Collaborative Service Arrangements: Patterns, Bases, and Perceived Consequences Peter J. May
Center for American Politics and Public Policy
Søren C. Winter
Danish National Centre for Social Research
This paper has later been published in Public Management Review 9 (4): 479-502.
Mads Stigaard, Mette F. Sørensen, Ina R. Bøge, Nina Friisberg, Helle N. Jensen, SFI-
Survey and UNI-C provided substantial research and other assistance with this project.
The authors are grateful to anonymous reviewers, Bodil Damgaard, Carolyn Hill,
Laurence Lynn, Kenneth Meier, Vibeke Lehmann Nielsen, and Laurence O’Toole for
their advice, to the municipal managers who participated in the study, and to AKF and
ECO-analyses for provision of some of the data. The research was supported by grants
from the Research Program of the Danish Ministry of Employment and the Danish
National Institute of Social Research for a project directed by Søren C. Winter. The
findings are not necessarily endorsed by the sponsoring organizations or the survey
Collaborative Service Arrangements: Patterns, Bases, and Perceived Consequences Abstract
While much of prior research on collaboration addresses the service delivery network as
a whole, we address collaborative relationships between one type of organization—
municipal employment services—and a range of governmental and non-governmental
partners for employment services in Denmark. Municipalities differ in the type, degree,
and character of collaboration with these partners. As others have found in prior
research, we find that organizational benefits, trust, and a variety of contextual factors
help shape the extent of collaboration. But, the relevance of these and problem-solving
benefits in particular differs among collaborators. Our modeling of the influence of
collaboration on perceived employment outcomes suggests that these impacts are
relatively minor. They are greater when there is active involvement of municipal
employment managers in fostering cooperative relationships with collaborators. In short,
collaboration requires a healthy and active relationship to foster improved outcomes.
These findings have implications for future research about collaborative service delivery
concerning the measurement of collaboration, different bases for it, and potential impacts. Collaborative Service Arrangements: Patterns, Bases, and Perceived Consequences
The literature about the provision of social services highlights the importance of
collaborative service arrangements. Two key presumptions underlie collaborative
arrangements. One is that they enhance service provision by some combination of
reducing costs, increasing efficiency, fostering innovation, and enhancing flexibility. A
second is that on balance collaboration leads to better service outcomes. These are
typically taken as truisms in the more popular treatment of the subject (e.g. Goldsmith
and Eggers 2004: 25-38; Osborne and Plastrik 1998: 203-240). But the validity of these
presumptions clearly has important implications that are the heart of current research
about governance and service delivery (see reviews by Forbes and Lynn 2005, Hill and
Any assessment of the implications of collaborative arrangements requires a fuller
consideration of what they entail. This research adds to the understanding of
collaborative arrangements and of the bases for inter-organizational cooperation. We
address collaboration for employment services in Denmark. This setting provides a
useful contrast to consideration of collaborative arrangements in the United States. Like
the U.S., employment services in Denmark are delivered through a multi-tiered
organizational structure of governmental and non-governmental entities. But, the Danish
context also engages to a much greater degree unions, private employers, and other
organizations in the shared delivery of employment services.
This research is part of a larger study of the design and implementation of reforms for
employment services. The Danish “Putting People to Work” initiative has entailed
stronger emphasis on job placement over other safety net benefits, increased reliance on
third-parties for employment training and assistance services, and more entrepreneurial
approach of employment service agencies.
Collaborative arrangements entail modification of traditional provision of services
through governmental hierarchies. Some like Salamon (2002) suggest that these new
forms of governance reflect fundamental transformations in the basic forms of
governmental services, labeling this “the new governance.” Others like Hill and Lynn
(2005) suggest that the changes are less dramatic with governmental organizations still at
the core of service delivery, labeling this “polycentric governance.” Consideration of the
different forms of collaboration, the reasons for and limits to cooperation among
organizations, and the perceived impacts of collaboration are central to our research.
