The determinants of jail use across large US cities: an assessment of racial, ethnic, and economic threat explanations

Social

Social Science Research 34 (2005) 538–569

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RESEARCH

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The determinants of jail use across large US cities: an assessment of racial, ethnic, and economic threat explanations q Jason T. Carmichael Department of Sociology, 300 Bricker Hall, 190 N. Oval Mall, Ohio State University, Columbus, OH 43210, USA Available online 7 July 2004

Abstract While empirical research on jails is not as neglected as it once was, there is no study that systematically tests theoretical assumptions on the use of this type of punishment. The present study fills this gap by examining theoretically based determinants of jail admission rates across 157 US cities. The consensus perspective assumes that the legal order reflects a widely held consensus about social harms that require punishment and that the use of formal crime-control mechanisms is a natural response to infractions of this order. Alternatively, the conflict perspective assumes that the legal order reflects the interests of the powerful and that the size of the state punishment apparatus is a response to perceived threats to these interests. Two prominent threat hypotheses are used to assess this approach. Racial and ethnic threat theories suggest that incarceration is more likely in areas with the most blacks or Hispanics. Economic threat theories claim that the rate of incarceration will be greatest where economic differences are the most pronounced. This study uses classical regression to test these perspectives with data from the 1983 Census of Local Jails. With the crime rate, measures of disorder, the size of the police force, the presence of young males, jail capacity, and regional effects held constant, the results provide strong support for the conflict perspective. Income differences between African-Americans and whites, and the size of both the African-American and Hispanic populations are found to explain variations in jail use across large US cities. The findq My thanks to James Moody, Paul Bellair, Benjamin Cornwell, Leo Carroll, and especially David Jacobs for their helpful comments and advice on prior drafts. This is a revised version of a paper presented at the annual meeting of the American Sociological Association in Chicago, IL, 2002. E-mail address: [email protected].

0049-089X/$ - see front matter Ó 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.ssresearch.2004.05.001

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ings supporting the threat hypotheses are strengthened in supplemental analyses that restrict the sample of cities to those with a sizable minority population and another that uses updated data from the 1999 Jail Census. Additionally, some models show a quadratic association between residential segregation and the rate of jail admissions providing some support for the contact hypothesis. Ó 2004 Elsevier Inc. All rights reserved.

1. Introduction Why do jail admission rates vary across jurisdictions? Why, for instance, does San Francisco have a jail admission rate nearly 10 times larger than that of Boston? This question is perplexing given that Boston has a significantly higher crime rate. What factors are responsible for this discrepancy? Considering that the removal of individual liberty through incarceration is one of the most serious legal sanctions imposed by the state, it is surprising that only a few studies have attempted to answer these important questions. Conventional wisdom suggests that the magnitude of local correction efforts is simply a function of the levels of criminal activity in each jurisdiction. The example given above illustrates the need to explore alternative explanations. While alternatives do exist, they have not been systematically tested on jail use. A variety of factors thought to be responsible for variation in imprisonment rates both in the US and cross-nationally have been tested (cf. Jacobs and Carmichael, 2001; Sutton, 2000), but differences in jail use have failed to receive the same attention. This study begins to fill this gap in the literature by assessing the ability of social threat accounts to explain variations in local crime-control efforts across jurisdictions in the United States. There are several reasons why it is meaningful to study variations in punitive outcomes across jurisdictions. First, from a theoretical standpoint, it is important to have a good understanding of the determinants of variation in all forms of social control. Such an understanding can provide valuable insights into the fundamental processes responsible for the maintenance of social order. Specifically, we may ascertain whether the primary role of the formal crime-control apparatus is to punish infractions of widely held normative beliefs or to maintain the inequalities of the social order. It is arguable that the question of social order is one of the most central theoretical problems in sociology. Social theorists who have attempted to construct comprehensive theories of society have been forced to address the means by which social order is maintained. Durkheim, for example, spent a considerable amount of time grappling with the fundamental aspects of social order and the role that punishment plays in maintaining that order. Studies that follow this tradition point out how punishment upholds broadly held sentiments pertaining to order. Weber also addressed this issue and claimed that the power to punish dissidents is the most crucial element of the state and that without such power the state would cease to exist. More recently, scholars have attempted to explain social order within the Marxist and neo-Marxist perspective (Garland, 1990). These analyses uncover ways in which the penal system reinforces class divisions and ensures ruling class dominance. The attention paid to this topic

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by social theorists suggests that the use of punishment to maintain order is a fundamental part of sociological theory and as such worthy of serious inquiry. An empirical assessment of jails is also important because individuals are far more likely to experience this form of state punishment compared to alternatives such as imprisonment. 1 In 1983, for example, over 8 million people had passed through jail doors while just 250,000 individuals were admitted to prisons during this same period (Bureau of Justice Statistics, 1985). Because such a large proportion of the population experiences this form of punishment, it is meaningful to understand the factors associated with variation in its usage. If the size of the US jail population can be understood as a response to the size of the crime problem, then this relationship would provide support for the conventional wisdom, but if legally questionable factors such as race and income inequality also matter then we need to find alternative theoretical explanations to account for these influences. While empirical research has demonstrated that such extraneous factors do influence state level prison use (Jacobs and Carmichael, 2001), no effort has been made to test similar explanations on the more widely used jail systems. The few studies that have attempted to explain variations in jail use have primarily been concerned with testing the influence of the official crime rates. Skyes (1987), for example, not only tests the effects of the overall crime rates on jail use but also these statistics disaggregated into violent and property crimes. He finds that neither of these official crime statistics has the expected significant positive effect. Skyes (1987) makes no attempt to test additional measures of crime that may more accurately capture the types of offenses that individuals commit to end up in jails. Klofas (1990) attempts to extend our understanding of variations in jail use by exploring a limited number of alternative explanations. These alternative accounts include tests for regional differences as well as the effect that variations in capacity may have on jail use. Klofas (1990) finds that after controlling for crime rates, the West and the South have significantly higher jail populations. He also finds that capacity restricts jail use where there is crowding. Liska et al. (1999) provide one of the better tests of alternative accounts of jail use to date. They test the effects of differences in population, divorce, economic conditions, segregation, and the percentage of AfricanAmericans while controlling for both the overall crime rates and the rated jail capacity of each city. Liska and his colleagues find that in their most inclusive 1983 models the only indicators with a significant influence on jail admissions were the percentage of the population that is African-American and the percentage divorced. The literature as a whole has provided only a limited understanding of variations in jail use across jurisdictions with several important questions remaining unresolved. While the Liska et al. (1999) article in particular contributes much to our 1 The jail system, much more so than the prison system, is seen primarily as a means by which the state, in the words of Irwin (1985) manages the rabble. Unlike prisons, which hold the most serious offenders, jails primarily hold those arrested for less serious, public order offenses such as vagrancy, prostitution, and public drunkenness. In a more technical sense, jails only house those individuals who are either awaiting arraignment/trial (often over 50% of the persons in a jail) or those serving sentences for less than one year. Prisons, on the other hand only hold those sentenced to more than a year of incarceration.

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understanding of jail use in the US, it has several limitations which this study hopes to overcome. First, I will include several theoretical important factors that are not part of the Liska et al. article. No study, including Liska et al. (1999), has attempted to systematically test alternative theoretical accounts of jail variation across jurisdictions. This study will fill that gap. Second, following the lead of Klofas (1990), I will include regional dummies in the analysis to control for regional differences in jail use. Third, I will restrict the sample to those cities with a substantial minority population to better test racial threat effects. Finally, while Liska et al. make only a modest attempt to control for less serious crimes, the present study will overcome this by introducing measures that capture minor criminal offenses. The analytical objective of this study, then, will be to find out whether a cross-sectional relationship exists between theoretically derived indicators and the rate of jail admissions across major US cities and to see if such relationships persist after crime and disorder are held constant. Doing so will not only broaden our understanding of why some cities have a greater propensity to use jails, but it will also provide greater insight into the role that formal sanctions play in the maintenance of social order. Because single factor explanations are not as effective as more extensive specifications, we must explore many hypotheses in this analysis. 2

2. Theorizing the use of jails While several social theories attempt to explain punishment, only two important ones will be addressed here. The first theoretical approach I draw from is the consensus or legalistic approach. According to this perspective, laws are constructed to reflect a value consensus. The punishment enacted to enforce these laws comes as a response to violations of these widely shared views. Conflict theory, on the other hand, assumes that the legal order reflects the interests of the powerful and that the volume of punishment in a given jurisdiction is a response to perceived threats to the interests of the powerful. The following discussion will explore these opposing theoretical approaches in detail. 2.1. The consensus approach: crime and disorder Consensus theory assumes that the majority of the individuals within a given society agree on what is right and wrong, and that the law is simply a codification of

2 Johnston says, ‘‘It is more serious to omit relevant variables than to include irrelevant variables since in the former case the coefficients will be biased, the disturbance variance overestimated, and conventional inference procedures rendered invalid, while in the latter case the coefficients will be unbiased, the disturbance variance properly estimated, and the inference procedures properly estimated. This constitutes a fairly strong case for including rather than excluding relevant variables in equations. There is, however, a qualification. Adding extra variables, be they relevant or irrelevant, will lower the precision of estimation of the relevant coefficients’’ (Johnston, 1984, p. 262), therefore, a more inclusive specification will typically provide more conservative results.

