Exploring the Determinants of Local Service Termination
Abstract: Objectives. In the past, research focusing on local service decisions was dominated by the
exploration of make-or-buy choices (i.e., how to produce services). In this article, we extend this venue
of research to explore service termination (i.e., whether to provide). In doing so, we adopt four
theoretical strands developed in the fields of public policy and management to guide our research: policy
termination, political economy, make-or-buy, and policy diffusion. Methods. We utilize multiple
International City/County Management Association survey data for our analysis. We supplement these
data with information gleaned from a variety of other sources, including Census data, to construct a
binary logistic regression model examining the determinants of service termination. Results. We find that
local governments are likely to terminate their services when they were previously outsourced to third
party contractors rather than produced by their own employees. Further, our results indicate that locales
tend to drop services more often when they are not commonly provided by peer jurisdictions, supporting
the idea of diffusion. Conclusions. Our findings suggest that, despite a dearth of research undertaken,
service termination is surprisingly common and widespread across service areas.
Exploring the Determinants of Local Service Termination
While termination is often explicitly (Brewer and deLeon 1983; Jones 1970; Lasswell 1971) or
implicitly (Anderson 2006) included in most “stages” models of the policy process (see Smith and
Larimer 2009 for a good review), it is generally considered an understudied phenomenon (Biller 1976;
Daniels 2001; Geva-May 2004). Daniels (2001, 251) argues that the paucity of research is mainly
attributable to the fact that termination is a fairly rare occurrence (as succinctly alluded to in the title of
Kaufman’s [1976] seminal book, Are government organizations immortal?), thus there are few examples
to study. Further, such occurrences have been posited to be idiosyncratic, implying that termination is not
amenable to theory-driven research (Bardach 1976). Small N problems and lack of theory have led most
termination studies to pursue in-depth, case analyses (e.g., Bothun and Comer 1979; Frantz 1992;
Katzenbach 1958; Kirkpatrick, Lester, and Peterson 1999; Shulsky 1976)1 or prescriptive works, offering
advice to practitioners regarding how to best approach such efforts to maximize the likelihood of success
(e.g., Bardach 1976; Behn 1978; Biller 1976; Geva-May 2001). The literature has almost exclusively
focused on highly visible and tractable federal programs and agencies, neglecting to examine termination
at subnational levels.
There have been a few attempts to surmount the seemingly innate weaknesses and limitations of
termination research. For example, Lewis (2002) increases his number of data points by longitudinally
(between 1946 and 1997) looking at federal agency mortality and systematically investigating fiscal and
political factors influencing agency termination. Graddy and Ye (2008) confine their analysis to a single
state (California), but utilize a fairly large sample by focusing on county-level public hospitals to examine
the closing of such entities. Kodrzyicki (1998) analyzes local service shedding based on two Census of
We should note, however, that Kaufman’s (1976) original work did employ a relatively large N, quantitative
analysis. He compared the number of federal organizations in 10 of the 11 executive departments (excluding DoD)
that existed in 1923 and the number of the same agencies that survived by 1973. He also investigated how many
new agencies were created after 1923 and still existed in 1973. What he did not explore was the possibility of
organizational births and deaths that might have occurred between 1923 and 1973 (for example, an agency might
have been created sometime after 1923 and terminated before 1973), which was a substantive methodological
weakness in his research design. Since the contribution from Kaufman’s seminal piece, however, researchers have
turned their attention more heavily towards conducting in-depth case studies where they could explore the detailed
events and contexts under which termination occurs.
1
1
Government data sets (1987 and 1992) that include over a thousand cities and towns and twelve different
services.2 While her main interest is examining local governments’ service delivery methods (i.e., inhouse or outsourced), she also explores why services are sometimes dropped all together. Building upon
these past efforts, we aim to develop a comprehensive model that explains when and under what
conditions local governments shed their services. Our study contributes to the termination and general
local governance literatures in several ways. First, there has been a dearth of empirical research that
systematically examines local service shedding. By analyzing a large number of cases over time for a
wide array of services local governments provide, our study brings unique information to the extant field
of termination studies. Second, in order to build a comprehensive model, we seek conceptual guidance
beyond the termination literature and broadly incorporate such theories as the political-economic
perspective (especially, public choice theory), the make-or-buy literature, and policy diffusion theory.
While these theories have been independently developed in the past, our study attempts to integrate them
in ways that allow us to construct a set of testable hypotheses for local service shedding.
We utilize a series of International City/County Management Association (ICMA) surveys to identify
termination. The results indicate that the traditional termination literature contributes little to the
understanding of local service shedding at least in terms of the fiscal and ideological factors we examine.
On the other hand, we find other theoretical frameworks to be useful. For example, our analysis suggests
that local governments are much more likely to drop services if the services are delivered through external
providers such as other government, nonprofit, or for-profit contract agents, as compared to their own
public employees. Local governments also tend to abandon redistributive types of services more than
other kinds and appear to be mindful of what services their peer institutions provide when making
decisions on whether to continue or discontinue their own service provision. The results of our study call
for future research to move beyond the conventional termination literature in explaining the dropping of
2
The Census of Government in 1987 provides service delivery information on the following twelve services and
facilities: airports, electric power, fire protection, gas supply, hospitals, landfills, libraries, nursing homes, public
transit, sewerage system, stadiums (and auditoriums and convention centers), and water supply. The 1992 Census
includes three more services. They are: ambulances, solid waste management, and road maintenance. Kodrzyicki’s
(1998) analysis is based on the services included in the 1987 Census of Governments.
2
local services and to incorporate a broader set of local governance theories to examine this critical policy
phenomenon.
Theoretical Exploration and Hypotheses Development
As previously mentioned, the notion that policy termination is a rare phenomenon is nearly
unanimous among scholars (Daniels 1997; but, see Lewis 2002 for a counterargument). We contend that
policy termination or, more precisely, service shedding, at the local level is a much more frequent event
than the general termination literature may recognize. A challenge to examining this proposition is that
there is no coherent body of theoretical and/or empirical literature at the local level that directs us to
develop hypotheses and build empirical models. The traditional termination literature focuses almost
exclusively on federal agencies and their programs and thus is only partially relevant. Recognizing the
limited utility of the termination literature as a guiding theory, we explore additional conceptual
frameworks that help us map out a more complete picture of local service termination.
Policy Termination Literature
As briefly mentioned, large parts of the termination literature have little relevance to the explanation
of local service shedding due to the different dynamics involved in different levels of government.