Coordination and Collaboration
The literature addressing collaborative arrangements is largely about different ways
of coordinating service delivery and is reflective of a longer-standing debate in the
organization literature (also see Milward and Provan 2000a). Collaborative arrangements
assume multi-organizational delivery of services that is aptly described as a network of
organizations. Collaborative arrangements differ in terms of the structure of the network
and the degree of formality that binds the organizations within the network.
A variety of mechanisms can be used to coordinate activities. The public
management networking literature has mainly focused on exchanges that involve formal
contracts for service provision by non-governmental organizations. These vary in terms
of funding arrangements and contract terms that by definition establish principal-agent
relationships (Milward and Provan 2000b). Less attention has been paid to other
mechanisms that can be usefully thought of as comprising a continuum that varies from
lesser to higher degrees of inter-organizational involvement and that can take on a variety
of forms (see Hill and Lynn 2003). The specific exchanges depend on the type of service
provision. For example, in studying collaboration activities of American cities, Agranoff
and McGuire (2003: 68-85) catalog interactions that include information seeking,
adjustments to rules or policies, policymaking assistance, resource exchange, and project-
specific actions. In studying delivery of mental health services, Milward and Provan
(1998) examine referrals received, referrals sent, case coordination activities, joint
The character of collaboration is also important to consider. The presumption in
much of the literature is that collaboration is purposeful and that the relevant
organizations are willing to cooperate in achieving those ends. But like any partnership,
the relationships can be conflict ridden, competitive, cooperative, or neutral. Agranoff
and McGuire (2003: 4) suggest collaboration should not be confused with cooperation in
that partners are not necessarily helpful to each other. Milward and Provan (2000b)
suggest that a challenge for network management is overcoming social dilemmas in
which one or more partners’ short-term interests undermine the broader policy objectives.
As such, it is useful to remember that each partner in a collaborative undertaking has
something at stake and brings in a host of preconceived notions to the partnership. The
stakes may be as ethereal as reputation, but often entail more substantive considerations
as resources (people and funds), turf, autonomy, or control (Bardach 1998).
Bases for Collaboration
There is no shortage of frameworks that have been employed in studying bases for
collaboration. A common theme among these is that collaborative partnerships are
purposive. Organizations collaborate in order to advance goals of the organization, of the
leaders of the organization, or of principals that mandate collaboration. Yet as Weiss
(1987) notes, cooperation is not a natural imperative of organizations (also see Hill and
Lynn 2003). With this basic caution, we consider several bases for collaboration with
particular attention to factors that might explain different patterns in collaboration. These
bases are not mutually exclusive. As such, they reinforce each other in ways that cannot
be easily disentangled. Our research interest is identifying the relative contribution of
The traditional perspective is that organizations will not enter into collaborative
arrangements unless they perceive greater benefits than costs from their involvement.
This perspective has been discussed with reference to rational choice perspectives (Hill
and Lynn 2003), transactions cost frameworks (Feiock et al. 2005, Kruger and McGuire
2005, Lubell et al. 2002), and resource dependency (Lubell 2004, Lundin 2005, O'Toole
and Montjoy 1984, O’Toole 2003). We consider hypotheses that relate to the overall
benefits of collaboration and more specific problem-solving benefits.
H1a: Resource Benefits – Organizations will seek collaborative relationships with others when there are perceived tangible net benefits from the relationship.
While the specifics of the rational choice, transaction cost, and resource dependence
theories differ, the central point is organizations will not collaborate unless they perceive
tangible gains. Resource dependence theories emphasize organizational
interdependencies and the benefits or resources that are extracted from collaboration.
Transaction-cost perspectives emphasize the costs involved in entering into and
maintaining collaborative relationships. An important part of this hypothesis is that the
decisions about collaboration are based on perceptions of net benefits, rather than actual
benefits. Tangible benefits include new personnel, funding, information, or political
support. Relevant costs include the negotiation and administrative costs (management
time, shared personnel, out of pocket costs) of entering into and maintaining collaborative
H1b: Problem-Solving Benefits– Organizations are more likely to seek collaborative relationships when the service delivery task presents stronger challenges.
