places they commit crimes; in fact, urban dwellers are often transported to rural prisons to serve their sentences. As for rural prisoners, there is no reason to believe that they are more likely to serve out their sentences near their homes either. As such, the community housing the imprisoned has more to do with local economies than with social attitudes toward crime.
Given that mass imprisonment disproportionately affects poor communities of color, one must ask whether there are disproportionate rates of mass imprisonment for people of color in rural areas. Using Eason, Zucker, and Wildeman’s (2017) framing of the rural communities as a producer of prisoners to investigate the contribution of rural concentrated disadvantaged to mass imprisonment, data from the Project on Arkansas Imprisonment, Reentry, and Health Disparities (PAIRHD) was surveyed. PAIRHD is an effort to examine the socioeconomic changes and health outcomes associated with imprisonment and prison reentry across towns in Arkansas. Overall, the project examines the incidence of health outcomes in Arkansas as a function of U.S. census place characteristics, imprisonment, and reentry rates.3
To understand trends in imprisonment across Arkansas towns over time, admissions rates were examined. Figure 13.8 shows the annual rate of admission across rural, suburban, and urban places for the Arkansas Department of Corrections (ADOC) from 1980 to 2005. Given that Arkansas is a predominantly rural state, Little Rock dominates the admissions trend for urban areas. In examining the trends in admission across rural, suburban, and urban U.S. census places, prison admissions into ADOC rose steadily from 1980 to 1995. During this period, there is not significant variation across rural, urban, and suburban communities; on average, admissions ranged between two hundred and three hundred admissions to ADOC per 100,000 residents. However, after 1995 the rate of admissions from urban U.S. census places in Arkansas outpaced that of suburban and rural communities, increasing to the highest point measured between 1980 and 2005 at nearly six hundred admissions per 100,000 residents by 1999. After 1993, rural areas began to outpace suburban areas in ADOC admissions, eventually peaking in 1999 at more than 300 admissions per 100,000 residents.
Figure 13.8 ADOC prison admissions by U.S. census place.
Source: John M. Eason. 2015. “Project on Arkansas Imprisonment, Reentry, and Health Disparities.” Compiled from data from the Arkansas Department of Health, the Arkansas Department of Corrections, and the U.S. Census Bureau. Available via the author.
Again, using admissions rates, racial differences in imprisonment are mapped across Arkansas over time. Figure 13.9 shows the annual rate of admission by race for ADOC from 1980 to 2005. The disproportionate rate of imprisonment for African Americans compared to whites has been consistent over time in admissions to ADOC, but the gap increased steadily from 1980 to 1998. In 1980, the rate of admissions to ADOC for African Americans was more than seven times that of whites. By 1990, the rate of admissions for whites had increased twenty-eightfold from 1980. Over this same period, the rate of African American admissions increased by more than fifteen times compared to 1980, closing the disproportionate rate of incarceration between African Americans and whites to 3.85 by 1990. By 2000, the rate of admissions for African Americans and whites reached its pinnacle, and the rate of disproportionate admissions also increased, with African Americans being nearly seven times as likely as whites to be admitted to ADOC. This section demonstrates the salience of rural communities in understanding the varied contours of mass imprisonment. The next sections consider the importance of rural communities’ structures and spatial contexts to the prison boom and prisoner reentry.
Figure 13.9 Total ADOC prison admissions rates by race.
Source: John M. Eason. 2015. “Project on Arkansas Imprisonment, Reentry, and Health Disparities.” Compiled from data from the Arkansas Department of Health, the Arkansas Department of Corrections, and the U.S. Census Bureau. Available via the author.
PLACING THE PRISON BOOM
The prison boom has reshaped the landscape of punishment in rural America (Lawrence and Travis 2004; Eason 2017). Although prison proliferation disproportionately affects rural communities, we are only now beginning to explore the causes and consequences of this phenomenon. If laid end to end, the prisons built during the boom would cover nearly six hundred square miles. Most of this acreage is in rural communities, and nearly 70 percent of prisons were built in nonmetropolitan communities. For comparison, this landmass is about half the acreage of Rhode Island. Figure 13.10 shows all U.S. prison facilities in 2010 according to size and location. Many larger prisons are located in, or near, urban areas, but there are also many large prisons in rural areas. An accurate count of the number of prisoners held in rural prisons cannot be provided, but a conservative estimate is that 1.3 million prisoners are held in rural prisons annually. This estimate was derived using a subsample of facilities that include the average daily population of the prison in 2010.