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these agreed upon social values. The criminal justice system, according to this model, is the mechanism of control used when some members of society deviate from what is considered acceptable behavior. Hence, criminal punishment is a reflection of the magnitude of infractions against the collectively agreed upon legal order. The most widely used measure of these infractions is the crime rate. While it does not reflect the true rate of crime, the official crime rates reported by various law enforcement agencies are the best means we have to measure criminal activity (Liska et al., 1981). At any rate, official crime statistics are typically the only avenue through which the public and authorities become informed about the extent to which crime is a problem in a given jurisdiction. The reported crime rates are therefore likely to play a significant role in creating a general perception of the crime problem and may be responsible for systematic variations in the public demand for punitive measures (Liska et al., 1981). Empirical evidence suggests that there is a positive relationship between crime rates and the size of formal crime-control mechanisms. Several studies, for example, report a positive relationship between crime rates and the size of police departments (Land and Felson, 1976; Liska et al., 1981). Research that examines the effect of crime rates on levels of imprisonment has also found a significant positive association (Arvanites, 1993; Colvin, 1990; Jacobs and Helms, 1996). If the consensus model is correct and if local jurisdictions respond to similar forces as the states, we can expect that jail admissions will be higher in cities with the most crime. While the consensus approach stresses the importance of crime rates on punishment, proponents do concede that structural determinants such as income inequality, segregation, unemployment, and race have an indirect effect on incarceration through crime. This assertion is based on the assumption that problematic social conditions like racial discrimination act to reduce legitimate economic opportunity. Groups faced with these obstacles may be more likely to resort to illegitimate means which in turn lead to incarceration (Merton, 1938). Thus, any direct association between extralegal factors and crime would be inconsistent with the consensus approach. While the serious crime rate is an important indicator of the crime problem, many of those who are admitted to jails are convicted of minor offenses that are not tabulated in the official statistics. In fact, as much as 80% of arrests are for non-indexed crimes (Bureau of Justice Statistics, 1985) and the majority of those jailed are incarcerated for these non-serious public order offenses that are not included in the most widely used statistics of crime (Irwin, 1985). Unfortunately, there are no alternative data sources that accurately capture public order offenses across all major US cities. For this reason, I include several theoretically inspired measures that may account for these minor offenses. One factor thought to be associated with both crime and disorder is the age distribution. For centuries, scholars have shown that young males are responsible for a disproportionate amount of crime (cf. Quetelet, 1984). Contemporary evidence continues to show that males aged 15–24 are responsible for the highest rates of crime, arrests, and convictions (Hirschi and Gottfredson, 1983). Empirical research also indicates that places with a larger proportion of young males have higher incarceration rates (Blumstein, 1988; Carroll and Cornell, 1985; Flanagan, 1975). Given

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these findings, we should expect that jail admissions will be higher in jurisdictions where young males make up a larger proportion of the population. Additionally, in recent years, social disorganization theory has been used to identify communities that are likely to have higher levels of both crime and disorder. Proponents of this theory argue that disorganization undermines formal and informal social controls within a community (Bursik, 1988; Sampson and Groves, 1989) making them more likely to need state control agents such as the police to maintain order. This is thought to be the case because without effective informal controls, communities are less able to channel young members into conventional activities thus increasing the likelihood that they will commit illegal acts that may result in incarceration. The relationship between social disorganization and crime appears to hold mainly for minor offenses (Johnson, 1986). For this reason, including commonly used measures of disorganization to account for non-enumerated crimes may be useful in this analysis. Social disorganization theory stresses the role of neighborhood organizational participation and network ties in creating and fostering formal and informal social controls in the community. Due to its potential to affect both network and organizational ties, family disruption is thought to play a crucial role in this process (Sampson, 1987; Shihadeh and Steffensmeier, 1994). High rates of family disintegration at the community level are believed to be partly responsible for undermining the capacity for social organization and mechanisms of social control, creating higher amounts of crime (Loeber and Stouthamer-Loeber, 1986; Sampson, 1987). This influence is largely due to the fact that intact families are more likely to have stronger organizational and network ties to the community than broken families. Intact families, for example, are much more likely to participate in formal community groups such as church, sports, and volunteer groups that can assist in control efforts (Kellam et al., 1982). Additionally, family disruption may reduce the size of informal networks that can provide valuable supervision of youth and guardianship of property. Studies suggest, for instance, that divorced mothers have less contact with neighbors than married mothers (Alwin et al., 1985). Hence, communities characterized by high levels of family disruption are likely to have higher rates of crime and disorder because formal and informal control mechanisms, that could reduce such antisocial behavior, are undermined by this situation. In support of this claim, Sampson (1987) showed that family disruptions at the city level had a large direct effect on crime. We can expect that jurisdictions with high levels of family disruption will have higher rates of jail use due to the higher levels of crime and deviance associated with this condition. The presence of some retail establishments may also be a viable measure of public order offenses. In particular, there is reason to believe that the number of liquor stores may be an important indicator of disorder. First, this measure may be a good proxy for alcohol consumption. Findings suggest that there is a link between alcohol and criminal behavior (Reiss et al., 1994). The presence of a larger number of liquor stores should be associated with an increase in offenses related to public order such as public intoxication, simple assaults, and vandalism. These infractions, while not serious enough to be enumerated in the official crime statistics, may be serious enough to lead to jail time, even if only for a few hours. Additionally, these retailers

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are more likely to be victimized (Reiss, 1969) so owners commonly request more aggressive crime-control in an effort to reduce losses (Rubenstein, 1973). In support of this idea, Jacobs (1979), for example, finds a positive association between the number of the number of drinking places and the size of the police force in large US metropolitan areas. Consequently, it seems reasonable to assume that other crime-control measures would also be strengthened under such conditions. Hence, because of their potential influence on public order offenses we should expect to see that jurisdictions with a larger presence of liquor stores have a higher rate of jail admissions. 2.2. The conflict perspective In contrast to the above views, conflict theory assumes that the legal order reflects the interests of the powerful rather than a widely held consensus about proper conduct and that the size of the state punishment apparatus is a response to perceived threats to these interests. Theorists who subscribe to such claims argue that the entire process of lawmaking, lawbreaking, and law enforcement is a direct reflection of the fundamental conflict between group interests and struggle for control over state power (Vold et al., 2002). The conflict perspective assumes an uneven distribution of political and economic resources that favors privileged groups in the construction and implementation of social policies that reflect their interests. In particular, the formulation and enforcement of the legal code is assumed to reflect the interests of the powerful who use the legal system to criminalize the activities that threaten their favorable social position. Proponents claim that violations of the law will be enforced with more vigor when the infractions are threatening to the powerful. Stone (1987, p. 250) writes that Ôthe criminal law but not the civil law was indeed in the last resort an instrument of the elite to protect their own and other peopleÕs lives and property by the use of selective terror.Õ This interpretation leads to views of the criminal law as a means by which the elite express their values. The criminal justice system is, then, one of the vehicles by which the elite enforce these views and regulate dissimilar populations. The conflict approach assumes that structural factors such as race, segregation, and income inequality directly influence punitive outcomes. Conflict theorists attribute these direct effects to the response of the dominant group to the real or perceived threat to the social order posed by oppressed minorities. These theorists hold that such threats will be intensified when there are significant income disparities between the dominant and subordinate groups (Quinney, 1977; Spitzer, 1975; Turk, 1969). This perceived threat of the ‘‘dangerous classes’’ might lead members of the majority group to pressure authorities to increase social control. The following discussion will outline factors believed to be associated with levels of threat. 2.3. Minority group threat 2.3.1. Racial threat Many theorists contend that the degree of threat associated with racial or ethnic groups is a function of the size of these groups relative to the dominant group. Accord-