Nonetheless, its theoretical development and empirical evidence provide some useful guidance as to the
general reasons behind terminating government programs or agencies. For instance, termination
researchers largely agree that there are three primary reasons regarding why terminations are attempted
(deLeon 1983). The first two are financial in nature – fiscal stress and efficiency – and the last has to do
with political ideology, which is generally argued to be a more significant force than the others (deLeon
1983, 1987; Frantz 1997; Lewis 2002; Best, Teske, and Mintrom 1997). Scholars later identified
additional facilitating factors such as advancement in behavioral theory (such changes in the views of the
psychiatric profession, for example, led to the deinstitutionalization movement in mental health) and
changes of view regarding problem solving and policy failures (Behn 1976; Daniels 1995, 1997).
3
No single cause, of course, can fully account for the occurrence of termination and it is only
reasonable to adopt a comprehensive and integrated approach to examine the phenomenon (Graddy and
Ye 2008). Kirkpatrick, Lester, and Peterson (1999) attempt such a comprehensive and systematic
approach to explain the termination of federal revenue sharing programs, although their effort is largely
descriptive. Their model accounts for both internal characteristics such as mission and longevity of the
target agency, issue salience, and the extent of stakes perceived by beneficiaries, and external political
forces including ideology and termination and anti-termination coalitions. Graddy and Ye (2008) later
revive this integrated approach with finer methodologies to examine public hospital closures in
California. They consider policy characteristics such as the size of the program, service demand, interest
group and community preferences, and triggering environmental factors such as fiscal stress and
ideological shifts in their analysis. While these comprehensive views are noteworthy, case studies
emphasizing fiscal concerns and political forces have been and remain a dominant approach in studying
termination. Therefore, we focus our attention on these two main factors to test termination theory when
examining local service shedding.
Fiscal Stress. The termination literature points to financial strains as one of the main reasons as to
why terminations might be attempted (deLeon 1983). Local jurisdictions experiencing financial distress
may look for various means to address the issue, including termination of services, and cuttingS their
spending to operate under balanced-budget requirements (MacManus and Bullock 2003). Running in
deficit in this case is an unlikely option to choose in dealing with fiscal hardship. According to the
National League of Cities that regularly surveys American cities’ fiscal conditions, it is not uncommon
for municipal governments to respond to financial strains by reducing the level of services, delaying
investments in infrastructure and capital projects as well as terminating noncore programs.3 Evidence
supporting these theoretical expectations is available in a limited way. Graddy and Ye (2008), for
example, report that public hospitals in California counties are more likely to be terminated when
3
The National League of Cities publishes the survey results annually. Electronic copies of these reports are
available from 2001 on (see: http://www.nlc.org/find-city-solutions/center-for-research-and-innovation/finance).
Survey results for the years 1993 through 2000 are available in the form of hard copies.
4
revenues grow slower than the normal pace. Conversely, Kodrzycki’s (1998) findings suggest that
termination of local services is more likely when jurisdictions have positive budgetary situations, which is
the opposite effect of what is predicted by theory. Lewis (2002) measures fiscal stress by unemployment
rates and reports that federal agencies are more likely to be terminated when unemployment rates
increase. Likewise, Kodrzycki (1998) finds that termination is positively associated with unemployment.
Despite some inconsistencies, the overall evidence from the termination literature seems to point to the
idea that unfavorable fiscal conditions do increase the chances of terminating programs or services.
Based on these previous findings, we hypothesize that: local governments experiencing times of fiscal
stress will be more likely to shed their services (hypothesis 1).
Political Ideology. Politics is another critical element the termination literature identifies to explain
the occurrence of policy abandonment. Considering potentially substantial losses the existing
beneficiaries will endure, it is important that rational decision-making prevails in assessing and
determining the necessity of termination (deLeon 1983). In reality, however, termination decisions are
often riddled with political calculation and ideology (deLeon 1983; see also Daniels 1997 and Frantz
1997). Several case studies show that what ultimately decides the fate of a targeted program is the
relative strength of the power held by termination or anti-termination coalition forces (Cameron 1978;
Daniels 1995; deLeon 1983, 1987; Frantz 1997; Kirkpatrick, Lester, and Peterson 1999). Large N studies
empirically examining political influences are rare, but the past research that did so only partially support
the idea that politics matters. For example, Graddy and Ye (2008) report that California counties’
decisions to close public hospitals are not significantly associated with the ideological leanings of the
president or state governor/legislature. On the other hand, Lewis (2002) finds a variety of political
indicators (e.g., Republican presidents, unified government, political control by the party not in power at
the time of the creation of the agency) to be associated with the likelihood of the termination of a federal
agency. It is possible that Graddy and Ye’s (2008) results are not statistically significant due to
measurement issues (that is, measuring federal and state level ideology to determine county-level
decisions). To address this potential concern, we focus on political ideology that prevails at the local
5
rather than national or state levels when determining local service shedding. More specifically, we posit
that conservative local jurisdictions are more likely to terminate services than are their liberal counterparts
(hypothesis 2) because conservatives generally favor smaller government.
Political-Economic Perspective
Besides the termination literature, a prominent intellectual development in the study of local
governance that may assist us in understanding and explaining local service shedding is the politicaleconomic perspective. Public choice theorists, for example, advocate for the polycentric character of U.S.
metropolitan areas, contending that the diverse sets of public policy and service packages offered by
different governments lead to higher levels of economic efficiency and citizen satisfaction than
consolidated governments are capable of (Ostrom, Tiebout, and Warren 1961). An early contributor to
this school of thought is Tiebout (1956) who argues that citizen-consumers vote with their feet when
choosing a place to live. According to him, the flexibility in revenue and expenditure structures local
jurisdictions enjoy sets them apart from the national government in that locales are able to offer service
packages appealing to residents whom they want to attract to their communities (Tiebout 1956). Ostrom,
Tiebout, and Warren (1961) contribute to this literature by conceptually separating government service
provision from the production of the services. This broadens our understanding of local service delivery
not only in the context of governmental institutions, but also with respect to intergovernmental
cooperation, special districts, and private markets. Warren (1964) further elaborates on the idea of local
governments as consumers in the market, looking for the most efficient ways to provide goods and
services to their residents.
