Organizational Decline and Fiscal Distress in Municipal Police Agencies

Abstract

In late 2007, the United States entered one of the most significant recessions in recent memory. While the consequences to individuals have been well documented, less attention has been paid to the effects on state-sponsored organizations such as police agencies. The current study examines fiscal distress (e.g., layoffs, hiring freezes) in a sample of large municipal law enforcement agencies. According to a framework proposed by Levine, departments should be most vulnerable to fiscal distress when the jurisdiction experiences economic shocks (environmental entropy), the demand for police services declines (problem depletion), the political system becomes less supportive of police agencies (political vulnerability), and the organization becomes more structurally complex (organizational atrophy). Structural equation modeling results suggest that fiscal distress is driven by the local economic context, changing crime rates, and organizational size. This research has implications for helping organizations address future economic declines.

Keywords

policing organizational theory fiscal distress recession cutback management

The general health of the United States economy ebbs and flows over time. According to the National Bureau of Economic Research (2012), the United States suffered through 33 separate recessions between 1857 and 2010. The cyclical nature of the economy suggests that economic distress for individuals and organizations is inevitable, with contraction occurring approximately every 17 years and, in recent decades, even more frequently. The recent recession, the most substantial economic decline since the Great Depression, began in December 2007 and officially lasted 18 months. Effects typically extend beyond the official period identified by economists as characterizing the recession. By late 2009, unemployment rates soared above 10%, more than twice the level from only a decade earlier, and would remain above 8% until 2012 (Bureau of Labor Statistics, n.d.). Not only did millions of Americans lose their jobs, they lost their homes and retirement savings as well. Similarly, many businesses were forced to either scale back or cease operations entirely.

The effects of the Great Recession also permeated the public sector. Cities and towns struggled to maintain funding for municipal services as property and sales tax revenues declined because of diminished home prices and decreased spending, respectively, and federal grant support dried up because of national austerity measures (Parlow, 2012). The Brookings Institution estimated a loss of nearly 600,000 government jobs between 2009 and 2011, including teachers, first responders, air-traffic controllers, and other workers (Greenstone & Looney, 2012). Police departments were not immune from the turbulent economic environment. One estimate placed the number of law enforcement jobs lost at 10,000 positions (Office of Community Oriented Policing Services, 2011). Some local agencies were, quite simply, decimated by layoffs and furloughs. The Newark, New Jersey Police Department, for example, faced a nearly $17 million shortfall in 2010, necessitating the layoffs of 167 of the city’s sworn officers (Queally & Friedman, 2010). Other agencies cut overtime spending or curtailed salary increases for agency personnel (Police Executive Research Forum, 2010). Nearly half of all agencies in a Police Executive Research Forum (2010) survey indicated that “services in their community have declined or will decline due to budget cuts” (p. 2).

Based on these accounts, the recession thrusted certain law enforcement agencies into a period of organizational decline marked by declining budgets, resource scarcity, cutbacks, and financial constraints (Cameron, Kim, & Whetten, 1987; Cameron, Whetten, & Kim, 1987; Guy, 1989). For some department leaders, workforce reductions are likely to be viewed as temporary challenges dictated by extraordinary economic circumstances (Weitzel & Jonsson, 1989). For others, the turbulent economic environment may have produced long-term organizational resource declines and a belief that funding levels will never return to prerecession levels (Druker & Robinson, 1994). Indeed, the downturn led the Office of Community Oriented Policing Services (2011) to conclude:

Rather than continuing to provide services through traditional means in hope that the economy will return to pre-recession levels, police nationwide are shifting, adapting, and redeveloping the ways in which they do their job—to ensure the highest levels of public safety (p. 34).

It is clear the recession adversely affected some police agencies, but not all agencies felt the pains of the poor economy equally. What factors account for organizational decline within municipal police agencies? Levine (1978, 1979) argued that a combination of organizational and environmental characteristics contributes to fiscal problems: environmental entropy, problem depletion, political vulnerability, and organizational atrophy. Specifically, Levine suggested that organizations are most susceptible to decline because of economic shocks, a declining need for the agency’s services, lack of political and community support, and organizational structures that hinder adaptation and innovation.

The current research examines organizational decline among police agencies. Some avoided deep fiscal strains, thereby escaping the detrimental outcomes often associated with decline such as lack of innovation, diminished employee morale, and turnover problems. To date, researchers have tended to focus only on the outcomes of recessions for police agencies and the specific cutback strategies used to deal with budgetary problems. They have not attempted to explain the causes of the decline in the first place using economic, environmental context, and organizational variables. Drawing upon Levine’s (1978) framework, we argue that less complex agencies with a well-defined community need, strong political/community support, and a stable economic environment should be able to avoid organizational decline better than other agencies.

Literature Review

Organizational Decline

Municipal government entities are often taken for granted, viewed as impervious to outside shocks (Adam, Bauer, Knill, & Studinger, 2007; Lewis, 2002). It is not until threats to their existence emerge that we acknowledge their vulnerability. For some scholars, these periods of crises and declines are part of an overall life cycle view of organizations, a framework which emphasizes various events and turning points that alter organizational trajectories (see, e.g., Cameron & Whetten, 1983; Kimberly, 1980; Quinn & Cameron, 1983; van de Ven & Poole, 1995). This research focused attention on a variety of points within an organization’s history, most notably its creation and its death or disbanding (King, 2009). Although most large police organizations in the United States were formed in the late 1800s or early 1900s, they may differ in their structures and activities because of imprinting caused by the specific time period of their founding (King, 2009). For instance, pre-1900s departments (political era organizations) were less likely to employ civilians than younger (reform era) departments (King, 1999). Life cycle research has also identified factors associated with police department disbanding, including lack of funding, internal crises, and small size (Kiedrowski, Jones, & Ruddell, 2017; King, 2014).

Just as important as organizational creation and death is organizational decline, periods that may alternate with growth and stagnation or represent the beginnings of a more permanent descent toward failure and disbanding. The conceptual definition of decline is murky, including everything from decreasing organizational size (e.g., number of employees) to declining financial resources (e.g., budgets, profits) to diminished views of the organization (e.g., prestige, reputation; Cameron, Kim, et al., 1987; Guy, 1989; King, 2009; Weitzel & Jonsson, 1989). Regardless of the specific indicator, there is a consensus about its dysfunction. It has been associated with organizational conflict, lack of innovation, negative structural changes, diminished employee participation, and declining morale (Cameron, Kim, et al., 1987; King, 2009; Levine, 1978).

