Regulating Hate: State and Local Influences on Law Enforcement Actions Related to Hate Crime

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2002 2: 126State Politics & Policy QuarterlyDonald P. Haider-Markel

EnforcementRegulating Hate: State and Local Influences on Hate Crime Law

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State Politics and Policy Quarterly, Vol. 2, No. 2 (Summer 2002): pp. 126–160

Regulating Hate: State and Local Influences on Hate Crime Law EnforcementDonald P. Haider-Markel, University of Kansas

abstract

I use elements of overhead democracy and policy implementation theory to explain hate crime law enforcement in American cities. I develop hypotheses of the rela-tionships between law enforcement, state and local policies, and the preferences of elected officials, bureaucrats, and the public. Using survey and demographic data, I find that local hate crime law enforcement is driven by the presence of state hate crime policies, the support and efforts of bureaucrats, the tractability of the hate crime problem, police funding and training, and public preferences. Law enforcement does not appear to be significantly influenced by the preferences of elected officials, local hate crime policies, or administrative procedures for hate crime cases. Thus, although political control by local elected officials is weak, state officials and citizens have some influence over local hate crime law enforcement.

Police and prosecutors are expected to implement policy faith-fully by finding and convicting those who violate criminal law. On the other hand, these officials must balance the imperatives of strong law enforcement with people’s basic rights and liberties. Law enforcement officials are the armed agents of the state, and as such, they have the potential to act as either the people’s protector or oppressor (Skolnick and Fyfe 1993). Given this im-portant but difficult balance, the level of control that the elected officials who supervise law enforcement officials have over them is a crucial question both politically and theoretically. Indeed, there is a growing literature examining the political control of law enforcement agencies and the determinants of law enforcement activity (Skolnick 1966; Wilson 1969, 1978; Brehm and Gates 1997; Chaney and Saltzstein 1998; Scholz and Wood 1998). My study adds to this literature by examining the forces influencing law enforcement and prosecutorial behavior in a new area of criminal law where political control may be especially problematic—hate crime policy.1

Examining bureaucratic activity related to hate crime provides a useful window to understanding bureaucratic activity generally for at least two reasons. First, hate crime policy has been argued to be a largely symbolic

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summer 2002 / state politics and policy quarterly 127

effort to satisfy interest group demands (Haider-Markel 1998), with little incentive for bureaucrats to expend much energy in its implementation (Haider-Markel and O’Brien 1999). As such, we might expect little bureau-cratic action unless political forces combine to encourage it. Second, because of underreporting and bureaucratic bias, hate crimes can be difficult to track and identify, making policy implementation even more problematic. In this way, hate crime implementation may be like implementing sexual assault and domestic violence laws (Backman 1998; Buzawa 1982). However, given the high salience afforded to some hate crimes (e.g., the killing of Matthew Shepard in Wyoming in 1998), bureaucrats may become responsive to a vocal public. These characteristics of hate crime policy suggest that it may provide a unique venue in which to examine the role of political forces on bureaucratic activity. I employ elements of two theoretical explanations of bureaucratic activity (overhead democracy and policy implementation) to develop hypotheses of agency behavior, focusing on the impact of state and local policies protect-ing lesbians and gays from hate crime. To test my hypotheses, I conduct multivariate analyses of demographic data and surveys of law enforcement officials from medium and large American cities. My findings suggest that law enforcement agency behavior is shaped by a variety of internal and external forces, but local political control of the en-forcement of hate crime laws may be relatively weak. Specifically, state laws, the support and efforts of rank-and-file police and their leaders, the tractabil-ity of hate crime, funding and training for police in hate crime procedures, and public opposition to hate crime policies drive local law enforcement activity on hate crime. Furthermore, law enforcement activity does not ap-pear to be influenced by the preferences of local elected officials, local hate crime ordinances, or administrative procedures for hate crime cases. These results suggest that there is limited local political control over the enforce-ment of hate crime laws in the United States.2

theories of bureaucratic activity

To develop hypotheses of law enforcement activity on hate crimes, I draw on elements from two theoretical frameworks for explaining bureaucratic activity—overhead democracy and policy implementation. The frameworks are complimentary, but each emphasizes different aspects of bureaucratic be-havior as well as the forces that may influence it. Overhead democracy places more emphasis on political control forces, such as the public and elected officials, while policy implementation tends to emphasize characteristics of



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bureaucratic agencies and the nature of the policy problem. The foremost concern of scholars working in the overhead democracy literature is the political control of bureaucrats by elected officials. As society and government have become more complex, elected officials have increas-ingly delegated decision-making authority to non-elected bureaucratic agents (Lowi 1969). Using this discretion, these bureaucratic agents make imple-mentation decisions that shape policy and can have significant impacts on its success or failure (Meier 1993). Normative concerns have been raised about the potential for these non-elected bureaucratic agents to misrepresent or even circumvent the preferences of their political principals and the public (Wilson 1969; Brehm and Gates 1997). Although some scholars argue that bureaucrats are often responsive to their political principals (Waterman and Meier 1998; Wilson 1978), others find that bureaucratic agents are insulated from political control, to one degree or another (Pressman and Wildavsky 1973; Lipsky 1980; Moe 1984, 1990). The literature on law enforcement bureaucrats suggests that they have considerable discretion in their work, allowing them to shirk political con-trol if they so desire (Skolnick 1966; Wilson 1969; Brehm and Gates 1997; Chaney and Saltzstein 1998). Like other bureaucrats, police and prosecutors’ offices can delay or hinder the implementation of policies with which their leadership disagrees (Skolnick 1966; Brown 1981; Buzawa 1982, 1988; Cohn and Sherman 1987; Carter and Sapp 1993; Chaney and Saltzstein 1998). Rank-and-file officers and prosecutors may hamper a policy’s implementa-tion if they do not believe it is important or if they disagree with its goals (Comstock 1991; Carter and Sapp 1993; Skolnick and Fyfe 1993; Brehm and Gates 1997; Chaney and Saltzstein 1998; Martin 1995, 1996). Proponents of bureaucratic efficiency and specialization have argued that bureaucrats can and should be accountable under certain conditions. Theo-ries of overhead democracy outline a framework through which bureaucratic agents can be made to carry out the wishes of their political principals, the public, and their elected representatives. Simply stated, 1) citizens elect their representatives, 2) elected officials shape bureaucratic behavior through po-litical appointments, legislation, executive orders, budget decisions, oversight, and bureaucratic reorganization, and 3) elected officials are held accountable for the actions of bureaucratic agents in subsequent elections (West 1996). If this process works as overhead democracy leads us to expect, agency activity is a function of public preferences (indirectly), elected officials’ preferences (directly), public policies set by elected officials, and potentially, the prefer-ences and discretion of bureaucrats, both administrators and street-level bureaucrats (Wilson 1978; McCubbins, Noll, and Weingast 1989; Wood and



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Waterman 1991, 1994; Ringquist 1995; Brehm and Gates 1997). This discus-sion of the overhead democracy theoretical framework suggests the following hypotheses:

H1. Agency activity supporting a policy will increase when public prefer-ences support the policy’s goals.

H2. Agency activity will reflect the strength of groups interested and active on the policy.

H3. Agency activity will reflect the policy preferences of elected officials.

H4a. Agency activity will reflect the relevant local government policies.H4b. Agency activity will reflect the relevant state government policies.H5. Agency activity will reflect the support rank-and-file bureaucrats

(minor agents) have for the policy’s goals.H6. Agency activity will reflect the support bureaucratic leaders (major

agents) have for the policy’s goals.

