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The Impact of Political Context on Local Law Enforcement Resourcing: AnAnalysis of Deputy Employment Rates in US CountiesRonald Helms

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To cite this Article Helms, Ronald(2007)'The Impact of Political Context on Local Law Enforcement Resourcing: An Analysis of DeputyEmployment Rates in US Counties',Policing and Society,17:2,182 — 206

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The Impact of Political Context onLocal Law Enforcement Resourcing: AnAnalysis of Deputy Employment Ratesin US CountiesRonald Helms

Determinants of municipal law enforcement have been studied intensively, but county

deputy rates have rarely been the focus of research attention. This study uses a sample of

190 sheriff ’s offices and OLS regression methods to assess diverse environmental and

internal organizational explanations for variation in the strength of law enforcement

agencies in US counties. The results show that Sheriff ’s agencies with highly

differentiated functional units, retain deputies at higher rates than their counterparts

in less complex organizations. Where agencies capture a larger percentage of the county

budget, law enforcement personnel are increased as well. Perhaps the most important

results are that agencies in economically unequal counties, and those operating in

counties where voters were strongly supportive of law and order political appeals,

employed deputies at the highest rates. These results reinforce research claims that

political influences and unequal social environments are key factors affecting local social

control arrangements.

Keywords: Deputies; Sheriff ’s office; Crime control; Social control; Law enforcement;

Environmental contingencies; Inequality; Counties; Personnel; Organizations

Introduction

Researchers have assessed ecological sources of municipal police employment, but

factors that may help explain variation in county law enforcement strength have not

Ronald Helms holds a PhD from the University of Oregon. He is currently an Associate Professor in the

Department of Sociology at Western Washington University. Dr Helms has published research in the American

Journal of Sociology, Social Forces and Social Science Research . Correspondence to: Ronald Helms, Associate

Professor, Department of Sociology, Western Washington University, Bellingham, WA 98225-9081, USA; E-mail:

[email protected]

ISSN 1043-9463 (print)/ISSN 1477-2728 (online) # 2007 Taylor & Francis

DOI: 10.1080/10439460701302743

Policing & Society, Vol. 17, No. 2, June 2007, pp. 182�206

Downloaded By: [Helms, Ronald] At: 20:25 28 April 2009

been studied. Early municipal police studies focused on economic inequality and

racial threat explanations (Jacobs 1978, 1979; Liska et al. 1981; Jackson and Carrol

1981; Liska and Chamlin 1984; Jackson 1989), while more recent aggregate research

emphasizes both political and social determinants of per capita police employment

(Jacobs and Helms 1997). Evidence supports claims that police forces are

strengthened when social inequality is greatest, and when there is greater support

for law and order political appeals.

Despite intensive research on municipal police, researchers have not studied county

law enforcement patterns with nearly the same intensity. Several scholars have noted

this lack of focus on county law enforcement, and have remarked that the Sheriff ’s

office has been overlooked by sociological researchers (Henderson 1975; Falcone and

Wells 1999; Helms, under review).1 Yet there are good reasons for focusing research

on county law enforcement. The county sheriff ’s office is the primary agency

responsible for social control service delivery, and the office is founded in the

constitution of most states. While the county Sheriffs office often defers to municipal

police in the county’s incorporated areas, the sheriff ’s office remains the fundamental

unit of county social control.

The office of the Sheriff is politically accountable to the citizenry through county

elections, and the agency is responsible for diverse services, such as traffic

enforcement, criminal investigations, serving warrants, jail operations, jail transport,

court security, and other functions as well. Perhaps because of its political link to the

citizenry, the Sheriff ’s office tends to be closely allied with the communities it serves,

and uses citizen volunteers to a greater extent than most municipal police

departments. All of this suggests that the sheriff ’s office is an important unit in

the state’s social control apparatus. Therefore, it would be a mistake to overlook this

potentially important unit of local government.

Research on per capita police rates emphasize the importance of state coercion as a

critical source of stable social conditions that foster inequality (Jacobs and Helms

1997). This research suggests that while domestic policing rarely involves direct

application of lethal force, this should not divert attention from law enforcement’s

critical social control function. According to Bittner (1990), control over violence

provides a unifying theme for most police work. If most people sense that the assets

of the state are far superior to their own, then these coercive resources will be rarely

challenged. Yet if law enforcement’s coercive capacity is used to enforce the rules that

control exchanges, then coercion or its threat becomes the basis of control through

rewards (Jacobs and Helms 1997).

One implication of this argument is that the state’s coercive capacity may be

particularly sensitive to conditions of heightened inequality. The operation of

advanced capitalist markets result in dramatic surpluses, but their distribution may

be skewed so that lavish rewards benefit a few, while the benefits are meager for a

majority. Police and the coercive resources they wield provide a critical political

function ensuring local stability and curbing disruptive threats to ongoing social

exchange processes.

Policing & Society 183


This study focuses on contextual explanations for variation in deputy employment

rates across county sheriff ’s offices.2 Deputy rates vary substantially across the

sampled jurisdictions. The mean rate is 7.63 deputies per 10,000 population, but

employment rates vary from less than one officer per 10,000 population (0.231) to

nearly 37 officers per 10,000 population (36.88), and so the mean rate for the sample

masks substantial variation across the surveyed jurisdictions. These differences may

stem from internal organizational and budgetary considerations. But employment

rates may also be affected by diverse threats arising from inequality in the

community, since the local external environment generates law enforcement

problems and helps to determine the political contingencies of this social control

agency (Henderson 1975).

The Constitutional basis of the sheriff ’s office and the local election of the county

sheriff imply that public concerns about local conditions will shape allocations for

law enforcement. Political demands for enhanced law enforcement are shaped by the

problems generated in the local environment, and therefore, we may expect that

demands placed upon the sheriff ’s office should be greatest where more threatening

conditions prevail. Since county law enforcement provides a first-line response to

local social order threats (Falcone and Wells 1999), and law enforcement does not

happen in a political vacuum,3 variation in local political-contextual environments

should affect personnel decisions for the local sheriff ’s agency. Formally, jurisdictions

characterized by threatening social conditions and corresponding heightened social

control contingencies should employ deputies at higher rates, but deputy employment

should be reduced in less demanding environments .

This analysis addresses agency and environmental determinants of deputy

employment with regressions of per capita deputy employment rates on organiza-

tional and county level contextual variables. Although this study does not ignore

internal factors, it draws attention to the local external context in which the sheriff ’s

office is situated, to see if local environmental conditions affect agency employment

decisions.

Literature Review

Empirical studies focusing on sources of county law enforcement strength are quite

limited. Two studies (Henderson 1975; Falcone and Wells 1999) lament that research

has paid little attention to the county sheriff ’s office. My own attempts to locate

research on deputy employment reaffirm the remarks of these other researchers. The

empirical results presented below begin to address this gap in previous county level

research.

