A longitudinal analysis of the effects of service consolidation on local government expenditures.
Maher, Craig S.
INTRODUCTION
The Great Recession of 2007-08 resulted in reductions in the public
sector labor force not seen in the previous four recessions (Dadayan and
Boyd, 2013). Five years following the recession, local government
employment remains down 2.9 percent (compared to employment gains in
three of the previous four recessions) and with limitations on tax
and/or spending growth (Amiel, Stallmann and Deller, 2009) there are few
resources available to add public employees. Despite these fiscal
pressures, local governments are responsible for providing essential
services to the public, including police and fire protection, road
repairs, assessments, building inspections, etc. Politically, the fiscal
gap between service demands and revenue limitations appears to be
perceived as a spending problem, not a revenue problem. Local managers,
administrators and policy makers continue to pursue alternative service
delivery options as a means of maintaining services while also reducing
costs. Unfortunately, the literature often provides either inconclusive
or conflicting evidence about the extent to which alternative service
options produce cost saving. In part that is due to the almost exclusive
use of pre-consolidation expenditure reduction estimates citied within
the existing literature (Holzer and Fry, 2011). Such estimates are often
very different from the final amounts, providing a false sense of
savings (Holzer and Fry, 2011).
Service consolidation and shared service agreements can take on a
variety of meanings (Nunn and Rosentraub 1997, McCabe 2000, Feiock 2009,
Scholz and Feiock 2010, Thurmaier and Wood 2002). According to Holzer
and Fry (2011), service consolidation means "... inter-local
agreements, shared services, service transfers, government partnerships,
contracts with government, and many other variants" (48). For our
purposes, service consolidation focuses on contractual agreements where
services are either shared or transferred from one governmental unit to
another, or to a newly formed unit (e.g., regional dispatch). Quite
often, the driving force behind the selection of service consolidation
over other forms of shared service agreements its perceived ability to
reduce expenditures through personnel reductions while also retaining,
or even enhancing, service quality. This paper attempts to examine the
effects on spending in Wisconsin communities that use a particular form
of alternative service delivery: consolidation. The panel dataset allows
for: 1) comparisons of local governments that did and did not
consolidate services; 2) comparisons of expenditures before and after a
community consolidated a service and; 3) examination of the effects of
various types of consolidated services. The paper follows with a
literature review, a discussion of local finances in Wisconsin to
provide context, a discussion of service consolidation in Wisconsin,
methodology, results and conclusions.
LITERATURE REVIEW
Government consolidation has a long and controversial history in
the U.S. (Fleischmann, 2000). It was a common strategy in the early-mid
twentieth century for school districts in the United States when their
numbers dropped from over 119,000 in 1938 to just over 22,000 in 1968
(Snyder, Tan and Hoffman, 2004). While never as common at the municipal
level, municipal consolidation--the merging of two communities or a city
and county government into one unit--dates back to 1805 when the City of
New Orleans merged with its county (Duvall, 1999). Yet large scale
consolidation of local governments does not have a successful history.
Since the 1950's many city-county consolidations have been proposed
but over 85 percent have been rejected by voters (Thurmaier and Leland,
2005). The high failure rate of city-county consolidations has led many
governments to seek out its closest alternative: service consolidation.
The advantage service consolidation has over government
consolidation is its ability to select those services appropriate for
merging (Holzer and Fry, 2011). Another benefit of service consolidation
is that, unlike municipal consolidation, the process typically does not
require voter or legislative approval (5) (Holzer and Fry, 2011). The
ability to by-pass voters has become increasingly appealing to local
government officials (Sparrow, 2004). For instance, after years of
failed city-county consolidation attempts, Sacramento and Sacramento
County turned to service consolidation to essentially achieve the same
ends (Sparrow, 2004). Furthermore, scholars have found that service
consolidation has stronger political support than service privatization
(Hawkins 2009, Delabio and Zeemering 2013).
The basic premise underlying consolidation is that it offers a
means of providing the same services more efficiently (Vojnovic, 2000;
Holzer and Fry, 2011, Benton 2013). By consolidating services, the
expectation is that costs can be lowered by eliminating positions
(Stenberg, 2011), eliminating duplicative services and, sharing
buildings and equipment (McAninch and Sanders, 1988; Holzer and Fry,
2011). Recent research on solid waste services has shown that
particularly for smaller cities lower costs are achieved through
cooperative agreements (Bel, Fageda and Mur year, Bel and Costas 2006,
Bel and Mur 2009).
Interestingly, others have contended that cost-savings associated
with service consolidation have been very difficult to predict (Hirsch
1959) and startup costs are often not accurately estimated or completely
ignored (Holzer and Fry, 2011; Piker and Maher, forthcoming). Claps
(2008) recommends that governments conduct a thorough economic impact
study when consolidation is initially proposed. Some communities have
hired consultants to evaluate the proposed merger, whereas other local
officials simply estimate expenditure implications themselves (Zettek,
2003). Self-estimation of costs has created research and comparability
problems for scholars and practitioner alike (Holzer and Fry, 2011).
Without clear expenditure evaluations (before, during, or after
consolidation), the results of a service consolidation are often
determined after it has occurred (Simon, 2011).