Figure 13.10 U.S. prison proliferation, 2010.
Source: Eason 2016.
Furthermore, the facilities sampled support an average daily population of 758 prisoners and 231 staff. Despite the vast amount of attention given to private prisons in the popular discourse, fewer than 10 percent of all facilities operate under private contracts. In analyzing the location of the entire population of U.S. prisons, roughly 81 percent are run by state governments; the remaining 9 percent are federal. Only about 1 percent of all U.S. census places builds a prison in any period.
Currently there are over 400,000 corrections officers, about one-third of whom are black or Latino (Ward 2006). The fact that prisons employ more than 100,000 corrections officers of color complicates the narrative of punishment as only contributing to racial inequality. Further complicating how we understand the racial landscape of prison building, penologist John Eason (2010) studied spatial inequality and found that during the height of the prison boom in the 1990s, prison building was a result of concentrated racial and economic disadvantage in U.S. census places. Eason argued that the essential local determinants of prison building in rural U.S. census places were the size of the black and Hispanic populations, the poverty level, and the total population of the community. Furthermore, during the height of the prison boom, the South was more likely than any other region to build a prison. Because of the prison boom, the prison, an undeniably stigmatized institution, is indelibly linked to the political economy of depressed rural communities of color (Eason 2012, 2017). Though the prison boom (figure 13.11) has reshaped the rural landscape, the contours of prison towns’ demographic characteristics across the entire prison boom period have not yet been mapped. This section uses data from the Prison Proliferation Project (Eason 2016) to expand on Eason (2010) and explore town-level correlates of prison building.
Figure 13.11 U.S. prison boom, 1970–2010.
Source: Eason 2016.
The Prison Proliferation Project4 includes novel data on prison building, allowing a comparison of predictors across different periods of the prison boom. A thumbnail sketch describes rural towns that built prisons versus those that did not. This is followed by an overview of national trends in prison building across different periods of the prison boom, paying particular attention to the role of spatial inequality. U.S. census place poverty rates and the residential segregation dissimilarity index are used as predictors of prison building. Certain trends are consistent across all periods of the prison boom. For instance, the South is the most dominant across all periods, with roughly 45 percent of all prisons in the prison boom period built in this region. The percent of residents living below poverty and percent black is higher in towns that received prisons than in those that did not across all periods of the prison boom. In addition, residential segregation was a strong predictor of prison building in the 1990s and 2000s across the nation and in the South.
During the 1970s and 1980s, trends in towns that built prisons and those that did not were relatively stable. For instance, prison-building towns had roughly 8 percent more black residents than towns that did not build prisons, and prison towns were only slightly poorer, with roughly 2 percent more of the population living below poverty. In the 1990s and 2000s, however, these trends shifted with increases in poverty, percentage of black residents, and residential segregation becoming strong predictors of prison building. These trends provide evidence of the power of concentrated disadvantage in U.S. census places in predicting prison building. This evidence provides further support for considering context and the neighborhood effect when trying to better understand crime and punishment in rural America. The problem of rural reentry for rural poverty is especially relevant in this context given the boom in prison construction in rural America.
