Transaction Costs and the Changing Patterns of Local Service Delivery
Trevor Brown
School of Public Policy and Management
The Ohio State University
Matt Potoski
Department of Political Science
Iowa State University
David Van Slyke
Maxwell School
Syracuse University
Paper for Presentation at the 2005 meetings of the Association of Public Policy Analysis and
Management, Washington, DC
Introduction
Public managers can choose from several approaches as they decide how to structure the
delivery of goods and services to citizens (see e.g. Salamon, 2002). The three most common
service delivery modes are internal service delivery in which the government produces the entire
service, contracts with other governments, private firms, or nonprofit organizations, or joint
service delivery arrangements, in which the government delivers part of a service and contracts
for the rest (Warner and Hedbon, 2001). Traditionally governments’ decision to “make or buy”
has been framed statically; public managers select one delivery mode over alternatives and then
remain committed to that delivery approach. Of course, in practice service delivery choices can
be more fluid: internal service delivery can later change to contract and contracts can later be
internalized (Hefetz and Warner, 2004; Lamothe, Lamothe, and Feiock 2004).
Changing service delivery modes can be quite costly both in terms of production costs –
the financial costs of fixed assets, labor and capital – and transaction costs – the management
costs of “…planning, adapting, and monitoring task completion…” (Williamson, 1981, 552553). Varying production and transaction costs make switching from some modes of service
delivery easier than others, depending in part on how the service was initially delivered. For
example, the costs of switching from internal service delivery to joint contracting with another
government may be lower than joint contracting with a private firm because the governments
may share common missions and organizational structures.
In this paper, we examine the changing pattern of local service delivery by focusing on
how governments’ previous service delivery choices structure future choices. We analyze panel
data from the 1992 and 1997 International City/County Manager Association’s Alternative
Service Delivery surveys along with data from the US Census and other sources. Our results
1
suggest that the previous service delivery mode affects the costs of switching to other services in
important ways. Internal service production is quite stable: governments that internally produce
a service in 1992 are highly likely to continue internal service provision in 1997. Contracted
service delivery is more fluid: governments that contract for service delivery in 1992 are more
likely to switch to another mode in 1997. Finally, joint service delivery in 1992 lowers the costs
of internalizing service delivery and engaging the market through contracting in 1997. Taken
together governments that have already internalized the upfront costs of switching modes of
service provision are more likely to approach service delivery choices dynamically.
This paper is divided into five sections beyond this introduction. In the first section we
lay out our theoretical arguments about the costs of changing service production mode. In the
second section we describe the data and methods we use to study these costs. In the third and
fourth sections we report the results of our analysis and discuss the findings, before concluding
the paper in the fifth section by outlining directions for future research.
The Transaction Costs of Switching Modes of Service Delivery
Managing service delivery is a complex and dynamic process. Rather than a formulaic
progression from one service delivery decision to the next, public managers frequently return to
choices they have already made. Notable among these iterative decisions is the question of
which mode of service delivery to employ, whether to “make” through internal service delivery,
or to “buy” through external contracts. An array of factors influence how public managers
choose to deliver services, including political pressures, fiscal constraints, bureaucratic routines,
growth demands, and features of services to be delivered (Benton and Menzel, 1992; Brown and
Potoski, 2003; Carver, 1989; Ferris, 1986; Ferris and Graddy, 1986, 1991; Hirsch, 1991, 1995;
2
Stein, 1990). Changing circumstances lead governments to reevaluate, and possibly change their
initial service delivery decision. With alternatives to direct service delivery becoming more
broadly accepted, public managers are increasingly assessing the opportunities to enter and exit
service markets (Lamothe, Lamothe, and Feiock, 2004; Hefetz and Warner, 2004).