On a practical side, the ideological perspective of policy makers
and citizens toward consolidation can often cause actors to disregard
information about expenditure changes (Holzer and Fry, 2011). Such
evaluation of expenditure changes is not uncommon and even
administrators can err in their estimates by focusing on the long-term
savings, while ignoring initial costs (Holzer and Fry, 2011). Service
consolidation can become contentious, particularly when it involves
high-profile services such as public safety (Simon, 2011; Namanny, 2013;
Superville, 2013). Citizens in rural communities in particular like to
see their community's name on service vehicles is both a source of
pride and identity (Simon, 2011). Rebranding essential services tends to
elicit fear and hostility among a community's citizens,
administrators, and elected officials (Idzerda, 2013).
At the state-level, large numbers of local municipalities and
special districts within states have been identified as a main cause of
government inefficiency (Karcher, 1998). Karcher (1988) contends that by
reducing duplication of services, the administration of the service is
improved and costs are reduced. Thus, by pooling resources, a better and
more effective service can be provided (Pachon and Lovrich, 1977). Such
arguments have also been used in a number of recent state-level
commissions on local government services, including New York (2007), New
Jersey (2006), Wisconsin (2012), Minnesota (2009) and Illinois
(forthcoming).
This movement is also consistent with classic civic reform theory
(Lyons and Lowery, 1989) where large cities are able to generate
economies of scale (Hilvert and Swindell 2013) and presumably, the same
end should be achieved through consolidation (Savitch and Vogel, 1995;
Feiock and Carr, 1997). There is also evidence suggesting that
improvements in service delivery through consolidation are attractive to
developers (Owen 1992; Rusk, 1993; Savitch and Vogel 1995; Feiock and
Carr, 1997; Vojnovic, 2000; Leland and Thurmaier, 2005). The promise of
increased efficiency, cost savings, and potential economic development
has created a favorable environment for consolidation among
administrators (Holzer and Fry, 2011). Furthermore, Ostrom, Tiebout, and
Warren's seminal work suggests that joint service provision is a
viable option to privatization (1961).
While the discussion of service consolidation often comes back to
cost savings, an array of other justifications have also been offered.
According to Feiock (2009), the research tends to identify political and
contextual reasons, as well as economic, for selecting alternative
service delivery options. Hilvert and Swindell (2013) note that in
addition to cost savings, service collaborations improve service
delivery and increase demand for services. Less obvious reasons for
service consolidation include, "needs to stimulate innovation,
desire to improve working relationships with other jurisdictions,
difficulty in solving problems that are multidimensional in scope, past
internal successes and observations of success by others" (Hilvert
and Swindell 2013, 243).
We are thus left with an expansive array of research on service
consolidation as an alternative to traditional forms of public service
provision or privatization. We have a sense of the issues local
officials need to consider prior to considering moving in the direction
of service consolidation (Hilvert and Swindell 2013, Blair and Janowsek
2013, Piker and Maher forthcoming, Carr and Hawkins 2013, Holzer and Fry
2011). We also have some research on the effects of service
consolidation for certain services e.g., public safety (Andrew and
Hawkins 2013, Lynn 2005, McEntire and Dawson 2007) and solid waste (Bel,
Fageda and Mur year, Bel and Costas 2006, Bel and Mur 2009). It is our
contention that a more extensive longitude study is needed that
considers the effects on expenditures on communities that consolidated
services, comparisons to communities that did not consolidate services
and one that examines an array of consolidated services.
LOCAL GOVERNMENT FINANCE IN WI
Municipalities in Wisconsin consist of 190 cities, 1,255 towns and
405 villages. Cities are generally the largest in population (average
17,246 in 2009) and villages (1,989) and towns (1,350) are generally
much smaller. Wisconsin cities and villages have home-rule powers,
meaning that they can do that which is not prohibited by the State
(Hintz, 2001). Towns, not having home-rule powers can only do that which
is expressly permitted by the State. In reality, municipal home-rule
powers are largely reserved for administrative functions. Wisconsin
municipalities focus on providing that which you would expect: cities
and villages focus on public safety, road work and general government
functions, whereas towns focus on road repair and construction. Not
surprisingly, in fiscal year 2009, cities spend the most per capita
($1,477) and towns the least ($469). Cities and villages generally spend
the same on debt service and, road and highway expenditures. Compared to
other types of municipalities, cities spend more on police ($296 per
capita) and fire protection ($154 per capita). Towns, on the other hand,
typically focus their expenditures on road and highway work (43
percent).
On the revenue side, Wisconsin municipalities have limited options:
no sales tax, only property taxes, intergovernmental aids and
fees/charges. In 2009, the average city collected $1,214, most of which
came from property taxes (39 percent) and state aids (31 percent).
Villages and towns are somewhat more reliant on property taxes (45
percent and 48 percent, respectively) and a little less reliant on state
aids (20 percent and 34 percent). Towns heavy reliance on state aid is
largely a function of the emphasis on road-related expenses which are
offset, somewhat, by state transportation aids.