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ing to this position, negative racial attitudes and discrimination are brought about in part by a feeling that dominant racial groups have prerogatives that entitle them to a superordinate position within society. Minorities are seen as a threat to existing social arrangements because they may seek to redistribute social resources in their favor. Proponents argue that a large minority group threatens dominant members of a society because they compete with them for jobs and other economic resources. Blumer (1958) and Blalock (1967), for example, make theoretically based assertions that dominant racial groups are threatened by larger minority populations. Blalock claims that threat is related to minority group size because a large minority presence heightens perceived competition for jobs and other economic resources as well as increasing the possibility of successful collective action against the dominant group. Majority group membersÕ ethnocentric beliefs accentuate majority assumptions that they are entitled to exclusive claims over rights and resources (Bobo and Hutchings, 1996). Hence, any attempt (perceived or otherwise) by racial or ethnic groups to redistribute these rights and resources is contested by majority members. To maintain their advantageous position, majority members will often use their political and economic resources to demand more repressive measures that are likely to target minorities. Empirical work has provided strong support for these theoretical assertions. First, there is convincing evidence that links whitesÕ attitudes about African-Americans and the perception of the crime problem to the size of this minority group. Studies have shown that whites hold criminal stereotypes of blacks and their lifestyles (Swigert and Farrell, 1976; Tittle and Curran, 1988). Additional research reveals that, after holding crime constant, fear of crime is greater in cities with more AfricanAmericans (Liska et al., 1982). Also, whites with higher levels of racial prejudice appear to have more punitive attitudes about crime (Cohen et al., 1991). While research has provided evidence that whites are fearful of crime when a large minority population is present and that they are more punitive when they hold racially prejudice attitudes, it does not show that this fear is translated into action. Some researchers have tried to establish whether majority group members can use their advantageous positions to shape criminal justice outcomes. The empirical evidence suggests that elite members of society are successful in their demands for more aggressive crime-control measures. Studies have found that after controlling for crime, a positive relationship between the percentage of African-Americans in a community and the size of the police force exists (Carroll and Jackson, 1982; Jackson, 1989; Jacobs, 1979; Liska et al., 1981). Others find a positive association between spending on corrections and the size of the non-white population (Jacobs and Helms, 1999). Studies examining court outcomes have found a positive correlation between the percentage of blacks and sentencing length (Chambliss, 1995; Chiricos and Crawford, 1995; Meyers, 1990). When blacks make up a large proportion of the population, punishment efforts are strengthened, regardless of the crime rate. We can expect that jurisdictions with the most blacks will have the highest rates of jail use. 2.3.2. Ethnic threat If whites perceive blacks as a threat, it is likely that other groups will also be seen as a threat. Hispanics are another minority that may be perceived as a threat by

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members of the majority (Anderson, 1995). The Hispanic population grew rapidly during the 1970s especially in cities of the South and West. The threat posed by the increasing presence of Hispanics should lead dominant group members to use their economic and political power to preserve the favorable social arrangements. In particular, elites may use their superordinate position to demand more repressive crime-control efforts directed at Hispanics. Several studies add credibility to this version of the threat hypothesis. First, Hispanics have a rate of arrest and incarceration disproportionate to their numbers in the population (Spohn and Holleran, 2000). Additionally, empirical evidence suggests that Hispanic presence increases law enforcement efforts on a variety of levels. Jackson (1989) finds a significant relationship between the percentage of Hispanics and police expenditure in cities. Research also shows that imprisonment may be influenced by the presence of Hispanics. Jacobs and Carmichael (2001) provide evidence suggesting that fear of Hispanics may lead to more repressive control efforts by showing that imprisonment rates are greater in states with a larger Hispanic population. If local crime-control measures follow similar processes, we can expect that those jurisdictions with the largest Hispanic presence will have higher jail admission rates. 2.3.3. Segregation and punishment Racial residential segregation may be one of the most persistent and pervasive dimensions of racial inequality in the United States (Massey and Denton, 1987; Peterson and Krivo, 1993; Quillian and Pager, 2001). The relationship between racial residential segregation and a variety of social ills including joblessness (Wilson, 1987), poverty (Krivo et al., 1998), and crime (Peterson and Krivo, 1993) has been well documented. Despite this interest, few researchers have attempted to explore the possibility of a direct association between residential segregation and levels of punishment. The threat model discussed in the previous section suggests that the size of a culturally dissimilar minority group will affect the likelihood that majority groups will feel threatened by such groups. An underlying assumption of this model is that increased size will translate into increased visibility. This, however, may not be the case. Restrictive residential arraignments may reduce the amount of interaction that minority group members may actually have with the majority. Specifically, racial residential segregation may limit the amount of contact between whites and minorities (even if the minority population is large) because segregation geographically isolates minorities into urban ghettos far from most whites. To the extent that contact with minorities is related to white perception of racial threat and this threat is subsequently related to state action, we can expect an inverse relationship between residential segregation and jail admissions. Additionally, a negative association between residential segregation and jail use may be expected because of its independent role in social control of Ôproblem populations.Õ Threat theorists have long claimed that racial residential segregation is a means by which the white population controls threatening populations (Spitzer, 1975). With racial residential segregation acting as a means of control, the need

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for alternative crime-control measures is largely eliminated (Blauner, 1972). Thus, the geographic isolation of racial/ethnic minorities, by reducing interracial contact and consequently interracial victimization (Blau, 1977; Messner and South, 1986; South and Felson, 1990), reduces whitesÕ perception of the crime problem and therefore may diminish white pressure on authorities to aggressively pursue crime. Prior research suggests that residential segregation is an important predictor of a variety of criminal justice outcomes. Liska et al.Õs (1982) find that after controlling for levels of crime, there is a strong negative correlation between residential segregation and the size of the police force in large US cities. Liska and Chamlin (1984) find that the same negative association exists between racial residential isolation and the rate of arrests in major cities. Finally, Liska et al. (1981) detect an inverse relationship between segregation and imprisonment rates after controlling for levels of crime. If the influence of segregation on local punitive responses is similar to its effect on other crime-control outcomes, we can expect jurisdictions with segregated minority populations to have a lower rate of jail use when compared to those with integrated communities. 2.3.4. Contact hypothesis While the theories and research mentioned above suggests a direct linear relationship between residential segregation and criminal justice outcomes, there is theoretical justification to believe that this relationship is curvilinear. Several studies have indicated that equal-status contact across ethnic lines correlates inversely with hostile beliefs (Ellison and Powers, 1994; Sigelman and Welch, 1993; Work, 1961). This account suggests that interracial and interethnic contact exposes people to the similarities in attitude and behavior of groups once seen as ‘‘different,’’ thus promoting communication and challenging traditional stereotypes. Thus, while the threat of a large culturally and racially dissimilar minority population may be alarming to the powerful, this fear may vary according to social proximity. By promoting or inhibiting interracial contact, segregation may influence levels of threat, and by theoretical extension, local punitive responses. Variation in social proximity between racial groups may have an important effect on punitive outcomes because this structural condition is connected to detrimental racial attitudes. Williams (1964) sharpens the contact hypothesis by suggesting that the type of contact is critical in reducing racial animosity. Specifically, he claims that intimate contact, such as those between neighbors, is the type of interaction most likely to produce tolerant attitudes. Tsukashima and Montero (1976) suggest that contact in a place such as work is not as effective at reducing intolerance because shared expectations about behavior at work tend to be highly specific, and largely governed by norms that restrict interpersonal relations. In other words, in a work environment oneÕs relations with others tend to be limited to a specific set of role relationships. Conversely, contact between neighbors is much more diffuse. For this reason, racial residential proximity, not contact at work, should be an important predictor of negative racial attitudes. While the contact hypothesis has only been examined under a few settings, it is thought to be a proposition with general applicability (Work, 1961). Most of the research on the contact hypothesis, however, has primarily focused on racial tolerance