In his classic work in the field, Levine (1978) adopted what has become a commonplace definition of organizational decline—a decrease in an organization’s financial resources (see also Cameron, Kim, et al., 1987; Whetten, 1987). Organizations which do not have the resources necessary to perform their basic roles are potentially faced with organizational decline. It is these organizations which fail to adapt that are in danger of permanent demise. While economic downturns can account for organizational decline, many organizations do not struggle during adverse economic cycles. Similarly, many organizations struggle even in periods of external economic stability. To understand the path toward organizational decline, Levine (1978) presented a typology which accounts for the locus (internal/external) and the nature (political/economic) of the decline.

Environmental Entropy

According to Levine (1978), environmental entropy represents an external economic cause of organizational decline where “the capacity of the environment to support the public organization at prevailing levels of activity erodes” (p. 318). He writes broadly of the detrimental effects of local population declines, supply costs and scarcity (detrimental to manufacturers), and overall market conditions. Environmental entropy could occur because of crises, such as local plant closings or economic recessions (see Brunet, 2015). During periods of economic decline, unemployment rates are likely to rise and tax revenues fall. This reduces the resources that the government can spend on public organizations, such as police agencies. For many agencies, this necessitates decline and restructuring, but for some agencies, it could signal the end.

Variations in organizational decline within police agencies during the Great Recession may be explained by the localized nature of the recession’s effects. The Great Recession disproportionately affected certain parts of the country and, consequently, individuals, businesses, and government agencies within these areas. For instance, poverty rates increased most dramatically during the recession in certain cities in Florida and California (Kneebone, 2010); other cities (e.g., Washington, DC) were somewhat insulated from its effects. Location may provide some measure of protection from broader economic forces.

Problem Depletion

Problem depletion, an external political cause of decline, represents a scenario where the demand for a public organization’s service deteriorates (Levine, 1978). Although the supply of crime is generally constant, if crimes rates were to diminish to the point where formal social control was no longer necessary from a police agency, then the health of the agency could decline or even cease to exist. There is some evidence linking crime rates to police organizational size (see Maguire, 2001 for a review), but the few studies of organizational decline in policing point to the limited relevance of crime. In fact, King (2014) and Brunet (2015) found that many of the disbanded agencies in their Ohio and North Carolina samples, respectively, failed to report crime data to the Federal Bureau of Investigation’s (FBI) Uniform Crime Reporting (UCR) program. Without any internal records, these agencies likely failed for reasons other than problem depletion. Where crime data were available, they varied widely, leading the authors to conclude that they played little role in the agency termination process. That said, crime did significantly affect at least two jurisdictions’ decisions to disband. According to Brunet,

one chief convinced the town council to do away with the department as there was little crime at the time [and] at least one interviewee noted that rising crime levels convinced town leaders of the need to shift policing responsibilities to the better equipped and trained sheriff’s office (p. 331).

Political Vulnerability

Levine (1978) identified political vulnerability as an internal risk factor that made organizations susceptible to budget changes and demands from external providers. For instance, younger organizations are more likely to struggle, a phenomenon known as the liability of newness (Brunet, 2015; Singh, Tucker, & House, 1986). Smaller organizations are also particularly vulnerable. As King (2014) noted, “It is relatively easy for sovereigns [e.g., local governments] to penetrate the skin of small police agencies because there is little to buffer the organization from external intrusion” (p. 686). Among the 19 disbanded North Carolina departments in Brunet’s (2015) sample, all had 10 or fewer officers. Organizations also risk decline if they are heavily reliant upon single external resource providers; diversifying resource flows provides some buffer against external control (Pfeffer & Salancik, 1978).

A city’s government form may also increase the likelihood of organizational decline. Elected mayors are more likely to respond to constituent demands, including demands for crime control services, leaving police departments vulnerable to external pressures (Stucky, 2003, 2005; Wilson & Boland, 1978). The city manager–council form of government is envisioned as superior to the mayor–council form, removing the political (reelection) motive from budgetary decisions. Levine, Rubin, and Wolohojian (1981) suggested that the city manager–council form allows for “centralized control over budget and personnel” (p. 620) in the hands of a professional decision-maker. Consequently, they predicted that city managers are better positioned to manage government agencies in times of fiscal uncertainty, thereby averting organizational decline. Morabito (2008) also found that the centralized decision-making power of a city manager facilitates innovation; a mayor must contend with the dissent and conflict arising from a more diffused set of constituents. She found that police agencies operating in city manager–council jurisdictions were more likely to adopt community policing. These benefits notwithstanding, other researchers have noted the benefits of a mayor form of government. Stucky (2005), for instance, found that traditional governments (e.g., mayor–councils, partisan elections, and district-based elections) employed more police officers than reformed governments (e.g., city managers, nonpartisan elections, and at-large elections). This is predicated on the belief that elected politicians must remain responsive to constituents and are less likely to allow police organizations to decline in size against public demands. Others predict that city managers are cost conscious and, all else being equal, would restrict public agency expenditures. Yet, empirical research more commonly shows no effect of government form on expenditures (see, e.g., MacDonald, 2008). As such, the precise relationship between government form and organizational decline, if any, is still unclear.

Community support is related to organizational decline for many of the same reasons as government form. If mayors feed organizations by responding to constituents, organizations can resist decline and attempt to disband the agency by maintaining a coalition of community support (Kaufman, 1976). The community becomes a lobbying voice for growth or maintenance of organizational resources and functioning.

Another way to foster community support and make police agencies more politically stable is by employing community-oriented policing strategies. Community-oriented policing became popular in the 1990s and reflected a move toward policing that incorporate citizen input, a broad policing function, personalized service, geographic focus, positive interactions, and partnerships within the community (see Cordner, 2015). Police agencies that employ this style of policing may be more likely to receive community support, thus making them less vulnerable to decline. According to a systematic review of the community policing literature, 78.3% of the studies reviewed found that community-oriented policing strategies have a beneficial effect on citizen satisfaction, and 60% found beneficial effects for police legitimacy (see Gill, Weisburd, Telep, Vitter, & Bennett, 2014).