Changing service delivery is costly because of the difficulty of reconstructing current
service delivery practices to ensure services continue to be delivered to recipients with as little
disruption as possible.1 There are two primary sources of switching costs – changes to
production systems and changes to management systems. In terms of production processes,
moving from one mode of service delivery to another typically requires defining, designing, and
coordinating new tasks necessary to deliver the service. In terms of management processes,
switching typically requires establishing new performance criteria, constructing monitoring and
evaluation systems, and hiring and integrating new employees or negotiating with existing
employees to change their job responsibilities. Both types of costs can arise in addition to any
financial costs (i.e. the fixed costs of assets, labor, and capital) associated with changing service
production. When these transaction costs of switching are high, public managers are less likely
to change the mode of service provision.
The current mode of service delivery plays a primary role in determining the level of
transaction costs associated with switching. The transaction costs of altering production and
management systems vary across different service delivery modes. Some moves are less costly
than others because of the nature of how the service is already being produced. The remainder of
this section examines the likelihood of changing mode of service delivery for the three primary
modes: direct; contract; and joint.
Sometimes these costs are referred to as “coordination” costs (Milgrom and Roberts 1992; Besanko, Dranove, and
Shanley 1996).
1
3
Direct Service Delivery
Switching from direct service delivery to external delivery is likely to be the most
difficult because internal production and management systems may be ill suited and challenging
to develop for external delivery. When making such a change, switching costs include:
downsizing public employees and perhaps negotiating with unions; crafting requests for
proposals, establishing systems and protocols for reviewing proposals and selecting vendors;
crafting contracts, including developing incentives and formalizing performance measures;
negotiating with vendors; integrating new work processes into existing systems, and
implementing oversight systems. All of these activities must be undertaken before the previous
system can be taken off-line and the new mode of service delivery initiated. Faced with these
upfront transaction costs, many direct service provision governments recoil at switching. When
direct service provision governments consider service delivery changes, they are likely to find
joint contracting the most attractive option because some portion of service delivery is contracted
and some is performed internally. There are obviously still endogenous fixed costs associated
with joint contracting, such as coordination and adaptation costs, but they are typically lower
than complete contracting because internal service production allows more flexibility and leeway
in the service production transition.
Contract Service Delivery
Under contracted service delivery, changing markets, underperforming vendors, and
other shifting circumstances may compel managers to seek changes to their service delivery
approach (Kettl, 1993; Sclar, 2000). The easiest move is to switch the contract to another type of
vendor. Since the government has already outsourced the service, the transaction and production
4
costs of switching are likely to be low. Production systems will only require minor changes
since the basic production tasks remain the same; governments that contract have already
internalized the costs of altering the production and delivery processes. Furthermore, changes to
management systems are likely to be minimal as well; governments can use the same request for
proposal, contract, and contract selection processes, perhaps with some slight modifications.
However, the costs of switching among vendor types is also likely to vary across circumstances.
Some service markets are dominated by one type of organization, leaving little opportunity to
switch to another type of vendor if the government is unsatisfied with the current arrangement.
For example, social and human service markets are dominated by other governments and
nonprofits, while infrastructure service markets are the province of private firms and other
governments.
A more costly move is to switch to direct service delivery. As is the case in moving
from direct to contract service delivery, governments face the transaction costs of changing and
adapting production and management systems described earlier. In addition, unless contracting
governments have retained some semblance of internal production capacity, the costs of
internalization may be prohibitive. Joint service delivery may be an attractive option therefore in
terms of production and transaction costs if the contracting government can take partial measures
to internalize a portion of the service without incurring the full costs of reintegration.
Joint Service Delivery
Governments are most likely to switch service delivery modes under joint service
delivery. In these cases, governments have positioned themselves to move to either complete
contracting or complete internal production. By carrying out some portion of service delivery,
5
these governments typically have established and maintained some of the service production and
management systems necessary for direct service delivery. As a result, they do not face the same
up front fixed costs of internalization that contracting governments do. At the same time, since
they are managing some form of contract service delivery (e.g. crafting contracts and developing
performance measures), the transaction costs of transitioning completely to contracted service
provision are not nearly as high as if the starting point were from direct service delivery. As a
result, joint service production is likely to be the least stable service delivery approach, with
governments moving either to internal production or complete contracting.