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in public housing. Ford (1973) found, for example, that tolerant attitudes among housewives were more likely in desegregated housing projects than they were in segregated projects. Similar findings have been shown in studies of tolerance between blacks and Jews (Tsukashima and Montero, 1976), and white and black students in desegregated schools (Longshore and Prager, 1985). Thus, if the contact hypothesis is correct, we can expect that individuals residing in the least segregated communities would have the most tolerant attitudes towards members of the ‘‘other’’ groups. Prior tests of the threat hypothesis suggest that a reduction of fear on the part of dominant group members should translate into fewer demands for repressive measures. Thus, we can expect that jurisdictions with the least amount of segregation should have the lowest rate of punishment. By combining the expectations of the contact hypothesis with those of the threat hypothesis, we see a clear indication that a nonlinear relationship between racial residential segregation and punitive responses may exist. When interaction between disparate groups is high, as we can expect they would be in the least segregated communities, negative racial attitudes should subside. Under such conditions, repressive measures conditioned by such negative attitudes should decline. Thus, we should expect low jail admissions in communities with little segregation. At moderate levels of segregation, though, we should expect higher rates of jail use. When social distance is greater, and interaction between different racial/ethnic groups is less common, the perception of minorities as a threat may be greater thereby increasing the chances that the powerful will request repressive measures that will likely target minority groups. In the most segregated cities, however, the influence of segregation on social control efforts may change. The most segregated cities may have lower jail admission rates because at these levels segregation begins to act as an alternative means of controlling threatening populations, reducing the need for more formal measures such as jails. According to this model, then, the jail admission rates will rise along with the level of racial residential segregation up to a certain point, level off, and then decline. Support for this model would be found if the squared term yields a significantly negative coefficient, while the non-squared term is significantly positive. Due to the theoretical plausibility of a nonlinear effect between the dependent variable and segregation, I will test this factor in its quadratic form. However, because prior research by Liska and his colleagues has found a linear relationship, I will test for both linear and quadratic effects. 2.4. Economic threat 2.4.1. Racially based income differences Some theorists have argued that rates of punishment are primarily shaped by an economic rather than a racial threat (Blalock, 1967; Garland, 1990). This perspective holds that disparities in economic resources produce a potentially unstable social order that must be maintained by force. Proponents claim that this instability is accentuated where economic stratification is the greatest. Turk (1969), for instance, claims that criminalization will be greatest when the dominant group has control over resources and the subordinate group is virtually powerless. Chambliss and Seidman

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(1980, p. 33) also argue that ‘‘the more economically stratified a society becomes, the more it becomes necessary for dominant groups to enforce through coercion the norms of conduct that guarantee their supremacy.’’ Thus, neo-Marxists believe that the potential antagonism created by high levels of economic inequality can lead to an unstable social order that must be maintained by strengthening formal social control mechanisms. Blau and Blau (1982) extend this argument by claiming that while overall inequalities generally foster conflict and violence, ‘‘ascriptive inequalities do so in particular’’ (p. 119). Lockwood (1986) supports this argument by claiming that ethnic divisions are far more likely than class divisions to lead to conflicts and these conflicts are more intense when they are accompanied by economic exploitation. Following these ideas, Jacobs and OÕBrien (1998) argue that hypotheses that focus only on overall economic inequality ignore the relative economic position of minorities. Explanations that examine overall economic disparities assume that economic differences between poor blacks and poor whites compared to the affluent have identical effects on the criminal justice system. To overcome this problem, I will examine the effects of economic inequality between blacks and whites. Economic disparities between racial groups may lead to an increased use of formal social control because the dominant group may feel more threatened by a minority group that is economically repressed. The high level of economic stratification that exists between racial groups may breed discontent that could lead to violent attacks against the dominant group. Jackson (1989) argues that economic inequality between blacks and whites produces interracial tension because relative depravation leads to greater minority discontent. Blau and Blau (1982) claim that the correspondence of economic and racial inequality accentuates the likelihood of inter-group conflict. It seems reasonable to assume that whites in cities with the highest levels of racial income inequality may be more likely to perceive blacks as a threat and they may act to maintain their position by pressuring authorities to step up control efforts of blacks and other minorities. If these assertions are correct, jurisdictions where the economic gap between blacks and whites is the greatest should have higher rates of jail admissions. 2.4.2. Unemployment There is a significant body of literature that tests the association between unemployment and incarceration rates. Neo-Marxists, following the Rusche–Kirchheimer tradition, have long claimed that punishment is a mechanism used by the dominant class to control Ôlabor surplusÕ (Jankovic, 1977; Quinney, 1977; Rusche and Kirchheimer, 1939). Rusche and Kirchheimer (1939) maintain that punishment must not be seen as a means of crime control but rather as an instrument used by the rich to dominate the poor. During harsh economic times, punishment is employed to reduce the labor surplus by using incarceration to absorb some of the unemployed population. Those most susceptible to unemployment are unskilled workers. It is argued that these members of society are less committed to the law and the dominant moral order and are therefore more likely to be punished for conduct that threatens this order. For this reason, Jankovic (1977) hypothesizes that ‘‘a rise in unemployment will lead to an increase in prison commitments because the policy of deterrence

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dictates an intensification of punishment to combat the increasing temptation to commit crime’’ (1977, p. 20). On the other hand, when demand for labor is high, penal institutions may be less inclined to keep (or bring under their control) a large number of individuals when their labor is needed by private employers. During times of economic expansion, therefore, penal institutions may (after instilling the discipline necessary for ÔlegitimateÕ work in inmates) release a labor force that is ready to conform to this dominant work ethic. Judges may also be less likely to choose incarceration when the labor supply is low, but rather choose a punishment that does not involve incarceration. Greenburg (1977) maintains that it is ‘‘plausible to assume that judges are less willing to grant probation to offenders when they are unemployed, or that unemployment affects levels of community tolerance toward offenders, to which judges respond in sentencing (p. 650). Findings from studies that have tried to analyze the correlation between unemployment and incarceration have provided mixed support for the neo-Marxist claims. Numerous studies report a significant and direct positive relationship between unemployment and incarceration (Chiricos and Delone, 1992; Greenburg, 1977; Inverarity and McCarthy, 1988; Jankovic, 1977; Meyers and Inverarity, 1992; Yeager, 1979). Many others found an insignificant unemployment effect (Arvanites, 1993; Colvin, 1990; Galster and Scaturo, 1985; Michalowski and Pearson, 1990; Parker and Horowitz, 1986). While the results are mixed, neo-Marxist theories suggest that any significant effect that unemployment would have on incarceration is positive. Hence, I expect those cities with the highest levels of unemployment to have the highest jail admission rates. 2.5. Controls I introduce a control for the number of police officers per capita in each city as well. The size of the police force has two possible effects on jail use. First, cities with a large law enforcement presence could have higher rates of jail use simply because criminal activity is more likely to be noticed if there are more police on the streets. Second, in cities where the police presence is small relative to the offending population, jail use could be high because officers may use their arrest powers more often in an effort to make their job on the street more manageable. Because the size of the police force could have a positive or negative effect on jail use I will use a two-tailed test for it in the analyses. I also include a control for jail capacity. Past research has suggested that capacity of penal facilities is positively associated with their use (Flemming, 1982; Klofas, 1990). State officials can only place individuals in jail if they have the space to put them. For this reason, I expect to find a positive association between jail capacity and jail admissions. 3 Finally, I will control for regional differences in jail use because 3 Jails tend to be more willing then prisons to experiment with capacity inflating techniques such as using tents or double-triple bunking. This may be because judicial restraints on jails have not been as extensive as they have been for prisons. The Census of Local Jails in 1983 reported that fewer than 8% of jails were under court order to control capacity.

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prior research on the criminal justice system shows that both police expenditures (Jackson, 1989) and jail use (Klofas, 1990) vary substantially by region. Specifically, both authors show that state managers in the south are more inclined to use jails at a higher rate and fund state control agencies (namely, the police).

3. Methods 3.1. Research design and the dependent variable I use data from the 170 cities with a population greater than 100,000 in 1980 along with data from the 1983 Census of Local Jails for both the admissions and the capacity measure. 4 The Census of Local Jails, conducted every five years, includes all locally administered jails that held inmates past arraignment. The census excludes drunk tanks, lock-ups, and ‘‘holding facilities,’’ as well as facilities that did not hold persons after being formally charged. The jail census also excludes federal and state facilities. Additionally, this census does not include data for those jurisdictions that have combined jail-prison systems. States with a combined jail-prison system are Alaska, Connecticut, Delaware, Hawaii, Rhode Island, and Vermont. The lack of data for these states reduces the sample size in this analysis from 170 to 161 cities. Four additional cities are excluded because information for some important explanatory variables was not available. Hence, the analyses will be conducted using the remaining 157 cities with a population greater than 100,000 in 1980. Jail admissions (logged) are used as the dependent variable instead of alternative measures such as the average daily jail population because admissions are a better 4 There are a number of reasons why it may be unwise to incorporate more recent jail data into the analysis. First and foremost, the data for the dependent variable in the 1983 and 1993 Census of Jails are incomparable. In 1983, jail admission figures are provided for an entire year (all persons admitted to the jails between July 1, 1982 and June 30, 1983), whereas the admission figures for 1993 are for only one day (June 30, 1993). This is problematic because simply multiplying the admissions for 1993 by 365 to get a figure for the year would produce substantial inaccuracies. The problem arises because the day that the admissions are counted in 1993 is a Friday (the first day of the weekend in jail terms). Weekend admission figures are nearly 50% higher than the weekday numbers. Thus, a simple multiplication of the one-day admission counts in 1993 to produce an annual number would produce artificially large figures thereby biasing the results. Additionally, while the 1999 Census of Jails is available, the incorporation of this particular dataset is not preferable because causation cannot go backward in time (ideally, 2000 explanatory variables should not be used to predict 1999 admissions but rather 2000 explanatory variables should be predicting 2003 jail data due to the necessary lag). There are two reasons, however, that the 1999 data are preferable to the 1993 data and may be useful for inclusions in this study as an exploratory analysis to see if findings from the 1983 models are still relevant today. First, given that social and demographic factors such as the percentage of African-Americans or Hispanics change little over a oneyear period, it is a fair assumption that 2000 demographic data are a good proxy for 1998 data. Second, unlike the one-day admission figure provided in the 1993 jail census, the 1999 census provides one-week admission figures. This is preferable to the one day totals for the reasons mentioned above. Thus, an updated analysis using 1999 jail data with 2000 census data for the explanatory variables will provide us with a way, at least tentatively, to assess the current relevance of the group threat indicators of interest here.