Redistributive Services. Tiebout’s (1956) idea of voter mobility has been widely accepted and
applied to various local governance and policy studies. Adopting the voter mobility premise, but
questioning Tiebout’s vague “optimum size” thesis (Peterson 1981, 19), Peterson turns his attention
instead to city policies that “maintain and enhance the economic position, social prestige, or political
power of the city, taken as a whole” (Peterson 1981, 20). Specifically, he argues that the primary interest
of cities is to secure and improve their fiscal bases by increasing land values (see also Peterson and Rom
6
1990). Such motives drive cities to compete with their fellow local jurisdictions in promoting
development and pro-growth policies while suppressing redistributive services (Williams 1966; Peterson
1981). The underlying logic is that skillful labor and capital are important assets to growth and investors
and well-to-do voters who possess such assets are mobile and will move if tax burdens increase to provide
social welfare programs (Craw 2006). Likewise, it is argued that local governments may fear becoming
“welfare magnets,” which could lead them to “race-to-the-bottom” (Shipan and Volden 2012, 789; see
also Bailey 2005). This logic is used to explain why local governments are generally considered to be
minor players regarding redistributive policies (Schneider 1989; Sharp and Maynard-Moody 1991).
However, we can extend this same logic to argue that local governments are likely to, perhaps
aggressively, abandon redistributive types of services they provide if and when circumstances promote
them to do so. Exploring those facilitating circumstantial factors is beyond the scope of our analysis.
However, scholars mention such conditions as changes in federal and state funding types and levels,
interjurisdictional competition, and the jurisdictions’ own fiscal capacity (Chamlin 1987; Craw 2006;
Peterson 1981; Schneider 1989; Sharp and Maynard-Moody 1991). Based on these discussions, we posit
that: redistributive types of services are more likely to be abandoned by local jurisdictions than any other
types of local services (hypothesis 3).
Services Delivered by Special Districts. Public choice theorists supporting polycentric local
governance structure strongly endorse the use of special-purpose governments in service delivery as they
contend this type of public institutions is an effective vehicle to achieve customization of citizen
preferences (Foster 1997). Special districts are also argued to be the mechanisms that governments can
use to address efficiency issues by right-sizing delivery infrastructure (Foster 1997; Krueger, Walker, and
Bernick 2011; McCabe 2000). Meanwhile, scholars adopting a different stream of the political-economic
approach contend that special districts are a good alternative to municipalities for business groups to
utilize in advancing their economic interests. For example, Burns (1994) argues that, while developers
often play significant roles in forming municipal institutions to control land use (i.e., zoning power),
incorporating and maintaining municipal entities can be an expensive option, politically as well as
7
economically. Municipalities are also subject to state restrictions on revenues, expenditures, borrowing
capacities, and other structural aspects (Bollens 1986; Carr 2006; Feiock and Carr 2001; Miller 1981).
Given the constraints associated with municipalities, special districts are a highly attractive public
mechanism for developers to utilize because of the financial flexibility they provide.4
Due to these advantages, special districts have become the fastest growing local governing units in
recent decades. For example, Stephens and Wikstrom (2000) report that between 1957 and 1997, the
number of special districts grew by 140%, while that of counties stayed virtually the same and
municipalities had only a modest increase (12.5%).5 Between 1992 and 1997 alone, which is our study
period, 3,128 special districts were created (Stephens and Wikstrom 2000). While many special districts
are simply created and used as funding mechanisms for capital projects, recent growth in this form of
government is largely accounted for by an increase in service-delivering special districts (i.e., singlepurpose government) that provide “system-maintaining or system-augmenting services like sanitary and
storm sewer, water supply and distribution, solid waste, airports, and gas and electric utilities” (Stephens
and Wikstrom 2000, 131). As such, the formation of special districts might be used as a way for generalpurpose governments to “drop” certain services, while ensuring they are still delivered to their
constituents.6 We thus hypothesize that: local governments are more likely to terminate their services
when a large number of special districts are available (hypothesis 4).
Special districts’ financial flexibility is largely attributable to their ability to issue nonguaranteed debt. According
to Burns (1994, 15), nonguaranteed debt is “debt that does not require repayment of all principal and interest to be
backed by local taxing power; instead, repayment is dependent upon successfully collected user fees.” The
popularity of this type of debt financing is obvious in that $120 billion out of the total $137 billion outstanding debt
reported at the end of the fiscal year 1986-87 for special districts were nonguaranteed types (see further discussions
from Burns 1994). It is also interesting to note how rapidly this type of financing has become relied upon by local
governments for providing infrastructure and service delivery – for example, the outstanding debt for special
districts in 1957 totaled $5.8 billion (or $23.5 billion in 1987 dollars), as compared to $137 billion in 1986-87.
5
The actual 1957 numbers for counties, municipalities, and special districts are 3,050, 17,215, and 14,424,
respectively. These numbers had changed to 3,043, 19,372, and 34,683 (for counties, municipalities, and special
districts) by1997. The figures are taken from table 1.2, Stephens and Wikstrom (2000, p.8). The original data
sources include 1957 through 1992 Census of Governments, vol. 1 and a 1997 document from Governments
Division of the Census Bureau.
6
Of course, special districts can receive support from general revenues, so the termination may not be complete.
Our measure of special districts includes things such as transit authorities and water districts, but does not include
school districts.
4
8
Make-or-Buy Literature
The make-or-buy literature may assist us to formulate hypotheses as well. The central idea of this
literature is that locales’ decisions on how they deliver services (e.g., via in-house or the use of other
government, for-profit, or nonprofit contract agents) depend on the transaction costs nature of the services
they provide and the institutional constraints by which they are bound (Brown and Potoski 2003; Hefetz
and Warner 2012). As such, it is argued that local governments tend to produce in-house when services
are hard to measure, monitor, and asset-specific because such traits increase the chances for vendor
opportunism, and thus transaction costs, if outsourced (Brown and Potoski 20037; Levin and Tadelis
2010; Williamson 1981). Governments are also likely to use their own employees if services are
considered inherently governmental, public unions are present, market competition is lacking, or
governments are less professionalized (Brudney et al. 2005; Clingermayer and Feiock 2001; Hefetz and
Warner 2004; Kettl 1993; Lamothe, Lamothe, and Feiock 2008; Moon and deLeon 2001; Nelson 1997;
Savas 2000). Contracting out to other governments or nonprofits is a reasonable alternative to internal
productions if governments can reap the benefits of the economies of scale, cheaper labor costs, or obtain
program expertise (Lamothe, Lamothe, and Feiock 2008). For goods and services that are easily
measurable, low in asset specificity, or have robust private for-profit markets, the likelihood that local
governments outsource to for-profit firms increases.
Previous Service Delivery Modes. While make-or-buy scholars accept the provision of services as
given and are primarily concerned with examining delivery arrangements only, it is of scholarly interest
to explore the aftermath of outsourcing and whether prior delivery methods affect termination decisions.