This hypothesis reflects a functionalist organizational problem-solving perspective.
One strand of this theorizing emphasizes the search for solutions to problems that stem
from difficulties in the task environment (Weiss 1987). Organizations seek to reduce
discordance in their task environment that stems from strong or new external challenges.
This may consist of relatively large client loads for which organizations seek to offload
those loads to other organizations, a problematic—or increasingly problematic—mix of
clients for which organization seek other organizations’ expertise, or other external
circumstances (e.g. rise in unemployment rates) that make it difficult to perform business
A competing, but not mutually exclusive, perspective to organizational
interdependence is that collaboration is based on mutual non-pecuniary relationships
among organizations. These foster “social capital” that is a primary basis for overcoming
collective action problems in recruitment and retention of members of voluntary
networks (Bardach 1998, Lubell et al. 2002, Lubell 2004, Lundin 2005). At issue is what
fosters and sustains the mutual relationships. Among the strongest bases for social
H2: Trust – Organizations will seek collaborative relationships with others that they trust to follow through on their commitments.
This hypothesis addresses at a key psychological basis for cooperation, which is the
foundation of much theorizing about collective action institutions (see Leach and Sabatier
2005, Lubell 2004). The basic argument is that organizations, just as individuals, are
more willing to cooperate with those they trust to follow through on their commitments.
That sense is, in turn, based at least in part on experience with a given organization for
which trust is built or destroyed over time (Bardach 1998).
How organizations view the benefits of collaboration also depends on their situation.
Those with more resources and abilities are presumably less likely to perceive benefits
from collaboration than those with fewer resources and greater demands. However, as
H3: Capacity– Organizations that have access to greater resources are less likely to cooperate with other organizations.
The general presumption is that organizations that have stronger resources do not
need to seek out additional help through collaboration. In contrast, smaller organizations
with fewer staff and less specialization are more likely to band together (a form of
collaboration) or to seek out additional resources through collaboration. In this respect,
capacity can be considered both as a matter of organizational expertise and as a matter of
Some evidence exists, however, that is contrary to the general presumption in
showing that organizations with greater resources are more likely to contract with third
parties for services. In studies of municipal outsourcing in Denmark and school district
outsourcing in Washington state, Thomas Pallesen (2004, 2006) hypothesized that
outsourcing is less of a threat to employees when a local government is wealthy than
when it is short of resources. Pallesen, and in separate research O’Toole and Meier
(2004a) in a study of school district contracting in Texas, found that more wealthy local
governments were more likely to outsource service to third party providers. These
researchers argue that contracting is used as a buffer, which can easily be reduced in
financially bad times. These findings suggests that the difference between contracting for
services with third parties (i.e., as a fiscal buffer) and collaboration over such things as
joint service provision needs to be taken into account when considering the influence of
Outcomes of Collaboration
It is often taken as a truism that collaboration fosters better outcomes. Despite the
plethora of literature about collaboration, relatively few studies of collaborative networks
address outcomes. This no doubt reflects the difficulty of measuring them. We address
outcomes by considering the extent to which employment managers perceive better
outcomes for clients. We assess the extent to which different collaborative arrangements
H4 – Perceived Outcomes: Managers of organizations that have healthy collaborative relationships with other organizations perceive better outcomes.
In reviewing 65 articles that address influences on public service performance, Boyne
(2003) finds “weak” evidence that contracting out improves services and “slightly more”
evidence that networked service delivery provides improvements. In one of the earliest
studies of network effectiveness, Provan and Milward (1995) found that service
improvements for community mental health centers depended on the specifics of network
management and stability more than upon the existence of the network. Consistent with
this, O’Toole and Meier (2004b) found in studying the influence of networking and
managerial behaviors for school performance that performance improvements were
associated with higher degrees of managerial networking along with stronger managerial
quality and stabilizing features of the network such as personnel stability.