The fiscal data also reveal the challenges faced by many Wisconsin
municipalities. In fiscal year 2009, operating expenditures exceeded
revenues, particularly in cities and villages. This suggests that many
Wisconsin municipalities are drawing down their reserve funds to balance
their budgets. More concerning is that this gap between municipal
revenues and expenditures has existed since the mid-1980s and got worse
when the Great Recession hit. The widening gap between municipal
revenues and expenditures is consistent with national trends (Pagano,
Hoene and McFarland, 2012), particularly for communities heavily reliant
on property taxes and state aid.
Exacerbating the challenges facing Wisconsin municipalities is the
strict levy limits imposed by the State. Following passage of Wisconsin
Act 10 in 2010, municipal levies were frozen, except for growth in new
construction. Given that this limit was imposed when the recession was
hitting cities the hardest (Pagano, Hoene and McFarland, 2012), this has
essentially meant levies have been unable to increase for most
municipalities. In addition, state aids have seen limited growth over
the years. Interestingly, the highly publicized Act 10 restriction on
collective bargaining passed on the basis of giving local governments
greater flexibility in dealing with fiscal challenges exempted
protective services, thus, offering little fiscal relief for most
municipalities.
Taking a page from the economies-of-scale argument, Wisconsin
Governor's Commission on Waste, Fraud and Abuse report (2011) cites
the "... need to create more initiatives for consolidation"
(49). Focusing on identified savings in dispatch service, the commission
recommended not only "encouraging" consolidation efforts, but
also offering loans for local governments (including school districts)
to engage in efforts to consolidate services (50). The Wisconsin
commission's recommendations regarding service consolidation were
largely based on a report, "Governor's Work Group: Public
Safety Answering Point Consolidation, A Guidebook for Consolidation
Strategies" presented to Minnesota Governor Pawlenty in December,
2009. As the title suggests, the report describes a Public Safety
Answering Point (PSAP) service which shares emergency communications
between of jurisdictions. Consolidation of PSAP services already
occurred at the state-level and the report argues the same efforts
should occur at the local level. In Illinois, Governor Quinn formed the
Local Government Consolidation Commission in 2011 for the purposes of:
"(i) permit effective management of local affairs, (ii) encourage
local policy decision making, (iii) reduce the multiplicity of local
governments, (iv) eliminate overlapping and duplicating of unnecessary
powers, (v) increase efficiency and economy in local governments, and
(vi) allow optional forms of local governments and increase their
authority for cooperation among the levels of government." The
final report was due December, 2012 and to date, is not available.
In an effort to gauge the level of fiscal stress facing Wisconsin
municipalities, Steven Deller has co-authored several reports since the
mid-1990s based on survey results of local officials that sought to
gauge local officials' perceptions of their community's
financial condition. In a survey of cities and villages conducted in
1997, less than one in five municipal officials expressed concern about
the adequacy of their fiscal position (Deller, Hinds and Hinman 2001). A
similar survey was conducted in 2010 and by Deller, Maher and Kovari
(2010) who found that compared to 1997, the fiscal health of Wisconsin
municipalities has fundamentally changed for the worse. Between 1997 and
2010, the percent of respondents expressing concern about their
community's current financial condition more than doubled.
When municipal officials were asked to consider their future (five
years) fiscal health, the picture again changes significantly between
1997 and 2010. A majority (52.4 percent) of survey respondents in 2010
believed that their revenues will be inadequate and 36.1 percent report
that they will be forced to reduce services. Only two of the 195
respondents believe that they will be in a position to reduce taxes.
Compare these results to the same question asked in 1997: a clear
majority of respondents in 1997 believed that they had adequate revenues
over the next five years and 17 percent thought that they would be able
to reduce taxes! Compared to the 2004 results--a time of modest economic
growth--today's result differ little (Deller, Maher and Kovari,
2010).
One of the enduring observations by Levine (1980) and his research
of fiscal retrenchment are the array of strategies adopted by
governments. For instance, during periods of low fiscal stress,
governments tend to focus on near-term strategies such as delaying
capital spending, drawing down fund balances, pursuing grants, etc. When
the fiscal stress level reaches a moderate level, actions need to be
ratcheted up, and include strategies such as salary and hiring freezes,
reducing employment through attrition, efficiency reforms, etc. When the
fiscal crisis escalates, the options change to things such as layoffs,
the closing of facilities, terminating of programs, transferring
services to other units of government or other sectors, etc. We have
experienced another level of fiscal stress in certain communities during
the last recession and that consists of bankruptcy (e.g., Detroit MI,
Jefferson County AL, San Bernardino CA, Stockton CA).
The work by Deller, Maher and Kovari (2010) sheds light on the
paths pursued by Wisconsin municipal officials in their efforts to cope
with fiscal stress. Their study focused on three broad categories:
service delivery or management, revenue alternatives, and changes in
expenditure policies. Administrative officials were asked to indicate
the degree to which they agree or disagree with the listed strategies as
they describe their community's recent efforts to cope with fiscal
stress. Interesting, the top three service delivery or management
strategies were improving productivity through better management (78
percent), contracting out services (49 percent) and pursuing regional
cooperative agreements (49 percent) (Deller, Maher and Kovari, 2010).
The strategies least supported by municipal officials were the reduction
of hours for public facilities (20 percent), eliminating services (25
percent) and department consolidation (34 percent) (Deller, Maher and
Kovari, 2010).