Data and Methods
To test our arguments about the impact of transaction costs on changing service delivery,
we primarily rely on data from the ICMA’s 1992 and 1997 “Profile of Local Government
Service Delivery Choices” surveys, with additional data from the 1997 U.S. Census of
Government, a survey conducted by the authors,. The ICMA survey asked a stratified random
sample of municipal and county governments a battery of questions about which of sixty-four
local services they provided and their service delivery modes. As a result, the ICMA survey is
possibly the strongest large sample study of governments’ service production practices. The
response rate for each of the two surveys is just over 30 percent: 1,504 municipal and county
governments governments participated in the 1992 survey and 1,586 participated in the 1997
survey. The surveys are generally representative of municipalities and counties along basic
criteria such as population, geographic location, and metropolitan status. As expected, the
samples are over-representative of council-manager governments: between 60 and 70 percent of
6
the respondents in the two samples use this form of government.2 Because we are interested in
switching decisions over time, we pair respondents from the two samples. This reduces our
working sample to 625 municipal and county governments. We use multinomial logit to
evaluate our hypotheses. Multinomial logit provides the analytical mechanism for examining the
effect of a constellation of independent variables on the likelihood of respondents choosing each
dependent variable category relative to each of the other categories (Long, 1997). The dependent
variable is governments’ service production choice for each service they deliver and the
independent variables are governments’ previous service production choice for the relevant
service and several control measures.3 All estimations are completed using the mlogit command
in Stata v. 6.
Dependent Variable
Respondents to the ICMA survey were asked which of 64 services their government
provides and which of a variety of modes are used to deliver each service. Our analyses focus on
five service delivery modes – internal delivery, joint contracting, complete contracts with other
governments, complete contracts with private firms, and complete contracts with nonprofits.4
The dependent variable is the service delivery mode chosen by each government for each service
2
The 1992 sample under-represents municipal and county governments with populations over one million that are
located outside central or suburban metropolitan areas, and municipal and county governments in the New England,
Mid-Atlantic, and Southern States. The 1992 sample over-represents municipal and county governments in the
Mountain and Pacific Coast states. The 1997 sample is not biased in terms of population or metropolitan status, but
again, under-represents municipal and county governments in the Mid-Atlantic and Southern states while overrepresenting those in Mountain or Pacific Coast states.
3
We tested for the independence of irrelevant alternatives (IIA) assumption using Hausman’s diagnostic test
(Hausman and McFadden 1984). Results indicated that the assumption is not violated and the choice options are
independent.
4
This strategy raises a potential endogeneity problem. That is, some factors that influence governments’ service
production choices may also influence their choices about whether or not to provide the service in the first place.
Failing to control for such effects risks biased coefficients in our analyses. However, we think such biases are
minimal because we believe that governments’ choices of production mechanisms have very little impact on their
choices about what services to offer, even at the margins. Governments, or more precisely the elected politicians
running them, offer services because their citizens want them.
7
it provides in 1997 so that the responses of one government could be incorporated 64 times in
our sample, although not every city provides every service. Service delivery choices are unlikely
to be independent within cities, although we can assume independence across cities. That is, a
city that chooses to contract for one service may be more likely to contract for other services.
However, treating these choices as independent risks artificially deflating the standard errors. To
address this issue, we follow White’s approach for robust standard errors, clustered by
government (Greene 1997). This adjustment essentially weights each observation (service
delivery choice) by the number of services a city provides
Independent Variables
Our primary independent variable of focus is the previous mode of service delivery for
each service. Consequently, we include five independent variables to measure the service
delivery mode chosen by each government for each service it provided in 1992. The variable
direct 1992 is a dummy variable coded 1 if the city or county directly provided the service in
1992, else zero. The variable joint 1992 is a dummy variable coded 1 if the city or county
provided the service in part with public employees in 1992, else zero. The variables other
government 1992, private firm 1992, and nonprofit 1992, are each coded 1 if the city or county
provided the service through a complete contract with the type of organization identified in the
label, else zero.