Organizational Atrophy

Levine (1978) identified organizational atrophy as a fourth cause of organizational decline (internal and technical). Organizational atrophy could occur with a large number of retirements, and the agency lacking the staff available to effectively police the community. Moreover, agencies with high levels of corruption or police misconduct could also face decline, particularly if such misconduct was met with public resistance (see King, 2014). Similarly, poor performance can also be linked to decline, as both unsatisfactory performance and misconduct are often linked to legitimacy concerns. In addition to these factors, the organizational structure of police agencies could contribute to decline (see Langworthy, 1986; Maguire, 2003). In particular, agencies that are too complex (e.g., too much differentiation) and taking on too many roles (e.g., task scope is too large) could face decline. Although the organizational literature suggests that some organizational characteristics (e.g., specialization through the presence of experts) may facilitate innovation, others are impediments (e.g., formalization removing discretion and innovation; see Damanpour, 1991). In periods of distress or when operating in dynamic environments, mechanistic or bureaucratic organizations may be slow to change and suffer accordingly (see Burns & Stalker, 1961). Moreover, complex organizations that use resources more effectively tend to have fewer slack resources, and cutbacks hit these organizations particularly hard (see Levine, 1979; Scorsone & Plerhoples, 2010).

Cutback Management

When faced with decline, public service organizations (e.g., police agencies) face tough decisions. It is inevitable that when resources become scarce, organizations must adapt to these conditions, whether they are temporary or permanent. This process of “managing organizational change toward lower levels of resource consumption and organization activity” is defined as cutback management (Levine, 1979, p. 180). According to Greenhalgh, Lawrence, and Sutton (1988), these strategies lie along a continuum related to the protection of employee well-being. For example, hiring freezes preclude the replacement of department employees or the employment of new workers, but the financial well-being of existing workers is maintained. Overtime and salary reductions more deeply affect existing employees, first by restricting opportunities for extra pay and then by reducing base compensation. Furloughs and layoffs differ only in their permanence. The point is that organizations tend to respond to decline through “a reduction of personnel, output, equipment, or services” (Weitzel & Jonsson, 1989, p. 95).

Current Study

In this article, we use Levine’s typology to examine whether environmental and organizational characteristics of police agencies contribute to organizational decline. Few prior empirical studies have addressed this question. To be sure, we know that some police organizations suffered during the Great Recession, experiencing furloughs, layoffs, reductions in training, discontinued units, and other cutbacks (Office of Community Oriented Policing Services, 2011). Unclear is whether the recession alone was responsible for this organizational decline and whether local economic conditions affected agencies differently. This study is important because it has implications regarding how police agencies can perform their goals efficiently, even in the face of harsh economic circumstances, declining need, political and community pressures, and internal challenges. We now turn to a discussion of the data and measures used to answer our research question.

Methodology

Sample

To examine Levine’s hypotheses, we merged together data from multiple archival and official sources. We used the 2013 wave of the Law Enforcement Management and Administrative Statistics Survey (LEMAS) as the study’s sampling frame (Bureau of Justice Statistics, 2015). The Bureau of Justice Statistics has administered the LEMAS survey to a sample of law enforcement agencies at irregular intervals of 1 to 6 years since 1987. Although the composition of the LEMAS sample varies across waves—for instance, special purpose agencies were included in 1997 but omitted in 2003—municipal, sheriff, and state police agencies with 100 or more full-time (FT) sworn officers are included each year (Groves & Cork, 2009). The remainder of the LEMAS sample is composed of a selection of smaller (<100 FT sworn officers) agencies. Given the wealth of organizational data (e.g., personnel, organizational structures, equipment) collected via the LEMAS survey from a large sample (over 2,500) of agencies with response rates exceeding 90%, the archived LEMAS datasets have proven to be valuable tools for police researchers (Groves & Cork, 2009; Langworthy, 2002; Matusiak, Campbell, & King, 2014). In fact, a recent study identified LEMAS as the second most used dataset produced by the Bureau of Justice Statistics, trailing only the National Crime Victimization Survey (Matusiak et al., 2014).

Data Sources

The 2013 LEMAS administration was the first administration since the Great Recession and the first wave to ask about organizational responses to fiscal crises. The 2,826 state, county, and local agencies within the dataset were filtered to include only municipal agencies employing 100 or more FT officers (n = 509). Data considerations forced us to restrict the sample to only larger local agencies. Several hypothesized predictors of fiscal distress were only available for metropolitan areas (e.g., gross domestic product, GDP) or municipal governments (e.g., government form), necessitating the exclusion of state, county, and sheriff’s departments. In addition, sheriff departments are politically, economically, and functionally distinct from most municipal agencies, further justifying the restricted sample. We also omitted smaller agencies (<100 FT sworn officers). The 2013 LEMAS was merged with the earlier 2007 wave to establish the time ordering of variable relationships. As the Bureau of Justice Statistics samples all large law enforcement agencies during each administration, over 90% (n = 465) of 2013 large agencies also participated in 2007. The sample of overlapping agencies with fewer than 100 FT sworn officers was considerably smaller and unrepresentative of the population of smaller agencies. Thus, the current study sample included 465 large (100 or more FT sworn officers) municipal agencies responding to both the 2007 and 2013 waves of the LEMAS.

In addition to the LEMASs from 2007 and 2013, we utilize data from multiple sources to examine environmental indicators of fiscal distress. These data come from the Bureau of Economic Analysis (BEA), the U.S. Census Bureau’s (n.d.) American Community Survey (2007, 2011), the Bureau of Labor Statistics (2011), the Uniform Crime Reports (2007, 2011), and the Municipal Year Book (International City/County Management Association, 2010). The measures extracted from these data sets are discussed in greater detail throughout this section.

Dependent Variables

Fiscal distress, a latent construct, served as the study’s dependent variable. The LEMAS addressed departmental fiscal challenges, including cutback management strategies, for the first time during the 2013 administration. LEMAS respondents reported the presence of salary reductions, furloughs, hiring freezes, and layoffs in agencies during the period January 1, 2010 to December 31, 2012 (exception was for layoffs, which referenced 2012 only). These cutback management strategies served as a proxy for organizational decline given the empirically established connection between decline and cutback management strategies (see, for instance, Levine, 1978). We employ the label “fiscal distress” instead of organizational decline to signify a deeper level of decline; organizations are not just facing budget reductions, but are forced to make significant adjustments to staffing. For study purposes, we make no distinction between strategies affecting only sworn or only nonsworn employees¹ nor do we examine the depth or magnitude of the fiscal distress (e.g., the percentage reduction in salaries). The four indicators capture the existence of fiscal distress only.²