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indicator of the number of people actually exposed to this type of punishment. Admissions are in rate form because we are interested in assessing the variation in jail use across jurisdictions with different population sizes. The natural log of jail admissions is used to correct for skewed distributions, to reduce the influence of outliners, and to ensure multivariate normality. I include both city and county jail admissions from June 1982 to July 1983 in the analyses. While jails primarily operate at the county level, the decision to move to a lower level of aggregation was made because important explanatory variables such as segregation are not available for counties. 5 3.2. Measurement of explanatory variables The data for the explanatory variables were collected from the 1980 Census. 6 To operationalize economic threat, I divide black mean household income by the same statistic for whites. This variable is reverse coded because whites usually earn more than blacks, so this coefficient should be negative. I assess racial threat with the percentage of the population that is African-American. Fossett and South (1983) evaluate many popular measures of relative income inequality (including the more commonly used measure—the ratio of median incomes) and find that the ratio of mean incomes is the most statistically appealing measure. Ethnic threat is measured with the percentage of Hispanics. Racial residential segregation in each city is operationalized using the index of dissimilarity computed by Taeuber and Monfort on block level data in 1980. While alternative segregation scores exist, the index of dissimilarity is the most widely used (see Messner and Golden, 1992; for a discussion). Unemployment is measured with rates taken from the decennial Census. Crime is operationalized using the serious crime rate per 100,000 residents taken from the FBIÕs Uniform Crime Report (UCR). The presence of young males in a city is measured by the percentage of the population that is male aged 15–24. Family disruption is assessed using the percentage divorced. The presence of liquor stores is measured using the number of these establishments per 10,000 residents taken from the Census of Retail Trade (1982). Law enforcement presence is measured with the rate of sworn police officers in the natural log form. Capacity is assessed with the rated capacity provided by the 1983 Census of Local Jails. The regional dummies are based on Census definitions.

5

Similar to a study conducted by Liska et al. (1999), each cityÕs jail admission rate is calculated by allocating a proportion of the entire countyÕs jail admissions and capacity in proportion to its city-tocounty population. For example, if a city population were 50% of the county population, that city would be allocated 50% of the entire countyÕs jail admissions and capacity. 6 A three-year lag on jail admissions will likely have little or no effect on the results given the high autocorrelation in admissions from one year to the next. In a study using jail admissions as one of the dependent variables, Liska et al. (1999) experimented with several lags and found that three-year lags produced the best results. This precedent is followed to give the forces I analyze sufficient time to influence jail admissions.

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4. Analyses 4.1. Variable means and distributions To assess the influence of the independent variables on logged jail admission rates in 1983, I use ordinary least squares (OLS) regression. Table 1 presents the predicted signs along with the means and standard deviations. In its unlogged form, the range of jail use was as low as 580 per 100,000 residents (St. Louis, MO) and as high as 15,000 (Columbia, SC) with a mean of 4886. It is clear that jurisdictions differ greatly in their willingness to impose this form of punishment. We also see that while the mean percentage of blacks in these cities was 19%, its values ranged from less than 1% in Livonia, Michigan to nearly 71% in Gary, Indiana. Hispanic ranged from less than one-half of a percent (Evansville, IN) to as high as 74% (Hialeah, FL). 4.2. Multivariate analyses Table 2 presents the findings from the OLS models. Because the main theoretical concern is those explanations associated with group threat, I begin with an assessment of these variables. Model 1 includes the racial and ethnic threat measures as well as those for relative income inequality, segregation in linear form, and joblessness. To assess the explanatory power of the measures derived from the consensus perspective, Model 2 includes the serious crime rate, the size of the young male population, the percentage divorced, and the presence of liquor stores. Model 3 combines both the threat and consensus measures in a single equation. In Model 4, I

Table 1 Predicted signs, means, and standard deviations for variables in the analysis Variable Ln jail admission rate Black to white income ratio Ln % black % Hispanic Ln % unemployed Segregation Segregation squared Serious crime rate Ln % males age 15–24 % Divorced % Divorced squared Ln liquor establishments (rate) Ln rate of sworn police Ln rate of jail capacity Midwest South West

Predicted sign

+ + + No + + + No + ± + No No No

prediction

prediction

prediction prediction prediction

Means

Standard deviation

8.206 .715 2.349 8.817 1.847 72.548 5387.124 9032.994 2.292 10.024 106.466 .422 5.265 6.249 .229 .359 .306

.742 .122 1.361 12.235 .381 11.164 1563.165 2510.684 .156 2.454 49.543 .632 .352 .554 .422 .481 .462

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Table 2 Regression estimates for 1983 jail admission rates for large US cities (Ln of admissions per 100,000; N = 157)

Conflict variables Black to white income ratio Ln % black % Hispanic Ln % unemployed Segregation Segregation squared Crime and disorder variables Serious crime ratea

Model 1

Model 2

Model 3

Model 4

2.3130*** (.5445) .0171 (.0494) .0100** (.0042) .2311 (.1703) .0065 (.0052) —

—

—

2.0719*** (.4845) .0034 (.0451) .0120*** (.0038) .0823 (.1599) .0012 (.0048) —

1.1067** (.4620) .0538 (.0498) .0073* (.0035) .0507 (.1582) .0054 (.0346) —

.8598* (.4383) .1086* (.0488) .0056* (.0033) .0251 (.1508) .1152*** (.0345) .0008*** (.0002)

.0326 (.0217) .9274** (.3273) .1462*** (.0217) —

.0078 (.0208) .6277* (.3161) .1467*** (.0216) —

.0037 (.0199) .3465 (.2859) .0430* (.0246) —

.1244 (.0830)

.0277 (.0732)

.0738 (.0675)

.0106 (.0189) .2017 (.2702) .4805*** (.1489) .0235*** (.0067) .1429* (.0654)

.3694* (.1780) .1696* (.0827) .5474** (.1834) .8738*** (.1985) 1.0000*** (.2070) 9.4514*** (1.3984) .538

.7502*** (.1976) .1307* (.0788) .9954*** (.2025) 1.3964*** (.2226) 1.5177*** (.2294) 9.5969*** (1.9963) .593

—

% Males age 15–24

—

% Divorced

—

% Divorced squared

—

Ln rate of liquor establishments

—

— — — —

Controls Ln rate of sworn police

—

—

—

Ln rate of jail capacity

—

—

—

Midwestb

—

—

—

South

—

—

—

West

—

—

—

Constant

10.6866*** (.5591) .209

4.2560*** (.8282) .244

Adjusted R2

Standard errors are in parentheses. a Coefficients and standard errors are multiplied by 1000. b The Reference group is Northeast. * P 6 .05. ** P 6 .01. *** P 6 .001.