SERVICE CONSOLIDATION IN WISCONSIN
Protective Services
Local government consolidation of police protection services is
uncommon in Wisconsin. To date, there are only six consolidated police
departments covering approximately 62,000 of the nearly 3.6 million
people living in Wisconsin municipalities that provide full-time police
protection (Wisconsin Taxpayers Alliance, 2008a). One of the largest
consolidated departments is the Fox Valley Metro Police Department that
consists of the villages Kimberly (population = 6,500), Little Chute
(11,020) and Combined Locks (3,114). The metro department was
established in April 1, 1995 and only included Kimberly and Little
Chute; Combined Locks joined in 2011. The initial discussions in 1994
were precipitated by previous success with a library merger, and the
retirement of Kimberly's police chief (Elsass, 2003). One of the
arguments for consolidating the departments was cost savings and
according to initial estimates, the consolidation was to generate
$100,000 annually in operating expenditures (Elsass, 2003). The
anticipated savings was not realized for several reasons, including the
reassignment of central administrative costs to the Police Departments
to reflect actual costs as well as enhanced service levels (Elsass,
2003). Based on the real per capita expenditures for the villages of
Little Chute and Kimberly, average real annual costs grew for both
communities following consolidation, particularly for Little Chute.
Little Chute's annual average real police protection operating
expenditures rose from $121 per capita (years 1987-1994) to $247 per
capita (years 1995-2009). Average annual operating costs also rose for
Kimberly from $139 per capita (1987-2004) to $169 per capita
(1995-2009).
Combined fire departments in Wisconsin are more prevalent than
police protection: in 2006, there were 110 such fire departments
accounting for 13 percent of all fire departments in Wisconsin
(Wisconsin Taxpayers Alliance, 2008b). The North Shore Fire District is
one the largest of the consolidated departments both in terms of the
number of municipalities involved (seven) and population served (approx.
64,000) and contains many of the most affluent communities in Milwaukee
County. The district was formed in 1995 following two serious apartment
fires in one of the communities coupled with the City of
Milwaukee's decision to stop providing mutual aid to surrounding
communities (Elsass, 2003). The discussion was also aided by the death
of one of the fire chiefs in 1993 (Elsass, 2003) and a history of
successful service-level mergers (Local Government Institute of
Wisconsin, 2012). Identified benefits of the agreement included service
enhancements, better code enforcement and improved insurance ratings for
residential, commercial and industrial properties (Local Government
Institute of Wisconsin, 2012). Also noted as a benefit was "more
efficient delivery of services--number of administrators reduced from 21
to 7" (42, Local Government Institute of Wisconsin, 2012).
Some of the most challenging aspects of the North Shore Fire
District merger included the division of assets and the development of a
cost-sharing formula (Elsass, 2003). According to Elsass (2003),
"The annual costs for each community was determined through a
financing formula based upon three factors: (a.) The population of each
municipality, (b.) The equalized valuation of each municipality, and
(c.) The average of the prior three-year usage." The financing
formula is frequently at the heart of threats to dissolve the Department
and as recently as November, 2013 one of the members faced the threat of
expulsion over payment (Whitefish Bay NOW, 2013). The municipal fire
protection spending patterns for North Shore Fire District members are
similar to that found with the Fox Valley Metro Police Department:
greater overall costs following consolidation. The real average cost of
fire protection operations rose from $136 per capita (1987-1994) to $192
per capita (1995 to 2009). Average costs rose for six of the seven
municipalities, with Bayside, WI experiencing the biggest jump: from $39
per capita pre-consolidation to $181 per capita post-consolidation.
The differences in average real annual per capita protective
services expenditures before and after consolidation for all identified
cases in Wisconsin are police protective services for all observations
in Wisconsin are stark (see Table 1). On average, real per capita police
protection expenditures for all consolidations rose from $70.57 prior to
consolidation, to $159.81 following consolidation (see Table 1). This
increase of 125.6 percent in real per capita expenditures is
substantially more than the 3.4 percent growth in real per capita
equalized valuation for those same communities. For all fire protection
consolidations, real per capita expenditures rose from $90.73 before
consolidation to $152.30 following consolidation (up 67.9 percent)
whereas real per capita equalized valuation rose only 29 percent during
the same period for the same communities.
Other Service Consolidations
For most of the other service consolidations we were able to
identify, the richness of contextual information was not readily
available. The Local Government Institute of Wisconsin released a
report, "Local Government Collaboration in Wisconsin: Case
Studies" in 2012 that summarized most of the consolidation we were
able to identify. Instead of going through each agreement we offer the
following summary: the words "cost saving",
"efficiency" were identified in nearly every rational for
seeking an agreement; the cost savings more often referred to operating
but many also identified capital cost savings; having a history of
working relationships was important and; enhanced service delivery was
frequently cited. Interestingly, the state of Wisconsin apparently
awards additional aids for recycling service consolidations because it
was cited as a reason for pursuing the endeavor. For the two sanitary
service consolidations we identified, the primary motivation was the
ability to spread the costs associated with the replacement of eroding
wastewater treatment plants. Based on our overview of these service
consolidations, the most consistent rational for pursuing these
agreements is cost savings.