Independent Variables

As noted earlier, Levine (1978) identified four broad determinants of public organizational decline (environmental entropy, problem depletion, political vulnerability, and organizational atrophy). We constructed the independent variables included in the following models with these broad categories in mind. As economic recessions affect cities quite differently, we employed Levine’s argument and hypothesize that police agency fiscal distress will be greatest in jurisdictions characterized by weak economies or, in Levine’s words, a form of environmental entropy. In these areas, local governments face more constraints in fully supporting public organizations. Environmental entropy was measured as a latent, unobserved construct. We selected three indicators based on their ability to reflect the underlying construct; in other words, variation in the environmental entropy construct, along with measurement error, is believed to produce variation within each of the observed indicators (Edwards & Bagozzi, 2000). The first indicator, GDP, is a widely used measure of economic activity at the national and subnational levels. The U.S. Department of Commerce, BEA (n.d.) collects and reports GDP data for U.S. metropolitan areas. For each metropolitan statistical area (MSA), we computed the percentage change in the GDP between 2007 and 2011 and subsequently attached each police agency in the LEMAS sample to its respective MSA, providing an indication of growth, stagnation, or decline in regional (i.e., metropolitan) economic activity. The second observed economic indicator captured the percentage change in county-level median home values between 2007 and 2011. Change scores were computed using data from the U.S. Census Bureau’s (n.d.) American Community Survey. Plummeting housing prices characterized the Great Recession and produced deleterious effects on individuals (e.g., personal bankruptcies; homelessness) and communities (e.g., widespread foreclosures; Ellen & Dastrup, 2012). A healthier economy is generally indicated by rising home prices or, at the very least, greater stability in housing markets. Finally, the U.S. Department of Agriculture’s Economic Research Service (n.d.; based on Bureau of Labor Statistics data) provided 2011 county-level employment rates.³ Employment rather than unemployment rates were used for consistency in the direction of all three observed indicators; positive values suggest a stronger local economy (greater GDP growth, growing home prices, and more workers employed).

Levine also argued that organizations become more susceptible to cutbacks when the very problems they are created to address—their raison d’être—subside. In the case of police organizations, declining crime rates may signal problem depletion, reducing the urgency to sustain existing staffing levels. We measured this problem depletion by collecting FBI UCR data for both 2007 and 2011 (FBI, 2009, 2013) and computing the percentage change in crime rates for each agency. Only Part I crimes were included in the rate computation (murder, rape, robbery, aggravated assault, burglary, larceny, and auto theft).

Political vulnerability was measured by four variables capturing an agency’s exposure to potential government, resource provider, and community pressures. The first measure captures the size of the department in terms of FT sworn personnel in 2007. Because of the significant positive skew in the dataset, the organizational size variable was log transformed in all models discussed in the Analysis Procedures section. Second, government form indicated the legislative and administrative structure for each city—council–manager, mayor–council, or other forms. There is some belief that the council–manager form of government represents a more professional model, where a city manager rather than a politicized mayor exerts business-like control over the everyday operations of the community, including its finances (see, e.g., Levine et al., 1981). If true, we would expect these professional governments to help police departments avoid agency-level fiscal distress. Data on government forms were collected from the Municipal Year Book, 2010, a publication of the International City/County Management Association (2010). The Association classifies each city with at least 2,500 residents according to its form of government: council–manager, commission, mayor–council, representative town meeting, or town meeting. For the purposes of the current study, government form was dichotomized into 1 = council–manager (the hypothesized professional form) or 0 = other form. Third, funding variety measures the diversity of sources from which police departments receive operational funding. The variable was constructed by summing seven indicators from the 2013 LEMAS dataset—one or more municipal governments, one or more county governments, one or more state government agencies, one or more federal government agencies, payment for contracted law enforcement services, asset forfeiture programs, and user fees (Cronbach’s α = .700). Presumably agencies receiving funding through multiple sources are better positioned to absorb shocks from any singular funding source. Fourth, a measure of community policing was included in the following models based on the belief that departments practicing strategies consisting with the philosophy may foster local community support. Consequently, agencies may draw upon this political capital as a buffer in times of need, such as during threatened cutbacks. The community policing variable was an index, constructed by summing nine dichotomous items from the 2007 LEMAS—agency maintained a mission statement with a community policing component, agency encouraged patrol officers to engage in problem-solving projects, agency conducted a citizens’ police academy, agency maintain a formal community policing plan, agency gave patrol officers responsibility for specific geographic beats, agency included collaborative problem-solving projects in officer evaluations, agency upgraded technology to support analysis of community problems, agency partnered with citizen groups, and agency conducted or sponsored community surveys (Cronbach’s α = .690).

Finally, organizational atrophy was measured by two variables. According to Levine’s framework, organizational characteristics may hinder a department’s ability to adapt in the face of financial crises, ultimately leading to cutbacks. This is especially true when organizations become structurally complex and inflexible. Horizontal complexity, measured with 2007 LEMAS data, represents an organization’s tendency to divide work into distinct divisions, units, or other occupational sections. Respondents were asked whether 22 different problems and tasks were handled with an FT special unit, specially assigned personnel, on an as-needed basis, or not at all.⁴ Responses were dichotomized into special unit (1) and other (0) and, consistent with prior police organizational research, summed to create a horizontal complexity index (Cronbach’s α = .832) with higher scores indicating greater levels of structural complexity (see Maguire, Shin, Zhao, & Hassel, 2003; Randol, 2012). The final variable, formalization, is a measure of structural coordination or control (see Maguire, 2003). It reflects the procedural or rule-bound nature of an organization. LEMAS 2007 respondents were asked about the presence of formal written policies in 17 areas (1 = agency has written policy/procedure, 0 = agency lacks written policy procedure).⁵ Responses were summed, creating a formalization scale (Cronbach’s α = .583) with higher scores indicating greater reliance on organizational rules (see Maguire et al., 2003; Randol, 2012).

Tables 1 and 2 display descriptive statistics for all study variables and correlations between independent variables, respectively. Figure 1 depicts the theoretical model as well as hypothesized relationships between constructs.

Table 1.

Descriptive Statistics for Endogenous and Exogenous Variables.

	n	M	SD	Minimum	Maximum
Economic distress indicators
Salary reductions	461	0.085	0.279	0	1
Furloughs	457	0.228	0.420	0	1
Hiring freezes	462	0.301	0.459	0	1
Layoffs	443	0.059	0.235	0	1
Environmental entropy indicators
Median home value change	461	−11.172	17.629	−54.84	32.40
GDP change	437	−1.031	1.663	−6.00	10.80
Employment rate	465	90.993	2.034	83.50	96.70
Problem depletion indicator
Crime rate change	442	−16.739	19.122	−96.05	35.90
Political vulnerability indicators
Organizational size (FT sworn)	465	494.865	1,872.103	87	35,216
Government (council–manager)	461	0.568	0.496	0	1
Funding variety	461	3.965	1.823	0	7
Community policing	462	5.896	2.149	0	9
Organizational atrophy indicators
Horizontal complexity	450	10.531	4.663	0	22
Formalization	462	14.509	1.750	8	17

FT = full time; GDP = gross domestic product.