6.4705*** (1.0967) .399

Model 5

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retain all variables in the prior model and add the controls for police strength, jail capacity, and region. Model 5 adds quadratic terms to two variables, residential segregation and the percentage divorced. An examination of the scattergram for divorce and the dependent variable suggests that a polynomial model might provide a better fit. For this reason, I test the percentage divorced in quadratic form to provide the most accurate test of the prominent theoretical positions. Consistent with ethnic threat expectations, the results in the first equation show that those jurisdictions with a large Hispanic population use jails more, but African-American presence does not matter. These initial findings also support economic threat predictions by showing that the ratio of black to white income helps explain jail admissions. Recall that the negative sign on this variable indicates that those cities with the greatest disparity in income between these two racial groups have higher rates of jail use. Labor market conditions do not appear to be important in this preliminary model. Because the dependent variable is logged, the coefficient (after being multiplied by 100) can be interpreted as the expected percent change in the jail admission rate per unit increase in each independent variable. The second model, testing the consensus perspective, shows that some measures of disorder have a significant influence on jail use. We see that the presence of a large young male population helps predict the rate of jail admissions. Additionally, we see that the percentage divorced significantly increases the use of local crime-control measures. Consistent with prior studies, the serious crime rates do not have a significant effect on local correction efforts. Model 3 combines the first two equations and shows the explanatory power of each set of variables. Variables that are influential in the separate equations remain important in the combined equation. With police size, capacity, and region held constant in Model 4, all but one variable that co-varies with jail admissions in the prior models continued to do so in this more exhaustive model. The one exception is the now non-significant coefficient for the presence of a large young male population. Model 5 tests the percentage divorced and residential segregation in quadratic form. We now see that in this more accurately specified model that jurisdictions with the largest proportion of blacks have higher rates of jail admissions. We also see that residential segregation is an important determinant of the rate of jail admissions. Consistent with the contact hypothesis, the plot of this relationship reveals that at the lowest levels of segregation, where blacks and whites live in desegregated communities, rates of jail use are low. Moderately segregated cities have the highest rates of jail use while in the most segregated cities rates of jail admissions are at their lowest levels. The control variables in this model also produce some interesting results. The significant negative sign on the sworn police officer rate in the last two models shows that the presence of more police officers in a city is associated with lower rates of jail use. This may suggest that when police are significantly outnumbered by potential lawbreakers, they use their arrest powers more often. With fewer officers patrolling the streets, jails may be used to reduce the number of potential offenders in an effort to ‘‘level the playing field.’’ By increasing the jailed population, a small police force may be able to reduce the number of repeat offenders to a level, which they can manage more effectively.

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The results presented here also provide some support for the consensus approach by showing that jail admissions are greater in jurisdictions with more disorder. Consistent with prior studies, however, the official crime statistics never reach statistical significance. The results also provide strong support for racial and economic threat influences on this outcome. Both economic and ethnic threat always account for the rate of jail admissions, while African-American presence and segregation influence this form of punishment after both divorce and segregation are properly specified as quadratics. The influence of these threat effects appear to be quite robust given that they persists even with the inclusion of crime and disorder measures and a number of other controls. 4.3. Additional methodological considerations and sensitivity tests 4.3.1. Sensitivity tests Several diagnostic tests were conducted. 7 In the best model, there were no correlations between any two explanatory variables above .6. The highest correlation (.548) was between the rate of police officers and the percentage of blacks (see Appendix A for the complete correlation matrix). The maximum VIF score for the variables (except the squared terms) is 3.67. This value is below a score of 4.0 that conservative statisticians claim is an indication of collinearity (Fisher and Mason, 1981; Fox, 1991). 8 In Table 3, I present three additional analyses using techniques that are helpful in assessing the sensitivity of the initial findings. All models use the best model from Table 2 (Model 5). Model 1 of Table 3 uses WhiteÕs (1980) correction for OLS to estimate the standard errors to correct for heteroskedasticity that may be present. While the Cook–Weisberg test for heteroskedasticity indicates that this problem is not present, some statisticians suggest using this correction even if the test is passed (Long and Ervin, 2000). When I re-estimate the model using this correction, the t values do not differ. In Model 2, I use robust regression to see if influential cases are distorting the results. This technique de-weights influential cases to ensure that the conclusions do not rest on such cases. 9 When the robust regression estimates are compared to those

7 Multicollinearity is inevitable in this situation because those variables are mathematical functions of one another and thus highly correlated. 8 While diagnostics indicate that the best model is not affected by multicollinearity, one variable was removed from the study because of this problem. In unreported analyses, both the zero order correlations and the VIF scores suggested that multicollinearity was present between the poverty rate and the percentage black. The correlation between these two indicators was .78. When both variables were in the equation, the poverty rate was not significant but the percentage of blacks continued to have a significant positive effect on jail admission rates. The coefficients on the other variables in the model were not affected by this collinearity. Because high levels of collinearity between explanatory variables make it difficult to determine their independent affects on jail use, and because the central concern of this study is to test racial threat hypotheses, I removed the poverty rate from the analysis. 9 Huber iterations weight cases with small residuals as 1. Cases with larger residuals receive gradually smaller weights (STATA, 1999).

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Table 3 Regression estimates of 1983 jail admission rates for large US cities (Ln of admissions per 100,000; n = 157) Model 1— Model 2— Model 3d— WhiteÕs Robust Errors-incorrection regression variables regression .8598* (.4001) Ln % black .1086* (.0463) % Hispanic .0055* (.0033) Ln % unemployed .0250 (.1426) Segregation .1152** (.0458) Segregation squared .0007** (.0008) Serious crime ratea .0106 (.0200) % Males age 15–24 .2017 (.2181) % Divorced .4805*** (.1327) % Divorced squared .0235*** (.0060) Liquor establishments (rate) .1429* (.0764) Ln rate of sworn police .7502*** (.2055) Ln rate of jail capacity .1307 (.0758) Midwestb .9954*** (.2025) South 1.3964*** (.2381) West 1.5177*** (.2204) Constant 9.5969*** (2.1951) R2 .635 F test 19.66*** Black to white income ratio

.7375* (.4459) .1239** (.0497) .0063* (.0034) .0731 (.1534) .1092*** (.0351) .0008** (.0003) .0092 (.0192) .2702 (.2750) .5152*** (.1515) .0257*** (.0068) .1700** (.0666) .8886*** (.2010) .1330* (.0802) .9999*** (.2060) 1.4072*** (.2265) 1.4642*** (.2334) 10.3367*** (2.0312) 15.74***

.8635* (.4383) .1052* (.0507) .0054 (.0034) .0262 (.1507) .1146*** (.0346) .0008*** (.0002) .0157 (.0279) .1948 (.2715) .4866*** (.1509) .0237*** (.0068) .1428* (.0654) .7610*** (.2023) .1283 (.0793) .9928*** (.2026) 1.4005*** (.2231) 1.5094*** (.2318) 9.6852*** (3.0148) .635 15.23***

Model 4e— Cities with a large hispanic presence

Model 5f—Cities with a large African-American presence

1.6922** (.7449) .0167 (.0717) .2235** (.0886) .5332* (.2630) .0450 (.0495) .0003 (.0004) .0284 (.0292) .5222 (.4191) .0689 (.1669) .0044 (.0078) .0793 (.1097) .7077* (.3445) .0409 (.1135) —

2.8432*** (.8324) .4639** (.1714) .0892 (.0622) .0195 (.2682) .0940 (.1399) .0006 (.0009) .0110 (.0259) .3745 (.4379) .0944 (.2601) .0078 (.0139) .0858 (.1226) .9698*** (.2979) .3310** (.1219) —

—

—

—

—

6.9280** (3.0148) .491g 6.300***

11.2004 (6.1779) .556g 8.32***

Standard errors are in parentheses. a Coefficients and standard errors are multiplied by 1000. b The Reference group is Northeast. c Two-tailed test used. d The assumed reliability for the serious crime rate is .8. e Sample restricted to those cities with Hispanic population greater or equal to the median (n = 78). f Sample restricted to those cities with African-American population greater or equal to the median (n = 77). g Adjusted R2 reported in models 4 and 5. * P 6 .05. ** P 6 .01. *** P 6 .001.