Naturally, we expect internally produced services to encounter stronger resistance to termination due to
the various stakes involved in such arrangements. For example, many of these services are supposedly
more costly to privatize in the first place due to the concerns over vendor opportunism, lack of private
markets, greater public interests, union resistance, and so on. For the services that are already outsourced
7
Brown and Potoski (2003), however, hypothesize that extremely high asset-specific services are likely to be
outsourced rather than internally produced because such services are often associated with high start-up costs. Their
empirical findings support this assertion.
9
to third party deliverers, shifting public provision to market-based private provision (i.e., complete
privatization) may face less resistance. We, therefore, hypothesize that: outsourced services are more
likely to be terminated than are internally produced services (hypothesis 5). Further, we expect that:
local governments are more likely to abandon the services contracted out to for-profit firms than the
services outsourced to other types of vendors (hypothesis 6) as the services delivered by for-profit
contractors may be more readily provided through fee-based private markets.
Isomorphism and Policy Diffusion Theory
The last theoretical strand that we believe is relevant to our study is policy diffusion and the theory of
isomorphism (DiMaggio and Powell 1983). Broadly speaking, policy diffusion is defined as “one
government’s policy choices being influenced by the choices of other governments” (Shipan and Volden
2012). Paths for diffusion are not settled and scholars have debated over regional influences (Walker
1969; Berry and Berry 1990, 1992; Berry 1994) versus national interactions (Gray 1973; Menzel and
Feller 1977; Brooks 2005; Weyland 2007). “Geographic clustering” is, however, argued to have become
increasingly an obsolete concept, given the rapid technological advancement in communication (Shipan
and Volden 2012, 789). Diffusion paths can also be top-down (national to local) or bottom-up (local to
national). As for the underlying reasons for diffusion, scholars identify competition, learning, imitation,
and coercion (Shipan and Volden 2012).
Imitation or competition are of particular interests to our study as we expect local governments to
compete with each other or mimic what they consider as their comparable peers, in gauging what services
to offer and what to abandon. Imitation, modeling, or “mimetic isomorphism” as DiMaggio and Powell
(1983) characterize it, occurs because uncertainty, whether it is caused by lack of information or
ambiguity of goals, is rampant in policy environments and, under such circumstances, institutional
decision makers often seek legitimacy rather than optimal solutions for their perceived problems (see also
Meyer and Rowan 1991). The idea of diffusion based on mimicking or conforming behaviors has been
accepted by scholars who studied local government reforms and other policy innovations at the local
level. Knoke (1982), for example, argues that between 1900 and 1942, municipal government structural
10
reforms (such as adopting commission or professional manager forms of government) spread rapidly,
regardless of the cultural, socioeconomic, and political differences among jurisdictions, and in fact were
most swiftly adopted by the least likely locales partly because such reforms were viewed as a way to
combat political corruption and improve urban conditions. Others have examined such policies as living
wage (Martin 2001), gun control (Godwin and Schroedel 2000), anti-smoking ordinances (Shipan and
Volden 2008), and charter school adoption (Zhang and Yang 2008) to see how innovative local initiatives
were diffused throughout cities via similar mimetic processes.
Services Delivered by Peers. While diffusion theory is most often used to explain why governments
adopt certain policies, we argue that terminating existing policies or shedding services may be subject to
similar dynamics. Research exploring termination based on diffusion theory is almost nonexistent (except
see Stokan 2012). There are, however, several studies investigating the role of diffusion in outsourcing of
local government services (Bel and Miralles 2003; Bivand and Szymanski 2000; Christoffersen and
Paldam 2003; Gonzalez-Gomez, Picazo-Tadeo, and Guardiola 2011). Given the evidence from this venue
of research suggesting that locales tend to follow suit in their service production decisions based on the
trends observed among peer institutions, it is not a far stretch to assume that local governments will heed
what other jurisdictions do in terms of the types of services they provide or terminate. We, therefore,
expect that: the services delivered by greater numbers of peer local governments are less likely to be
terminated than the services that are offered by fewer numbers (hypothesis 7).
Research Design
The Data
We use the 1992, 1997, 2002, and 2007 versions of the International City/County Management
Association’s (ICMA) alternative service delivery arrangements (ASD) survey to allow us to identify
cases of service termination by local governments. This survey, administered approximately every five
years since 1982, gathers information as to what services jurisdictions provide as well as how they are
delivered. We determine if a service was provided by a locale in 1992 and then examine if its provision
11
continued or was terminated in 1997. The 2002 and 2007 surveys are also used to verify whether the
services identified as terminated in 1997 continue their termination status in 2002 and/or 2007.8 The
purpose of this additional verification is to minimize the possibility that a case is coded as termination
based on inconsistent survey responses over the years.
ICMA’s sampling frame includes all municipalities with over 10,000 inhabitants and counties larger
than 25,000. They also randomly select one in eight smaller communities, although not all such entities
are recognized by ICMA. ICMA reports the following response rates for the surveys: 1992 – 30.5
percent; 1997 – 32.0 percent; 2002 – 23.9 percent; and 2007 – 26.2 percent. While there are slight
variations in the number of services accounted for in each iteration, all include the same seven categories
of services – public works/transportation (e.g., residential solid waste collection, street repair), public
utilities (e.g., electric utility operation and management), public safety (e.g., crime prevention/patrol, fire
prevention/suppression), health and human (e.g., animal control, child welfare programs) , parks and
recreation (e.g., operation and maintenance of recreation facilities), cultural and arts (e.g., operation of
libraries), and support services (e.g., building security, data processing). In total, 64 services were
utilized in our analysis.
Each case in our dataset represents a jurisdiction-service pair. For example, case 1 might signify
service “a” for jurisdiction 1; case 2, service “b” for jurisdiction 1, etc. We begin with all cases in which
a local government provides a service in 1992, as this is necessary requirement for later termination. We
then ensure the case also has data for 1997 and at least one of the remaining years (i.e., 2002 and 2007).
After identifying eligible cases in this fashion and dropping those missing relevant data, we are left with
17,092 cases from 398 unique local jurisdictions. As both our and ICMA’s selection criteria might lead
to biases in the data, we include table A1 (see the Appendix) which reviews the representativeness of our
sample. This table shows that we over-sample larger jurisdictions (a function of ICMA’s sampling
frame), professional manager forms of government (not surprising in that ICMA is the professional
8
We discuss the logic of this decision in more detail below, when we discuss the operationalization of or dependent
variable, below.
12
organization for these individuals), as well as wealthier locales and those in the western portion of the
country. We also have a disproportionate number of counties in our sample. Hence, our findings may be
more applicable to jurisdictions characterized by these traits than the overall population of local
governments in the nation.