The empirical study of Henderson (1975) on professionalism among Florida

sheriffs’ agencies is one of the few studies to address variation in sheriff ’s office

outcomes of any kind. His research shows that local contextual factors are useful in

accounting for variance across sheriffs offices in agency professionalism. Henderson’s

work provides useful insight into some of the key factors affecting sheriff ’s office

184 R. Helms


professionalism, and offers some indication that other outcomes in the sheriff ’s office

may also be affected by political-contextual factors. Falcone and Wells (1999) provide

additional descriptive evidence of the close relationships that often emerge between

the sheriff ’s office and the local community, and conceptualize the sheriff ’s office as

one that is highly responsive to localized political-contextual factors.

The paucity of empirical research focusing on the sheriff ’s office means that

theoretical guidance must come from other related areas of research. Studies of other

local public agencies, e.g., the municipal police and the criminal courts, should offer a

basis for development of plausible hypotheses concerning sources of variation in

sheriff ’s office outcomes.4

Research on Municipal Police and the Criminal Courts

Empirical studies that focus on determinants of social control expenditures or the level

of police employment, and studies of local sentencing decisions, indicate the potential

importance of a contextual approach, and suggest fruitful directions for the present

research. The conceptualization efforts of Blalock (1967) stimulated a substantial body

of research centering on minority group threats. Researchers assessing a racial threat

hypothesis generally report that cities with a larger percentage of African Americans

had the strongest police forces. Findings on comparative police rates (Jacobs 1978,

1979; Liska et al. 1981) and research on city law enforcement expenditures (Jackson

and Carroll 1981; Jackson 1989) report support for a racial threat explanation.

Jacobs (1979) analysis of police employment across SMSAs in 1960 and 1970

provides empirical evidence supporting a hypothesis that unequal economic

conditions lead to larger police forces. Jacobs used an indicator most sensitive to

the gap between the rich and all other income recipients, and he found that it was the

strongest predictor of the rate of police. Jacobs and Helms (1997) analyzed the per

capita rate of police since 1953 and report that economic inequality and political

support for the law and order Republican Party, particularly after 1968, are strong

predictors of per capita police employment. Other researchers analyzed police arrests

(Williams and Drake 1980; Liska et al. 1985) and found that unequal cities are more

likely to clear crimes through arrest actions.

Research concerning the criminal courts provides additional evidence about

contextual hypotheses. The analysis of Myers and Talarico (1987) of criminal

sentencing in Georgia counties offers evidence that minority population size and

unequal economic conditions help predict variation in court punishments. In a

follow-up study, Huang et al. (1996) studied Georgia sentencing decisions, but

included a statistical control for local conservative political environments. After

statistically holding constant other previously studied explanations, they found that

courts in politically conservative jurisdictions imposed the harshest punishments.

Helms and Jacobs (2002) developed comprehensive statistical controls in their

analysis of court punishments, and report general evidence of political sentencing

effects with a study covering jurisdictions in many states and regions of the country.



The empirical findings from these and other punishment studies reinforce

conceptual claims that criminal justice processes do not operate in isolation from

their socio-political environments (Garland 1991, 2000; Jacob 1995; Hughes 1995).

Analyses of municipal police expenditures and employment and studies of county

sentencing patterns highlight the importance of a political-contextual approach. The

research presented below takes these study results as a point of departure on the

plausible assumption that similar environmental factors may also affect sheriff ’s

office outcomes. The following section develops hypotheses about organizational

and environmental sources of county law enforcement employment. But there is a

caveat. Johnston (1984) argues persuasively that exhaustive specifications have

important methodological advantages. His rationale is worth repeating here.

Johnston asserts:

. . . 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:

262) and so exhaustive specifications will result in more conservative significance

tests.

The advantages of inclusive specifications are substantial, and so the following

theoretical section draws attention to many alternative explanations for variation in

deputy rates.

Theory and Hypotheses

Sheriff ’s agencies provide basic law enforcement services, much like urban police,

but they are uniquely situated in county government, and generally have more

diverse service responsibilities than the typical urban police agency (Falcone and

Wells 1999). The popular election of the sheriff means that the agency most likely

will exhibit strong political ties with the local community. Thus, deputy hiring may

be a function of agency factors relating to the diverse services the agency provides

as an agent of county government. Alternatively, law enforcement employment may

be a function of the politically responsive relationship between the agency and area

residents. With this in mind, in this study I develop hypotheses associated with

internal agency considerations and community conditions as well.

Functional Specialization within the Sheriff ’s Office

Agency Functional Specialization. Sheriff ’s agencies vary substantially in their

functional responsibilities, therefore I introduce a control for the number of

agency specialized units. Elaboration of the internal structure of the organization

186 R. Helms


may have little relevance to actual street enforcement tactics (Mastrofski et al.

1991). Nevertheless, deputy employment should be closely linked to the number of

specialized units, since deputies are responsible for performing many of the core

tasks.5 Functionally elaborate organizations may additionally be more successful in

county resource allocation struggles, since they are able to document their needs for

stronger law enforcement presence (Benson 1975; Mastrofski et al. 1991). For all

these reasons, agencies characterized by greater functional specialization should

employ deputies at higher rates than those with less elaborated organizations .

Local Resource Extraction and Citizen Service Demands

Resource Allocation. In any jurisdiction, county law enforcement competes with other

public social service providers for revenue drawn from the locally limited tax base. But

not all communities are equally willing to maintain a high tax burden to support

expanded public services. While all local jurisdictions provide basic law enforcement

services, there is substantial variation in the rate of badged personnel available to

address social control contingencies. One expectation is that where public per capita

revenues are expanded, local sheriff agencies will pursue an expanded deputy force.

Citizens of these same tax-rich jurisdictions are likely to demand increased law

enforcement services. This expectation follows from the fact that law enforcement

often serves middle class property owners directly, unlike other locally funded social

services that are targeted to population groups whose tax contributions are minimal. It

would be no surprise to find that in areas with an increased tax burden services,

beneficial to middle class tax payers would be expanded. Formally, where local tax

revenues are heightened deputy employment rates should be increased. By contrast

evidence of inferior community tax revenue should be associated with reduced deputy

rates.

Citizen Service Requests. I include an indicator for citizen requests for emergency

police response (911 calls responded to by sheriff agencies), since this is a direct

measure of incident-driven demand placed on the county sheriff ’s office for law

enforcement services. Note that this indicator captures an array of potential service

contingencies that are not enumerated in the crime rate (discussed below). Maguire

and Uchida (2000) emphasize the difficulty of measuring, with precision, what police

do. The measure adopted here reflects deputy responses to local citizen demands for

immediate services. A contingency approach emphasizes the importance of main-

taining reserve capacity to handle fluctuations in demand for law enforcement

services. I expect that where there is a high level of citizen demand for law

enforcement-related service, county agencies will heighten their employment to

maintain adequate response capacity. Formally, where the rates of calls for service are

highest deputy employment should be enhanced. Reduced citizen service demands should

translate into lower deputy employment rates.