These studies suggest that the existence of a collaborative relationship in itself does
not necessarily lead to better outcomes. Relationships that are not strong or that are
conflict ridden will presumably have less beneficial outcomes than healthier collaborative
relationships. As such, we hypothesize that the “health” of the relationship is an
important consideration in affecting outcomes. Following Bardach (1998), we
conceptualize the health of collaboration as a function of the extent to which mangers are
actively involved in the collaboration and the extent to which the relationship is viewed
as being supportive. Active involvement of managers serves as the glue for the
collaboration. By definition, arrangements that collaborators view as supportive are
healthier than ones that they view as conflictual. The reinforcing nature of these forces in
producing healthy collaboration suggests they are appropriately modeled with interaction
The Setting: Danish Employment Services
The context of this study is the collaboration of Danish municipalities in the
implementation of employment policy. The 269 Danish municipalities are multi-purpose
local government entities that have responsibility for implementing many different
national policies, including administering social assistance and employment measures for
unemployed persons that are not eligible for unemployment insurance. The main
municipal tasks are checking eligibility for and paying social assistance, giving advice on
job search and career and vocational guidance, checking availability for work, and
placing unemployed clients into employment or employment promoting measures. Part
of the municipal costs for employment services are paid by the national government.
Municipal employment services work to varied degrees with governmental and non-
governmental partners in the delivery of employment services. A key governmental
entity is the national Public Employment Service (PES) that focuses on unemployed
clients who are eligible for unemployment insurance benefits. The PES provides
employment services for these clients as well as for non-insured recipients of social
assistance. Historically, there has not been strong collaboration between municipalities
and the PES, and many municipalities have been very critical towards the PES (Larsen et
al. 2001). A second set of governmental actors is neighbor municipalities. Some,
particularly small, municipalities have formed inter-municipal collaborative employment
administrations. Other municipalities collaborate with each other in more specific issue-
areas, in buying services from one another, or for exchanging information.
Three different types of non-governmental partners are relevant. One is 31 private
Unemployment Funds and their affiliated trade unions. These funds administer
unemployment insurance, check unemployed member’s availability for work, and offer
career guidance. A second group is employer associations that together with unions give
advice to municipalities and the PES on local employment policy and perform bridging
between these local authorities and firms. A final group is comprised of private for
profit, non-profit, and public third-party providers of education, training, case
management, and job placement services typically under contract with municipalities the
Until 1990 municipalities had to rely on labor market related services from the
national PES that had a monopoly of contacting private firms for jobs or activation.
From 1990 municipalities had permission to contact private firms for jobs or
employment-training offers. Most municipalities have contacts with private firms for the
provision of jobs and subsidized employment-training offers, and some have contact with
local unions, unemployment funds, and employers associations (Damgaard 2006,
Changes in national employment policy under the “Putting More People into Work”
reform enacted in 2002 (Damgaard 2003) place greater emphasis on collaboration in
employment service delivery. Among other considerations, the reform puts more
emphasis on getting unemployed person into jobs more quickly and on monitoring that
clients are available for work and regularly looking for jobs. The reform urges
municipalities and the Public Employment Service to cooperate more closely. Separate
local government reforms to take effect in 2007 reduce the number of municipalities to
98 by amalgamations and mandate municipalities to cooperate with local and regional
PES offices by partly merging services in order to achieve a national policy objective of a
more unified employment policy implementation system. To a varying extent
municipalities have responded to this challenge prior to 2007 by increasing their
cooperation with the PES. Also as part of the “Putting More People into Work” reform,
both the PES and municipalities have been asked to contract out more services to third-
Data and Measures
Much of the prior research on collaboration focuses on the network as a whole and
the degree to which organizations cooperate. We break this down in considering
collaborative relationships between one type of organization—municipal employment
services—and a range of prospective partners. Our analyses follow from the preceding
conceptualization of collaboration and bases for collaborative relationships.