The Dependent Variable
Termination is in many ways a challenging concept to account for in that government is constantly
reorganizing in efforts to deliver services more efficiently. As such, programs may go away, while
policies, organizations, and functions, as defined by deLeon (1978), may remain. At what point is
delivery diminished enough to claim that termination has occurred? For the purpose of our study, we
define termination simply as the cessation of the provision of a service that the local jurisdiction
previously paid for with public money. Termination, defined as such, may be considered true
privatization and differs from “contracting out” in that jurisdictions merely shift service production from
in-house to contract agents, while maintaining responsibility for provision, in the latter case. To identify
termination, we isolate those cases coding as providing a given service in 1992. We then examine
provision status in 1997. If the case codes as delivered through an identifiable method in that year (i.e.,
in-house, joint, or contracting with another government or for-profit or nonprofit entity), this indicates
continuing provision. If, however, provision cannot be confirmed in 1997 (that is for example, the survey
respondent marks the “no longer provided” option), this is a possible case of termination. To verify
termination, we then look at the case’s status in the next time period (i.e., 2002 and/or 2007). If the
service continues to not be subsequently provided, we code it as an instance of service shedding. For
those jurisdictions that responded to all four surveys, nonprovision must be indicated in 1997, 2002, and
2007 for it to be declared a termination. We do so because, while it is possible that services are moved in
and out of provision, it is also possible that such movement indicates a lack of reliability in the data more
so than true termination.9 As such, we opt for this conservative coding scheme.
9
We thank Skip Krueger for pointing this out to us.
13
Independent Variables
To test termination theory, we account for fiscal stress and political ideology. To measure fiscal
stress, two variables are included. The first measure (Budget stress, 1992) is the ratio of local government
expenditures to revenues in 1992. Higher values on this measure indicate increasing fiscal imbalance
where expenditures exceed revenues (Kodrzycki 1998). Such imbalances should put pressure on local
officials to balance these streams. Service termination could be seen as a tool to lower expenditures to
assist in this effort. The second (Avg. Unemployment, 1992-97) is the average unemployment rate for the
county in which the jurisdiction resides (unless the jurisdiction is a county) for the years 1992 through
1997. Higher unemployment rates indicate challenging financial circumstances that that have been found
to impact termination decisions at the federal level (Lewis 2002).
Hence, it is expected that jurisdictions
facing higher levels of unemployment should, on average, be more inclined to terminate delivery of some
services as a way to alleviate these stresses.
Our final measure generated from the termination literature relates to ideology, which is captured by
taking the average of the Republican two-party vote share (Avg. % Republican vote, 1992-96) in the
county for the 1992 and 1996 presidential elections. This variable taps into the general tendency of
Republicans to prefer smaller government. Thus, we expect Republican leaning locales (i.e., those
residing in more Republican counties) to be more likely to drop services, regardless of their financial
status.
We account for redistributive types of services and special districts to examine the political-economic
perspective. Defining exactly what constitutes redistributive policies and related services is not an easy
task because there is no government service classification system to determine such types. Based on
Peterson’s (1981, 51-65) discussions and his examples of redistributive policies, we identify eight out of
sixty-four ICMA services examined in this paper as of the redistributive type. They are: operation of
daycare facilities, child welfare programs, programs for the elderly, operation/management of hospitals,
public health programs, drug and alcohol treatment programs, operation of mental health/mental
retardation programs and facilities, and operation of homeless shelters. Day care and behavioral health
14
care services are included in this category because government-run facilities providing these services
heavily benefit low-income families who are welfare and Medicaid eligible. Redistributive service is a
dummy variable that is coded “1” when the case is one of the above services and “0” otherwise. A
positive relationship is expected for this variable as local governments are eager to minimize their roles in
providing redistributive type services and thus are likely to terminate them whenever they can (Peterson
1981; Burns 1994).
We consider special districts through two measures: Service w/ high SD growth, 1957-92 and Δ
number of SDs, 1992-1997. Service w/ high SD growth, 1957-92 is a dummy variable capturing the
sixteen local services whose delivery through special districts increased by more than 100% between
1957 to 1992 according to Census of Government data covering these periods (see Stephens and
Wikstrom 2000, 132, table 7.4). In terms of absolute numbers, the most common purpose of special
districts is the management and regulation of natural resources (Stephens and Wikstrom 2000). However,
natural resource special districts experienced little change (that is, 12.4% increase) during the same period
(i.e., 1957-1992) and thus are less relevant to account for as a potential factor to explain shedding in the
current context. Δ number of SDs, 1992-97 is measured by subtracting the number of special districts
reported in the county in the 1992 Census of Government survey from the number reported in 1997. Both
of these measures indicate increased use of special districts, which may serve as avenues for jurisdictions
to drop direct responsibility for service provision while ensuring delivery continuity. Thus, we expect
both variables to be positively related to the likelihood of termination.
Other explanatory variables we include in our model account for prior service delivery methods.
We identify six delivery methods based on the ICMA ASD surveys, from which we construct five prior
delivery method variables. The first Joint, 1992, reflects a blend of public and contracted delivery in the
base year, 1992. The last four are forms of contracting. Other government, 1992, For-profit, 1992, and
Nonprofit, 1992 indicate contracting exclusively with these types of entities in the prior time period.
Finally, Mixed contracting, 1992, represents contracting with a variety of types of vendors (in-house
delivery serves as the reference category). The expectation is that each of these variables should be
15
positively related to the likelihood of termination since each represents a circumstance in which there
should be less institutional resistance owing to the lack of a direct impact (or in the case of joint
contracting, a mitigated impact) on the jobs of public employees.
The last theory we examine is policy diffusion. To evaluate this effect on termination, we construct
% peers providing services, 1992. This measure captures the percent of “peer” jurisdictions, as defined
by level of government (i.e., city, county) and metro status (i.e., urban, suburban, rural) that provided the
service in 1992. We expect that locales may be mindful of what service packages their peers offer and
factor that information into making decisions on termination. As such, we posit that as the percentage of
peers previously providing the service increases, the less likely termination should be.
In addition to the above variables we include to examine the four different theoretical strands, we also
account for a number of jurisdictional characteristics in our analysis. First, we control for the fact that
cities and counties have different organizational structures and functional responsibilities and hence
possibly different service packages that, on average, might impact their proclivity to shed services. We
further break down jurisdictions into core, suburb, and rural as citizen preferences regarding service
provision might vary based on the environments they find themselves in. We differentiate between cities
and counties through inclusion of a dichotomous variable, County, that codes “1” if the jurisdiction is a
county and “0” otherwise. We also include two other dummy variables, Suburban and Rural, to control
for metro status.