The analyses also include a slate of control variables replicated from a previous crosssectional analysis with the 1997 ICMA data (Brown and Potoski, 2003). The previous analysis
demonstrated that service specific transaction costs played an important role in determining
mode of service delivery decisions when controlling for other competing explanations and
8
factors. The advance of this current analysis is to build off of a robust empirical model by
incorporating a temporal dimension. This allows us to assess the “stickiness” of service delivery
choices while controlling for factors previous research has identified as important determinants
in the “make or buy” decision.
To assess the impact of service-specific characteristics that risk contract failure we
incorporate measures of asset specificity and ease of measurement. Asset specificity refers to
whether specialized investments are required to produce the service. Ease of measurement refers
to how easy or difficult it is for the government to measure the outcomes of the service and/or to
monitor the activities required to deliver the service. To measure these service characteristics,
we surveyed 75 randomly selected city managers and mayors across the country asking them to
rate the asset specificity and service measurability of the 64 ICMA listed services.5 The survey
instrument provided half-a-page description of the two specific transaction cost risk factors
associated with services – asset specificity and ease of measurement. An appendix presents the
definitions used on the survey instrument. The instrument then asked respondents to rate each of
the 64 services included in the ICMA survey on two scales of one to five, one scale for asset
specificity and one for ease of measurement. We then averaged these ratings across respondents
to create the service characteristic independent variables asset specificity and ease of
measurement. Higher values indicate that the service is more asset specific and therefore more
difficult to measure. Because the asset specificity and ease of measurement may vary across
different levels of these transaction costs, we also include squared measures of these variables,
asset specificity2 and ease of measurement2 (Brown and Potoski 2003).
Governments were randomly selected with two sample stratification criteria – population and type of government
(council-manager versus mayor-council). Thirty-six usable surveys were returned for a response rate of 48 percent.
5
9
To measure transaction cost risks stemming from market characteristics, we focus on the
metropolitan status and the size of the population of the government’s jurisdiction. Metropolitan
areas have larger markets of potential vendors that facilitate external production. The analyses
therefore include a dummy variable (metropolitan area) scored one if the government is located
within an SMSA, else zero. Governments with large populations within metropolitan areas are
likely to decrease external production, while governments with large populations outside of
metropolitan areas are likely to increase their use of external service delivery. We measure
population as the number of residents living within the government’s jurisdiction as reported in
the 1990 US Census. To investigate these market competition hypotheses, the analyses include
the variables population and population squared along with interaction terms for metropolitan
area x population and for metropolitan area x population squared.
We employ several approaches to assess the influences of cities’ historical-political
development on their service delivery patterns. First, we distinguish cities according to when
they achieved metropolitan status under the Census Bureau’s Standard Metropolitan Statistical
Area (SMSA) guidelines (Bogue, 1958). Stein (1990) argues that 1929 marks the end of the
industrial period and the beginning of the post-industrial period era. The variable industrial city
identifies those cities located in a SMSA prior to 1929 (coded as one, otherwise zero). Because
of the rich market of suburban governments around these governments and their weak tax bases,
these cities may be more likely to produce services externally through complete or joint
contracting than others cities. Second, to assess the affect of annexation limitations, we
operationalize the variable annexation limitation. Following Hill’s (1978, 1993) examination of
annexation authority dimensions, we combine state level annexation regulations to create an
annexation scale, ranging from least to most restrictive. This variable is scaled so that its mean is
10
zero and standard deviation is one. We expect that an increase in annexation limitation increases
the likelihood of external service production. Research suggests that council-manager
governments are more likely to produce services externally through complete or joint contracting
than other forms of government. To control for this argument we include a dummy variable,
called council-manager, scored one if the government is a council-manager form of government,
else zero.