Policy implementation theory suggests that a similar set of variables may shape bureaucratic behavior, but it helps clarify the relationships among these variables by considering the context in which agencies implement policy (Mazmanian and Sabatier 1989; Meier 1993; McFarlane and Meier 2000). The difficulty officials might have in solving the targeted policy problem, that is, the tractability of the problem, can influence bureaucratic activity. Problems may be more or less tractable depending on available technolo-gies, issues of measurement and problem identification, and the extent of behavior modification needed in the target group (Ringquist 1995). Agencies may work more actively on tractable problems simply because they are more likely to obtain quantifiable measures of success, thereby justifying their efforts to their political principals (Mazmanian and Sabatier 1989). Policy implementation theorists also have elaborated on the role of interest groups in shaping bureaucratic activity. Organized interests may try to influence policy implementation, hindering, helping, or shaping it to match their policy preferences (Mazmanian and Sabatier 1989; Moe 1990; McFarlane and Meier 2000). These efforts can include lobbying bureaucratic officials, protesting or litigating agency actions, and even seeking reduced funding for established programs (Brehm and Gates 1997; Haider-Markel 1998). Bureaucrats may feel especially constrained or emboldened by an interest group if that group benefits directly from their agency’s services (Meier 1993). In addition, policy implementation theorists conclude that policy success or failure also depends on the level and allocation of bureaucratic resources (Mazmanian and Sabatier 1989; Moe 1990; McFarlane and Meier 2000).



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Without adequate funding, personnel, training, and equipment, an agency cannot implement policy successfully. Even if a program is funded well, these resources can be poorly allocated or ineffectively used. For example, educa-tion reform advocates often argue that public school funding is adequate, but that these resources are inefficiently allocated, with too many administrators and too few teachers, leading to poor education for children (Mintrom and Vergari 1998). Thus, agency activity is believed to be influenced by both the amount and allocation of resources. Once policies are enacted, elected officials may consider the problem solved and move on to the next issue. But without the leadership and sup-port of elected officials and top bureaucrats, effective policy implementation is difficult (Mazmanian and Sabatier 1989). The preferences and interests of elected officials thus shape their oversight of bureaucrats, which subsequently influences bureaucratic behavior. Similarly, agency administrators themselves can promote effective policy implementation by providing clear leadership and strong support for a policy and its objectives. One way for administra-tors to signal their support for effective policy implementation is to devote resources to it by creating and implementing internal procedures and training programs for street-level bureaucrats (Brehm and Gates 1997). The signals that street-level bureaucrats receive from their bureaucratic leaders should influence overall agency activity on the issue (Meier 1993). Rank-and-file bureaucratic support also influences agency activity and effectiveness (Keiser and Soss 1998; Keiser 2001). Since bureaucrats’ policy discretion allows them to tailor their activities according to their own preferences, particularly in law enforcement (Brehm and Gates 1997). Finally, the level and direction of public support for a policy may also shape bureaucratic behavior (Mazmanian and Sabatier 1989; Keiser and Soss 1998). Public preferences and opinion may influence agency behavior indirectly through elected officials, or directly as bureaucrats monitor public opinion and obtain feedback on their activities from their clients and others (Meier 1993; Haider-Markel 1999). Thus, policy implementation theory suggests that bureaucratic behav-ior will be shaped by a number of the variables that overhead democracy theorists suggest could influence it, including the preferences of citizens and elected officials. However, at least three hypotheses can be derived from policy implementation theory that cannot explicitly be derived from over-head democracy theory:

H7a. Agency activity on a policy will increase when more resources are devoted to it.

H7b. Agency activity on a policy will increase when the agency develops administrative programs or takes administrative actions related to the



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policy problem.H8. Agency activity on a policy will increase as the problem it is designed

to address is perceived to be more tractable.

hate crime

Hypotheses H1 to H8 were derived from the overhead democracy and policy implementation theoretical frameworks to apply to bureaucratic behavior in general. However, the unique characteristics and politics of hate crime policy may make some of these factors more influential than others. Under pressure from minority, women, and gay rights groups, a number of local, state, and federal hate crime laws have been passed in recent years. Furthermore, some law enforcement officials have developed their own procedures for dealing with these crimes in the absence of new laws. The implementation of these policies falls largely on local police and prosecutors. To implement these laws properly, these bureaucrats must be educated in the recognition and classification of hate crimes, the collection of hate crime statistics, and the pursuit of hate criminals. Unlike non-crimi-nal justice policy, proper implementation of hate crime laws often involves working closely with victims and representatives of related interest groups (Czajkoski 1992; Martin 1995, 1996; Boyd, Berk, and Hamner 1996; Franklin 1999). Furthermore, the enforcement of these laws is completely discretion-ary (Haider-Markel 1998), making the attitudes of police and prosecutors es-pecially important for their effective implementation (Skolnick 1966; Chaney and Saltzstein 1998). None of the existing laws require police to classify a potential hate crime as such. Officers are allowed considerable discretion to determine a crime’s classification based on the circumstances surrounding it (Jacobs 1992; Franklin 1999; Loftin, Logan, and Addington 1999; McDe-vitt, Bennett, and Balboni 1999). Some officers may decide that classifying a crime as a hate crime is not worth the extra time and effort, others may believe that the victim deserved to be attacked, and others may think that successful hate crime prosecution is unlikely, reducing their incentive to classify a crime as a hate crime (Martin 1996, 477).3 Thus, the attitudes of rank-and-file officers and prosecutorial staff, as well as police administrators and district attorneys (DAs), (hypotheses H5 and H6) should be especially important for understanding the implementation of hate crime laws. Bureaucrats’ considerable discretion on hate crime also suggests a greater potential for high responsiveness to vocal citizens and interest groups, but low responsiveness to public opinion if the issue is not salient (Haider-Markel and O’Brien 1999). Elected officials tend to view hate crime laws as symbolic



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and therefore should be less likely to influence bureaucratic activity, unless the issue is salient (Haider-Markel 1998). Although I do not control for is-sue salience explicitly in my empirical models, if citizen and interest group forces do play a significant role, I will presume that the case of hate crimes supports the indirect political control elements of the overhead democracy and policy implementation frameworks. In addition, such results will be more clearly generalizable to other issues where bureaucrats have high discretion in a politically charged atmosphere, such as domestic violence and sexual assault laws.

multivariate analysis of hate crime policy implementation

The most obvious way to test my hypothesis of bureaucratic activity related to hate crime would be to assess the determinants of reported crime-to-ar-rest ratios for local police departments. However, documentary data on hate crime incidents and subsequent law enforcement responses are notoriously weak (Martin 1995, 1996; McDevitt, Bennett, and Balboni 1999). The data problems stem from the lack of reporting by hate crime victims as well as from police failure to document these crimes (Herek and Berrill 1992). To avoid these and other problems associated with official hate crime statistics, I use surveys of law enforcement bureaucrats that elicit attitudes through the use of hypothetical scenarios. The use of these survey data means that my analyses are based on respondents’ perceptions and should therefore be interpreted with caution. Although hate crime policy often covers a variety of potential victim groups, my analysis is limited to the enforcement policy on hate crimes com-mitted against lesbians and gays, for three reasons: 1) the use of a hypotheti-cal scenario within the survey required the identification of a victim from a specific group, 2) hate crimes against lesbians and gays have generally been increasing at a faster rate and tend to be significantly more violent than crimes against other groups (U.S. Department of Justice, 1999), and 3) hate crimes against gays and lesbians are likely to have the weakest official documentation, making assessment of effective law enforcement with official records especially difficult. The weak official documentation results from underreporting by victims who fear being identified as homosexual (Herek and Berrill 1992; Martin 1996; Haider-Markel 1998; U.S. Department of Justice 1999).