Further, we control for the presence of professional managers (Professional manager) since these
actors have been found to manage local government services differently than their elected counterparts
(Hefetz and Warner 2004, 2012). Additionally, we control for the total number of services provided by
the jurisdiction in 1992 (# services provided, 1992) as jurisdictions with larger service packages simply
have more opportunities to drop services (i.e., larger risk sets; Kodrzycki 1998). Next, we include
controls for population (Population 1990), income (Avg. per capita income, 1992-97), and the percentage
white (% white, 2000). Larger population might be an indicator of potentially higher level of demands for
services, which may reduce the probability of termination. On the other hand, higher population may also
16
indicate more robust private markets, leading to an easier transition from public provision to fee-based
private market supply for services. Population information is taken from the 1990 Census. The direction
of income is uncertain as well. For example, wealthy jurisdictions may lead to less termination because
they have more resources to mobilize before contemplating the termination of services. However, it is
also possible that high-income jurisdictions have less resistance to terminating publicly provided services
due to lower levels of need for public assistance. Finally, we account for the percentage of the
population in the county that is white as this might impact service package preferences (Burns 1994).
Please see table one for a summary of the measures, data sources, and hypothesized directions. We also
provide the descriptive statistics for the variables in our model in table 2.
[tables 1 and 2 about here]
The Model
Based on the above discussion, we estimate the following logit model to examine the determinants
of local government service termination:
Service termination = b0 + b1Budget stress, 1992 + b2Avg. Unemployment, 1992-97 + b3Avg.
%Republican vote, 1992-96 + b4Redistributive service + b5Service w/high SD growth, 1957-92 + b6Δ
number of SDs, 1992-97 + b7Joint, 1992 + b8Other government, 1992 + b9For-profit, 1992 +
b10Nonprofit, 1992 + b11Mixed-contracting, 1992 + b12% peers providing service, 1992 + b13County +
b14Suburban + b15Rural + b16Professional manager + b17 # services provided, 1992 + b18 Population,
1990 + b19 Avg. per capita income, 1992-97 + b20 % White, 2000 + e
Results and Discussions
Since comprehensively examining local service termination has, to our knowledge, only received
limited attention in the past, we open our discussion of the results by providing descriptive statistics
regarding shedding rates by ICMA service category. Three groups of services stand out as most likely to
face termination: public utilities, health and human services, and cultural and arts programs. Of these
health and human services appear to be shed at the highest rate. This is generally in-line with our
17
expectations as many of these services qualify as redistributive in nature. Public utilities are dropped at
the next highest rate with many jurisdictions discontinuing electric and gas utility and management
services. Culture and arts may not be viewed as core functions of government, which could explain the
prevalence of termination in these services. The other service categories (i.e., public
works/transportation, public safety, parks and recreation, and support functions) are shed at notably lower
rates. Unlike culture and arts, public works/transportation and public safety may tend to be seen as core
services, making them resistant to being dropped. Parks and recreation services likely have strong
support across the socioeconomic strata of the population owing to their tendency to enhance the quality
of life by providing green spaces. Finally, support services may be viewed as more easily managed inhouse since many of these services (e.g., secretarial and personnel services) are associated with the
mundane, day-to-day workings of municipal government.
Overall, 9.3 percent of the services at risk for shedding in our dataset are terminated. Further, 69.3
percent of jurisdictions in the analysis shed at least one service over the timeframe of the study.
Therefore, we can comfortable say that service termination is a relatively common phenomenon at the
local level. Having briefly reviewed our descriptive findings, we now turn to our multivariate analysis
into the determinants of service shedding.
We present our results in table 4. Our most robust finding is the importance of the previous delivery
method. All forms of contracted delivery are associated with greater probability of termination than inhouse delivery. Local governments also abandon redistributive services at higher rates than other types
and tend to drop the services that their peers do not commonly provide. Fiscal distress and political
ideology appear to have little influence on the termination decisions of the locales in our analysis.
[tables 3 and 4 about here]
More specifically, the variables identified by termination theory (Budget stress, 1992; Avg.
unemployment,1992-97; and Avg. % Republican vote, 1992-96) are not statistically related to the
likelihood of local service shedding, contrary to expectations generated from the termination literature.
On the other hand, the prior delivery method appears to be very influential in service shedding decisions.
18
Each of the variables from the “make-or-buy literature” reaches both statistical and substantive
significance. In all cases, the impact is notable.10 For example, partial privatization (i.e., joint production
in 1992) has the smallest impact, but it still doubles the likelihood of termination (increasing the
probability by 10.0 percentage points over the baseline probability of 0.093 when in-house production
was used in 1992). The greatest influence is associated with other government contracting in 1992 (a
41.9 percentage point increase). This was a bit of a surprise in that we expected for-profit contracting to
lead to the highest probability of shedding. However, this does not appear to be the case. The coefficient
for other government contracting is larger and statistically distinguishable from that of the for-profit
variable (difference = 0.573, p = 0.000). Overall, these variables lend support to our supposition that
internal resistance associated with in-house delivery makes service termination less likely.
Moving to our next grouping of independent variables, we do not find much support for the politicaleconomic perspective. The change in the number of special districts in the county from 1992 to 1997 is
not related to the likelihood that services will be dropped. And while the coefficient for Redistributive
service, is significant in the posited direction, its substantive impact is negligible. Further, Services w/
high SD growth, 1957-92 has a non-zero influence on the dependent variable, but it is in the wrong
direction (and again, substantively small). So overall, this package of measures is not very useful in
assisting in the identification of service shedding. It is possible to think about the role of special districts
as gap fillers rather than institutions directly competing with general-purpose governments in providing
services. In other words, the recent increases in special districts might have resulted from unincorporated
areas and communities preferring special districts for meeting service needs as opposed to absorbing the
costs of incorporation or allowing themselves to be annexed by adjacent cities. If so, special districts
might not necessarily replace the current service delivery functions carried out by existing generalpurpose city and county governments.
Our last variable of interest is the diffusion measure, % peers providing service, 1992. This indicator
performs as expected – higher numbers of peer jurisdictions (as defined by city/county and metro status)
10
See the note below table 4 for an explanation of how “impact” is calculated.
19
providing the service in the previous time period is associated with decreased likelihood of termination.