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from the original OLS model, the same variables are significant. Because there is doubt as to the accuracy of the crime statistics, Model 3 uses an Errors-in-Variables regression routine to see if measurement error in this variable affects the other results. This routine is employed because all coefficients in an OLS model can be biased if they are correlated with the poorly measured variable (Chatterjee and Hadi, 1988; Greene, 1997). We see from the results that only one variable that was significant in the prior models fails to reach significance in this model. While the coefficient for the presence of Hispanics does not reach the same level of influence as it has in prior models, it does reach the .1 level of significance. Thus, the findings in Table 3 suggest that the OLS estimates are not degraded by heteroskedasticity, overly influenced by outliers, or biased by possible measurement error in the crime rates. The results continue to provide evidence for economic, racial, and ethnic threat effects on jail use even after substantial controls have been included. Additional models (not shown) indicate that adding explanatory variables such as overall income inequality, the presence of female-headed families, median family income, or the violent and property crime rates entered separately have no effect on jail admissions. Political differences across jurisdictions also did not matter. 10 In some models, overall arrest rates were substituted for the crime rates (not shown but available from the author) the results remained substantially the same. This is consistent with Liska et al.Õs (1999) research that finds a similar nonsignificant effect between arrest rates and jail admissions. Arrest figures for Part II offenses (less serious/public order offenses) were also substituted for the theoretically derived measures of less serious crimes. The results from these models (available from the author) again produced substantially the same results. 4.3.2. Additional analyses In an effort to explore further how minority group threat contributes to levels of jail use, I present additional models with analyses restricted to those cities where Hispanics and African-Americans make up a sizable proportion of the population. 10 In unreported analyses, I tested two additional political variables. The first was the presence of a black mayor. I hypothesized that the presence of a large minority group would have a significant positive effect on jail admissions because it is likely that once minority groups reach a sufficient size, they will gain enough political strength to reduce repressive efforts directed at them. The presence of an AfricanAmerican in a high level political office should reduce repression directed at members of this group (Chambliss and Seidman, 1980). I found no support for this hypothesis. The second political variable was the presence of determinant sentencing legislation. Recently, several researchers have explored the effects legislative attempts to reduce judgesÕ discretionary powers (and the potential discriminatory bias that goes with it) by introducing sentencing guidelines (DÕAlessio and Stolzenburg, 1995; Marvell and Moody, 1996). While these determinant sentencing laws are an attempt to provide equity in sentencing, some have argued that they will increase prison use because judges will no longer have the option of placing offenders on probation. When prisons become overcrowded, judges in these states may begin to send offenders to local jails instead. Some scholars suggest that sentencing guidelines increase the use of jails because judicial concerns about prison overcrowding motivate them to get around the guidelines and shift the burden of incarcerating offenders from the state to local levels (DÕAlessio and Stolzenburg, 1995). Marvell and Moody (1996), however, find that determinant sentencing laws have little or no effect on jail use. I also found no evidence to support the idea that sentencing guidelines influence jail use.

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Because both African-Americans and Hispanics are not evenly distributed across the United States, one should assume that the salience of race and ethnicity varies across jurisdictions. More specifically, the processes associated with group threat are unlikely to take place in jurisdictions where these groups are not present. 11 Restricting the analysis to those cities where specific minority groupÕs numbers are sizable should provide a more accurate picture of threat influences on jail use. To evaluate this possibility, separate regressions are reported with a substantial Hispanic (Model 4; n = 78 cities) or black population (Model 5; n = 77 cities). Model 4 is estimated using cities with a Hispanic population greater than or equal to the median (or 3.65% of the total city population). The results from this equation provide even stronger support for two versions of the threat hypothesis. As one would expect, the threat results are stronger when the model is limited to those cities where Hispanic presence is greatest. Yet, coefficients for the racial threat variables did not reach significance. These findings suggest that in jurisdictions where the Hispanic population is considerable, they become the most prominent threat. The explanatory power of other threat determinants is also strengthened in this restricted model. The coefficients on income inequality are more substantial. Additionally, the coefficient for unemployment reaches significance for the first time. This finding suggests that those cities with a sizable Hispanic population have higher rates of jail use where jobless rates are high. But, neither segregation nor any of the crime and disorder variables are significant in this restricted model. While the first two models provide evidence that a large Hispanic presence has a strong influence on jail use, we do not yet know if sizable African-American presence has a similar effect. To see if restricting the analysis to cities with a large AfricanAmerican population produces similar patterns, Model 3 includes only those cities where the presence of African-Americans is at least equal to the median (14.73%). Again, we see that the influence of the threat variables is stronger in this limited model. Both racially based income disparities and the presence of African-Americans become stronger predictors. As in Model 4, we see that many of the factors that mattered in earlier models are no longer significant once the model is restricted. These additional analyses provide consistent support for threat explanations. The findings from both restricted models suggest that threat determinants become the only factors responsible for variations in local punitive efforts in jurisdictions where minorities are a sizable proportion of the community. The results reported thus far show rather convincingly that both racial and ethnic threat are strong predictors of jail use in 1983. We do not know, however, if these findings persist today. While analyses for more recent time periods can only be tentative at this time (see Footnote 4 for further explanation), I have analyzed data taken from the 1999 National Jail Census and data from the 2000 census for the explanatory variables (not shown but available from the author). This tentative 11 Walker et al. (2002) point out that more than half of all racial and ethnic minorities live in just five states (California, Texas, New York, Florida, and Illinois—20% in California alone). They also show that more than half of the African-American population is located in the Southeastern part of the US and that half of the Hispanic population lives in just two states (California and Texas).

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model uses a similar set of explanatory variables and produces results that are largely consistent with those produced using the 1983 data. Findings from the 1999 model show that sizable African-American and Hispanic populations continue to be important predictors of jail admissions. Thus, these results show that racial and ethnic threat indicators continue to be important predictors of the variation in jail use across cities. The model also indicates that racial income inequality continues to matter. Combined, the results from this tentative model suggest that those variables of primary theoretical concern in this paper are not historically contingent but rather remain important determinants of jail use today. Segregation continues to have a direct significant influence on variations in jail use in the 1999 models. Unlike the earlier models, however, these tentative results suggest that the effect is negatively associated with the outcome and that this effect is linear. While this finding is consistent with the idea that segregation acts as its own means of social control by isolating minorities (thereby reducing the need for formal control measures to control these threatening groups), it does not support the contact hypothesis. Thus, the inclusion of multiple theoretical explanations for this outcome plus the stability and persistence of the threat effects after using diverse specifications, restricted samples, and a tentative model updating the data to the present suggests that these findings are robust and applicable today. All of these considerations suggest that this analysis has captured the major social processes that produce differences in the rate of jail admissions across jurisdictions.

5. Conclusions 5.1. Results The primary theoretical concerns that motivated this analysis were supported by the findings. Several group threat factors explain the rate of jail admissions across jurisdictions. The consensus approach findings were theoretically interesting as well. In both the model limited to measures of crime and public disorder and the more exhaustive models, the findings always show that the amount of serious crime does not influence jail use. This is not unexpected given that prior qualitative and quantitative research suggests that the majority of those in jail have not committed serious offenses (Irwin, 1985). The results, however, show that public order offenses do lead to increases in the use of jails. Family disruption and the presence of liquor stores are both consistent predictors of this punitive outcome. These findings support the consensus approach by suggesting that the menace produced by minor public order offenses leads to expansions in the use of jails. The threat findings are more complicated. While the results for racial threat are weak in the initial models, the coefficients consistently reach significance in the properly specified equations. As many social theorists (Blalock, 1967; Blumer, 1958) would expect, the results show that a positive association exists between the presence of African-Americans and jail admissions. This association is strengthened in a sec-

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ondary equation that restricts the sample to those cities with a large African-American population. Additionally, the significant positive result persists in the tentative model using more recent 1999 jail data. Combined, these results provide compelling evidence that suggests that racial threat factors may be the most meaningful determinants of the use of jails. The racial threat findings presented here are consistent with prior research that shows a relationship between the presence of blacks and the rate of imprisonment (Jacobs and Carmichael, 2001). I also find consistent support for the ethnic version of threat theory. Unlike the results for racial threat, the findings support this threat perspective in all but one equation of the analysis. The results in this study corroborate prior findings about prison usage (Jacobs and Carmichael, 2001) by showing that a large Hispanic population is associated with higher rates of jail use. This finding suggests that Hispanics may occupy a role similar to that of blacks and may therefore be subject to similar repressive measures at both the state and local level. Such findings provide added support for threat hypotheses. The findings also provided strong support for the economic version of the group threat hypothesis. Consistent with many theoristsÕ claims (Blalock, 1967; Chambliss and Seidman, 1980), a large racial disparity in income was found to be a significant predictor of jail use in all 10 models presented here (and the updated model using 1999 jail data). Cities where income is stratified predominantly along racial lines tend to have higher jail admission rates. The evidence fits with claims that economically influential groups feel more threatened in jurisdictions where a large economic underclass exists and that these same groups will act to reduce this threat by pressuring authorities for more aggressive law enforcement. Additionally, when the resources of the majority greatly exceed that of the ‘‘threatening’’ group, the elites are much more likely to be successful in their requests for control measures because the powerless are less capable of mounting an effective campaign against such actions. The results presented here support these assertions by showing that when racial income inequality is great, local punitive measures are used at higher rates. Although it seems reasonable to assume that similar processes affect all aspects of the criminal justice system, it appears that this is not the case. Findings from a study exploring the explanatory power of racial income differences on imprisonment came to a different conclusion than the one presented here. In a time-series analysis, Jacobs and Helms (1996) found that the economic gap between non-whites and whites had no effect on prison admissions. While this finding is inconsistent with the results from this study, it is plausible that at the local level, effects of racial economic inequality are more salient than they are at higher levels of aggregation. It may be that both judges and prosecutors are more attentive to public demands when sentencing minor offenders facing relatively short jail terms but they are less concerned with these demands when adjudicating serious wrongdoings because these cases are more likely to receive the scrutiny of appeals courts. The findings concerning racial residential segregation in the main models using the 1983 data ran counter to those found in studies exploring other aspects of the criminal justice system. While Liska et al. (1981) reported a negative linear association between segregation and another facet of crime-control (police strength), the