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Data Collection

Unless otherwise noted, my data were collected through a national survey of police departments and DAs. The police department survey was conducted during the summer of 1999. The survey was mailed to the office of the police chief in the 250 most populous American cities. The police chief was asked to complete the survey. If she or he was unavailable, the chief’s staff was asked to complete the survey. Over 61 percent (152) of police departments returned the survey, and over 85 percent of the persons completing these surveys were either the police chief or a staff member from the chief’s office.4

For these police department survey respondents, the mean annual de-partmental budget was $60 million, and the mean number of sworn officers was 607 (see Appendix A for descriptive statistics and raw survey response distribution). In these and other ways, these survey respondents were rep-resentative of the population of large and medium city police chiefs and departments. In the full 250–city population, the west and south are over-represented because these regions have more large cities, and the distribution of participants in the survey reflects this pattern. Police departments in larger cities were marginally more likely to complete the survey. This difference is small and does not represent any significant bias in my sample. A second survey was mailed in the fall of 1999 to a random sample of 100 DAs from the original population of the 250 largest United States cities. The county DA covering each of these cities was asked to complete the survey, or if she or he was unavailable, to have her or his staff complete it. Only 37 percent of the DAs contacted agreed to participate in the survey, but the DA rather than an assistant completed 91 percent of these surveys.5

For the offices of the DA, the mean annual budget was about $42 million, the mean number of full-time staff members was 443, and the mean number of full-time attorneys was 144 (see Appendix B for descriptive statistics and raw survey response distribution). Analysis of respondents versus non-re-spondents revealed no city size or regional bias. Western and southern cities were over-represented in the population and in my sample of respondents. A comparison of median populations for the full 250 cities and the respon-dent sample suggests that slightly smaller cities were somewhat more likely to respond, but this difference does not represent a significant bias in my sample. Because the survey population was limited to the largest 250 cities in the country, the results can only be generalized to medium and large cities, and not to cities with less than roughly 100,000 persons. However, based on previous research (Haider-Markel 1998; Haider-Markel and O’Brien 1999; McDevitt, Bennett, and Balboni 1999), we can assume that these small cities



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134 haider-markel

are doing less than medium and large cities on hate crime. For example, small cities are much less likely to report hate crime statistics to the FBI voluntarily under the Hate Crimes Statistics Act of 1990 (McDevitt, Bennett, and Balboni 1999).

Dependent Variables

I operationalize bureaucratic activity on hate crime from my surveys in two ways. First, I capture administrative actions on hate crimes with a series of survey questions relating to administrative procedures and practices. Second, I use a hypothetical hate crime scenario and related survey questions about police and prosecutor actions following the crime.

Dependent Variables I: Administrative Procedures and Practices. Police chiefs and DAs were asked questions about administrative procedures and practices to assess their departments’ and offices’ efforts on hate crime. The allocation of resources and administrative procedures are a measure of bureaucratic activity (Brehm and Gates 1997). These questions included whether their department or office had a liaison for the gay community, made special ef-forts to hire lesbians and gays, had a bias crime task force or officer, provided training on hate crime identification and reporting, and had received training from federal agencies on hate crimes (see Appendices A and B). Each question on administrative actions and procedures was converted to a dichotomous variable coded zero if the department or office did not have the program or procedure and one if it did have it. These variables were also combined into an index for use as an independent variable to help explain the second set of dependent variables.

Dependent Variables II: Hypothetical Scenario Questions. To capture each police chief’s and DA’s perception of hate crime law enforcement in his or her city, a hypothetical scenario was presented to survey respondents. Respondents were asked to read the scenario and then answer several ques-tions designed to tap into the respondents’ perceptions of the attitudes of individual officers and prosecutors. The scenario and questions were pre-sented as follows:

Please read the following hypothetical scenario and respond below.

A white male is beaten and robbed near a gay bar in your city. Officers arrive on the scene along with medical personnel. The injured man tells the officers he believes he was attacked and robbed because he is a homosexual. In fact, he heard both of his two assailants call him a “fag” as he was beaten. Although evidence



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suggests the man was clearly robbed, the initial evidence related to the potential that the crime was motivated by bias towards homosexuals is limited to the state-ment of the victim. The victim is able to give a description of both assailants.

Now based on your department’s/office’s policies and the attitudes of your officers/staff, please rate the following on a scale from 0 to 10, with 0 indicating very unlikely and 10 indicating very likely:

How likely is it that the police officers on the scene will classify the crime as a hate crime?

How likely is it that a hate crime arrest will be made in the case?How likely is it that the district attorney will pursue the case as a hate

crime?How likely is it that a hate crime conviction will be made in the case?

The descriptive statistics for the responses to these questions are dis-played in Appendices A and B. Although it is likely that many respondents biased their answers in the politically correct direction, the variation among respondents should still track variation among police departments and DA offices’ hate crime law enforcement efforts. To this end, respondents’ answers to these four questions are used as dependent variables.

Independent Variables

In this section, I outline how I operationalized my hypotheses of bureaucratic behavior for the multivariate analysis. Unless otherwise noted, the questions and raw response distributions for these independent variables are found in Appendices A and B. Key hypotheses from overhead democracy theory suggest that state and/or local hate crime policies will influence bureaucratic activity on hate crime (H4a and H4b). Although all police departments are subject to the federal Hate Crimes Statistics Act of 1990, not all departments are subject to laws that create criminal penalties for hate crimes. A number of states and localities have such policies, but these vary considerably in their scope and coverage (Haider-Markel 1998). Furthermore, some departments classify, track, and pursue hate crimes even though no state or local policy requires them to do so (Jacobs 1992; Balboni and McDevitt 2001), and sentencing guidelines in some states allow prosecutors to use bias motivation as grounds for lengthening a sentence (Comstock 1991; Herek and Berrill 1992). I measure the existence of state and local policies in a city with dichotomous variables coded one for the presence of a hate crime policy covering sexual orientation, and zero otherwise.6

The extent to which resources have been devoted to hate crime law en-



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forcement is captured with several survey questions, including whether the police department had a liaison for the gay community, made special efforts to hire lesbians and gays, had a bias crime task force or officer, provided train-ing on hate crime identification and reporting, and had received training from federal agencies on hate crimes. Because the use of these administrative practices was highly correlated among the cities in my sample, the responses to these questions were combined into an additive index, ranging from zero to six. Higher scores on this resource index should be associated with stronger efforts to pursue hate crime cases (H7b). Administrative procedures in DA offices were captured with questions on the existence of a bias crime staff, requiring training on hate crimes for DA staff, and special efforts made to hire lesbians and gays. These questions were combined into an additive index, ranging from zero to three. Higher scores on this index should be associated with stronger efforts to pursue hate crime cases (H7b). I also capture bureaucratic resources with two additional questions: 1) “How much of a problem is lack of funding in your department’s/office’s efforts to enforce hate crime laws and collect statistics on hate crime?”; and 2) “How much of a problem is lack of training in your department’s/office’s efforts to enforce hate crime laws and collect statistics on hate crime?”7 Both questions had a one to five response scale, with five suggesting greater prob-lems. Responses to variables developed from these questions should be nega-tively related to the dependent variables (H7a). Political elite support for hate crime policies is captured with the following question: “How concerned have local politicians in your city been about the issue of hate crime in the past three years?” Respondents answered on a one to four scale, with one indicating that these elites were not at all concerned and four indicating that they were very concerned.8 I expect bureaucratic activity on hate crime to be greatest in cities where local politicians are most concerned about hate crimes (H3). I measure the support and leadership of bureaucratic officials with the following question: “How strongly do you agree with the following statement: ‘Hate motivated crimes are more serious than other, similar but non-bias motivated crimes.’” Respondents answered on a one to five scale, with one indicating that the respondent strongly dis-agreed and five indicating that he or she strongly agreed. Since police chiefs (or their assistants) and DAs completed the survey, this question captures the support of the agencies’ leadership. Higher scores should be associated with more bureaucratic activity on hate crime (H6). The support or preferences of rank-and-file officers and DA staff are measured with the following two questions: 1) “On a scale from 0 to 10, with 0 meaning not serious at all and 10 meaning very serious, please rate your