Environmental Sources of Sheriff ’s Deputy Employment Rates

While organizational functional specialization, county law enforcement allocations,

and citizen service demands may account for significant variation in deputy

employment, local environmental conditions may also be expected to affect this

visible component of law enforcement. Falcone and Wells (1999) offer a political

conceptualization of the sheriff ’s office, emphasizing that, among other distinguish-

ing factors, the county sheriff is an elected political official, and, thus, the sheriff ‘s

office is less insulated from democratic politics than municipal police and other

public agencies. Additionally, Henderson (1975: 116) emphasizes the functionally

responsive relationship the sheriff ’s office exhibits with its environment, arguing that

‘the environment impinges on the organization insofar as it contains the objects with

which the county sheriff department must deal. Increased environmental complexity

will affect the kinds and incidence of criminal activity as well as the difficulty in

defining what is required to maintain public order.’ As local environments are the

source of significant contingencies for the sheriff ’s office, it would be a mistake to

ignore the potential role these factors play in shaping highly visible sheriff ’s office

outcomes.

The overarching model emphasizes that heightened local inequality affects social

control contingencies for the sheriff ’s office and its locally elected leadership, and,

therefore, should also affect deputy employment decisions. This contingency

perspective is reinforced by insights from the literature on the municipal police,

where indicators of economic inequality and racial and criminal threats are strong

correlates of municipal police size. If this conceptualization has merit, contextual

controls should help explain cross-jurisdictional variation in deputy employment.

Next, I develop hypotheses regarding environmental contingency explanations for

deputy employment variation.

Criminal Threats and African American Population Size Explanations

Criminal Threats. Local crime may indeed be a key source of contingency for the

sheriff ’s office. But unlike urban police, who emphasize crime control and focus on a

narrow law enforcement role, the sheriff ’s office provides county law enforcement and

other related services on behalf of a typically rural constituency (Falcone and Wells

1999). One implication is that local sheriff ’s agencies are likely to be confronted with

diverse demands for social control. The local sheriff is responsible for policing both

urban and rural areas throughout the county, but typically defer to municipal police in

incorporated (typically urbanized) areas. Researchers note that violent crime in the US

is disproportionately located in urban areas (Kovandzic et al. 1998; Blumstein and

Rosenfeld 1998). One implication is that the sheriff ’s office service responsibilities will

be focused disproportionately on calls for non-violent and property-related offenses.

An overall measure of crime that emphasizes the diversity of offense categories to

which the sheriff ’s office must be responsive is most likely to explicate the

contingencies associated with crime control at the county level. Therefore, in this

188 R. Helms


analysis, an indicator of the overall index crime rate should be positively associated

with enhanced law enforcement employment. Formally, a cross county jurisdictions

heightened rates of crime should be associated with higher rates of deputy employment,

but jurisdictions with reduced crime problems should have lower employment rates.

African American Population Size. The presence of African Americans may also exert

independent effects on deputy employment rates, since a large minority population has

been conceptualized in previous research as a factor linked to increased crime rates.

These effects are generally conceptualized as a consequence of structured disadvantages

African Americans face as a group throughout the US (Wilson 1987; Hagan 1994).

Hagan (1994) emphasizes that the size of African American populations is likely to be

associated with reduced economic and social opportunities, therefore making criminal

behaviors more likely. Other researchers who studied fear of crime report that after

statistically controlling for crime rates, the presence of larger African American

populations is associated with an increase in community anxiety about crime (Liska

et al. (1982); see also Fossett and Kiecolt (1989), Bobo and Hutchings (1996) and Taylor

(1998) on the link between the size of African American populations and negative

attitudes about this racial group). These characterizations in the literature lead to the

following expectation: across county jurisdictions, larger African American populations

should be associated with expanded deputy employment but where African American

populations are reduced deputy employment should also be reduced.

Threatening Economic Conditions: Income Inequality, Poverty, and Unemployment

Income Inequality. Unequal environments are difficult to police, and so may be a

source of enhanced social control effort. But law enforcement hiring may also result

from unequal power available to local economically advantaged groups since some

theorists assert a link between economic and political power in modern society

(Blalock 1967; Collins 1975; Lindblom 1977; Chambliss and Seidman 1980). If so,

those who benefit most from existing economic arrangements can be expected to use

their economic advantages to encourage expansion of law enforcement (Jacobs 1979;

Chambliss and Siedman 1980; Jacobs and Helms 1997). Economically-ascendant

groups may be expected to favor more law enforcement over other social welfare

enhancing public expenditures, since these do not benefit the well off as much as

other less advantaged consumers of public services. By contrast, enhanced law

enforcement may benefit the economically privileged who have substantial stakes in

existing arrangements, and may also have extensive property holdings in non-

incorporated county areas. Therefore, I expect that economically unequal jurisdictions

should be characterized by stronger law enforcement. Where economic inequality is

greatest I expect counties to hire more law enforcement officers while in more equal

jurisdictions law enforcement employment rates should be reduced.

Local Poverty. Economically unequal communities may experience increased political

contingencies for the local sheriff ’s office, but that does not mean that more deputies



will automatically be hired. A high level of community poverty may inversely affect

local law enforcement hiring despite the conceptual link between poverty and

physical and social disorder. Absolute poverty creates conditions of intense

competition for scarce public revenues, and so may undermine local efforts to

employ a large deputy contingent. Therefore, I include an indicator for local poverty

and hypothesize an inverse relationship between the percentage of families in poverty

and deputy employment rates.

Area Unemployment. Local unemployment may coincide with conditions of high or

low poverty (absolute deprivation), and income inequality (relative deprivation), and

may independently influence hiring decisions, and so an indicator for local

unemployment should be included in this analysis. A plausible argument for this

variable emphasizes the risks associated with the unemployed as a factor contributing

to more formal social control. Local environments with high unemployment may

pose an increased risk of social disruption, since marginalized workers have less to

lose and more to gain through redistributive violence (Blau 1964). Therefore, I

include an indicator for the annual unemployment rate to assess its effects on county

law enforcement employment. Formally, across counties, where local unemployment

rates are highest sheriff ’s deputy employment should be enhanced but low unemployment

should be associated with reduced deputy rates.

Law and Order Political Environments and Local Demand for Law Enforcement

Electoral Support for the Law and Order Presidential Candidate. Law and order has

been an important staple in US political discourse at all levels of government at least

since 1968. Conservative politicians have emphasized the importance of policing as a

primary source of deterrence, and have expressed unwavering support for enhanced

law enforcement. Law and order environments should be particularly supportive of a

strong local sheriff ’s office. One obvious way this might develop is through enhanced

employment of badged deputies. Formally, I expect to observe a positive association

between law and order political environments and local deputy employment rates.

Counties that most strongly supported the law and order Republican Party in previous

national elections should exhibit enhanced deputy employment, but where there is less

support for the law and order party, deputy employment should be reduced.