RETHINKING RURAL REENTRY
What about prisoner reentry? Each year nearly 800,000 formerly incarcerated individuals leave prisons to return home (Carson and Sabol 2012). There are fewer social services and health care organizations for prisoners reentering in rural areas than in urban regions (Wallace, Eason, and Lindsey 2015). The return is more than a singular event; prisoner reentry is a process that reshapes neighborhoods, institutions, families, and individuals (Miller 2014). The concentration effects of prison reentry produce a host of negative outcomes for urban neighborhoods (Clear, Waring, and Scully 2005). Reentering prisoners suffer many challenges, such as barriers to employment, poor access to safe and affordable housing, and obstacles to health care. Despite the catalog of scholarship mapping the effects of reentry on urban communities, little is known about how prisoner reentry affects rural communities (Wodahl 2006). Given that prisoner reentry has lasting and meaningful effects on communities, it is important to understand how reentry affects rural communities. How does spatial inequality shape the contours of ex-convicts reentering life in rural America? Specifically, what are the effects of concentrated disadvantage and racial composition on reentry across the rural–urban continuum?
Again, data from PAIRHD is used to examine rates of reentry across nonmetropolitan and metropolitan counties. The reentry data consist of approximately 118,000 reentries (not individuals) between 1990 and 2008, across 541 U.S. census places and seventy-five counties in the state of Arkansas. Figure 13.12 provides a preliminary analysis, illustrating the constant increase in reentry rates in Arkansas between 1990 and 2008 across metropolitan and nonmetropolitan counties. Once again, Little Rock dominates the trend in reentry for urban areas because Arkansas is predominantly a rural state.
Figure 13.12 The rate of prisoner reentry into the general population, 1990–2008.
Source: John M. Eason. 2015. “Project on Arkansas Imprisonment, Reentry, and Health Disparities.” Compiled from data from the Arkansas Department of Health, the Arkansas Department of Corrections, and the U.S. Census Bureau. Available via the author.
During this period, there is significant variation in reentry trends among metropolitan and nonmetropolitan communities. Metropolitan communities have a reentry rate of more than 500 per 100,000 residents, whereas nonmetropolitan areas have a rate of just over 200 per 100,000 residents. This analysis provides only a very cursory description of trends; it warrants a deeper analysis segmenting the nonmetropolitan communities into suburban versus rural areas. Furthermore, future analyses need to focus on characteristics of concentrated disadvantage within U.S. census places. In any case, it is clear that many elements of context—a community’s rurality, percent black, poverty level, and degree of concentrated disadvantage—matter for explaining trends in the prison boom, mass incarceration, and prisoner reentry in rural America.
CONCLUSION
Context, spatial inequality, and poverty within and across U.S. census places are important in determining key crime and punishment outcomes in rural America. Using the square of crime theory, poverty is criminalized through the interdependent actions and beliefs of a multiplicity of actors, both at the individual level (i.e., the victim, the offender) and beyond (i.e., the public, the state). First, place matters with reference to three recent trends in rural crime. To this point, the downward trend in murder is far from universal across spatial scales and place-level demographics. More specifically, although there has been a downward trend in murder across U.S. cities, rural communities effectively traded places with their urban counterparts and have witnessed an increase in homicide rates in recent years. Second, rural rates of prisoner reentry and incarceration have likewise increased over time. Finally, using novel data on the U.S. prison boom, towns with higher levels of poverty, larger black populations, and greater residential segregation were more likely to build a prison in the 1990s and 2000s.
Given what is known about the mutual reinforcement of poverty and crime in the United States, the findings of this chapter have serious implications for the future of rural America. If we continue to direct our preventative resources and public attention to curtailing crime in urban regions alone, we stand to neglect rural poor indivdiuals and communities. If inequality continues to rise in the United States, with more and more prisons being built in economically depressed rural communities, crime and punishment may remain entrenched in impoverished regions of rural America.
Ultimately, the findings of this chapter illustrate the importance of taking into account context when discussing crime and punishment. Neighborhood-level poverty rates matter, but so, too, do measures of spatial inequality, racial composition, and place along the rural–urban continuum. Overall, this chapter suggests that if rural, low-income communities experience a continued growth in poverty and spatial inequality, the varied actors within the square of crime may continue to interact in unique ways—fueling not only crime and punishment but also exacerbating socioeconomic disadvantage for the rural communities and people of the United States.