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perception of how seriously regular uniformed officers take hate crimes and the enforcement of hate crime laws;” and 2) “On a scale from 0 to 10, with 0 meaning poor and 10 meaning excellent, please rate your perception of your department’s effort to track, classify, and report hate crime incidents.” The former question captures respondents’ perceptions of the preferences of rank-and-file officers, while the latter question captures their perceptions of the efforts of rank-and-file officers. On the DA survey, the questions referred to the DA’s staff’s attitudes and efforts. Responses to each question should be positively associated with the dependent variables (H5). I assess respondent perceptions of problem tractability with two ques-tions. First, I asked, “Based on your experience, how much impact do you think hate crime laws have on reducing the number of hate crimes in your city?” Responses ranged from zero, “no impact,” to four, “a very large im-pact.” This question captures the bureaucratic leadership’s perception of the ability of laws to solve this problem. Second, respondents were asked, “Based on your knowledge of the behavior of hate crime victims, would you say that hate crime victims: never report hate crimes, often do not report hate crimes, sometimes do not report hate crimes, often do report hate crimes, or always report hate crimes?” This question captures the respondents’ perceptions of the difficulties in identifying, classifying, and tracking hate crimes. Clearly, if victims do not report hate crimes, solving these crimes will be extremely difficult.9 Responses to both tractability questions should be positively as-sociated with the dependent variables (H8). Respondents’ perceptions of the influence of organized interest groups on hate crime law enforcement were assessed with the following question: “Please rate the degree to which local groups concerned about hate crimes attempt to influence your department’s activities related to hate crimes.” Respondents rated these attempts on a zero to four scale, with zero meaning “never” and four meaning “always.” I expect that as interest group efforts increase, law enforcement activity will also increase (H2). Finally, I use two surrogate measures to assess the influence of public opinion on bureaucratic activity.10 The first measure is a surrogate for the visibility of the lesbian and gay population in a city, which may reflect toler-ance for homosexuals, as well as potential gay interest group strength (Button, Rienzo, and Wald 1997; Haider-Markel 1997). This variable is the number of same-sex unmarried partner households per 100,000 population.11 I expect this variable to be positively associated with bureaucratic hate crime efforts (H1 and H2). The second public opinion surrogate measures the anti-gay religious values in a city.12 Because anti-gay religious beliefs are held most strongly by Protestant fundamentalist denominations (Haider-Markel and



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Meier 1996), I measure anti-gay religious values as the percent of the county population (in which a city is located) that is affiliated with a Protestant fun-damentalist denomination.13 In cities with a higher proportion of Protestant fundamentalists, support for hate crime policies covering sexual orientation may be weaker (Haider-Markel and O’Brien 1999). As a consequence, I expect that bureaucratic activity on hate crime will be negatively associated with this variable (H1 and H2).

analysis and results

Administrative Procedures and Activities

I used a series of logistic regression models to estimate police department hate crime administrative procedures and activities, given the dichotomous nature of each dependent variable. For these models, I only used variables that measure city characteristics and none of the measures of bureaucratic percep-tions since these programs and procedures may pre-date current bureaucratic leaders. Economies of scale may increase the chances that cities with greater population and more police personnel adopt these procedures, so controls were also included for city population and the number of police officers per 1,000 persons. The results of these models are shown in Table 1. The results in Table 1 are inconsistent across the various administra-tive procedures and training efforts.14 The existence of state and local hate crime policies generally appears to have no influence on the adoption of administrative procedures to combat hate crime. State laws only influence the creation of a hate crimes task force, but local ordinances appear to have no influence on the adoption of any of these procedures (H4a and H4b). Simi-larly, the Protestant fundamentalists variable, my indicator of conservative religious opinion, appears to have no statistically significant influence (H1 and H2). The same-sex unmarried households variable is positively related to administrative procedures to a statistically significant degree in four of these models, as are larger populations, suggesting that police departments respond to population characteristics that indicate more tolerance towards gays (H1 and H2). Interestingly, four of the six models estimate that the more police officers per capita a city has, the less likely it is that the department will adopt hate crime administrative procedures and training efforts. This might occur because cities with more police officers per capita are more authoritarian and less tolerant of alternative lifestyles (Brown and Warner 1992). Indeed, further analysis (not shown) indicates a statistically significant negative correlation between how seriously officers take hate crimes and the number of officers per capita.



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Tabl

e 1:

Det

erm

inan

ts o

f Po

lice

Dep

artm

ent

Hat

e C

rim

e A

dmin

istr

ativ

e P

roce

dure

s an

d Tr

ain

ing

Eff

orts

Inde

pend

ent

Hat

e C

rim

e R

equ

ire

Liai

son

Offi

cer

Eff

orts

to

Col

lect

Sta

tist

ics

Fede

ral

Var

iabl

es

Task

For

ce

Trai

nin

g fo

r G

ays

Hir

e G

ays

for

HC

SA o

f 19

90

Trai

nin

gSt

ate

polic

y (H

4b)

.904

**

.115

–.

203

.574

.2

98

.636

(.

450)

(.

417)

(.

431)

(.

560)

(.

517)

(.

553)

Loca

l pol

icy

(H4a

) .5

39

.653

–1

.389

1.

310

7.35

4 8.

209

(1

.007

) (1

.237

) (1

.015

) (1

.081

) (2

1.04

4)

(24.

424)

Sam

e-se

x u

nm

arri

ed

Hou

seh

olds

(H

1 &

H2)

.6

86**

.9

98**

.3

00

.999

**

1.40

5**

.213

(.

301)

(.

417)

(.

240)

(.

345)

(.

678)

(.

292)

Pro

test

ant

Fun

d.

(H1

& H

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140 haider-markel

In summary, the adoption or use of administrative procedures and train-ing designed to reduce hate crime is more likely in larger cities and cities with more visible lesbians and gays. These anti-hate crime efforts are some-what less likely to be found in cities with more police officers per capita and perhaps a larger Protestant fundamentalist population. Importantly, state and local policies on hate crime do not generally influence these police procedures. These results suggest that law enforcement may be resistant or indifferent to efforts at political control by elected officials, but that they are responsive to public preferences. Further analysis is needed to explore this very interesting result.

analysis of hypothetical scenario questions: hate crime efforts by the law enforcement bureaucracy

I estimate a full model and a more parsimonious reduced model for each de-pendent variable based on questions about the hypothetical scenario (Tables 2a, 2b, 3a, and 3b). I used ordinary least squares regression because each of these dependent variables ranges from zero to ten (Hamilton 1992). In developing the reduced models, independent variables were removed from the full model based on their ability to improve the model goodness of fit statistics, statistical significance, and degree of colinearity with other variables in the model. For clarity, I focus my discussion on the reduced models. The results of the reduced police department models are shown in Table 2b and the reduced DA models are shown in Table 3b. The results of the police department models in Table 2b are mixed, with reasonably high levels of explanation, but inconsistently performing vari-ables. Overall, the full models predict respondents’ expectations of police and DA office activity fairly well. The subsequent reduced models show similar levels of explanation but also show significantly reduced standard errors and higher F scores. This suggests that the reduced models have lower colinearity problems and an improved goodness of fit, justifying the reduc-tion of the models. As suggested by the overhead democracy theoretical framework, the existence of state hate crime policy has a consistent and statistically significant influence on arrests, DA pursuit of this case, and the likelihood of a conviction in the case, but it appears to have no significant influence on classification that, in fact, may be the easiest activity for depart-ments to undertake (H4b). The fact that neither state nor local hate crime policies influence classification efforts suggests that state and local elected officials may have little influence over this particular police activity on hate