Many contracting scholars (e.g. Ferris 1986; Stein 1990; Hirsch 1991, 1995) point to the
seminal importance of post-1978 property tax limitations that began in California and were
subsequently employed in some other states. While any overall property tax limitation may
create fiscal pressures for municipal governments, the post-1978 state tax limits sought to reduce
governments’ role in society and consequently tended to be highly restrictive. These limitations
created incentives for governments to be more efficient and creative in service delivery.
Governments in states with extensive property tax limitations may therefore be inclined to seek
alternatives to internal delivery, particularly those in states with post-1978 property tax
limitations. We include two measures to assess the affect of overall tax limitations on service
delivery choices. The variable tax limit identifies those respondents located in a state that
adopted an overall property tax limitation prior to 1978 (coded as one, else zero). The variable
tax limit 1978 identifies respondents located in states that adopted an overall property tax
limitation in 1978 or after (coded as one, else zero). While we expect that both groups of
respondents are more likely to produce services externally than other respondents, we
particularly expect this to be the case for governments included in the tax limit 1978 variable.
Similarly, governments with low levels of human and fiscal resources are likely to produce more
services externally. Low revenue and human resource capacity create fiscal imperatives to either
11
not deliver services or to find low cost service delivery approaches (Gargan 1981; Honadle
1981). The variable fiscal capacity is the overall general revenue per capita, as reported in the
1997 US Census of Governments.6 We include both municipal and county governments
because, as general service units, the services they provide are typically quite extensive and in
many instances similar, but because the services responsibilities of the two types sometimes
differ, the analyses include a government type dummy variable. The variable county is scored
one if the respondent is a county, else zero.
Results
This section reports the results of the empirical analysis. Table 1 reports descriptive
statistics for all variables. Figure 1 and 2 report the percentage of services that are delivered via
each mode in 1992 and 1997, respectively. Table 2 reports results of the multinomial logit
analyses of the determinants of service delivery choices in 1997. This table compares the
likelihood of respondents selecting the base service delivery mode (listed in the table title)
relative to each of the service delivery modes listed on the four right-hand columns. Specifically
Table 3 reports the likelihood of municipal and county governments selecting direct delivery in
1997 relative to selecting joint delivery, contracts with other governments, contracts with private
firms, and contracts with nonprofits. To help with interpretation, we calculated the “predicted
effects” of the independent variables of interest in this study – the service delivery modes in
1992 (Long 1997). These results are reported in Figure 3.
[INSERT TABLE 1 HERE]
6
We elect not to include a measure of human resource capacity since our measures of staffing are highly positively
correlated with fiscal capacity.
12
Before turning to the results that inform the focus of our inquiry, it is important to
examine the overall use of different service delivery modes in the ICMA data as reported in
Figures 1 and 2. Figure 1 and 2 report the percentages of service delivery practices across the
five delivery modes in 1992 and 1997. In both 1992 and 1997, over 60 percent of all services
delivered are delivered directly. Joint service delivery is the second most frequently used mode
of service delivery, accounting for just over 20 percent of all services delivered in both time
periods. Contracting with other governments, private firms, and nonprofits accounts for fewer
than 20 percent of all services delivered in each time period. While these figures do not
specifically speak to switching between modes, they do suggest that movement across service
delivery modes occurs at the margins and there is inertia associated with service delivery
choices.
[INSERT FIGURES 1 AND 2 HERE]
Table 2 reports the results of our multinomial logit analysis of municipalities’ service
delivery decisions. Overall, the results support our theory about how the transaction costs of
switching influences governments’ service delivery decisions from one time period to another.
The multinomial results in Table 2 indicate that service delivery choices in 1992 have the most
consistent significant effect on service delivery choices in 1997, and generally speaking they do
so in the manner in which we predicted. Service specific transaction costs – notably asset
specificity and ease of measurement – display statistically significant impacts on service delivery
choices consistent with previous studies (Brown and Potoski, 2003). The other variables included
in the analysis demonstrate less consistent impact on service delivery choices in 1997, if they
13
display any impact at all. We discuss the impact of past service delivery choices in more detail
below by presenting the predicted effects of 1992 service delivery modes on 1997 service
delivery modes in Figure 3.