Table 2.

Correlation Matrix and Collinearity Statistics for Study Variables.

		1	2	3	4	5	6	7	8	9	10
1	Median home value change	1.000
2	GDP change	0.629	1.000
3	Employment rate	0.529	0.445	1.000
4	Crime rate change	0.034	0.038	0.063	1.000
5	Government form	−0.156	−0.196	−0.114	−0.104	1.000
6	Funding variety	−0.091	−0.044	−0.065	−0.030	0.048	1.000
7	Community policing	−0.110	−0.078	−0.017	−0.008	0.172	0.024	1.000
8	Organizational size (logged)	0.005	−0.033	−0.028	−0.030	−0.180	0.111	0.153	1.000
9	Horizontal complexity	−0.102	−0.135	−0.103	−0.004	0.076	0.160	0.292	0.639	1.000
10	Formalization	−0.086	−0.032	−0.050	0.006	0.027	0.000	0.166	0.262	0.289	1.000
	VIF^a	1.94	1.78	1.42	1.02	1.16	1.04	1.13	1.91	1.99	1.11

GDP = gross domestic product; VIF = variance inflation factor.

VIF based on sample of n = 391 after listwise deletion.

Figure 1.

Theoretical model of fiscal distress and hypothesized relationships.

Analysis Procedures

Given that our measurement of Levine’s model includes both measurement (fiscal distress and environmental entropy are measured as unobserved latent constructs) and structural (regression paths) components, it is most appropriately assessed using structural equation modeling techniques. We follow the two-step modeling approach recommended by Anderson and Gerbing (1988) and Schumacker and Lomax (2010) wherein the validity of the measurement portion of the theoretical model is first established (in this case, fiscal distress and environmental entropy) using confirmatory factor analysis before proceeding to an analysis of structural/prediction models. All analyses were performed using Mplus, a powerful structural software package. The program is particularly useful because it includes a model estimator (weighed least squares means and variance adjusted, WLSMV) to handle latent construct indicators that violate assumptions of continuous normality (Muthén & Muthén, 2010). In this study, four dichotomous indicators measure fiscal distress, thereby necessitating the alternative estimator.

The individual parameters within measurement (i.e., factor loadings) and structural models (i.e., standardized coefficients) provide some indication of the importance of specific model paths but do not provide any indication of the degree of congruence between the hypothesized model (Figure 1) and the data. We rely upon four of the many available goodness-of-fit statistics developed over time to assess the overall model fit (Byrne, 2012; Gau, 2010; Hu & Bentler, 1999; Kelloway, 2015; Schumacker & Lomax, 2010). The Chi Square Test of Model Fit (χ²) is one of the most commonly reported statistics and tests the difference between the sample covariance matrix and the more restricted covariance matrix based on the hypothesized model (Byrne, 2012). A nonsignificant χ² suggests model fit or no differences between the two covariance matrices. Results must be interpreted cautiously, however, as the test is sensitive to sample size, sometimes with as few as 200 cases (Gau, 2010; Hoelter, 1983; Schumacker & Lomax, 2010). Consequently, χ² is evaluated along with the root mean square error of approximation (RMSEA; values < .06 indicate a good fit), comparative fit index (CFI; values > .95 indicate a good fit), and the Tucker–Lewis Index (TLI; values > .95 indicate a good fit). Following Hu and Bentler’s (1999) suggestion, we adopt an approach whereby the indices are examined holistically; a slight deviation from the aforementioned cutoffs is acceptable if the bulk of the evidence points to a strong-fitting model.

Findings

Measurement Models

Before proceeding with the full structural model, we first constructed a measurement model inclusive of the two latent constructs—fiscal distress and economy—described earlier. Individual factor loadings for each construct were examined (four for fiscal distress and three for economy) and the overall fit of the model was assessed using common goodness-of-fit statistics. As shown in Table 3, all fit statistics (χ² = 10.387, df = 13, p = .662, RMSEA = .000, CFI = 1.000, TLI = 1.008) are well within the acceptable range suggesting that the hypothesized measurement model fits the data well. Although factor loadings are lower (<.40) for two of the four fiscal distress measures (hiring freezes and layoffs), we decided to retain all four indicators because of their theoretical relationship to the underlying concept and the overall model fit statistics.⁶

Table 3.

Fiscal Distress and Economy Measurement Model Estimates.

Fiscal distress		Economy
Indicator	Item loading	Indicator	Item loading
Salary reductions	.828	Median home value change	.877
Furloughs	.759	GDP change	.712
Hiring freezes	.399	Employment rate	.623
Layoffs	.355

Note. Fiscal distress/Economy R = −.552. χ²= 10.387, df = 13, p = .662, RMSEA = .000, CFI = 1.000, TLI = 1.008. GDP = gross domestic product.

Structural Model

The next step involved testing the full structural model shown in Figure 1.⁷ Results are displayed as Model 1 in Table 4. The model (inclusive of both measurement and structural components) fits the data according to goodness-of-fit statistics (χ² = 73.898, df = 55, p = .045, RMSEA = .029, CFI = 0.963, TLI = 0.953). A review of individual parameters shows only partial support for Levine’s (1978) arguments. As expected, the local economy (β = −.544, p < .001) and crime rate change (β = −.129, p < .05) are both negatively related to fiscal distress within large local municipal police agencies. Organizations are less likely to experience the detrimental effects of the great recession if they operate within stronger local economic environments characterized by higher employment rates, rising home values, and a stronger GDP. Similarly, community need inoculates organizations from fiscal distress, even after accounting for the local context. Large increases in serious crime rates between 2007 and 2011 also buffered departments from major cutbacks. In contrast, drops in crime are associated with greater fiscal distress. Among the four indicators of political vulnerability and two indicators of organizational atrophy, only organizational size is significantly related to fiscal distress. Larger organizations are more likely to experience some combination of salary reductions, furloughs, hiring freezes, and layoffs. Overall, Model 1 accounts for approximately 45% of the variation in the fiscal distress latent construct.

Table 4.