We analyze data concerning municipal-level implementation of employment reforms
for which our data are primarily based on a national survey of middle managers for
municipal employment services. These are the most relevant respondents for providing
information about collaborative relationships since they are the ones who are responsible
for those relationships. The survey responses have been supplemented by secondary data
based on register data on population size, resources, and task-difficulty.1 For most
municipalities the relevant respondent is a middle manager with responsibility for
employment measures for recipients of social assistance who are available for work. In
those small municipalities that have no middle manager, the respondent is the chief
executive officer for social affairs and employment services. Relevant respondents were
identified by telephone calls to every municipality. We sent two reminders by email and
Our analyses are based on 204 Internet-based survey responses collected from mid
December 2005 through May 2006. The response rate is 75 percent in relation to a total
of 269 municipalities. The collected survey data are representative of all Danish
municipalities in terms of population size and difficulty of the employment task.
Responses for middle managers from municipalities with less than 10,000 inhabitants are
marginally underrepresented by 3.8 percentage points in comparison to census
distribution, while those from municipalities with between 20,000 and 45,000 inhabitants
are slightly overrepresented by 3 percentage points.
Among the 204 municipalities that we consider, 31 are part of eight inter-municipal
employment centers. These centers provide job services for the participating
municipalities, which tend to be smaller municipalities. These centers and the remaining
municipalities either provide services on their own or enter into collaborative
Two sets of potential concerns arise from the use of these survey data. One is that
reliance on middle managers of municipal employment services as informants about
collaborative relationships and perceived outcomes of employment services leads to
biases in our characterization of collaboration. Some respondents may consistently
provide a rosy picture while others are less enthusiastic. However, we do not find
evidence of any systematic biases as might be expected due to age or length of time
serving as a middle manager of employment services. We fail to find meaningful
correlations between these variables and our measures of extent of cooperation with
different organizations, degree of trust in different organizations, and perceived
outcomes.2 Nor do we find evidence of systematic biases when comparing mean scores
for the same collaboration variables between respondents who are males and females.3
Questions were phrased in ways to minimize any response method effects in responding
to the questionnaires, aided by the fact that the Internet-based survey administration
prevented respondents from viewing multiple questions on the same screen.
A second potential concern is our reliance on perceived outcomes rather than actual
outcomes of employment services. Middle managers may have incentives to report more
positive outcomes than is actually the case. But, our concern is the relative variation in
outcomes and not the absolute levels. Moreover, as discussed below, we ask about
perception of different outcomes in employment services and not about outcomes of
collaborative relationships per se. This phrasing, along with far separation in the
question ordering, disconnects any cognitive link between responses about the nature of
the collaboration with the perceived outcomes. Actual outcome data would, of course, be
preferred. But, these are not available until well after the collection of our survey data.
The limited independent survey data about client outcomes that are available within a
relevant timeframe do not provide sufficient information for municipal-level analyses.
We conceptualize collaboration as a combination of extent of cooperation and effort
that goes into collaboration with each of the relevant potential partners.4 Municipalities
that cooperate regularly in sharing information should rate lower for collaboration than
those that cooperate with more intensive activities like sharing personnel. We get at this
by computing an overall collaboration score that is the product of the score for extent of
cooperation and the score for the effort put into collaboration. This has a scale of 0 to 75.