[INSERT TABLE 2 HERE]
The Impact of Past Service Delivery Modes on Current Service Delivery Modes
Figure 3 reports the likelihood (i.e. the predicted effect) of a municipal or county
government selecting each mode of service delivery in 1997 for a given service based on how the
service was delivered in 1992, with all other variables in the analysis held constant at their
means. For example, the first column in the figure reports the likelihood of a municipal or
county government selecting each of the five modes of service delivery in 1997 given that they
delivered the service directly in 1992. Here we discuss the likelihood of switching from each of
the three primary modes of service delivery.
[INSERT FIGURE 3 HERE]
Direct Service Delivery
As expected, governments are unlikely to switch service delivery modes when they
provide services directly; the likelihood of a government providing a service directly in 1997
given that they provided it directly in 1992 is .8, controlling for other factors. This supports our
contention that the costs of changing direct production and management systems are high when
governments are producing services internally. To the degree that these governments do switch,
14
they are most likely to switch to joint service provision (.15), supporting our claim that the
transaction costs of such a change are substantially lower than moving to a complete contract
with either another government, a private firm, or a nonprofit.
Contracted Service Delivery
Figure 3 also shows that governments that contract are much more dynamic in their
service delivery decisions. Governments that contract with other governments, private firms or
nonprofits in 1992 have a smaller than .5 probability of continuing their service delivery
approach in 1997, holding constant the effects of other variables. Governments that contract
with nonprofits in 1992 are the least likely to continue with the same vendor type in 1997 (.4).
The predicted effects provide only mixed support for our argument that contracting governments
are most likely to change vendor types – should they elect to change – because the transaction
costs of switching are low. The probability of a government that contracted for service delivery
with another government in 1992 and then switching to a private firm or a nonprofit in 1997 is
only .05 and .04, respectively. Similarly, the probability of a government that contracted with a
private firm in 1992 switching to another government or a nonprofit in 1997 is only .06 and .03,
respectively. These patterns may be due to the vendor types selected in 1992 dominating the
service markets in question, thus leaving few alternatives among other types of vendors.
Governments that contract with nonprofits provide the most support for our contention about the
costs of switching among vendor types. The probability of a government that contracted with a
nonprofit in 1992 switching to another government or a private firm in 1997 is .15 and .16,
respectively.
15
Across all three contract delivery modes, governments are most likely to move to joint
service delivery if they are to change their service delivery mode. The probability of switching
to joint service delivery ranges from .14 to .23 across the three vendor types, while the likelihood
of switching to direct service delivery ranges from .14 to .29. This supports our contention that
partial steps are less costly than fully changing service delivery mode to complete contracting.
Joint contracting can sometimes serve as an intermediary stage between one mode and the other.
Taken together, contracting governments are highly likely to switch with their choice of service
delivery mode being the most diverse.
Joint Service Delivery
Governments that jointly delivered services in 1992 are the most likely to switch to direct
service delivery in 1997. In fact, joint service delivery governments are about as likely to
switch to direct service delivery (.45) as they are to remain with joint service delivery (.41). This
is consistent with our argument that joint service delivery governments retain some degree of
production and management capacity necessary for direct service delivery and consequently
incur lower transaction costs in returning to direct service delivery. The results also support our
contention that joint service delivery governments can easily move in the other direction toward
contracted service delivery. However, the relatively lower likelihoods of joint service delivery
governments switching to complete contracts with other governments (.06), private firms (.07),
or nonprofits (.02) suggests that the transaction costs of completely turning to the market for
service delivery are higher than bringing the service back in-house.