Population Effects

Population Size. Henderson (1975) asserts that larger populations should affect the

kinds and incidence of crime. Also, in highly populated jurisdictions, crime is more

difficult to detect and informal controls may be less effective in controlling criminal

behavior. The analysis includes a statistical control for population size. However,

since the outcome of this study is a rate, I expect that the largest populations will not

necessarily be associated with the highest deputy rates. The largest counties are likely

to experience just the opposite, in fact, so that as population size increases, the per

190 R. Helms


capita deputy rate is reduced. This is possible even if the actual number of officers is

greatest in the largest jurisdictions, and, of course, this highlights one of the

important reasons for standardizing measures. But, there are other reasons for

expecting an inverse relationship. Large populations are likely to correspond with the

presence of municipal governments, and, therefore, more urban police. If urban

police are a substitute for county law enforcement, then where the population size is

greatest, county law enforcement employment may be expected to diminish. For all of

these reasons, population size should be statistically controlled for in the regression

models and should exhibit an inverse relationship with deputy employment rates.

Urban Populations. I also statistically control for urbanization, since the most densely

populated areas may experience the greatest social control challenges. Consistent with

the logic developed for the population size indicator, as urbanization increases, areas

are increasingly likely to incorporate and also hire municipal police to intensify patrol

in those areas. Therefore, the indicator for urbanization should exhibit an inverse

association with the dependent variable. Formally, since the most densely populated

areas are the most difficult to police, but are likely to be policed by municipal police

agencies, I expect to find an inverse association between an indicator for the percentage of

urbanized population and the county rate of law enforcement employment.

State Level Effects on Local Public Agencies. Finally, I introduce state dummies to see if

state level conditions affect local sheriff ’s deputy employment rates. State mandated

expenditures for education, social welfare, and other services might substantially limit

local deputy hiring decisions despite strong local support for enhanced law

enforcement and locally demanding environmental conditions. Also, at least one

state (Pennsylvania) places restrictions on the arrest powers of their deputies, and

may, therefore, alter the expected relationships between localized threatening

conditions and county law enforcement personnel size. In order to statistically

hold constant all systematic state level effects, I include dummy indicators for the

various states whose local agencies make up the sample.

Summary

The analysis in this study focuses on sources of variance in sheriff ’s deputy

employment rates in county agencies covering 10 states. This analysis does not

ignore internal structure of the agency, locally generated tax revenues, or citizen

demands for law enforcement emergency response. Rather, it emphasizes local

environmental contexts that are conceptualized as presenting diverse law enforce-

ment contingencies, and assesses these empirically to see if contextual indicators

help to account for variation in deputy employment rates. In addition to indicators

for functional specialization and an indicator of per capita county government

revenues, I assess the effects of citizen 911 calls and local crime rates. I also assess

hypotheses about the effects of African American population size, economic



inequality, local law and order political support, population size and urbanization,

and state level dummies, to see if these exert independent effects on county deputy

employment. If theorized political contingencies in the local environment

contribute to variation in law enforcement strength, then this analysis should

detect significant relationships between environmental measures and the depend-

ent variable. The following sections present information about the method and

sample.

Methods

Analytic Approach, Dependent Variable and Sample

OLS is used to estimate the police employment models, since the outcome is

operationalized as an interval level indicator, which is the natural log of the number

of full-time badged employees per 10,000 population. Descriptive statistics reveal

adequate variance on all of the respective indicators, and observation of bivariate

scatterplots and other diagnostics from the regression models (Neeter et al. 1996)

provide reinforcing evidence that a linear model provides a reasonable approxima-

tion of the structural relationships. Additionally, most aggregate social control

research has taken a similar approach, and so the choice of methods has ample

precedent.

The sample is taken from the Sentencing and Law Enforcement Database

developed by Helms and Jacobs (2002), covering 10 states.6 These were matched

with agency organizational and personnel data taken from the 1993 LEMAS (Law

Enforcement Management and Administration Statistics) database.7 Incomplete data

on county finances and results from the sampling procedure employed in the LEMAS

collection resulted in a usable sample of 190 sheriff ’s agencies from the 10 states. The

sample size is limited, but the database includes extensive contextual controls, and

there is no reason to believe that the sample is characterized by systematic bias; it,

therefore, is utilized in this research.

Measurement of Explanatory Variables

The various indicators used in this study are measured as follows; organizational

functions, county social control spending and citizen service demands : agency

functional specialization is measured as a simple count indicator (range�2, 25);

county tax revenues are measured as the total county tax revenues per 10,000

population; citizen service demands are measured as the per capita rate of 911 calls to

the Sheriff ’s office. Each of these indicators exhibited positive skew, and so a natural

log was applied to normalize their distribution. County contextual controls : the crime

rate is measured as the index crime rate per 10,000 population. African American

population size is measured as the per cent African American population and is in

natural log form. The standard deviation of household incomes is used to assess the

192 R. Helms


effects of county income inequality. Poverty is measured as the per cent of the county

population living below the federally established poverty line. Unemployment is

measured as the county-level annual unemployment rate and is averaged over three

years (1990�1992). Politically conservative environments are estimated with the

mean of the county per cent Republican votes in the 1988 and 1992 presidential

elections.8 Population size is in natural log form. Urbanization is measured as the per

cent of county residents living within urbanized areas of 50,000 population or more.

Dummies (coded 0�1) are included for the various states represented in the sample.

The omitted category is California. Next, I discuss the results of this analysis.

Analyses

Table I shows the number of valid cases, mean, standard deviation, and minimum

and maximum values for all of the key theoretical variables used in the regression

models.

Table II shows zero-order correlations between the dependent variable and the

included indicators. Note that the strongest zero-order relationships with the

dependent variable include high organizational specialization (0.487), per capita

tax revenues (0.488), and 911 calls (0.325). These indicators are perhaps the most

intuitive, since they are linked directly to the agency’s diverse functions, its ability to

attract county resources, and direct requests for law enforcement services. The

Pennsylvania state dummy exhibits a strong negative association (�0.586) with the

dependent variable. This result may, in part, be linked to the state’s general policy

limiting sheriff deputy arrest powers.

High correlations between the crime rate and population size indicator (0.716),

and also between the population size indicator and the per cent urbanized population

indicator (0.858) suggest that collinearity may be a problem. One option is to drop

one or more of the offending indicators. Another option is to assess the variance

inflation in the model. I ran models with and without the respective indicators, but

found that the substantive results on other theoretically relevant indicators did not

change. The variance inflation scores for the offending indicators in the most

comprehensive model are all below 8, and the average for all of the indicators is

always well below 4, suggesting that collinearity is probably not disproportionately

affecting the model results (StataCorp 2001).

Research by Land et al. (1990), however, highlights problems that arise in the

presence of collinearity. Their insights suggest that further analysis using principle

components is warranted to assess the dimensional space of the structural covariates.

The results show that population size and per cent urbanized population indicators

share substantial overlapping dimensional space, with quite large eigenvector values.