The extent of cooperation is measured by respondent rating of frequency of interaction on
a scale of 1 (none) to 5 (regular) for each organization. Collaboration effort is measured
by assigning a score of 0 if there is no collaboration and increasing scores from 1 to 5 for
each of the different types of collaboration: information sharing, client referrals, receive
clients, joint programs, and sharing of personnel. This ordering reflects increasing
degrees of effort that is involved in collaboration for which information sharing and
client referrals involve relatively little effort while undertaking joint programs and
sharing of personnel involves substantially more effort. Assignment of scores on a 1 to 5
scale is a simple way of recognizing these differences. Because any given municipality
may engage in one or more of the different types of collaboration, the potential score for
Our theorizing suggests several factors that help account for variation in
collaboration. One consideration is the degree of organizational benefits that an agency
receives from collaboration. This entails perceived resource benefits, which we measure
as the municipal respondent rating on a scale of 1 (not important) to 5 (very important) of
the “importance of the organization for fulfillment of our goals” with reference to each
potential collaborator. Organizational benefits also include more specific problem-
solving benefits from collaboration. We get at this with an overall measure of task
difficulty under the logic that municipalities that face more difficult employment services
tasks will have greater problem-solving benefits from collaboration. The measure is an
index of the expected mean duration of temporary cash benefits for all adult citizens in
each municipality in 2004 based on characteristics of the population and local labor
market conditions (e.g. unemployment rate). Higher scores indicate more problematic
task environments.5 Natural log values are employed to address skewed data.
The degree to which organizations trust potential collaborators is another relevant
consideration. This has been measured in a variety of ways in the literature (see Lundin
2005 in particular). Rather than employing a generalized measure of trust, we consider
perceptions of the degree to which respondents trust potential collaborators to meet their
obligations. We measure this as the municipal respondent rating on a scale of 1 (low) to
5 (very high) of the degree to which “you trust these other authorities and organizations
to follow through on their commitments in carrying out employment services.” Separate
ratings were provided for each potential collaborator.
We consider two aspects of the capacityof municipalities to address employment
problems. One is municipal size of population following the logic that larger
municipalities will have more options for addressing employment problems. A second
measure gets at the resource capacity of the municipality measured as the municipal
budgeted basis of taxable income and land value per capita for 2005 with a correction for
central government grants and inter-municipal transfers.
A related consideration is whether a municipality is a member of a municipal
employment center or not. As we discuss below, some smaller municipalities have
banded together to form combined employment centers as a way of sharing their
resources. We create a dummy variable to indicate whether a given municipality is part
of a municipal employment center or not.
In order to examine consequences of collaboration for perceived outcomes, we
employ an index of perceived outcome based on the responses of middle managers about
the extent to which the municipality has succeeded in getting clients to search for jobs, to
be available for work, and to enter ordinary employment on a scale of 1 (not at all) to 10
We consider two factors that we hypothesize above as affecting the influence of
collaboration on perceived employment outcomes. One is the degree of manager
involvement with each collaborative entity. We measure this each manager’s rating of
the frequency with which they personally met with representatives of each potential
collaborator within the past year on a scale of 1 (not at all) to 6 (once a week).7 The
second consideration is the character of the collaborative relationship. We measure this
as the employment services manager’s rating for each collaborator of the relationship as
being conflict ridden (score 1), competitive (score 2), neutral (score 3), supportive (score
We present our findings in first considering patterns in collaboration between
municipal employment services and other organizations. We next consider factors that
account for variation in collaboration. We then consider the influence of different types
of collaboration and other factors on perceived employment outcomes.
Table 1 summarizes the extent of cooperation and types of collaboration for the 204
municipalities in our study. For each prospective collaborator, we report the mean extent
of cooperation, the percentage of municipalities that engages in different forms of
collaboration, the resultant mean effort scores, and the combined collaboration score.
The ordering of municipal involvement with different collaborators is the same regardless
of which summary score is considered. However, there is clearly greater variation in
collaborative efforts than in the reported extent of cooperation. For this reason, we argue
the overall collaboration score is a better measure than the extent of cooperation score.