Estimates and Fit Indices for Fiscal Distress Structural Model.^a

	Model 1		Model 2
	β	SE	β	SE
Environmental entropy
Economy (unobserved latent)	−.544***	.064	−.555***	.065
Problem depletion
Crime rate change	−.129*	.064	−.129*	.064
Political vulnerability
Government form	.084	.081	−.016	.077
Funding variety	.023	.077	.023	.077
Community policing	−.072	.076	−.072	.076
Organizational size (FT sworn)	.240*	.098	.240*	.098
Organizational atrophy
Horizontal complexity	.186	.102	.186	.102
Formalization	−.055	.085	−.055	.085
Government form > economy			−.180**	.061
Government form > economy > fiscal distress (indirect)			.100**	.036
	n	419		419
	χ²	73.898		60.811
	df	55		54
	p	.045		.244
	RMSEA	.029		.017
	TLI	0.953		0.983
	CFI	0.963		0.987
Fiscal distress R²		.453		.455

Measurement model parameters omitted.

***

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

We estimated a second model (see Model 2 in Table 4) based on software-produced modification indices, suggestions for additional paths or constraints designed to improve overall model fit (see Gau, 2010; Schumacker & Lomax, 2010). Adjusting models based on empirical data should be done cautiously and with relevant theory in mind to avoid producing a strong-fitting model unable to be reproduced with different samples. Although Model 1 fits the data, we added a single path based on the suggested modification index—a pathway connecting government form with the unobserved latent economy construct. This allows us to estimate the same parameters as in Model 1 but also examine the indirect relationship between government form and police department fiscal distress through the local economy. The results in Table 4 show that there is only a slight improvement in overall model fit from Model 1 (χ² = 60.811, df = 54, p = .244, RMSEA = .017, CFI = 0.987, TLI = 0.983) with the addition of the new direct path (government form > economy) and indirect path specification. Interestingly, while a council–manager form of government is typically associated with “sound budgeting and accounting practices” (Levine et al., 1981, p. 620), the results here suggest that the government form is linked to weaker local economic conditions (β = −.180, p < .01) and, indirectly, local police agency fiscal distress (β = .100, p < .01).

A final model was estimated to further explore Levine’s (1978) notion of problem depletion. In this study, problem depletion is operationalized as the percentage change in UCR Part I crime rates between 2007 and 2011. We operate under the assumption that police departments increase their exposure to cutbacks during times of fiscal austerity when the problems they are designed to handle become less pressing. By accounting for percentage change, we can measure the fluctuations in the magnitude of a jurisdiction’s crime problem over a relatively short period. Indeed, the results in Models 1 and 2 indicate that departmental distress is more common in agencies experiencing the greatest drops in crime during the period. Note, however, that the operationalization only considers the change in crime rates, not the absolute magnitude of the problem. We estimated the indirect path model a second time (reproducing Model 2), substituting 2011 crime rates for the percentage change in crime rates from 2007 to 2011. As predicted by Levine’s theory, the absolute magnitude of the problem is less important than its change over time. The revised model (not shown) does not fit the data as well as Model 1 or 2 (χ² = 77.711, df = 54, p = .019, RMSEA = .032, CFI = 0.944, TLI = 0.957) and the 2011 crime rate variable is not a significant predictor of fiscal distress (β = −.061, p > .05).

Discussion & Conclusion

Our goal in this study was to examine the relationship between environmental indicators of financial turbulence, structural arrangements of police departments, and fiscal responses by those agencies to economic hardship. Using Levine’s (1978) organizational decline typology, we found that both environmental and size variables affect agency responses.

First and foremost, we found that police departments operating within areas where the economy remained strong were the least likely to experience decline during the Great Recession. This is intuitive, and on the surface, not something that police agencies have much control over; however, there are steps that police departments can take to prepare for these inevitable fluctuations in economic conditions. For example, agencies with more slack resources may be able to withstand economic turbulence for longer periods of time before actively making cutbacks. Ironically, this can penalize the most efficient organizations, who tend to use all of their resources efficiently. Perhaps agencies that are best able to come in under budget can use some of these slack resources to prepare for potential hardships.

We also found that police departments operating in contexts with rising crime rates were less likely to make cuts. Where crime rates are high, more officers are needed to address this problem, both proactively and reactively. With falling crime rates, agencies may be able to survive with smaller numbers of officers, and some of these agencies may even be absorbed into larger agencies (Brunet, 2015). Thus, the response to financial hardship may be influenced by the demand for the services that the organization provides.

With the exception of organizational size, we did not find evidence of any direct relationships between our measures of political vulnerability and fiscal distress; however, we did find that council–manager government forms are less likely to appear in places where the economy is doing well. This finding was unexpected, but is explainable in that our measure of economic well-being is linked more closely to individual economics (e.g., housing values, employment rates) than government resources. It could be that while the economy has improved broadly, more resources are not necessarily available at local government levels. Alternatively, the role of city managers may be overstated. Perhaps council–manager governments are not enough to prevent local economic downturns. However, the presence of a city manager may allow for “quick and effective retrenchment action” in the face of a recession, adapting more quickly than other jurisdictions (Levine et al., 1981, p. 626).

Finally, we found that larger agencies, employing more officers were more likely to experience fiscal distress. These larger departments may be better able to withstand some belt-tightening by simply asking some of their remaining officers to do more. It is less likely that larger departments disappear altogether, so in order to remain functional, they might have to look to measures such as hiring freezes, layoffs, or salary reductions. Larger agencies may be better situated to survive economic challenges, but that does not mean that they will not be forced to adapt (see King, 2014).

Our study was not without limitations. While we merged multiple iterations of the LEMAS together, we were only able to examine our variables cross-sectionally. This is because the items that appear in the different waves of the LEMAS are not necessarily consistent. For example, the 2007 LEMAS includes a measure of the number of substations, which allows for measurement of spatial complexity. The 2013 LEMAS contains no such measure, so it is not possible to examine changes in this construct across the waves. As such, a longitudinal examination of police agencies (see King, 2009) using data from the LEMAS series is quite challenging. Ideally, we could examine whether agencies that have become more complex experience more financial distress. The answer to this question is not possible given the data at hand.

We were also lacking some measures that could help us apply Levine’s typology to police agencies (see Brunet, 2015). Measures of the age of the organizations in our sample, and whether there was some type of political changeover would help us further develop the political vulnerability concept, which received only indirect support in our study. Moreover, organizational-level information regarding retirements, turnover, misconduct, or slack resources would help examine organizational atrophy more effectively. Even when economic circumstances are challenging, a large number of retirements and voluntary resignations may prevent the organization from taking more formal measures such as layoffs or salary reductions.