This is a very impactful variable. An increase from the mean to one standard deviation above the mean
leads to a 4.9 percentage point decrease in the probability of the dependent variable coding one. This
translates to a bit over a 50 percent decrease in the likelihood of termination. Hence, it appears that other
jurisdictions may serve as guides in determining appropriate service packages. We close with our control
variables. Surprisingly, none of our controls appear to be related to service termination. Only Popultion,
1990 reaches even a marginal level of significance (p = 0.071). We find it particularly interesting that the
total number of services provided in 1992 is not related to shedding, since it seems very reasonable to
assume that jurisdictions with larger risk sets (i.e., more services that could possibly be dropped) should
be more likely to shed services, but that does not appear to be the case. Further, this finding is in
contradiction to Kodrzycki’s (1998) results. The last measure worth commenting on is Professional
manager. While it does not quite reach marginal significance (p = 0.133), its negative coefficient
indicates that jurisdictions with professional managers might show some sign of being reluctant to the
dropping of services. It is possible that they see service termination as not in their interest as it decreases
the size and scope of local government.
Conclusion
In this paper, we examine why local jurisdictions choose to stop providing services. Borrowing from
four independently developed bodies of literature, we hypothesize that locales are more likely to abandon
their services when fiscal stresses are high, conservative ideology is dominant (termination literature),
special districts are present, provision is redistributive in nature (political-economic perspective), prior
service delivery arrangements involve outsourcing (make-or-buy literature), and fewer peer institutions
offer the target services (policy diffusion theory).
The results indicate that prior service delivery methods (i.e., make-or-buy) are strong and persistent
predictors of how likely a local jurisdiction is to cease provision of services. In the past, scholars
examined what happens to the services that are privatized and found that reverse contracting (i.e.,
20
bringing outsourced services back in-house) is as relatively common as contracting out (Brown, Potoski,
and Van Slyke 2008; Hefetz and Warner 2004; Lamothe, Lamothe, and Feiock 2008). What has gone
unnoticed in this literature is the fate of the services that stay outsourced. Our findings clearly suggest
that these services are more likely subject to future shedding. But why? We speculate such factors as the
existence of potentially viable markets and lack of internal stakes (e.g., public employment) due to
outsourcing. However, this does not effectively explain why the services contracted out to other
governments are dropped at higher rates. Future research should delve deeper into this aspect by directly
inquiring what facilitates different termination rates among different types of vendors. Lastly, the strong
connection found between local service delivery methods and shedding suggest that research in these
respective areas of study should give serious consideration to adopting broadly integrated approaches to
analyzing local service delivery.
We also find evidence that local governments may pay attention to their peers when determining the
composition of their service packages. Interestingly, such diffusional effects appear to be more impactful
than nearly all of the other predictors in our model, with the possible exception of the previous delivery
mode measures. Since “peers” are defined based on the locales’ city/county and metro status in the
model, this finding also indicates that local governments imitate from what they consider to be their
appropriate reference groups, not just their geographically determined neighbors, which supports the idea
of national interaction rather than regional clustering (although we are unable to test regional influences
in this paper). Broadly, however, our results are in-line with the general findings of the diffusion
literature; that is, governments often copy each other, whether they are adopting, or in our case
abandoning, policies.
While we were a bit surprised to find no evidence supporting the importance of the variables
generated from termination theory (i.e., Budget stress, 1992, Avg. unemployment, 1992-97, and Avg. %
Republican vote, 1992-96), this does not necessarily mean that the theory is discredited. Our data are
very expansive, covering 64 services from 398 jurisdictions. While this is advantage for uncovering
broad trends, it necessarily makes our operationalization of variables rather blunt. As such, we are unable
21
to account for jurisdiction-specific dynamics that may be at play. For example, a key component of
termination theory is the concept of termination and anti-termination coalitions. While we do try to
account for general partisan proclivities in this regard with our political variable, we are unable to capture
the existence, or lack thereof, of particular groups pushing such agendas for given services in each
jurisdiction. As such, future research should be conducted to capture this dynamic. For example, it
would be interesting to explore whether the low level of shedding we observed among internally
produced services is at least partly caused by the work of anti-termination coalition forces within
governments.
In conclusion, we argued in the beginning of our paper that termination at the local level is neither
rare, although it is not prevalent, nor purely idiosyncratic in nature. Our results support these assertions.
Termination seems to occur broadly across the local jurisdictions, but appears to take place at
systematically higher levels for outsourced services, services provided by fewer peers, and services that
are redistributive in nature. Our study also suggests that local governance, including service shedding, is
a dynamic and complicated phenomenon, requiring multidisciplinary, cross-field approaches. We hope
our paper contributes to this effort.
22
Characteristic
Appendix: Representativeness of jurisdictions in the analysis
Jurisdictions in the
U.S.
analysis
Population:
Less than 10,000
10,000 to 50,000
50,000 to 250,000
Greater than 250,000
0.5%
66.2
28.2
5.1
86.6a
10.4
2.6
0.3
Region:
Northeast
North Central
South
West
13.2
28.1
32.3
26.4
13.2a
32.5
35.8
18.5
Form of government
Council-manager
Other
77.5
22.5
53.8b
46.2
% white
77.7
75.1c
$23,271
$18,335d
6.0
6.4d
13.6
86.4
7.8e
92.2
Avg. per capita income, 1992-97
Avg. unemployment, 1992-97
Level of government
County
City/town
a
ESRI (2003).
b
ICMA (2001).
c
U.S. Census Bureau (2000).
d
Kim, Elliott, and Wang (2003).
e
U.S. Census Bureau (2011).
23
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29
Table 1: Summary of measures and data sources
Variable
Service shedding (DV)
Budget stress, 1992
Avg. unemployment,
1992-97
Avg. % Republican
vote, 1992-96
Redistributive service
Service w/ high SD
growth, 1957-92
Δ number of SDs, 199297
Joint, 1992
Other government, 1992
For-profit, 1992
Nonprofit, 1992
Mixed contracting, 1992
% peers providing
service, 1992
County
Suburban
Rural
Professional manager
# services provided,
1992
Population, 1990
Avg. per capita income,
1992-97
% white, 2000
Measures
Codes “1” when the service is marked: 1) provided and delivered
through one or more of the following production modes – in-house,
joint, other government, for-profit, and nonprofit in 1992; and 2) “no
longer provided” or “never provided” in 1997 and 2002, 1997 and
2007, or 1997, 2002, and 2007.
Code “0” when the service is marked: 1) provided and delivered
through one or more of the production modes mentioned above in 1992
and 1997
Expenditures divided by revenues in 1992 for each jurisdiction.
Unemployment rate, 1992-97, for the county in which a jurisdiction
resides.
Average of the Republican two-party vote share (percentage) for 1992
and 1996 presidential elections.