I, therefore, created a single index that includes these empirically overlapping

indicators (cumulative variance for this component�0.82) and substituted this

indicator in some models (see Table II).9



Multivariate Regression Results

Table III shows the regression results. The first model includes a count indicator for

the number of functionally specialized units, to see if this indicator of internal

functional elaboration is associated with the size of law enforcement personnel. Since

local crime and efforts to control it are so fundamental to the performance of the

sheriff ’s duties, the crime rate indicator is also included in the first model.

Additionally, 911 service calls are included, since citizen service requests should be

a factor that affects the strength of local law enforcement.10 The first equation also

includes an indicator for per capita county tax revenues, since local resource

availability should be a strong determinant of personnel size.

Model 2 includes all of the previous indicators, but introduces contextual

indicators to see if more difficult (unequal, threatening) environments are associated

with enhanced deputy employment. Factors common to all agencies within a state

may affect local hiring decisions, and so this model also includes state dummies to see

if features constant to all agencies within any state, but that may vary across states,

affect local deputy rates. Model 3 substitutes the population/urbanization index for

the population and urbanization indicators. All other indicators from Model 2 are

retained in this model. Model 4 includes all of the previously mentioned indicators,

but introduces an interaction for African American population percentage and the

Table I Descriptive statistics

Variables n Mean Std. Dev. Minimum Maximum

Ln deputy employment rate 190 1.93 0.585 0.441 3.13Ln org. functional units 190 2.59 0.693 0.693 3.22Ln county tax revenue per cap. 190 15.57 0.888 10.789 17.149Crime rate 190 347.99 207.85 0 1072.99Ln 911 Calls 190 3.93 3.10 0 9.82Ln population size 190 11.30 1.75 7.61 16.00% Urban population 190 36.89 41.25 0 100Pop/urban index 190 .093 1.34 �2.01 3.02Ln per cent Afric. Am. pop 190 1.37 1.09 0 4.27Poverty rate 190 11.88 5.38 2.57 37.91Std Dev hh incomes 190 6781.72 1278.41 4687.17 12707.38Unemployment rate 190 6.58 3.09 1.6 25.47Republican votes (88�92) 190 48.2 8.6 26.7 69.35D. Alabama 190 0.063 0.244 0 1D. California 190 0.159 0.366 0 1D. Idaho 190 0.032 0.175 0 1D. Minnesota 190 0.095 0.294 0 1D. Missouri 190 0.095 0.294 0 1D. Nebraska 190 0.111 0.314 0 1D. New Jersey 190 0.079 0.270 0 1D. New York 190 0.153 0.361 0 1D. Pennsylvania 190 0.089 0.286 0 1D. Virginia 190 0.121 0.327 0 1

ln: natural log.

D. variables: Dummy indicators.

194 R. Helms


Table II Zero order correlations

Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

1. Ln dep empl rate 1.002. Ln org. units 0.487 1.003. Ln county tax rev 0.488 0.174 1.004. Crime rt. 0.046 �0.037 0.232 1.005. Ln 911 calls 0.325 0.337 0.304 0.234 1.006. Ln population size �0.144 �0.188 0.245 0.716 0.179 1.007.% Urban pop �0.069 �0.170 0.289 0.648 0.226 0.858 1.008. Pop/urban index �0.111 �0.186 0.277 0.708 0.210 0.965 0.963 1.009. Ln% Afric. Am. pop 0.020 �0.124 0.180 0.432 0.076 0.539 0.542 0.561 1.0010. Poverty rt. �0.078 0.145 �0.324 �0.109 �0.099 �0.423 �0.483 �0.470 �0.127 1.0011. Std dev. hh inc. 0.125 0.049 0.226 0.504 0.173 0.592 0.623 0.630 0.550 �0.145 1.0012. Unempl rt. 0.029 �0.057 0.001 0.193 0.024 0.022 �0.193 �0.088 0.011 0.434 �0.065 1.0013. Republican votes 0.203 �0.003 �0.006 �0.266 �0.032 �0.256 �0.229 �0.252 �0.043 �0.150 �0.238 �0.205 1.0014. D. Alabama �0.112 0.081 �0.307 0.032 �0.032 �0.012 �0.027 �0.020 0.370 0.294 0.229 0.040 0.160 1.0015. D. California 0.166 0.177 0.315 0.499 0.236 0.390 0.265 0.340 0.028 0.005 0.322 0.328 �0.230 �0.112 1.0016. D. Idaho 0.212 0.100 �0.046 0.018 0.002 �0.123 �0.102 �0.117 �0.203 0.011 �0.036 0.021 0.005 �0.047 �0.078 1.0017. D. Minnesota �0.072 0.200 0.104 �0.174 0.111 �0.165 �0.116 �0.146 �0.311 0.030 �0.181 �0.027 �0.284 �0.084 �0.140 �0.058 1.0018. D. Missouri 0.021 0.112 �0.461 �0.293 �0.135 �0.186 �0.159 �0.179 �0.101 0.280 �0.145 0.116 �0.164 �0.084 �0.140 �0.058 �0.105 1.0019. D. Nebraska 0.025 0.189 �0.106 0.244 0.001 �0.449 �0.277 �0.378 �0.377 0.118 �0.298 �0.411 0.327 �0.092 �0.153 �0.064 �0.114 �0.114 1.0020. D. New Jersey �0.081 �0.352 0.018 �0.008 0.004 0.261 0.327 0.305 0.278 �0.240 0.122 �0.017 0.017 �0.076 �0.127 �0.053 �0.095 �0.095 �0.103 1.0021. D. New York 0.059 0.100 0.241 �0.128 �0.029 0.190 0.046 0.123 0.106 �0.206 0.031 0.016 �0.023 �0.110 �0.184 �0.077 �0.137 �0.137 �0.150 �0.124 1.0022. D. Pennsylvania �0.586 �0.643 �0.282 �0.154 �0.342 0.062 �0.004 0.030 �0.093 0.001 �0.136 0.074 �0.038 �0.081 �0.136 �0.057 �0.101 �0.101 �0.111 �0.092 �0.133 1.0023. D. Virginia 0.361 �0.002 0.377 0.657 0.126 �0.090 �0.011 �0.053 0.287 �0.190 0.063 �0.153 0.281 �0.096 �0.161 �0.067 �0.120 �0.120 �0.131 �0.109 �0.158 �0.116 1.00

Policin

g&

Society

19

5


population/urbanization index, to assess the possibility that police employment is

responsive to African American population size, particularly in the most heavily

populated and most urbanized jurisdictions. For each model, the Cook-Weisberg test

for heteroskedasticity resulted in a non-significant chi-square value, suggesting that

the respective models are characterized by constant variance. Therefore, all of the

tabled results are uncorrected estimates.