METHODOLOGY
The previous sections about municipal finance in Wisconsin reflects
both growing fiscal pressure and a willingness to pursue alternative
service delivery methods. Unknown is the extent to which regional
cooperation includes service consolidations. Furthermore, the existing
literature on service consolidation is restricted to case studies and
lacks empirical investigation of the policy effects. This research
endeavor has two aims, the first is to begin the process of cataloging
service consolidations in Wisconsin municipalities. The second objective
is to answer the question: does service consolidation affect spending?
The challenge was collecting data that would enable a pseudo
pre-consolidation vs. post-consolidation test. The data were collected
from multiple sources, the most important of which were the University
of Wisconsin-Extension's Local Government Center (LGC) and the
Local Government Institute of Wisconsin. The LGC annually works with the
Wisconsin Department of Revenue to collect local government revenue and
expenditure data and then makes the financial data available to local
Extension agents and officials (6). The LGC financial panel dataset
consists of revenues and expenditures for all municipalities and
counties between fiscal years 1987 and 2009. The available revenue and
expenditure categories are the same as those presented in Table 2.
The data on service consolidation were collected from multiple
sources, the most important of which was the Local Government Institute
of Wisconsin's website:
http://localgovinstitute.org/_casestudies2012. The Institute's
website lists a number of service consolidation contracts that typically
provided the communities involved, the affected service(s) and the year
of creation. These data were supplemented by telephone interviews with
local officials known by the authors who have service consolidation
contracts, and reports published by the LGC (Elsass, 2002) and Wisconsin
Taxpayers Alliance (2008a; 2008b). All told, we identified 94
municipalities and counties that had a service consolidation agreement
between 1987 and 2010.
Table 2 (below) lists the 17 services identified where there were
consolidation agreements. The services are varied both in terms of type
and locations in Wisconsin. The most frequently identified were
protective services: both fire and police. The fire service
consolidations involve six different agreements that cover towns,
cities, villages and counties. The most notable is the North Shore Fire
Department. Created in 1994, this Department comprises seven affluent
suburbs of Milwaukee. The agreement is often cited as a model for
service consolidation in Wisconsin (Elsass, 2002). A report by the
Wisconsin Taxpayers Alliance (2008a) identified six joint police
departments. The report noted that for these departments, the number of
officers per 1,000 population was lower than other municipalities and in
2006, "... spent an average of $152.33 per capita on law
enforcement, nearly $84 less than the municipal average ..."
(2008a, 7).
In most cases I was only able to identify one consolidated service
offered by the local governments. The exception is the North Shore area,
consisting of six villages and one city in northern Milwaukee County.
These communities have service agreements for public works, capacity
management, a senior center (only two of the seven municipalities),
animal control (with the county), data services and fire protection.
That said, it would be inaccurate to characterize service agreements as
being unique to southeastern Wisconsin. I identified consolidation
agreements as far north as Bayfield, WI and as far west as LaCrosse, WI.
I was also pleased to find that the agreements were relatively evenly
divided between cites (27 percent), villages (32 percent) and towns (34
percent). Of the 72 counties, we also found seven with service
agreements.
Dependent Variable
This analysis focuses on the relationship between service
consolidation and spending. I take two approaches to operationalizing
expenditures. The first is to regress a set of variables against total
operating expenditures. It can be argued that given the complexity
associated with service agreements and consolidations (Thompson and
Perry, 2006) those organizations that reflect an organizational culture
committed to such an endeavor do so to lower overall costs, not just
those expenses specific to a particular service. Therefore, the first
dependent variable is total operating expenditures for each Wisconsin
community between 1987 and 2010. These expenditures were adjusted for
inflation, population and intergovernmental charges. With respect to the
latter, it is often the case that one of the communities serves as the
fiscal agent for the service consolidation which affects their reported
expenditures and is offset on the revenue side through intergovernmental
charges. Given the nice subset of police and fire protection services, I
was also able to conduct a more focused analysis on the influence of
protective service consolidations on police and fire protection services
expenditures from 1987 to 2010.
Independent Variables
Service consolidation. The primary hypothesis I sought to test is
the relationship between service consolidation and expenditures. The
expectation was that following a service consolidation agreement, the
community's expenditures would decrease. To capture this effect, a
dichotomous variable was created and coded 0 for the years prior to the
service consolidation agreement; and 1 for the years following the
service consolidation (7). In the first model, the variable is coded 0
for the years that those identified communities had identified no
service agreement; and 1 for the years that an array of service
agreements were in effect (refer to Table 1). For the subsequent
analyses, I focused on two specific services: police and fire. For the
communities that have consolidated police protection, we coded 0 for the
years prior to the consolidation, and 1 for the years following
consolidation. The same coding methodology was used for fire protection
services.
Form of government. Municipalities in Wisconsin fit the traditional
model where cities and villages are incorporated and typically provide
an array of services, including full-time police protection, full-time
fire protection, administrative services, etc. Towns are unincorporated
and typically few services other than road maintenance. These
differences in service delivery are reflected in expenditures: in fiscal
year 2009, the average city's operating expenditures were $1,477
per capita; villages spent $1,216 and towns $419. Counties are different
from municipalities both in terms of their size and services provided
and which are reflected in their spending. Dichotomous variables we
created for cities, towns and counties, with villages representing the
comparison group.