Codes “1” for the following local services: operation of daycare
facilities, child welfare programs, programs for the elderly,
operation/management of hospitals, public health programs, drug and
alcohol treatment programs, operation of mental health/mental
retardation programs and facilities, and operation of homeless shelters;
“0” otherwise.
Codes “1” for the sixteen local services whose delivery through special
districts increased by more than 100% between 1957 and 1992. These
services are: residential solid waste collection, commercial solid waste
collection, solid waste disposal, operation/maintenance of bus transit
system, operation of airports, water distribution, water treatment,
sewage collection and treatment, electric utility operation and
management, gas utility operation and management, fire
prevention/suppression, operation/management of hospitals, public
health programs, operation and maintenance of recreation facilities,
parks landscaping and maintenance, operation of libraries.
The number of special districts in the county in 1992 subtracted from
the number of special districts in that county in 1997.
Codes “1” for the services that were delivered jointly by the jurisdiction
and contractor(s) in 1992, “0” otherwise.
Codes “1” for the services that were delivered by other governments in
1992, “0” otherwise.
Codes “1” for the services that were delivered by for-profit vendors in
1992, “0” otherwise.
Codes “1” for the services that were delivered by nonprofit vendors in
1992, “0” otherwise.
Codes “1” for the services that were delivered by more than one type of
contractor in 1992, “0” otherwise.
Percentage of peer institutions that provided the service in 1992. Peer
institutions are based on the jurisdiction’s city/county and metro status
(i.e., city-core, city-suburb, city-rural, county-core, county-suburb, and
county-rural).
Codes “1” for the county governments, “0” otherwise.
Codes “1” for the suburban governments, “0” otherwise.
Codes “1” for the rural governments, “0” otherwise.
Codes “1” for the council-manager form of governments, “0”
otherwise.
The number of services the jurisdiction provided in 1992.
The jurisdictions population in 1990
The average per capita income, 1992-97, for the county in which the
jurisdiction resides.
Percentage of the jurisdiction’s population that is white, 2000.
Data Source
Posited
direction
ICMA (1992; 1997;
2002; and 2007).
N/A
U.S. Census Bureau
(2008)
Kim, Elliot, and
Wang (2003)
Kim, Elliot, and
Wang (2003)
Peterson (1981, 5165)
+
+
+
+
Stephens and
Wikstrom (2000,
132, table 7.4)
+
U.S. Census Bureau
(2011)
ICMA (1992)
ICMA (1992)
ICMA (1992)
ICMA (1992)
ICMA (1992)
+
+
+
+
+
+
ICMA (1992)
ICMA (1992)
ICMA (1992)
ICMA (1992)
ICMA (1992)
ICMA (1992)
ICMA (1992)
Kim, Elliot, and
Wang (2003)
U.S. Census Bureau
(2000)
+/+/+/+/+
+/+/+
Table 2: Descriptive statistics for variables in the analysis
Variable
Service shedding
Budget stress, 1992
Avg. unemployment, 1992-97
Avg. % Republican vote, 1992-96
Redistributive service
Service w/ high SD growth, 1957-92
 number of SDs, 1992-97
Joint, 1992
Other government, 1992
For-profit, 1992
Nonprofit, 1992
Mixed contracting, 1992
% peers providing service, 1992
County
Suburban
Rural
Professional manager
# services provided, 1992
Population, 1990
Avg. per capita income, 1992-97
% white, 2000
N = 17,092
Mean
0.093
1.016
5.965
48.344
0.079
0.216
3.991
0.193
0.144
0.084
0.031
0.009
77.276
0.136
0.520
0.226
0.775
45.321
0.808
2.658
77.662
Std. Dev.
0.290
0.153
2.596
10.164
0.270
0.411
9.027
0.394
0.351
0.278
0.174
0.097
21.854
0.342
0.500
0.418
0.418
9.132
2.339
0.725
14.505
Min
0.000
0.354
1.764
25.090
0.000
0.000
-20.000
0.000
0.000
0.000
0.000
0.000
6.250
0.000
0.000
0.000
0.000
1.000
0.041
1.148
27.040
Max
1.000
1.657
21.699
80.187
1.000
1.000
40.000
1.000
1.000
1.000
1.000
1.000
100.000
1.000
1.000
1.000
1.000
64.000
34.854
5.823
98.208
Table 3: Shedding by service
Service
Public works/transportation
Public utilities
Public safety
Health and human services
Parks and recreation
Cultural and arts programs
Support functions
Total
% shed
7.6
17.3
5.5
24.7
3.7
16.5
3.4
9.3
N
4,926
585
2,347
2,738
864
630
5,002
17,092
Table 4: Logit analysis of the determinants of service shedding
Variable
Termination theory
Budget stress, 1992
Avg. unemployment, 1992-97
Avg. % Republican vote, 1992-96
Political-economic perspective
Redistributive service
Service w/ high SD growth, 1957-92
 number of SDs, 1992-97
Make-or-buy literature
Joint, 1992
Other government, 1992
For-profit, 1992
Nonprofit, 1992
Mixed contracting, 1992
Diffusion theory
% peers providing service, 1992
Controls
County
Suburban
Rural
Professional manager
# services provided, 1992
Population, 1990
Avg. per capita income, 1992-97
% white, 2000
Constant
b
Robust sea
p-value
Impactb
-0.266
-0.037
0.005
0.448
0.038
0.009
0.552
0.340
0.612
-
0.354
-0.165
0.003
0.114
0.077
0.012
0.002
0.032
0.774
0.008
-0.006
-
0.847
2.328
1.755
1.878
2.006
0.127
0.134
0.140
0.163
0.266
0.000
0.000
0.000
0.000
0.000
0.100
0.419
0.278
0.308
0.339
-0.037
0.002
0.000
-0.049
-0.203
-0.012
0.116
-0.289
0.009
-0.166
0.215
-0.002
-1.269
0.279
0.255
0.282
0.193
0.009
0.092
0.135
0.007
1.063
0.467
0.962
0.681
0.133
0.343
0.071
0.111
0.778
0.233
-
N = 17,092 (398 jurisdictions)
Chi-squared = 1334.85 (p = 0.000)
Note: p-values are for two-tailed tests.
a
Robust standard errors clustered on jurisdiction (see White 1980).
b
“Impact” is calculated as follows. First, the variable of interest is set at zero, if dichotomous, or at its mean, if
continuous, and all other variables are set at values that produce a baseline predicted probability of 0.093 (the
probability of termination in the dataset). The variable of interest is then increased by one or a standard deviation,
as appropriate, and a new probability is calculated. Impact is simply pnew – pbaseline.