Model 1 (Table III) introduces a count indicator to assess the effects of increased

agency functional specialization, and indicators for county tax revenues, index crimes,

and local 911 calls. The significant F value for the overall model (32.41**) and the

Table III OLS regression estimates of natural log of county deputy ratesa

Dependent variable�Ln full- and part-time sheriff deputies per 10,000 population 1993Model 1 Model 2 Model 3 Model 4

IndependentConstant �3.114**

(0.602)�2.637**

(0.958)�4.214**

(0.794)�4.010**

(0.824)Ln Num. org. units 0.386** 0.159* 0.150* 0.157*Per capita county tax, $ 0.408** 0.425** 0.448** 0.432**Total crime rate �0.054 0.161* 0.151* 0.134*Ln. 911 calls 0.084 0.085 0.089* 0.090*Ln. pop. Size � �0.351** � �% Urban pop. � �0.070 � �Pop/urban index � � �0.397** �0.477**ln% African Am. pop. � �0.059 �0.055 �0.040Poverty rate � �0.144* �0.150** �0.152**Standard dev. hh incomes � 0.192** 0.202** 0.189**Unempl. rate 90�92 � 0.089 0.079 0.082Republican votes 88 and 92 � 0.163** 0.156** 0.153**D. Alabama � �0.018 0.000 �0.010D. Idaho � 0.147** 0.163** 0.156**D. Minnesota � �0.136* �0.114* �0.126**D. Missouri � 0.210** 0.243** 0.223**D. Nebraska � �0.057 �0.013 �0.043D. New Jersey � �0.008 0.008 �0.007D. New York � �0.021 �0.025 �0.035D. Pennsylvania � �0.255** 88** �0.246** �0.258**D. Virginia � 0.104 0.119 0.118Afric. Am. pop�pop/urban

index� � � 0.097

Pearson’s R 0.412 0.678 0.674 0.676Adj. R2 0.399 0.639 0.638 0.638Model F 32.41** 17.75** 18.53** 17.63**Average variance inflation 1.17 3.25 2.83 3.26Number of cases 190 190 190 190

*Significant at 0.05 level with one-tail test.

**Significant at 0.01 level with one-tail test.aModel constant and its standard error are reported in original units; standardized coefficients are reported for

all other indicators.

196 R. Helms


adjusted R2 value (0.399) indicates that this initial specification accounts for a modest

amount of variance in deputy employment for this sample of agencies.

The indicator for agency functional elaboration exhibits a positive and statistically

significant association with the dependent variable in this first equation. Additionally,

a comparison (within the model) of standardized coefficients shows that this

indicator accounts for the greatest amount of variance in deputy employment rates.

Also, sheriff agencies in counties with the highest per capita tax revenues generally

employ more deputies. Both of these indicators exhibit relatively large associations

with the dependent variable. For example, a one standard deviation change in the

functional elaboration indicator is associated with approximately a 0.40 standard

deviation change in the logged deputy rate. The tax revenue indicator exhibits a

similarly strong positive association with the dependent variable in this model.

Heightened local crime should be a positive predictor of law enforcement

personnel, since criminal threats are a key source of political contingency that

must be addressed by the sheriff ’s office. Surprisingly, the crime rate indicator does

not exhibit a statistically significant association with deputy employment rates in this

initial model.11 Also, agencies that field higher rates of emergency response requests

(911 calls) are expected to retain larger forces as a buffer against variance in service

demand. The regression results in Model 1 do not support this hypothesis, however,

since the coefficient on this indicator does not approach statistical significance.

Model 2 (Table III) offers insights concerning the effects of contextual indicators

on deputy employment rates. All of the indicators included in the first model are

retained, but the second model includes the size of African American populations,

income inequality, a measure of local political support for the Republican Party, and

other conceptualized indicators to see if these are helpful in accounting for variance

in deputy employment rates. Model 2 also includes dummy indicators for the various

states to hold constant all systematic state level influences on local law enforcement

hiring. The increase in the model’s adjusted R2 value (0.399 to 0.638) shows that the

addition of structural indicators and state dummies indeed enhances the prediction

model.

Both of the statistically significant indicators reported in Model 1 remain

significant in Model 2. Also, after introducing structural indicators and state level

dummies, the crime rate is now a significant predictor of deputy rates. Evidence in

Model 2 supports claims that agencies in economically unequal environments hire

more police than agencies characterized by more equal environments, since where

household income inequality is greatest, sheriffs’ agencies are strengthened. Also, the

law and order political indicator is significantly associated with the size of law

enforcement personnel. Where local citizens most strongly supported the republican

law and order candidate (George Bush, Sr) in the two previous national elections

(1988 and 1992), sheriff ’s agencies are expanded. The poverty rate indicator also

exhibits a statistically significant association with the dependent variable, and the

negative sign on the coefficient is consistent with conceptual expectations. The



evidence from this model shows that deputy rates are reduced in counties with a large

impoverished population.

Several predictors are not statistically significant in this model. The 911 calls

indicator does not approach statistical significance in Model 2. Also, the per cent

African American population indicator is not significantly correlated with deputy

rates. This result is surprising, since so much of the quantitative social control

literature reports a positive association between the size of African American

populations and the strength of law enforcement agencies. One possibility is that this

conceptual source of police expansion influences personnel size only in the most

densely populated and urbanized jurisdictions. This possibility is assessed in Model 4

with the introduction of an interaction term.

Finally, the population size indicator is significant and negatively correlated with

the law enforcement size indicator. While the raw number of personnel in the most

populated jurisdictions in the sample generally is quite large, the per capita rate of

police protection is reduced, and of course this highlights the importance of

standardization in quantitative research. One possible explanation for this inverse

association is that, in the most populated jurisdictions, county law enforcement

agencies often defer to municipal police, who take primary responsibility for law

enforcement and emergency response.

While the overall population size indicator is negative and statistically significant,

the indicator for the percentage of the population that is urbanized does not exhibit a

significant association with deputy rates in this model. This may reflect underlying

estimation problems due to collinearity between the two population indicators.

Model 3 (discussed below) addresses this potential problem by dropping the

population and urbanization indicators, and introducing a population/urbanization

index based on the results of a principle components analysis.

The standardized coefficients (Model 2) show that the tax revenue indicator

(0.425) exhibits the strongest association with the deputy rates measure. Standardized

indicators for population size (�0.351), income inequality (0.192) and law and

order political environments (0.163) show that these also are key sources of explained

variance in deputy employment for this sample of agencies.

As previously noted, the estimates in Model 2 may be affected by strong

intercorrelations among several of the predictor variables. To assess this possibility,

I analyzed the indicators using un-rotated principle components (StataCorp 2001).

The results suggested strong overlap in the regression space for the population size

and urbanization indicators. Therefore, I substituted a population/urbanization

index in place of the component indicators into the deputy rates model. The results

can be seen in Model 3 (Table III). In the third model, the population/urbanization

index exhibits a strong and significant negative association with deputy rates, while

estimates on other coefficients are largely unchanged.