It is also possible that police departments begin to mirror each other in attempts to successful address organizational decline and economic turbulence. The literature on isomorphism among organizations suggests that this process can be coercive when organizations change because of pressures from larger, more powerful organizations, or it can be mimetic if it results from one organization imitating another (see Burruss & Giblin, 2014; DiMaggio & Powell, 1983). In both cases, police organizations experiencing decline are likely to follow the lead of those agencies which have provided an effective blueprint for addressing such decline. We did not have any measures of this potential process.

In addition to examining the relationship between organizational decline and police organizational structure longitudinally, future researchers may examine whether a reciprocal relationship exists between fiscal distress and organizational structure. That is, do agencies that experience fiscal distress become less complex in the future? Moreover, what long-term effects might fiscal distress have on police effectiveness or police legitimacy? We believe that a number of policing outcomes may be influenced by the economic stability of the police agency.

To conclude, our study examined how both police organizational structure and environmental economic conditions affect fiscal decisions by police departments. We found that both economic conditions and factors linked to the organizations themselves predict responses. In a social reality where economic conditions will continuously ebb and flow, it is important that police agencies have plans in place to adapt to economic turbulence.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Matthew J. Giblin is a professor in the Department of Criminology and Criminal Justice at Southern Illinois University Carbondale. His primary research interest involves the application of organizational theories to the study of criminal justice agencies. He holds a PhD in criminal justice from Indiana University.

Jeffrey S. Nowacki is an assistant professor in the Department of Sociology at Colorado State University. His primary research interests include social responses to crime such as disparity in sentencing outcomes and characteristics of police organizations. He has recently published in Policing & Society, Criminal Justice Policy Review, and Feminist Criminology.

References

Adam

Bauer

M. W.

Knill

Studinger

(2007) The termination of public organizations: Theoretical perspectives to revitalize a promising research area. Public Organization Review 7(3): 221–236.

Anderson

J. C.

Gerbing

D. W.

(1988) Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 103: 411–423.

Brunet

J. R.

(2015) Goodbye Mayberry: The curious demise of rural police departments in North Carolina. Administration & Society 47: 320–337.

Bureau of Economic Analysis. (n.d.). Regional data: GDF and personal income. Retrieved from http://www.bea.gov/iTable/index_regional.cfm.

Bureau of Justice Statistics (2015) Law enforcement management and administrative statistics (LEMAS), 2013, Washington, D.C: Author.

Bureau of Labor Statistics (n.d.) Top picks (most requested statistics). Unemployment rate (seasonally adjusted), Washington, DC: Author. Retrieved from http://data.bls.gov/cgi-bin/surveymost?bls.

Burns

Stalker

G. M.

(1961) The management of innovation, New York, NY: Oxford University Press.

Burruss

G. W.

Giblin

M. J.

(2014) Modeling isomorphism on policing innovation: The role of institutional pressures in adopting community-oriented policing. Crime & Delinquency 60(3): 331–355.

Byrne

B. M.

(2012) Structural equation modeling with Mplus: Basic concepts, applications, and programming, New York, NY: Routledge.

10.

Cameron

K. S.

Whetten

D. A.

(1983) Models of the organizational life cycle: Applications to higher education. The Review of Higher Education 6(4): 269–299.

11.

Cameron

K. S.

Kim

M. U.

Whetten

D. A.

(1987) Organizational effects of decline and turbulence. Administrative Science Quarterly 32(2): 222–240.

12.

Cameron

K. S.

Whetten

D. A.

Kim

M. U.

(1987) Organizational dysfunctions of decline. Academy of Management Journal 30(1): 126–138.

13.

Cordner

G. W.

(2015) Community policing: Elements and effects. In: Dunham

R. G.

Alpert

G. P.

(eds) Critical issues in policing: Contemporary readings, 7th ed. Prospect Heights, IL: Waveland Press, pp. 481–498.

14.

Damanpour

(1991) Organizational innovation: A meta-analysis of effects of determinants and moderators. The Academy of Management Journal 34(3): 555–590.

15.

DiMaggio

Powell

W. W.

(1983) The iron cage revisited: Collective rationality and institutional isomorphism in organizational fields. American Sociological Review 48(2): 147–160.

16.

Druker

Robinson

(1994) Implementing retrenchment strategies: A comparison of state governments and higher education. New England Journal of Public Policy 10(2): 83–96.

17.

Edwards

J. R.

Bagozzi

R. P.

(2000) On the nature and direction of relationships between constructs and measures. Psychological Methods 5(2): 155–174.

18.

Ellen

I. G.

Dastrup

(2012) Housing and the Great Recession (A Great Recession brief), Stanford, CA: Stanford Center on Poverty and Inequality.

19.

Federal Bureau of Investigation (2009) Uniform Crime Reporting Program data: Offenses known and clearances by arrest, 2007, Ann Arbor, MI: Inter-university Consortium for Political and Social Research.

20.

Federal Bureau of Investigation (2013) Uniform Crime Reporting Program data: Offenses known and clearances by arrest, 2011, Ann Arbor, MI: Inter-university Consortium for Political and Social Research.

21.

Gau

J. M.

(2010) Basic principles and practices of structural equation modeling in criminal justice and criminology research. Journal of Criminal Justice Education 21: 136–151.

22.

Gill

Weisburd

Telep

C. W.

Vitter

Bennett

(2014) Community oriented policing to reduce crime, disorder and fear and increase satisfaction and legitimacy among citizens: A systematic review. Journal of Experimental Criminology 10(4): 399–428.

23.

Greenhalgh

Lawrence

A. T.

Sutton

R. I.

(1988) Determinants of work force reduction strategies in declining organizations. Academy of Management Review 13(2): 241–254.

24.

Greenstone, M., & Looney, A. (2012). A record decline in government jobs: Implications for the economy and America’s workforce. Retrieved from http://www.brookings.edu/blogs/jobs/posts/2012/08/03-jobs-greenstone-looney.

25.

Groves

R. M.

Cork

D. L.

(2009) Ensuring the quality, credibility, and relevance of U.S. justice statistics, Washington, DC: National Academies Press.

26.

Guy

M. E.

(1989) From organizational decline to organizational renewal, New York, NY: Quorum Books.

27.

Hoelter

J. W.

(1983) The analysis of covariance structures: Goodness-of-fit indices. Sociological Methods & Research 11(3): 325–344.

28.

Bentler

P. M.

(1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6: 1–55.

29.

International City/County Management Association (2010) The municipal year book, 2010, Washington, DC: ICMA Press.