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crimes (H4a and H4b). This conclusion is supported by the fact that my indicator of local political elite support for hate crime policies does not have a statistically significant coefficient in any of these police models (H3). This finding is consistent with Haider-Markel and O’Brien’s (1999) finding that hate crime policies may largely be symbolic, and that local elected officials have little incentive to force strong implementation of them. The percentage of Protestant fundamentalists in a city is estimated to decrease the likelihood of arrests, DA pursuit, and conviction, as predicted (H1 and H2). Thus, police and prosecutor perceptions again appear to reflect public preferences when enforcing hate crime laws since high levels of anti-gay religious orthodoxy tend to reduce efforts to address crimes motivated by bias towards those with non-traditional sexual orientation. Although this finding may be troubling for gays as potential victims, it suggests that non-elected bureaucrats are directly responsive to the preferences of the populace. This conclusion is consistent with some models of overhead democracy (West 1996; Haider-Markel 1999). Although my index of hate crime administrative procedures does not appear to influence perceptions of police activity directly in my model, the lack of funding and training do reduce at least two types of hate crime related police activity, at least as perceived by police chiefs (H7a and H7b). Furthermore, the support of rank-and-file officers is estimated to increase police activity, and their efforts are estimated to increase prosecution ef-forts and convictions (H5). This finding makes sense in that officer support will influence their own efforts, while their overall effort will determine the prospects for the successful prosecution of a particular case. In my analysis, police leadership support influences only classification efforts to a statistically significant degree (H6). This suggests that the po-lice leaders’ preferences influence perhaps the most basic activity on hate crime, but once the law enforcement process begins (e.g., once the case has been classified), their preferences matter little. Again, this makes sense. Officers have much discretion in crime classification, but once an act has been classified as a potential hate crime, discretion for its further pursuit is greatly reduced. It is police officers’ discretionary actions that should be most influenced by their leaders’ preferences. Finally, the tractability of the problem, as measured by police chiefs’ per-ceptions of impact of hate crime policies, has a consistent and positive impact on activity in my models (H8). If police chiefs believe that hate crime policies can reduce hate crime, then law enforcement activity in the area increases. Although this measure of tractability is a rough indicator of how difficult the problem is to solve, the results suggest that the perception that these laws



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Table 2a: Determinants of Effort on Hate Crime, Full Models Based on Police Department SurveyIndependent Classification Police DA Pursuit ConvictionVariables as Hate Crime Arrest of Case in CaseState policy (H4b) .425 1.712** 1.695** 1.671** (.447) (.633) (.645) (.623)Local policy (H4a) .868 .212 –1.584 –.444 (1.057) (1.479) (1.503) (1.512)Same-sex unmarried

households (H1 & H2) .007 .206 –.247 –.168 (.197) (.277) (.281) (.293)Protestant fund

(H1 & H2) –.001 –.037 –.044* –.047* (.018) (.025) (.025) (.024)Interest group

lobbying (H2) –.120 –.352 –.070 –.140 (.223) (.318) (.325) (.315)Lack of funding (H7a) –.398 –.462 –.076 .025 (.285) (.400) (.406) (.390)Lack of training (H7b) –.461 .134 –.221 –.057 (.333) (.780) (.484) (.467)Index of admin.

procedures (H7b) .152 –.091 .077 .046 (.147) (.210) (.210) (.212)Rank-and-file support

(H5) .325** .352* .152 .179 (.149) (.210) (.214) (.217)Rank-and-file effort (H5) .259** .111 .164 .167 (.107) (.144) (.150) (.145)Police leader support

(H6) .457** .165 .089 .010 (.020) (.277) (.275) (.279)Political elite support (H3) –.029 .390 .272 .088 (.243) (.344) (.348) (.345)Tractability: victim

reporting (H8) –.108 –.216 –.432 –.262 (.293) (.415) (.417) (.400)Tractability: impact of

laws (H8) .511* .936** 1.111** 1.092** (.268) (.385) (.391) (.391)Constant 1.716 .192 2.058 1.406 (1.753) (2.461) (2.485) (2.379)R-square .49 .35 .35 .34Adjusted R-square .42 .27 .27 .24Standard error 2.09 2.91 2.96 2.81F 7.494** 4.153** 4.075** 3.579**Number of cases 125 120 119 113Notes: Coefficients are OLS regression coefficients. Standard errors are in parentheses. Significance levels in

two-tailed test: **p < .05; *p < .10.



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Table 2b: Determinants of Effort on Hate Crime, Reduced Models Based on Police Department SurveyIndependent Classification Police DA Pursuit ConvictionVariables as Hate Crime Arrest of Case in CaseState policy (H4b) .560 2.078** 1.828** 1.685** (.399) (.555) (.583) (.550)Local policy (H4a) — — — —Same-sex unmarried

households (H1 & H2) — — — —Protestant fund.

(H1 & H2) — –.049** –.043* –.046** (.022) (.022) (.021)Interest group lobbying

(H2) — — — —Lack of funding (H7a) –.434* –.553* — — (.260) (.315)Lack of training (H7b) –.514* — — — (.310)Index of admin.

procedures (H7b) .196 — — — (.130)Rank-and-file support

(H5) .300** .358** — — (.127) (.146)Rank-and-file effort (H5) .213** — .262** .248* (.097) (.097) (.092)Police leader support

(H6) .359** — — — (.180)Political elite support (H3) — — — —Tractability: victim

reporting (H8) — — — —Tractability: impact of

laws (H8) .555** .992** 1.248** 1.225** (.236) (.318) (.329) (.324)Constant 2.029 1.517 1.667* 1.492 (1.435) (1.594) (.972) (.926)R-square .46 .33 .33 .33Adjusted R-square .43 .30 .30 .31Standard error 2.05 2.80 2.86 2.68F 13.132** 12.292** 14.820** 14.557**Number of cases 130 130 127 120Notes: Coefficients are OLS regression coefficients. Standard errors are in parentheses. Significance levels in

two-tailed test: **p < .05; *p < .10. — indicates omitted variable.



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Table 3a: Determinants of Effort on Hate Crime, Full Models Based on District Attorney SurveyIndependent Classification Police DA Pursuit ConvictionVariables as Hate Crime Arrest of Case in CaseState policy (H4b) 2.619* 1.434 2.759 2.270 (1.422) (1.685) (2.136) (2.044)Local policy (H4a) –2.280 –3.837* –2.221 –3.053 (1.792) (2.123) (2.691) (2.576)Same-sex unmarried

households (H1 & H2) 2.125** 1.156 1.213 1.571 (.723) (.857) (1.086) (1.040)Protestant fund.