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present study shows that the relationship between jail use and segregation is better specified as a quadratic. This curvilinear relationship is consistent with the contact hypothesis, which suggests that when interaction between rival groups is high, negative racial attitudes tend to subside because social proximity tends to reduce fear of other groups. Bursik and Grasmick (1993) provide additional justification for this quadratic relationship by claiming that negative racial/ethnic stereotypes and prejudicial attitudes may lose their relevance when social interaction between once hostile groups increases. Under such conditions, the use of repressive punitive measures may decline when blacks and whites live in highly integrated communities because the main source of fear (minority size) no longer appears threatening. The results support this prediction by showing that the least racially segregated communities utilize their jails less than those with higher levels of segregation. At moderate levels of segregation, however, we see higher rates of jail use. When social distance is greater, and interaction between different racial/ethnic groups is less common, fear of minority groups may be greater. Yet, limited contact between unfamiliar racial/ethnic groups may increase the likelihood of the ÔoutsideÕ group being seen as ‘‘different’’ and potentially threatening to the dominant groups position thereby increasing the chances that the powerful will request repressive measures that will likely target minority groups. In the most segregated cities, though, the influence of segregation on social control efforts changes. Specifically, the most segregated cities have lower jail admission rates. This drop in jail use is consistent with the theory that segregation acts as an alternative means of controlling threatening populations so that more formal means of control (in this case jail use) will not need to be employed. Blacks in highly segregated communities may become socially invisible to whites, thereby reducing the perceived threat of this group. While the results from the primary models using 1983 data provides strong support for the contact hypothesis, it is important to mention that the results from the tentative analysis that introduces more recent (1999) jail data do not maintain contact hypothesis expectations. Rather, the results from the exploratory model using the 1999 jail data support the negative findings reported by Liska et al. (1981). One possible explanation for this change in the results may be that the reduction in the sample of cities has changed the findings (the sample size is reduced from 157 in the 1983 model to 104 in 1999 model). This possibility cannot be eliminated without further analyses that are not possible at this time due to limitations in the available data. 5.2. Wider implications The results presented in this study challenge claims that racial stratification has begun to lose its overall importance (Wilson, 1978). The findings suggest that variations in jail use may be contextual in origin and not simply be reducible to differences in rates of crime. Specifically, this study provides evidence that racial divisions influence the use of punishment. The size of the black and Hispanic population relative to that of whites accounts for a significant amount of the variation in

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jail use in both 1983 and today. Racial income inequality between blacks and whites further explains this variation in both time periods studied. The persistence of the group threat results after crime rates and other urban conditions have been held constant does not fit with the conventional wisdom, which suggests that punishment is only used in reaction to criminal activity. Instead, the findings concerning minority group threat at least hint at the idea that the neo-Marxists are right in their assertion that punishment is best seen as a mechanism intimately connected to the class struggle between the dominant and subordinate groups rather than as a means of crime control (Garland, 1990; Rusche and Kirchheimer, 1939). In the US criminal justice system, there are just a handful of actors who are in an official capacity that gives them the authority to influence jail use. Police can decide whom to arrest, prosecutors have the discretion to prosecute, and ultimately only judges (and sometime juries) decide to detain or release criminals. Conventional wisdom suggests that these state agents follow impartial legal guidelines to make these decisions. If this were the case, we should expect that, once crime has been controlled, differences in jail admission rates across cities would not be determined by extralegal factors such as racial economic inequality, the percentage of the minority population, or segregation. Instead, they should be influenced only by legal considerations. The findings of this study suggest that this is not the case. In particular, the results show that group threat theories are helpful in explaining much of the variation in jail use between cities. With the results of this study in mind, we may have come closer to answering the question of whether the penal system is largely concerned with crime-control or merely acts as a mechanism used to preserve the inequities in society. This study provides some evidence that suggests that viewing the penal system solely as a device for regulating criminal behavior may be somewhat misguided. By showing that cities use their jails more if they have larger percentages of minority populations and greater economic disparities between racial groups, these findings at least hint at the idea that the punishment apparatus of the state may be used as a means of regulating culturally dissimilar groups. The results show that penal measures may not just be shaped by criminality but by the perception of disadvantaged and dissimilar groups as a social problem. Past research on jail use has been based primarily on atheoretical assumptions that assume a general consensus about crime control. But my research helps establish theoretically based explanations for fluctuations in jail admission rates. These results are clearly at odds with the belief that social control efforts are primarily driven by the incidence of criminal behavior. I find considerable evidence that minority group size influences jail use. The size of both the African-American and Hispanic population increases admissions to jails. Both of these findings point to the idea that despite civil rights advances and the apparent reduction of racial tensions, race still has an influence on local social control efforts. A large economic gap between whites and blacks also has a consistent affect on jail use. With the influences of many additional forces controlled, racial animosity and fear still exerts an independent effect on variations in admissions. The persistence of threat effects suggests that antagonisms that exist between majority and minority group members may have consequences beyond

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attitudes. Higher rates of incarceration at both the state and local level appear to be the price of racial divisions. The results do, however, provide some evidence to suggest that specific environmental conditions may mitigate these negative racial sentiments. The findings presented here, which show that desegregated cities use jail less, suggest that attempts to reduce the repressive use of crime-control mechanisms might be achieved through programs aimed at reducing the isolation of minorities. Having a diverse community, without the geographic isolation of minority group members, can reduce racial tensions such that repressive crime-control efforts are not employed to protect interests of the powerful at the expense of the powerless. In fact, scholars have long claimed that segregation is to some degree a creation of the middle class used to separate themselves, physically and socially, from the encroachment of the ‘‘dangerous classes’’ (Lofland, 1973, p. 90), and that this structural condition acts as a formidable obstacle between middle and working class whites and their African-American counterparts by detracting from their ability to form mutual identification and solidarity (cf. Tolnay et al., 1996). Cox (1959) furthers this argument by claiming that segregation is the most powerful mechanism through which racial antagonisms are maintained. Results from the models using the 1983 jail data suggest that reducing the social isolation of minorities may not only have the affect of improving race relations but may also reduce the likelihood that repressive control measures will be used against them.

Bivariate correlations, means, and standard deviations (n = 157)

1. Ln jail admit rate 2. B/W income ratio 3. Ln % black 4. % Hispanic 5. Ln % unemployed 6. Segregation 7. Segregation squared 8. Crime rate 9. Ln % young males 10. % Divorce 11. % Divorce squared 12. Ln liquor stores 13. Ln police rate 14. Ln capacity rate 15. South 16. West 17. Midwest Means Standard deviations

1

2

3

4

5

1.000 .386 .007 .185 .299 .058 .067 .119 .076 .506 .475 .088 .265 .271 .362 .283 .318 8.21 .74

6

7

8

1.000 .401 .087 .309 .327 .318 .104 .002 .094 .081 .192 .121 .206 .539 .197 .329 .72 .12

1.000 .196 .375 .398 .397 .381 .086 .233 .251 .199 .548 .267 .361 .456 .023 2.36 1.37

1.000 .040 .283 .284 .048 .063 .006 .007 .112 .126 .065 .015 .272 .284 8.82 12.24

1.000 .192 1.000 .193 .995 1.000 .266 .188 .185 1.000 .178 .248 .248 .081 .401 .169 .167 .075 .410 .166 .166 .058 .135 .071 .072 .233 .396 .438 .433 .397 .208 .004 .003 .260 .299 .395 .398 .054 .075 .546 .571 .103 .240 .151 .154 .012 1.85 72.55 5387 9032 .38 11.16 1563 2510

9

10

11

12

13

1.000 .079 .087 .034 .191 .058 .021 .020 .006 2.29 .16

1.000 .985 .079 .333 .103 .156 .362 .114 10.02 2.45

1.000 .090 .268 .116 .127 .353 .139 106.5 49.5

1.000 .286 .206 .105 .088 .057 .42 .63

1.000 .156 .004 .252 .002 5.50 .32

14

1.000 .104 .173 .321 6.25 .55

15

16

17

1.000 .485 .425 .36 .48

1.000 .352 1.000 .31 .23 .46 .42

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