The regression results show that diverse environmental and political indicators are

empirically associated with the rate of deputy employment. Perhaps most

importantly, an indicator of local economic differences that is most sensitive to the

198 R. Helms


income gap between high income earners and all other households is a strong and

significant predictor of the rate of law enforcement for this sample of counties.

Similarly, the law and order political environments indicator is always a significant

and positive predictor of enhanced deputy employment.

The final regression (Model 4) uses the same specification as Model 3, but assesses

the possible joint effects of large urbanized populations, and the presence of a large

percentage of African American population on deputy rates. Many municipal

policing studies have demonstrated a statistical association between the size of

African American populations and the strength of law enforcement agencies (Jacobs

1978, 1979; Jackson and Carrol 1981; Liska 1981; Jackson 1988; Jacobs and Helms

1998), but the models presented here do not show a similar race effect. When African

American population size is interacted with population size (in unreported models)

or the population/urbanization index (Model 4), the interaction coefficient is not

significantly associated with variation in the strength of law enforcement agencies.

These non-significant findings suggest that arguments linking African American

population size with expanded law enforcement in the municipal police literature do

not provide an adequate explanation for expansion of county law enforcement.

Substantive findings on all other key indicators are consistent with those reported in

Model 3.

Theoretical Conclusions and Implications from this Research

County sheriff agencies are conceptualized as maintaining close links to communities

they serve, highlighting the close political association that quite often characterizes

the relationship between county law enforcement agents and local citizens. This

conceptualization implies that resource allocation decisions will not be insulated

from conditions in the local social environment. Henderson’s statements regarding

the effects of local contextual conditions on sheriff ’s office outcomes are noteworthy

here. He observes that external environments contain the objects that must be dealt

with by local law enforcement, while emphasizing that ‘increased complexity will

affect the kinds and incidence of criminal activity as well as the difficulty in defining

what is required to maintain public order’ (1975: 116). Where social conditions

heighten law enforcement contingencies, we should not be surprised to find that the

sheriff ’s office is strengthened. The empirical results of this study generally support

this conceptual claim.

Summary of This Study’s Empirical Findings

The work on municipal police strength emphasizes the effects of inequality on police

employment. Chambliss and Siedman (1980: 31) assert 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.’

Jacobs (1978, 1979) emphasizes that economically ascendant groups have an



enhanced need for law enforcement in order to sustain unequal exchange

arrangements that benefit them so much. But, their economic resources also give

these same groups a heightened ability to make effective demands for more law

enforcement (see also Jacobs and Helms 1997). Together these claims suggest that in

jurisdictions with greater economic differences between the most affluent income

earners and the rest of the population law enforcement should be expanded, and that

is what I find. The empirical results show that county sheriff ’s offices are

strengthened where economic differences between the highest income earners and

the rest of the population are greatest.

Furthermore, the results support an argument that a large impoverished

population constrains county law enforcement, since in the most comprehensive

model specifications, the poverty indicator exhibits an inverse association with the

deputy rate indicator. The presence of large impoverished populations most likely

increases demand for local social services, and contributes to resource constraints that

limit law enforcement expansion. In sum, the results show that the rate of badged

personnel is altered under conditions of heightened economic inequality. Where

upper and middle class income inequality is greatest, law enforcement is

strengthened, but a large impoverished population is associated with smaller county

agencies.

The results from this study also show that conservative political environments are

an important source of strengthened law enforcement agencies. Since at least 1968,

Republican politicians have emphasized law and order themes to win elections.

Consistent with their law and order rhetoric, these same politicians have been

relentless advocates for stronger law enforcement agencies. This emphasis leads to an

expectation that jurisdictions exhibiting the strongest political support for the

Republican Party in previous elections should also favor strengthened law enforce-

ment agencies. The findings from this research show that jurisdictions where voters

most strongly supported the Republican Party platforms in the two previous national

elections, which were notable for their strong emphasis on law and order themes,

indeed tended to have higher deputy rates, while fewer deputies were retained in less

politically conservative counties.

Research presented here also show that those agencies in the most populated

counties employ deputies at lower per capita rates than their counterparts in less

populated counties that make up the sample. This result is not unreasonable, since

absolute numbers of police generally are greatest in the largest jurisdictions, even if

the per capita rate in those jurisdictions is reduced.

One surprising result of this analysis is that the indicator for African American

population size is never a significant predictor of deputy employment rates for this

sample of sheriff ’s agencies. This result contrasts with the empirical evidence on

municipal police. The interaction between African American population size and the

population/urbanization index was also insignificant in this study. The presence of a

large minority population has been widely conceptualized as a source of municipal

police expansion. Positive empirical results have typically been explained by reference

200 R. Helms


to reduced consensus about social norms and long-standing perceptions linking the

presence of minorities with socially-threatening conditions. But the findings of this

study show that for this sample of counties, African American population size does

not predict variation in the size of county law enforcement agencies.

In sum, the sheriff ’s office is conceptualized as an agency that is attentive to the

political requirements associated with the delivery of adequate law enforcement and

order maintenance services. The conceptualization developed in this analysis

emphasizes the importance of an environmental contingency approach to help

account for variation in law enforcement strength. The results of the empirical

analysis show that organizational and local tax revenue indicators are significant

correlates of deputy employment variation. Perhaps most importantly, two economic

inequality indicators (standard deviation of household incomes, poverty rates)

exhibit strong empirical associations with the size of deputy forces. Similarly, an

indicator of support for the Republican Party in previous presidential elections is

always significantly associated with the size of law enforcement agencies in US

counties.

Theoretical Implications

Resource allocation decisions affecting the sheriff ’s office do not occur in

administrative isolation. Expansion of law enforcement personnel in any community

is rarely, if ever, politically neutral, and it would be a mistake to ignore political-

contextual factors that undoubtedly do shape local coercive control capabilities. The

foregoing analysis conceptualizes diverse social conditions that should contribute to

heightened contingencies for the sheriff ’s office.

The empirical results show that the most unequal economic environments and

strong political support for the Republican Party are closely associated with expanded

rates of county law enforcement personnel. These statistical results parallel the

findings concerning determinants of law enforcement strength (Jacobs 1979; Jacobs

and Helms 1997) and criminal sentencing decisions (Myers and Talarico 1987; Huang

et al. 1996; Helms and Jacobs 2002). The literature on these related law enforcement

outcomes suggested directions for development of contextual hypotheses in the

present study, since there is so little available research on sheriff ’s agencies. The

findings of this study offer insight into the potential importance of a shared

perspective in the analysis of these related local, social control, institutions.

Local criminal justice processes have been characterized as being loosely coupled,

according to Hagan (1994). Notwithstanding this plausible claim, variation in diverse

outcomes across agencies may be a function of contextual factors operating

simultaneously in the overlapping areas of their respective external environments,

and, therefore, theoretical integration should be possible. Future county law

enforcement research should seek to further develop conceptual and empirical links

between environmental indicators and agency outcomes, and in so doing, can expect



to contribute to general understanding of these apparently overlapping processes that

affect local criminal justice agency outcomes.