16
Conclusion
The high transaction costs of altering existing production and management systems and
constructing new systems make switching service delivery modes costly. Governments do not
often change service delivery modes. However, the production and transaction costs of
switching vary across service delivery modes and make some moves more likely than others. In
general, the costs of exiting direct service delivery for contract service delivery are high;
managers have to dedicate significant time and effort to dismantling existing production and
management systems and building new ones. As a result, direct service delivery governments
are most likely to stick with their current mode. On the other hand, because they have typically
already incurred the transaction costs of switching in the past, contracting governments’ service
delivery decisions are more dynamic. Sometimes these governments move back towards
internalization from joint or contracted service delivery, while other times they remain in the
market by switching vendor type. Finally, governments are most likely to change delivery
modes for jointly produced services, perhaps because the switching costs are lower. Service
delivery mode is dynamic, but the likelihood of switching modes of delivery is strongly
influenced by the prior service delivery mode.
The argument we present here, while powerful, is straightforward. The changing patterns
of service delivery are certainly influenced by other factors beyond the prior service delivery
mode. Building from our argument about the production and transaction costs of switching, we
hypothesize that there are other related factors that influence the costs of switching. In
particular, there may be spillover effects for governments that engage market alternatives to
direct service delivery across a range of services. As governments contract for more services –
or engage in more joint service delivery – relative to direct service delivery, they may achieve
17
economies of scale in altering production and management systems, at least for similar types of
services (e.g. infrastructure services, social services). For example, governments that contract
for high numbers of social services may find that they face lower transaction costs of switching
additional social services from direct to contract service delivery because they have experience
in constructing similar kinds of production and management systems. In future research we
intend to test this and related arguments about other factors that influence the transaction and
production costs of switching modes of service delivery.
References
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19
Table 1: Descriptive Statistics
Variable
Mean
Std. Dev.
Min
Max
1a. Direct 1997
.62
--
0
1
1b. Joint 1997
.21
--
0
1
1c. Other Government 1997
.08
--
0
1
1d. Private Firm 1997
.07
--
0
1
1e. Non-Profit 1997
.02
--
0
1
1a. Direct 1992
.63
.48
0
1
1b. Joint 1992
.20
.40
0
1
1c. Other Government 1992
.09
.29
0
1
1d. Private Firm1992
.06
.24
0
1
1e. Non-Profit 1992
.02
.13
0
1
2. Asset Specificity
3.07
.63
1.76
4.26
3. Asset Specificity Squared
9.84
3.96
3.11
18.16
4. Ease of Measurement
2.67
.63
1.53
4.3
5. Ease of Measurement Squared
7.53
3.69
2.34
18.53
6. Metropolitan Area
.075
.44
0
1
2115
2122101
4473225
4.5x1012
Dependent Variables
Independent Variables
7. Population
67252.03
149982.9
10
11
8. Population Squared
2.7x10
9. Metro*Population
59303.06
151899.9
0
2122101
10
11
0
4.5x1012
10. Metro*Population Squared
2.66x10
2.3x10
2.3x10
11. Council-Manager
.68
.47
0
1
12. Industrial City
.03
.167
0
1
13. Annexation Limitation
-.02
.78
-1.78
.84
14. Tax Limit
.12
.33
0
1
15. Tax Limit 1978
.16
.36
0
1
16. Fiscal Capacity (1 = $1000/capita)
1.21
.74
.01
4.74
17. Fiscal Capacity Squared
2.02
2.70
.00
22.46
18. County
.15
.35
0
1
20
Table 2: Multinomial Logit Analysis of Determinants of Service Delivery Mode in 1997
(Direct 1997 as Base of Comparison)
Direct 1997 versus….