Intergovernmental aids. There is convincing evidence of a
"fly-paper effect" in Wisconsin municipalities (Deller and
Maher, 2009; Deller, Maher and Lleudo, 2007, Deller and Maher, 2005).
This means that state aid payment, more specifically state shared
revenues, have a stimulative effect on spending greater than personal
income. To account for this effect, real per capita shared revenues
payments from the state to the local governments is included as a
control variable.
Equalized property valuation. To capture service demand, we include
real per capita equalized valuation as a control variable. The
expectation is that, controlling for other factors, per capita real
equalized property valuation will be positively associated with
municipal spending.
The North Shore. The seven municipalities in the northern part of
Milwaukee County are unique for a couple of reasons. The first is, as
stated earlier, they are engaged in a number of service consolidation
and sharing programs. Second, these are smaller (average population is
9,551 compared to 18,706 (8)) and more affluent than most of the other
communities in Wisconsin that have consolidated a service. This is
captured when comparing average real per capita equalized property
valuation ($137,168 vs. $75,273) and average real per capita shared
revenues received from the State ($62 vs. $144). These differences
translate into differences in service-level demands measured in average
real per capita expenditures ($1,706 vs. $957). Given their uniqueness,
a dichotomous variable was created and coded 1 if the community is part
of the "north shore" group and 0 otherwise.
Table 3 provides a set of descriptive statistics comparing
communities in Wisconsin that had a service consolidation agreement in
2009 to those without such an agreement. Interestingly, communities with
agreements tend have larger populations (14,180 vs. 6,952) and higher
average operating expenditures ($1,121 vs. $790). State shared revenue
payments and equalized property valuation capture community wealth, and
there is no difference in either measure when comparing the two groups.
RESULTS
Tables 4 and 5 presents fixed-effects regression models which
include the dependent variable lagged one year as an independent
variable to address problems with serial correlation. Due to the nature
of the fixed-effects models--chosen over the random effects after
conducting the Hausman Test--the form of government variables dropped
out of the models. Given the size of the overall [R.sup.2]'s, I
feel comfortable with the overall models. Table 4 includes all
municipalities and counties in Wisconsin between 1987 and 2009. Table 5
includes only those communities that consolidated a service between 1987
and 2009. The two sets of tables help answer the research question in
slightly different ways. Table 4 focuses on the effect of consolidation
on operating expenditures when compared to spending patterns in all
municipalities and counties during the time period. Table 5 is a bit
more targeted and examines the effect of consolidation in those
communities that adopted such an agreement between 1987 and 2009.
Turning our attention to Table 4. Of the 13 consolidated services
in the model (9) seven services are significantly associated with
operating expenditures. Of those services, six are positively (animal
control, building inspection, data services, library, municipal court
and sanitary) and only capacity management is negatively associated with
operating expenditures. Given the small number of identified communities
that consolidated each of these services it is a stretch to read the
coefficients and extrapolate their effects on overall operating
expenditures. Suffice it to say that the overwhelming pattern in this
model does not support the argument that these agreements resulted in
cost reductions which was a stated purpose for most. The one service we
should have expected a positive effect was sanitary since the agreements
were created to replace multi-million dollar wastewater treatment
plants. The second model in Table 3 focuses on police services and
suggests that for communities that consolidated police protection
services, spending increased by approximately $28 per capita annually
after controlling for inflation. A similar result occurred for fire
protection services, following consolidation average spending rose $22
per capita in real dollars. The spending patterns police and fire are
consistent with our case studies where we demonstrated spending growth
following service consolidation.
Consistent with expectations, equalized property valuation is
positively associated with spending in each of the three models in Table
4. Shared Revenue payments are also positively associated with overall
operating expenditures and fire protection services, but negatively
associated with police protection expenditures. Consistent with previous
fly-paper research on Wisconsin communities, overall operating
expenditures increases by 50 cents for each dollar of Shared Revenues.
The effect of Shared Revenues on fire protection expenditures is much
less dramatic: real per capita spending on fire protection increases by
seven cents with each dollar of Shared Revenues. Interestingly, the
relationship is reversed when examining police protection services. The
model suggests that for each dollar of shared revenues, local spending
on police services decreases by two and a half cents.
Table 5 is, in essence, a set of difference in means tests, the
goal of which is to ascertain the effects of service consolidation on
expenditures. The first model examines the effect of an array of service
consolidations on total operation expenditures. Similar to Table 4, the
model here suggests that five of the six services associated with
spending are in the positive direction. As stated previously, caution is
warranted when interpreting the results given the small number of
identified communities that have such agreements. It is, however, worth
noting that the evidence does not support the argument that these
agreements were pursued as cost-saving measures and the model more
directly captures the pseudo pre-post effects of consolidation in those
communities.
The next sets of results in Table 4 focus on police and fire
protection expenditures. With an [R.sup.2] = .71 and lack of significant
variables other than the lagged dependent variable, it appears that
incremental budgeting, at least for police protection, is the norm in
these communities. The model does not support the hypothesis that police
protection expenditures were affected by service consolidation. There is
only marginal evidence that fire protection services were positively
affected by service consolidation. These findings for police and fire
are significant because they suggest that for those communities that
consolidated their services, not only did it enhance service delivery
(mentioned in all cases as a reason for service consolidation) but it
did so without significantly increases costs. In both models, the
control variables are insignificant.