The crime fighter imagery that has so often been embraced by local law

enforcement agencies (Manning 1997) suggests that crime will be reduced by

stronger social control forces. But this optimistic imagery is inconsistent with the

difficult circumstances local law enforcement agencies confront in their attempts to

address and significantly affect crime problems. Political success for any sheriff may

be linked less to the agency’s direct capacity to reduce crime and more to the local

office holder’s ability to politically manage law enforcement contingencies. A stronger

law enforcement presence would seem to facilitate that objective, since a more

numerous deputy force is a primary mechanism for social control, even if it cannot

realistically be expected to produce substantial reductions in crime. The results of this

study suggest that county sheriff offices, similar to their municipal police counter-

parts, respond to environmental conditions and employ more deputies where

inequality is heightened.

The foregoing discussion highlights the important influence of economically

unequal social conditions and local politics on the distribution of social control in US

counties. Law enforcement employment most likely is shaped, in part, by middle class

concerns, but this study suggests that pluralist or consensus arguments are likely to

prove unsatisfactory as a complete account for deputy strength. Some theorists argue

persuasively that the coercive resources of the state are a source of stability in

contemporary societies and should not be overlooked (Jacobs and Helms 1997).

Researchers should focus attention on the environmental conditions under which

coercive strategies are made more or less appealing.

This analysis expands current research by assessing diverse contextual explanations

for one local sheriff ’s office outcome. The conceptual model points to environmental

factors as key sources of social control contingencies. The conceptual claims of this

paper strongly imply that localized conditions of heightened inequality should be

closely correlated with variation in social control. The analytical results reinforce

these claims, providing systematic evidence of the social and political foundations of

county law enforcement strength.

Acknowledgements

The author is grateful to Jim Inverarity, Kyle Crowder, and the journal manuscript

reviewers for their useful reactions to a previous draft of this manuscript. The author

is responsible for any errors in conceptualization and analysis.

Notes

[1] Several researchers note a general absence of research focusing on county level government

units (Marando and Thomas 1977; Schneider and Park 1989; Marando and Reeves 1991).

One obvious exception to this lack of attention is the extensive literature concerning court-

sentencing decisions.

202 R. Helms


[2] Recent research by Osgood and Chambers (2000) supports using data aggregated at the

county level to assess hypotheses previously tested in cities or urban neighborhoods. Their

logic is as follows: ‘. . . we test the proposition that social disorganization theory is based on

principles of community organization and social relations that are applicable to commu-

nities of all types and settings’ (p. 81). They cite Laub’s conclusions from his research (Laub

1983), stating that ‘most theories of crime and delinquency are likely to apply to rural

settings, even though they were developed in reference to urban settings’ (2000: 82) Osgood

and Chambers continue their discussion, asserting that community-level theories should be

more widely applicable, particularly since ‘The rural-urban dimension is itself an essential

aspect of communities, and our current theories of communities and crime would be far

more useful if they apply to the entire range of this dimension’ (2000: 82). Furthermore, their

work cites several additional studies that use city units rather than neighborhoods (Blau and

Blau 1982; Messner 1982; Shihadeh and Steffensmeier 1994) to assess comparisons among

geographical subareas and subpopulations of cities (e.g., racial segregation or income

inequality). The analysis presented here follows the logic of Chambers and Osgood, and

presents a county-level analysis of police strength to test whether prominent theories that

have guided municipal police research are also applicable to non-metropolitan units.

[3] This study draws from arguments developed in research on the criminal courts. McIntosh

emphasizes the importance of a contextual perspective, arguing that researchers should assess

the courts’ acknowledgement of various forces at work in the community. He argues that

‘. . . judges are influenced by, and must take into account, the world around them. Their

decisions are not crafted in an intellectual vacuum’ (in Gates and Johnson 1991: 283).

Similarly, Jacob (in Gates and Johnson 1991: 223) asserts that among other contributions

made by the community, it supplies the needs the courts are to address, and among them are

demands for justice. The present paper extends this argument, emphasizing that similar

political-contextual factors should also be useful in explaining sheriff ’s office outcomes.

[4] Marando and Reeves (1991) caution that county government units may differ substantially

from other local government units. Extending their caution, explanations for sheriff agency

outcomes developed from studies of outcomes in other local agencies, such as the police and

courts, should be viewed as exploratory and require empirical assessment beyond the models

presented here.

[5] Maguire and Uchida (2000) note a lack of consensus about the best measures of internal

organization. This analysis takes a simple approach, assessing the possibility that

organizations characterized by greater differentiation of functional units will tend to have

more personnel to implement the diverse specialized tasks.

[6] The states represented in this analysis are as follows: Alabama, California, Idaho, Minnesota,

Missouri, Nebraska, New Jersey, New York, Pennsylvania, and Virginia.

[7] Data sources are as follows: the outcome variable and various organizational indicators are

from the US Department of Justice, Bureau of Justice Statistics, Law Enforcement Manage-

ment and Administration Statistics (1993); county law enforcement revenue figures are from

Government Finances (1994) and (1988), and 1992 county-level Republican vote totals are

from America Votes (1989) and (1993); county population and social and economic data are

from the US Bureau of the Census (1990) Economic and Social Indicators , Summary Tape File

3 on CD-ROM Technical Documentation; data used to estimate top-end income categories

are from the Internal Revenue Service (1994) Statistics of Income Bulletin ; county

unemployment rates are taken from the Bureau of Labor Statistics, Unemployment in States

and Local Areas 1990�92 ; county crime data are taken from the Federal Bureau of

Investigation, Uniform Crime Reports 1990 , County-Level Tape Files, from the ICPSR.

[8] This operationalization addresses an important concern. A single presidential contest may be

shaped by idiosyncratic politics, and, thus, may offer only minimal insight into core

community political sentiments. By averaging over several election cycles, the stability of the



estimates of the local law and order climate is increased. Thus, the average of several

measures of the political environment should yield a more conceptually sound estimate of

the local political climate in which law enforcement personnel decisions are made than

would a measurement taken at a single point in time.

[9] One limitation of this approach is that I am unable to assess the independent effects of the

underlying factors that make up the index in the models that include the index. Therefore,

Model 2 includes the component indicators (population, urbanization) while later models

substitute a population/urbanization index.

[10] The argument developed here emphasizes the effects of public demand for law enforcement

services (911 calls) on deputy employment (this link follows from a theoretical expectation

that organizations will maintain excess capacity, or organizational slack, as a buffer against

environmental uncertainty) (March and Simon 1957; Thompson 1967; Mintzberg 1979), but

a reciprocal association is also plausible since shifts in personnel size may also result in

heightened community awareness and corresponding increases in service demands.

[11] Potential estimation problems due to collinearity between crime rates and the urbanization

and population indicators is not a factor here, since these other indicators are not included

in the first specification.

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