30.

Kaufman

(1976) Are government organizations immortal?, Washington, DC: Brookings Institution.

31.

Kelloway

E. K.

(2015) Using Mplus for structural equation modeling: A researcher’s guide (2nd ed.), Los Angeles, CA: Sage.

32.

Kiedrowski

Jones

N. A.

Ruddell

(2017) ‘Set up to fail?’ An analysis of self-administered indigenous police services in Canada. Police Practice and Research 18(6): 584–598.

33.

Kimberly

J. R.

(1980) The life cycle analogy and the study of organizations: Introduction. In: Kimberly

J. R.

Miles

R. H.

(eds) The organizational life cycle: Issues in the creation, transformation, and decline of organizations, San Francisco, CA: Jossey-Bass, pp. 1–17.

34.

King

W. R.

(1999) Time, constancy, and change in American municipal police organizations. Police Quarterly 2(3): 338–364.

35.

King

W. R.

(2009) Toward a life-course perspective of police organizations. Journal of Research in Crime & Delinquency 46: 213–244.

36.

King

W. R.

(2014) Organizational failure and the disbanding of local police agencies. Crime and Delinquency 60(5): 667–692.

37.

Kneebone, E. (2010). The great recession and poverty in metropolitan America. Metropolitan Policy Program at Brookings. Retrieved from https://www.brookings.edu/wp-content/uploads/2016/06/1007_suburban_poverty_acs_kneebone.pdf.

38.

Langworthy

R. H.

(1986) The structure of police organizations, New York, NY: Praeger.

39.

Langworthy

R. H.

(2002) LEMAS: A comparative organizational research platform. Justice Research and Policy 4: 21–38.

40.

Levine

C. H.

(1978) Organizational decline and cutback management. Public Administration Review 38: 316–325.

41.

Levine

C. H.

(1979) More on cutback management: Hard questions for hard times. Public Administration Review 39: 179–183.

42.

Levine

C. H.

Rubin

I. S.

Wolohojian

G. G.

(1981) Resource scarcity and the reform model: The management of retrenchment in Cincinnati and Oakland. Public Administration Review 41(6): 619–628.

43.

Lewis

D. E.

(2002) The politics of agency termination: Confronting the myth of agency immortality. The Journal of Politics 64(1): 89–107.

44.

MacDonald

(2008) The impact of government structure on local public expenditures. Public Choice 136(3–4): 457–473.

45.

Maguire

E. R.

(2001) Research evidence on the factors influencing police strength in the United States. In: Koper

C. S.

Maguire

E. R.

Moore

G. E.

Huffer

D. E.

(eds) Hiring and retention issues in police agencies: Readings on the determinants of police strength, hiring, and retention of officers, and the federal COPS program, Washington, DC: Urban Institute, Justice Policy Center, pp. 7–26.

46.

Maguire

E. R.

(2003) Organizational structure in American police agencies: Context, complexity, and control, Albany: SUNY Press.

47.

Maguire

E. R.

Shin

Zhao

J. S.

Hassell

K. D.

(2003) Structural change in large police agencies during the 1990s. Policing: An International Journal of Police Strategies & Management 26(2): 251–275.

48.

Matusiak

M. C.

Campbell

B. A.

King

W. R.

(2014) The legacy of LEMAS: Effects on police scholarship of a federally administered, multi-wave establishment survey. Policing: An International Journal of Police Strategies & Management 37(3): 630–648.

49.

Morabito

M. S.

(2008) The adoption of police innovation: The role of the political environment. Policing: An International Journal of Police Strategies & Management 31(3): 466–484.

50.

Muthén

L. K.

Muthén

(2010) Mplus user’s guide, 4th ed. Los Angeles, CA: Author.

51.

National Bureau of Economic Research. (2012). US business cycle expansions and contractions. Retrieved from http://www.nber.org/cycles.html.

52.

Office of Community Oriented Policing Services (2011) The impact of the economic downturn on American police agencies, Washington, DC: Author.

53.

Parlow

M. J.

(2012) The Great Recession and its implications for community policing. Georgia State University Law Review 28(4): 12–12.

54.

Police Executive Research Forum (2010) Is the economic downturn fundamentally changing how we police?, Washington, DC: Author.

55.

Pfeffer

Salancik

G. R.

(1978) The external control of organizations: A resource dependence perspective, New York, NY: Harper and Row.

56.

Queally, J., & Friedman, A. (2010, November 30). Newark finalizes 167 police layoffs after union refuses Booker’s plea to return to negotiating table. Retrieved from http://www.nj.com/news/index.ssf/2010/11/union_head_expects_167_newark.html.

57.

Quinn

R. E.

Cameron

(1983) Organizational life cycles and shifting criteria of effectiveness: Some preliminary evidence. Management Science 29(1): 33.

58.

Randol

B. M.

(2012) The organizational correlates of terrorism response preparedness in local police departments. Criminal Justice Policy Review 23(3): 304–326.

59.

Schumacker

R. E.

Lomax

R. G.

(2010) A beginner’s guide to structural equation modeling, 3rd ed. New York, NY: Routledge.

60.

Scorsone

E. A.

Plerhoples

(2010) Fiscal stress and cutback management amongst state and local governments: What have we learned and what remains to be learned? State and Local Government Review 42: 176–187.

61.

Singh

J. V.

Tucker

D. J.

House

R. J.

(1986) Organizational legitimacy and the liability of newness. Administrative Science Quarterly 31(2): 171–193.

62.

Stucky

T. D.

(2003) Local politics and violent crime in US cities. Criminology 41(4): 1101–1136.

63.

Stucky

T. D.

(2005) Local politics and police strength. Justice Quarterly 22(2): 139–169.

64.

U.S. Census Bureau. (n.d.). American factfinder. Retrieved from http://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml.

65.

U.S. Department of Agriculture, Economic Research Service (n.d.). County-level data sets. Retrieved from http://data.ers.usda.gov/reports.aspx?ID=18242.

66.

van de Ven

A. H.

Poole

M. S.

(1995) Explaining development and change in organizations. The Academy of Management Review 20(3): 510–540.

67.

Weitzel

Jonsson

(1989) Decline in organizations: A literature integration and extension. Administrative Science Quarterly 34: 91–109.

68.

Whetten

D. A.

(1987) Organizational growth and decline processes. Annual Review of Sociology 13: 335–358.

69.

Wilson

J. Q.

Boland

(1978) The effect of the police on crime. Law & Society Review 12(3): 367–390.