Walter S. DeKeseredy and Amanda Hall-Sanchez
Within criminological and sociological circles, rural American communities are well known for experiencing myriad social problems, especially poverty and high levels of unemployment. Much less is known, however, about violence in these areas. In fact, rural crime has ranked among the least studied topics in criminology (Donnermeyer and DeKeseredy 2014). This selective inattention is now being addressed mainly by a growing international cadre of critical criminologists. They view the major causes of crime as the unequal class, racial/ethnic, and gender relations that control our society. To date, one such group of scholars in particular has contributed the bulk of empirical, theoretical, and policy work on criminological issues in rural communities. These are feminists who study intimate violence against women, and we are active members of this cohort. Feminism is defined as “a set of theories about women’s oppression and a set of strategies for change” (Daly and Chesney Lind 1988, 502).
Feminist research on violence against rural women quickly exploded on the scene in the latter part of the last decade, with much of the U.S. work being done in the rural Appalachian part of southeast Ohio. This case study is based on this region and spans ten years in which rich, in-depth interviews were conducted with fifty-five women who were assaulted in ways few of us could possibly imagine when they wanted to leave, were trying to leave, were in the process of leaving, or finally left their male partners.
The women who revealed their stories were all at great risk of being killed. In fact, regardless of where women live in the United States, women who are separated run a sixfold increase of being murdered (DeKeseredy and Schwartz 2009; Ellis, Stuckless, and Smith 2015). Using aggregate National Crime Victimization Surveys (NCVS) from 1992 to 2009, Rennison, DeKeseredy, and Dragiewicz (2012) found that a higher percentage of rural divorced/separated women are victims of rape/sexual assault than are urban divorced/separated women. In addition, rural separated women are victims of intimate rape/sexual assault at significantly higher rates than their suburban and urban counterparts. Rennison, DeKeseredy, and Dragiewicz (2013) also found that rural divorced and separated women are victimized by other types of violence at rates exceeding those of their urban and suburban counterparts.
Interviews conducted since 2003 and up to 2013 provide further support for the empirically informed claim by Rennison et al. (2012, 2013) that separation/divorce assault is a major problem in rural American communities. The most important themes that stood out were male peer support, men’s patriarchal dominance and control, men’s consumption of violent and racist pornography, and membership in the all-male hunting subculture, which also involved male peer support. This concept is defined as the attachments to male peers and the resources that these men provide that encourage and legitimate woman abuse.
In addition to these risk factors, interviews revealed that the system of social practices that dominates and oppresses rural and urban women alike operates differently in rural places. For example, the masculization of the rural, the dominance of man and mankind over women and nature, is represented as natural and unproblematic (Carrington, Donnermeyer, and DeKeseredy 2014). Furthermore, whereas many men in urban areas (usually males of color) report adversarial relationships with police, men who abuse women in rural communities are more likely to be friends with and be protected by criminal justice officials.
This case study also confirmed what other scholars in the field are finding. For instance, there is widespread acceptance of violence against women and community norms prohibiting victims from seeking social support. Moreover, compared to urban women, rural women have fewer social support resources, and those available cover large geographic areas. Rural women face additional barriers, including geographic and social isolation and inadequate (if any) public transportation. Another factor exacerbating rural women’s troubles is being uninsured; rural women are less likely to be insured than are urban and suburban women.
Popular films, documentaries, news stories, and television shows may portray rural women as weak, unintelligent, seductive, or strong, but only rarely do they focus on male-to-female violence in rural communities. Sadly, this research, and studies done by a few other scholars, show that numerous rural women continue to suffer in silence, and those interviewed in this case study constitute just the tip of the iceberg.
NOTES
1. Concentrated disadvantage is in general a key predictor of negative neighborhood outcomes such as crime—not only for violent crimes, such as homicide. For instance, living in a community in which residents are exposed to violence (hearing gunshots) makes people more likely to live in fear. Similarly, living in a high-crime area where neighbors keep to themselves increases the likelihood that someone will be the victim of crime.