(H1 & H2) –.364** –.302** –.238 –.273* (.105) (.124) (.157) (.151)Interest group lobbying

(H2) .350 .681 –.222 –.246 (.770) (.912) (1.156) (1.107)Lack of funding (H7a) –.488 –.992 –.657 –.507 (.728) (.862) (.828) (1.046)Lack of training (H7b) –1.752 .221 .071 –.194 (1.105) (1.310) (1.660) (1.589)Index of admin.

procedures (H7b) 1.193 1.457 1.212 1.184 (.861) (1.020) (1.292) (1.237)Rank-and-file support

(H5) .401 .924 .329 .283 (.504) (.598) (.757) (.725)Rank-and-file effort (H5) .020 -.029 .014 .013 (.038) (.045) (.057) (.054)DA support (H6) .264 .301 .633 .694 (.551) (.653) (.828) (.793)Political elite support (H3) –1.173 –1.342 –.784 –.766 (.813) (.963) (1.221) (1.169)Tractability: victim

reporting (H8) 2.339** 1.170 1.216 1.447 (.693) (.821) (1.040) (.995)Tractability: impact of

laws (H8) –1.526 –1.323 –.780 –1.013 (.866) (1.026) (1.300) (1.245)Constant 3.177 .352 2.295 3.101 (4.464) (5.290) (6.704) (6.417)R-square .79 .76 .66 .65Adjusted R-square .61 .56 .37 .36Standard error 1.77 2.09 2.65 2.54F 4.470** 3.739** 2.252* 2.177*Number of cases 32 32 32 32Notes: Coefficients are OLS regression coefficients. Standard errors are in parentheses. Significance levels in

two-tailed test: **p < .05; *p < .10.



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Table 3b: Determinants of Effort on Hate Crime, Reduced Models Based on District Attorney SurveyIndependent Classification Police DA Pursuit ConvictionVariables as Hate Crime Arrest of Case in CaseState policy (H4b) 3.466** 2.699** 3.480** 2.716** (.809) (.987) (1.176) (1.178)Local policy (H4a) — — — —Same-sex unmarried

households (H1 & H2) 1.422** 1.315** 1.169 1.347* (.499) (.609) (.726) (.727)Protestant fund.

(H1 & H2) –.253** –.307** –.238** –.253** (.059) (.072) (.085) (.085)Interest group lobbying

(H2) — — — —Lack of funding (H7a) — — — —Lack of training (H7b) –1.059* .136 –.098 –.165 (.565) (.690) (.822) (.824)Index of admin.

procedures (H7b) — — — —Rank-and-file support

(H5) — — — —Rank-and-file effort (H5) — — — —DA support (H6) .343 .653* 1.035** .997** (.305) (.373) (.444) (.445)Political elite support (H3) –.831* –.824 –1.115 –1.162* (.462) (.616) (.672) (.674)Tractability: victim

reporting (H8) 1.989** 1.850** 1.751** 1.972** (.505) (.616) (.734) (.736)Constant 4.046* 2.139 1.447 1.230 (2.032) (2.479) (2.954) (2.961)R-square .73 .66 .57 .52Adjusted R-square .65 .58 .46 .40Standard error 1.67 2.03 2.42 2.43F 9.797** 7.391** 4.984** 4.086**Number of cases 33 33 33 33Notes: Coefficients are OLS regression coefficients. Standard errors are in parentheses. Significance levels in

two-tailed test: **p < .05; *p < .10. — indicates omitted variable.

can make a difference appears to give police the incentive to enforce them. If bureaucratic leaders do not believe policies will solve a problem, their efforts are weaker, presumably because they do not want to waste resources on what they see as a lost cause. The results of the DA’s perceptions models in Table 3b show a pattern similar to those of the police chiefs’ perceptions. These models suggest that the crime specified in my scenario is more likely to be pursued strongly throughout the process if state hate crime policies exist (H4b), the same-sex



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146 haider-markel

household population is higher (H1 and H2), the Protestant fundamental-ist population is lower (H1 and H2), and bureaucratic elites (the DA) are supportive (H6). Furthermore, the tractability of the problem, measured by the perception that hate crime victims are less likely to report the crime, negatively influences the pursuit of hate crimes cases (H8). The support of local political elites for hate crime laws has a less consistent influence on DAs’ perceptions of law enforcement efforts, but it suggests a negative relationship (H3). This result is inconsistent with policy implementation theory, but it was not supported in the analysis of police chiefs’ perceptions. Further analysis is needed to understand this finding more fully.

conclusions

Local police departments and prosecutors are responsible for implementing criminal law, including laws against crime motivated by bias or hate toward particular groups of people. This study examined police department and district attorney implementation of hate crime laws, with particular attention to hate crimes against lesbians and gays. I specified a number of hypotheses of law enforcement activity on hate crime based on the overhead democracy and policy implementation theoretical frameworks and tested these hypotheses using multivariate analysis of survey and demographic data from medium and large American cities. These analyses suggest several important conclu-sions. However, since they are based on survey data of bureaucratic leaders’ perceptions rather than on behavioral data from actual policy implementa-tion, these conclusions are not definitive and suggest the need for future research as better data become available. First, police and prosecutors in America’s largest cities have taken significant steps to address hate crime, such as adopting special training programs and assigning key personnel to investigate and prosecute hate crimes. However, these administrative efforts do not appear to influence the extent to which hate crime suspects are pursued and prosecuted. At best, my analyses suggest that only hate crime training for law enforcement may enhance agency activity on hate crime. Thus, efforts to enhance the enforce-ment of hate crime laws might be most efficiently targeted on training. Second, as suggested by both overhead democracy and policy implemen-tation theories, bureaucrats’ attitudes toward policy can have a significant influence on agency activity. In my analyses, I found that if both rank-and-file bureaucrats and their leaders are supportive, the agency will more forcefully pursue hate crimes. In police departments, the preferences and perceived efforts of rank-and-file officers had a significant impact on agency activity, while in prosecutors’ offices, the preferences of DAs had more influence.



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The preferences of their leaders may matter more to prosecutors simply be-cause the latter have less discretion in pursuing cases than do police officers. This finding is consistent with previous research on bureaucratic discretion (Brehm and Gates 1997), but further research is needed to clarify the role of bureaucrat preferences and discretion in law enforcement bureaucracies. Third, policy implementation theory suggests that implementation is more effective if the problem being addressed is more tractable, but no pre-vious quantitative study has attempted to measure this illusive concept. I measured hate crime problem tractability with the perceptions of police chiefs and DAs, asking them how much impact they believed hate crime laws have on the level of hate crime and how often hate crime victims report these crimes. My results indeed suggest that problem tractability has a significant influence on implementation efforts. If bureaucratic leaders believe that a law can have an impact or that the problem can be identified and measured, they are more likely implement the law strongly. Fourth, and perhaps most important for overhead democracy theory, although elected officials in many cities appear to be at least moderately concerned with hate crime, their concern does not appear to translate into greater agency activity, controlling for other relevant factors. Furthermore, although state hate crime policies were found to influence agency efforts, local ordinances appear to have no independent influence. This combination of results suggests that local-elected officials’ control of police and prosecutors may be relatively weak, or at best, mixed on hate crime policy. The influence of state laws suggests that local bureaucrats may respond best to guidance from state officials, perhaps because state officials pass the majority of crimi-nal law. Previous research found effective political control of law enforcement bureaucracies through state and national legislation (Chaney and Saltzstein 1998; Haider-Markel 1999) and through the preferences of elected officials (Wilson 1978; Wood 1988, 1992; Wood and Waterman 1991, 1994; Ringquist 1995; Scholz and Wood 1998), but my findings are only consistent with the previous research on the impact of state legislation. Because most of this earlier research is focused on federal agencies, their inconsistency with my results may suggest that federal law enforcement bureaucrats are more re-sponsive to their political principals than are local bureaucrats. This pattern is evident, for example, in the comparison of Wilson’s 1969 study of local law enforcement agencies with his 1978 study of federal law enforcement agencies. Chaney and Saltzstein’s (1998) findings on domestic violence policy implementation by local police are also consistent with my results, showing that state legislation has a significant influence on implementation efforts. Thus, although local political control appears relatively weak in hate crime