CONCLUSION
I am generally pleased with the overall strength of the models of
local spending in Wisconsin communities between 1987 and 2009. In
addition, I believe that adding the time element--where I was able to
track spending over time--I am adding to the discussion about the
effects of alternative service delivery when prior works relied on
pseudo controlled studies comparing communities that consolidated to
those that did not consolidate (Feiock and Carr, 1997). While still a
case-study in the sense that only communities in Wisconsin were
examined, the approach, coupled with an array of different service
agreements, sheds light on how to expand future research in communities
across states. A qualitative element to the research was added by
getting a sense of the intent behind most of the consolidation efforts
as well as a quantitative analysis of the effects of the agreements on
expenditures. In an era of fiscal retrenchment when alternative service
delivery is getting increased attention, this latter point cannot be
overstated.
I acknowledge that service consolidation is not solely about cost
savings and our examination of the intent behind many of the agreements
reflects the argument that they often include multiple goal statements.
These goals are often about enhancing service quality and, in the cases
of sanitary services, spreading/sharing the costs associated with
multi-million dollar infrastructure upgrades. That said, it cannot be
denied that nearly every explanation for service consolidation mentioned
cost: whether stated in general terms such as "efficiency", or
specifically stating operating and/or capital cost savings. This study
focused on this latter point with a more thorough time-series analysis.
I was also able to examine the effects of a variety of types of service
agreements and the effects on operating expenditures. The data also
afforded us the opportunity to focus on two of the more costly operating
expenditures police and fire protection--to determine the extent to
which consolidation agreements were associated with the operating costs.
Reviewing the results, scant evidence was found that communities
that committed to service consolidation experienced overall spending
reductions. In only one circumstance--capacity management--did we find a
negative relationship between the service consolidation and lower
operating expenditures. I, however, caution using this result to assert
a larger association given that this service consolidation only
pertained to seven of the more than 2,000 counties and municipalities in
the study. From my perspective, the more important takeaway from the
analysis is the lack of evidence that service consolidation lowered
operating costs despite its mention as a justification for consolidation
in nearly every circumstance. The same point applies to police and fire
protection service consolidations: justification pursuing many of these
agreements is based on service enhancement and cost savings. Again,
little overall evidence was found to support the cost-savings claim. In
fact, there is some evidence to suggest that fire protection operating
costs increase following consolidation both when compared to spending by
all municipalities and counties, and in only those jurisdictions that
consolidated fire protection services in Wisconsin during the given
time-frame. The evidence is weaker for police protection services
although a positive relationship was found between police protection
operating expenditures and service consolidation when all municipalities
and counties are included in the model. This relationship is
insignificant when only the communities that consolidated police
services are examined.
I acknowledge that one of the greatest shortcomings is the lack of
data measuring service quality. Future work needs to incorporate both
cost and service quality measures to really assess the effects of these
agreements. Furthermore, the ability to examine both operating and
capital expenditures would also be helpful given that the cost of
"big ticket" items such as buildings or wastewater treatments
can be sufficient incentive to pursue consolidation agreements.
Craig S Maher
University of Nebraska at Omaha
This paper was previously presented at the 25th Annual Conference
of ABFM, October 3-5 2013. Washington DC. I would like to thank
Katherine Piker for her help on earlier versions of this paper and the
anonymous reviewers for their helpful suggestions.
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(5) However, see Piker and Maher (forthcoming). A recent attempt to
consolidate police services was prevented because it crossed over county
boundaries, thus needing state legislative approval, and the union which
opposed the consolidation was able to block legislative approval.
(6) University of Wisconsin-Extension's Local Government calls
the dataset and associated program GREAT (Graphing Revenues,
Expenditures and Taxes).
(7) Per our earlier example of the Fox Valley metro Police
Department, we also created dummy variables to capture each year of the
consolidation with the expectation that costs would increase in the
first years of the agreement, then taper off and perhaps decrease. This
did not occur in any of the models and in an effort to save space we
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population to 31,252.
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collinearly problems.
Table 1
Descriptive Statistics for Consolidated Protective Services
Fire Protection
Std.
N Mean Deviation
Before Consolidation
PC Expenditures 291 90.73 99.56
Eq Value Per 88.32 35.24
After Consolidation
PC Expenditures 285 152.30 100.68
Eq Value Per 113.95 81.04
$1,000
Police Protection
Std.