Independent Variable
Direct 1992
Joint 1992
Other Government 1992
Private Firm1992
Non-Profit 1992
Asset Specificity
Asset Specificity Squared
Ease of Measurement
Ease of Measurement Squared
Metropolitan Area
Population
Population Squared
Metropolitan Area * Population
Metropolitan Area * Population Squared
Council-Manager
Industrial City
Annexation Limitation
Tax Limit
Tax Limit 1978
Fiscal Capacity (1 = $1000/capita)7
Fiscal Capacity Squared
County
Joint 1997
Other
Governments 1997
Private Firm
1997
Nonprofits 1997
-5.63***
(.64)
-4.06***
(.64)
-4.47***
(.64)
-3.79***
(.64)
-3.92***
(.67)
.24
(.28)
-.05
(.04)
2.66***
(.31)
-.49***
(.05)
.34*
(.20)
4.22x10-6
(7.48x10-6)
-1.25x10-11
(4.95x10-11)
-3.13x10-6
(7.41x10-6)
1.18x10-11
(4.94x10-11)
-.00
(.12)
-.29
(.29)
-.13
(.07)
-.13
(.15)
.15
(.14)
.05
(.19)
-.01
(.05)
-.16
(.19)
-1.12
(.89)
.25
(.92)
2.90***
(.91)
1.04
(.88)
2.37***
(.93)
-.37
(.46)
.14**
(.07)
-1.29***
(.46)
.17***
(.08)
.02
(.25)
2.06x10-6
(9.42x10-6)
1.4x10-11
(4.25x10-11)
-3.6x10-6
(9.24x10-6)
-1.54x10-11
(4.24x10-11)
.12
(.19)
-.41
(.53)
-.10
(.09)
.12
(.20)
.21
(.19)
-.35
(.29)
.04
(.08)
-.18
(.32)
.66
(.89)
2.17***
(.90)
2.43***
(.89)
5.04***
(.89)
4.21***
(.55)
.73
(.55)
-.08
(.09)
-3.47***
(.40)
.57***
(.07)
.00
(.23)
-9.08x10-6
(8.55x10-6)
5.32x10-11
(4.03x10-11)
7.57x10-6
(8.45x10-6)
-5.26x10-11
(4.02x10-11)
-.14
(.16)
-.03
(.38)
.00
(.09)
.03
(.20)
.36*
(.20)
-.36
(.25)
.07
(.07)
-.65*
(.27)
-3.79**
(1.6)
-1.75
(1.57)
-.34
(1.58)
-.34
(1.58)
2.59*
(1.58)
-3.01***
(.75)
.46***
(.12)
2.44***
(.82)
-.39***
(.14)
-.57*
(.32)
-1.5x10-5
(1.06x10-5)
8.01x10-11*
(4.47x10-11)
1.44x10-5
(1.04x10-5)
-7.97x10-11*
(4.47x10-11)
.45*
(.26)
.30
(.27)
-.07
(.13)
.05
(.28)
-.15
(.28)
-.39
(.33)
.09
(.08)
.57*
(.36)
Log likelihood
-16042.71
Pseudo R2
.46
N (observations)
18510
N (clusters, governments)
625
Notes: standard errors in parentheses; *** p < .01, ** p < .05, *p< .10, two tailed tests.
7
We use the log of fiscal capacity in all of the multinomial logit analyses.
21
Figure 1: Percentage of Services Delivered by Mode 1992
Nonprofit
2%
Private Firm
6%
Other Government
9%
Joint
20%
Direct
63%
Figure 2: Percentage of Services Delivered by Mode 1997
Nonprofit
2%
Private Firm
7%
Other Government
8%
Joint
21%
Direct
62%
1
Figure 3: Service Delivery Mode Likelihood '97 by Mode '92
1
0.8
Nonprofit
0.00
Nonprofit
0.02
Nonprofit
0.04
Private
Firm
0.03
Private
Firm
0.07
Private
Firm
0.05
Other
Govt
0.03
Other
Govt
0.06
Joint
0.15
Nonprofit
0.03
Nonprofit
0.40
Other
Govt
0.49
Joint
0.41
Private
Firm
0.49
0.6
Other
Govt
0.06
0.4
Joint
0.16
Joint
0.23
Private
Firm
0.16
Other
Govt
0.15
Joint
0.14
0.2
Direct
0.80
Direct
0.45
Direct
0.26
Direct
0.19
Direct
0.14
0
Direct 1992
Joint 1992
Other Government 1992
2
Private Firm 1992
Nonprofit 1992