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148 haider-markel

policy implementation, political control exists through state law and fund-ing priorities (Jacoby and Schneider 2001). State officials may simply have better policy tools for directing law enforcement bureaucrats, especially with controversial policies. Finally, although few studies have uncovered a direct connection between public preferences and bureaucratic behavior (Wilson 1978; Haider-Markel 1999), my findings suggest that such overhead democracy may be at work in hate crime policy. I found that the religious conservatism of a city’s popula-tion reduced police and prosecutor efforts related to crimes committed based on sexual orientation, and the level of same-sex unmarried households (my surrogate for gay population) increased these efforts. So while my findings suggest that local officials have little influence on hate crime law enforce-ment, overhead democracy may work more directly through bureaucratic responsiveness to citizen preferences, rather than indirectly through elected officials. While such a pattern of responsiveness may be positive for normative theories of overhead democracy, potential hate crime victims, who are often in the minority, may not appreciate this type of majoritarian democratic responsiveness.15

Fundamentally, this returns us to the dilemma raised at the beginning of this article. How do law enforcement agencies balance competing demands? In the case of hate crime policy, it appears that they are most responsive to the diffuse demands and preferences of the citizens they meet daily on city streets, rather than those of their political principals. If these political prin-cipals are more attuned to concerns for civil rights and justice for minorities than the citizenry at large, potential victims of hate crimes may suffer.

appendix a

Survey of Police Chiefs: Questions and Descriptive Statistics

Comparison of the Cities of the Population and Respondents

Population (N = 250) Response Group (N = 152)Northeast 13.4% 7.3%Midwest 19.8% 20.5%South 30.4% 34.4%West 36.4% 37.7%Mean Population 274,430.9 267,462.7Median Population 140,891 142,560

Raw Survey Response Distributions

I. Administrative Procedures and Practices Questions



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Does your police department have a special officer, task force or unit to investigate and track bias or hate crimes? (By hate crimes, we mean crimes committed because of the victim’s race, ethnicity, gender, disability, or sexual orientation.)

No 55.7% Yes 35.6% Other 8.7% N= 149

Does your department require training for officers on the identification and reporting of hate or bias crimes?

No 33.1% Yes 60.8% Other 6.1% N= 148

Does your department have a liaison officer for the following communities? (By liaison, we mean an employee whose job is to meet with the community and help solve that community’s problems or complaints with the department.)

—African Americans No 47.4% Yes 44.7% N/a 8.6%

—Lesbians and gays No 57.2% Yes 36.2% N/a 6.6%

Does your department have any programs or make any special efforts to hire persons from the following groups?


—Lesbians and gays No 66.4% Yes 24.3% N/a 9.3%

Does your department participate in the voluntary federal program to collect statistics on hate crimes under the Hate Crime Statistics Act of 1990?

No 15.1% Yes 77.6% Other 2.6% N/a 4.6% N= 152Has your department participated in federal training programs for the identification, tracking, and reporting of hate crimes?

No 38.2%



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150 haider-markel

Yes 27.6% Don’t know 34.2% N= 152

II. Implementation Effort Questions

On a scale from 0 to 10, with 0 meaning not serious at all and 10 meaning very serious, please rate your perception of how seriously regular uniformed officers take hate crimes and the enforcement of hate crime laws.

0 1 2 3 4 5 6 7 8 9 10 MeanN= 143 0% 2.1% 0% 0.7% 0% 1.4% 4.2% 13.3% 26.6% 21.0% 30.8% 8.38

On a scale from 0 to 10, with 0 meaning poor and 10 meaning excellent, please rate your perception of your department’s effort to track, classify, and report hate crime incidents.

0 1 2 3 4 5 6 7 8 9 10 MeanN= 138 3.6% 1.4% 2.2% 2.2% 2.2% 10.9% 2.9% 5.1% 16% 22.5% 31.2% 7.70

Based on your knowledge of the behavior of hate crime victims would you say that hate crime victims:

Never report hate crimes 0.0% Often do not report hate crimes 14.5% Sometimes do not report hate crimes 49.3% Often do report hate crimes 29.6% Always report hate crimes 2.0% No answer/don’t know 4.6% N= 152

Please rate the degree to which local groups concerned about hate crimes attempt to influence your department’s activities related to hate crimes:

Never try to influence our activities related to hate crimes 9.9% Almost never try to influence our activities 32.2% Sometimes do not, sometimes do try to influence our activities 36.2% Often try to influence our activities 15.8% Always try to influence our activities 2.7% No answer/don’t know 3.3% N= 152

Based on your experience, how much impact do you think hate crime laws have on reducing the number of hate crimes in your city?

No impact 17.8% Very little impact 42.1% Some impact 29.6% Quite a bit of impact 5.3% A very large impact 0.0% No answer/don’t know 5.2% N= 152



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How strongly do you agree with the following statement: “Hate motivated crimes are more serious than other, similar but non-bias motivated crimes.”

Strongly disagree 3.3% Disagree 11.8% Neither disagree nor agree 27.0% Agree 35.5% Strongly agree 18.4% No answer/don’t know 3.9% N= 152

How much of a problem is lack of funding in your department’s efforts to enforce hate crime laws and collect statistics on hate crime?

Not a problem 58.6% Slight problem 21.7% Problem 11.8% Severe problem 2.0% Severe and continuing problem 0.0% No answer/don’t know 5.9% N= 152

How much of a problem is lack of training in your department’s efforts to enforce hate crime laws and collect statistics on hate crime?

Not a problem 50.7% Slight problem 30.9% Problem 11.2% Severe problem 1.3% Severe and continuing problem 0.0% No answer/don’t know 5.9% N= 152

How concerned have local politicians in your city been about the issue of hate crime in the past three years?

Not at all concerned 18.4% Somewhat concerned 40.1% Concerned 22.4% Very concerned 11.2% No answer/don’t know 7.9% N= 152

III. Hypothetical Scenario Questions

Given the hate crime scenario (in the text), respondents were asked to answer the fol-lowing questions:




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152 haider-markel

Very Very Unlikely Likely 0 1 2 3 4 5 6 7 8 9 10 MeanN= 143 4.2% 1.4% 2.1% 1.4% 2.1% 7.7% 6.3% 9.1% 20.3% 18.9% 26.6% 7.56

How likely is it that a hate crime arrest will be made in the case?


How likely is it that the district attorney will pursue the case as a hate crime?


How likely is it that a hate crime conviction will be made in the case?


appendix b

Survey of District Attorneys: Questions and Descriptive Statistics

Comparison of the Cities of the Population and Respondents

Region Population (N = 100) Response Group (N = 37)Northeast 3.5% 5.4%Midwest 23.9% 24.3%South 27.4% 27.0%West 45.1% 43.2%Mean Population 265,536.4 324,424.3Median Population 141,993 135,160

Raw Survey Response Distributions

I. Administrative Procedures and Practices QuestionsDoes your office have staff who are devoted to or specialize in prosecuting bias or hate crimes? (By hate crimes, we mean crimes committed because of the victim’s race, ethnic-ity, gender, disability, or sexual orientation.)

No 43.2% Yes 56.8% N/a 0.0% N=37

Does your office require training for your staff on the identification and prosecution of



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hate or bias crimes?

No 48.6% Yes 51.4% N/a 0.0% N=37

Does your office have any programs or make any special efforts to hire persons from the following groups?


—Lesbians and gays No 48.6% Yes 37.8% N/a 13.5% N=37

II. Implementation Effort Questions

On a scale from 0 to 10, with 0 meaning not serious at all and 10 meaning very serious, please rate your perception of how seriously your staff members (including lawyers) take hate crimes and the enforcement of hate crime laws.