2. The key dependent variable is annual murder rate at the U.S. census place, and key independent variables include year, population size, percent urban population, race, poverty, residential segregation, and foreign- or native-born status. Other controls include percent vacant housing, owner-occupied housing, and party of state government. Residential segregation, percent black, poverty, and vacant housing are commonly used in the neighborhood effects literature to measure concentrated disadvantage. A fixed-effects regression was used with robust clustered standard errors around FIPS-place level to parse out the effects within and between geographic locations.
3. The first data set consists of individual inmate reentry records from the Arkansas Department of Corrections (ADC) between 1990 and 2008. Address data are included in the ADC data, allowing inmates to be geocoded to communities after release. The second data set consists of U.S. census and county-level data related to new incidents of HIV annually between 1990 and 2011, the density of community organizations collected from annual county and zip code business pattern census data between 1990 and 2010, and structural census covariates associated with socioeconomic and demographic characteristics of residents collected from the 1990, 2000, and 2010 decennial census. U.S. census and county-level segregation data is created using block-group census data on race and ethnicity from decennial censuses. The index of dissimilarity is included for all pairs of whites, blacks, and Hispanics. The P* isolation index is included for each of these three groups as well.
In 1997, the Standard Industrial Classification (SIC) code system used to classify types of businesses was replaced with the North American Industry Classification System (NAICS). The NAICS system was developed under supervision by the Office of Management and Budget and was a collaborative effort between the United States, Canada, and Mexico. Although SIC and NAICS codes are not strictly comparable, and the NAICS system has evolved over time, it is possible to establish near comparability in broad classifications of industry (e.g., farming/forestry, mining, construction, retail, finance). All SIC and NAICS codes have been recoded to the 2007 NAICS system; the latest system developed during the study period. Additionally, the county-level census data set includes rural–urban continuum codes (i.e., Beale codes) obtained from the U.S. Department of Agriculture.
The data were originally constructed so that each individual sentence received an entry. That is, individuals who entered prison on a single date for multiple sentences were counted by the number of sentences. This means that an individual who entered prison after being convicted of four counts of a crime was counted four times. Because the focus of this study is the number of prison admissions, the data needed were adjusted to reflect only admissions. As such, the data were collapsed to reflect only the number of entries for an individual on a given date. Even if a person entered prison on a single date for ten different crimes, that person was still only counted once.
4. These data were augmented and merged with Geolytics’ decennial U.S. Census demographic and economic data using GIS software resulting in a data set with every U.S. census place from 1970 to 2000 normalized to 2000 decennial boundaries (N=25,150) and 1,663 prisons spread across all fifty states. Unlike most data on prisons, these data are not a sample of prisons or towns—the data set includes every adult prison facility and every municipality in the United States. These data were then merged with files containing U.S. state-level economic and program transfer data covering the years 1980 to 2011 as maintained by the University of Kentucky Center for Poverty Research (UKCPR). The UKCPR data include such state-level predictors as party affiliation of the governor and both chambers of the legislature, as well as state-level demographic, poverty, and employment variables. The data are additionally delineated along the rural–urban continuum according to the Beale Rural-Urban Classification Codes (RUCC). The RUCC designates the degree of rurality (9 the most rural) or urbanity (1 the most urban) for each census location in every period of the analysis, with 1974, 1983, 1993, and 2003 as designated years for determining rural–urban classifications. Because of the potential for prison populations to tip the RUCC designation from rural to urban, 1974 was used as a baseline for this analysis.
To this end, data were obtained from several sources including the 2010 Directory of Adult and Juvenile Correctional Departments, Institutions, Agencies, and Probation and Parole Authorities and the Inter-University Consortium for Political and Social Research data holdings. The latter includes a listing of the 1,600-plus U.S. prison facilities by latitude and longitude coordinates, U.S. census place, name of facility, and year of facility construction. The directory is regarded as the “gold standard” of prison locations by a contact at the United States Bureau of Prisons. The location of each facility was verified using the Coding Accuracy Support System, a location verification system used by institutions such as the United States Postal Service, and each case was reconciled multiple times. This extensive process was used to ensure the accuracy of facility locations within each U.S. census place along with the corresponding year it opened. However, only residential segregation measures to 1990 and 2000 were available for this analysis.
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