N Mean Deviation
Before Consolidation
PC Expenditures 56 70.57 75.24
Eq Value Per 65.67 22.67
After Consolidation
PC Expenditures 198 159.81 87.79
Eq Value Per 67.93 28.23
$1,000
Table 2
Types of Consolidated Services
Towns Cities Villages Counties
Fire Protection 11 14 7 1
Police 3 13 2
Recycling 17 3 2
Animal Control 10 9 1
Data Services 1 8 1
Public Works 1 6
Capacity Mgmt. 1 6
Dispatch 5 1
Municipal Court 1 3
Emergency Services 1 3
Transit 2 2
Storm Water Mgmt. 2 1 1
Building Inspection 3
Sanitary 3
Library 2
Parks 1 1
Senior Center 2
Table 3
Descriptive Statistics for Communities With and Without
Consolidated Services: 2009
Without Consolidated Services
Min. Max. Mean St. Dev
Population 2 969,252 6,952 51,846
PC Operating $118 $18,730 $790 $778
Expenditures
PC Shared Revenues $2 $699 $100 $108
PC Equalized Value $20,158 $5,091,918 $107,812 $149,215
N 1,969
With Consolidated Services
Min. Max. Mean St. Dev
Population 100 196,321 14,180 29,508
PC Operating $219 $4,891 $1,121 $776
Expenditures
PC Shared Revenues $6 $524 $97 $100
PC Equalized Value $36,694 $737,115 $107,975 $88,588
N 91
Table 4
Fixed--Effects Regression Models: All Wisconsin Communities
All Consolidated Services
Coeff. S.E. p-value
Consolidated Services
Animal Control 168.43 48.54 0.001
Building Inspection 610.99 135.91 0.000
Capacity Management -759.72 199.08 0.000
Data Services 391.35 184.52 0.034
Emergency Services -4.23 160.31 0.979
Library 158.95 222.09 0.000
Municipal Court 404.29 154.07 0.009
Parks 2.74 148.45 0.985
Public Works 28.95 162.55 0.859
Recycling -12.16 45.61 0.790
Sanitary 245.85 111.76 0.028
Senior Center -83.59 160.01 0.601
Stormwater -28.08 98.21 0.775
Police
Fire
Equalized Property Value 1.79 0.072 0.000
Shared Revenues 0.501 0.06 0.000
Lag Expenditures 0.245 0.005 0.000
Constant 364.76 11.39 0.000
[R.sup.2] overalls 574
N = 39,385
Police Protection
Coeff. S.E. p-value
Consolidated Services
Animal Control
Building Inspection
Capacity Management
Data Services
Emergency Services
Library
Municipal Court
Parks
Public Works
Recycling
Sanitary
Senior Center
Stormwater
Police 28.19 4.51 0.000
Fire
Equalized Property Value 0.038 0.0003 0.000
Shared Revenues -0.025 0.003 0.000
Lag Expenditures 0.599 0.004 0.000
Constant 21.77 0.548 0.000
[R.sup.2] overall=.951
N = 39,385
Fire Protection
Coeff. S.E. p-value
Consolidated Services
Animal Control
Building Inspection
Capacity Management
Data Services
Emergency Services
Library
Municipal Court
Parks
Public Works
Recycling
Sanitary
Senior Center
Stormwater
Police
Fire 22.35 10.050 0.026
Equalized Property Value 0.023 0.001 0.000
Shared Revenues 0.073 0.009 0.000
Lag Expenditures 0.122 0.005 0.000
Constant 29.31 1.650 0.000
[R.sup.2] overalls 266
N = 39,385
Table 5
Fixed--Effects Regression Models: Only Communities That Consolidated
Services
All Consolidated Services
Coeff. S.E. p-value
Consolidated Services
Animal Control 181.29 39.46 0.000
Building Inspection 480.51 116.31 0.000
Capacity Management -785.79 159.46 0.000
Data Services 391.66 147.91 0.008
Emergency Services 8.45 129.20 0.948
Library 184.39 177.92 0.300
Municipal Court 374.19 124.28 0.003
Parks 2.58 120.40 0.983
Public Works 27.05 130.29 0.836
Recycling -34.16 37.94 0.368
Sanitary 379.87 90.66 0.000
Senior Center -128.03 128.18 0.318
Stormwater 18.84 79.49 0.813
Police
Fire
Equalized Property Value 3.93 0.378 0.000
Shared Revenues 0.501 0.06 0.000
Lag Expenditures 0.215 0.022 0.000
Constant 158.99 38.19 0.000
[R.sup.2] overall=.566
N = 1,822
Police Protection
Coeff. S.E.
Consolidated Services
Animal Control
Building Inspection
Capacity Management
Data Services
Emergency Services
Library
Municipal Court
Parks
Public Works
Recycling
Sanitary
Senior Center
Stormwater
Police 3.38 13.16
Fire
Equalized Property Value 0.314 0.300
Shared Revenues -0.005 0.214
Lag Expenditures 0.831 0.118
Constant 8.729 16.19
[R.sup.2] overall=.709
N = 212
Fire Protection
p-value Coeff.
Consolidated Services
Animal Control
Building Inspection
Capacity Management
Data Services
Emergency Services
Library
Municipal Court
Parks
Public Works
Recycling
Sanitary
Senior Center
Stormwater
Police 0.797
Fire 14.59
Equalized Property Value 0.298 0.051
Shared Revenues 0.809 0.07
Lag Expenditures 0.000 0.505
Constant 0.591 40.67
[R.sup.2] overall=.694
N = 480
S.E. p-value
Consolidated Services
Animal Control
Building Inspection
Capacity Management
Data Services
Emergency Services
Library
Municipal Court
Parks
Public Works
Recycling
Sanitary
Senior Center
Stormwater
Police
Fire 8.550
Equalized Property Value 0.083
Shared Revenues 0.094
Lag Expenditures 0.039
Constant 16.410