0 1 2 3 4 5 6 7 8 9 10 MeanN=36 0.0% 0.0% 5.6% 0.0% 0.0% 13.9 % 0.0% 0.0% 13.9% 16.7% 63.9% 9.28

On a scale from 0 to 10, with 0 meaning poor and 10 meaning excellent, please rate your perception of your police department’s effort to track, classify, and report hate crime incidents.

0 1 2 3 4 5 6 7 8 9 10 MeanN=35 0.0% 2.9% 0.0% 5.7% 2.9% 8.6% 0.0% 5.7% 25.7% 14.3% 31.4% 8.00

Based on your knowledge of the behavior of hate crime victims, would you say that hate crime victims:

Never report hate crimes 0.0% Often do not report hate crimes 13.9% Sometimes do not report hate crimes 47.2% Often do report hate crimes 36.1% Always report hate crimes 0.0% No answer /don’t know 2.8% N=36

Please rate the degree to which local groups concerned about hate crimes attempt to influence your office’s activities related to hate crimes:

Never try to influence our activities related to hate crimes 8.1% Almost never try to influence our activities 16.2% Sometimes do not, sometimes do try to influence our activities 56.8%



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154 haider-markel

Often try to influence our activities 16.2% Always try to influence our activities 0.0% No answer/don’t know 2.7% N=36

Based on your experience, how much impact do you think hate crime laws have on reducing the number of hate crimes in your city?

No impact 13.5% Very little impact 29.7% Some impact 32.4% Quite a bit of impact 21.6% Avery large impact 0.0% No answer/don’t know 2.7% N=36

How strongly do you agree with the following statement: “Hate motivated crimes are more serious than other, similar but non-bias motivated crimes.”

Strongly disagree 8.1% Disagree 5.4% Neither disagree nor agree 24.3% Agree 48.6% Strongly agree 13.5% No answer/don’t know 0.0% N=37

How much of a problem is lack of funding in your office’s efforts to prosecute hate crimes?

Not a problem 73.0% Slight problem 10.8% Problem 10.8% Severe problem 0.0% Severe and continuing problem 2.7% No answer/don’t know 2.7% N=36

How much of a problem is lack of training in your office’s efforts to prosecute hate crime laws?




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How much of a problem is resistance from police officers in your office’s efforts to pros-ecute hate crimes?


How much of a problem is a lack of cooperation from police department officials in your office’s efforts to prosecute hate crimes?


How concerned have local politicians in your city been about the issue of hate crime in the past three years?

Not at all concerned 5.4% Somewhat concerned 37.8% Concerned 40.5% Very concerned 13.5% No answer/don’t know 2.7% N=36

III. Hypothetical Scenario Questions

Given the hate crime scenario (in the text), respondents were asked to answer the fol-lowing questions:


Very Very Unlikely Likely 0 1 2 3 4 5 6 7 8 9 10 MeanN=36 5.6% 0.0% 0.0% 5.6% 5.6% 16.7% 2.8% 11.1% 11.1% 25.0% 16.7% 6.97

How likely is it that a hate crime arrest will be made in the case?




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How likely is it that the district attorney will pursue the case as a hate crime?


How likely is it that a hate crime conviction will be made in the case?


endnotes

A previous version of this article was presented at the State of the States: State Poli-tics and Policy Conference, Texas A&M University, March 2001. The author thanks Elaine Sharp and Mark Joslyn for their comments on earlier versions of this article. This research was funded by a 1998 Wayne F. Placek Award from the American Psy-chological Foundation.

1. Hate crimes are usually defined as crimes that are committed, wholly or in part, because of the victim’s group identification. This group identification might include race, ethnicity, religion, sexual orientation, gender, veteran status, and disability, among others (U.S. Department of Justice 1999). 2. Because my law enforcement data are based on survey responses from police chiefs and district attorneys, I only have information on the perceptions of my respondents and not observed, behavioral data on bureaucratic performance. However, bureaucratic leaders have considerable knowledge of agency performance and their perceptions have been used successfully in similar studies (Chaney and Saltzstein 1998; Gormley 1998). Furthermore, official data on law enforcement activity is notoriously inaccurate, for example, when compared to reported crime in victimization surveys (Marenin 1997). Thus, it is not clear that police arrest data and the like would be more reliable or valid than my survey data. 3. Hate crime policies come in two major forms (Haider-Markel 1998). First, some policies call for police to identify crimes motivated by bias toward a particular group and to collect statistics on those crimes. Second, other hate crime policies enhance the penalty for crimes motivated by bias, with some laws distinguishing between specific regular and bias-motivated crimes, such as robbery versus harassment. In 1999, at least 25 states required statistics collection for certain hate crimes, and 42 states allowed a criminal penalty or sentence enhancement for hate crimes against certain groups. At least 28 states also have laws that enable hate crime victims to file civil lawsuits (Anti-Defamation League 1999). At least 16 localities and the District of Columbia had hate crime statistics collection and/or penalty enhancement ordinances, but their coverage varies considerably (Haider-Markel and O’Brien 1999). In each hate crime policy, covered victim groups are identified explicitly. For example, the FBI defines hate crimes as crimes that are committed, wholly or in part, because of the victim’s race, ethnicity, religion, or



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sexual orientation (U.S. Department of Justice 1993, 1). Most state and local hate crime policies cover race, ethnicity, and religion, but only 23 state policies cover sexual orienta-tion (Anti-Defamation League 1999). 4. In a few departments, the survey was referred to a hate or bias crime task force or officer, but each of the completed surveys was cleared by the chief’s office before being returned to me. Analysis of the survey data revealed no systematic differences in responses of those surveys completed by police chiefs and those completed by their staff. 5. Analysis of the survey data revealed no systematic differences in responses of those surveys completed by DAs and those completed by staff. 6. Data on state policies are from the Anti-Defamation League (1999) and data on local policies are from Haider-Markel and O’Brien (1999). Cities in Texas are counted as being covered by a gay and lesbian hate crime law even though the Texas law did not mention any specific target group in 1999. 7. The specific wording of the questions varied for the police department and DA surveys (see Appendices A and B). 8. McDevitt, Bennett, and Balboni (1999) used a similar survey question. 9. The problem of non-reporting seems as pervasive for hate crime (Comstock 1991; Herek and Berrill 1992; Cramer 1999) as it is for sexual assault and domestic violence (Buzawa 1988; Backman 1998). 10. My surrogate measures of public opinion also serve as proxies for potential interest group strength (H2). 11. These data are from the U.S. Bureau of the Census (1990). The 1990 census asked respondents if they lived with an “unmarried partner.” Only those respondents indicating that they lived with a same-sex unmarried partner were counted for my measure. While this measure is crude, it has face validity as a surrogate measure of the openly gay and lesbian population (Button, Rienzo, and Wald 1997). 12. As with the same-sex unmarried partner variable, this variable could also be a surrogate for conservative religious interest group strength (Haider-Markel and Meier 1996). 13. These data are from Bradley et al. (1992) and the classification of Protestant fun-damentalist denominations follows Haider-Markel and Meier (1996). 14. Because the survey of DAs asked fewer questions about administrative procedures, I only estimated these models for police departments. In similar models estimated on my limited DA office data, only the number of attorneys in the office per 1,000 population and the size of the city were related to the adoption of administrative procedures related to hate crime to a statistically significant degree. Both relationships were positive. Further-more, both of these variables were highly collinear with demographic variables such as the same-sex unmarried partner population, perhaps covering up the potential relationship between these demographics and administrative procedures for hate crimes. 15. See Gamble (1997) for her discussion of a similar concern for the effect of initiatives on minority interests.

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Regulating Hate: State and Local Influences on Law Enforcement Actions Related to Hate Crime

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