Table 1. Cooperation and Collaboration
Municipal Cooperation and Collaboration with: a
Collaboration Score f 17.24 14.39 8.39
a Cell entries are mean scores or percentages, as indicated, for municipal collaboration with each of the actors indicated in the column headings. Scores are based on 204 municipal respondents. b Services provided by for profit, non-profit, and public organizations such as consulting firms and training institutes. c Mean score for extent of cooperation on a scale of 1 (none) to 5 (regular). d Percentage of municipalities that report each form of cooperation; multiple responses were allowed. e Mean value for summary score for types of involvement with each organization on a scale of 0 to 15, where no collaboration is scored 0, information sharing scored 1, client referrals scored 2, receive clients scored 3, joint programs scored 4, and sharing of personnel scored 5. f Mean value for summary collaboration score that is product of extent of cooperation and collaboration effort scores on a potential scale of 0 to 75.
Municipal collaboration is greatest with neighboring municipalities that includes the
inter-municipal center collaboration. As indicated by the type of collaboration, these
arrangements run the gamut of different forms of collaboration with a relatively strong
emphasis on joint programs. The second greatest municipal collaboration is with the
national Public Employment Service. These arrangements emphasize client referrals and
receipts with some joint programs and sharing of personnel. Thirty one of the responding
municipalities have formed joint labor market centers with the PES. Six of those centers
include more than one municipality. The center collaboration with the PES includes 12
government sponsored pilot schemes with joint labor market centers with municipalities
and the local PES that are forerunners for the mandated future joint job centers. We
assume that some of the collaboration between municipalities and the PES has been
stimulated recently by the planned merger of the two organizations.
Municipal collaboration with other organizations is less extensive. Cooperation with
unions and their affiliated unemployment funds and with third party providers (such as
consultants and training institutes) is on average fairly frequent, but the types of
collaboration and resultant collaboration effort are more limited. Municipal cooperation
with employer associations is the least frequent with the emphasis on information
sharing. As mentioned above, the Danish government have urged municipalities to use
more third party providers and indicated that municipalities will be required to do so in
the planned joint job centers with the Public Employment Service.
Bases for Collaboration
Our theorizing about collaborative arrangements leads us to consider the role of
organizational benefits, social capital, and different aspects of capacity in explaining
variation in the extent to which municipalities collaborate with different actors. As noted
in our theorizing, we consider these to be contributing bases for collaboration that are not
mutually exclusive. Of research interest is the relative contribution of each consideration.
The cross-sectional nature of the data do not permit analyses about the dynamics of
collaboration in reinforcing trust and subsequent collaboration or of changes in
collaborative relationships overtime as the result of different outcomes of the
Table 2 presents regression models for these factors in explaining variation in the
extent of collaboration with each collaborator. The dependent variable is the relevant
collaboration score that combines extent of cooperation and collaboration effort. Given
the construction of this measure, it is reasonable to treat it as an interval variable. Higher
scores indicate greater degrees of collaboration. The models have been estimated using
Ordinary Least Squares regression with appropriate transformations of relevant variables
to meet assumptions of linear relationships. Appropriate visual inspections and statistical
tests were conducted to verify that OLS regression assumptions were met for these
models. The cell entries are the standardized coefficients. Keeping in mind differences
in distributions of the independent variables, these suggest the relative magnitude of
influence of each. The models explain a reasonable amount of variation in the extent of
collaboration with greater explanatory power for those organizations with which
municipalities tend to collaborate more.
One caveat about these models is the differences in sample sizes that arise from the
fact that we only gauged the degree of trust in other organizations when there was some
form of collaboration. If no collaboration existed with a potential partner, the trust
variable was coded as missing. Excluding non-collaborative relationships potentially
biases key relationships especially for collaboration with employer associations and other
actors for which the extent of collaboration with municipalities is more limited. We
assessed the degree of this potential bias by computing regression models that use mean-
value substitution for missing values. Except for the trust variable, the effects and
significant levels of other factors are virtually the same as reported in Table 2. The
influence of trust is reduced and becomes statistically non-significant for the models of
collaboration with employer associations and with other actors. Given the arbitrariness
of mean-value substitution, we report results as computed with appropriate caveats.8
Table 2. Explaining Variation in Collaboration Regression Models for Collaboration Addressing a Explanatory Factors Employment Neighbor Unemployment Employer Municipalities Funds/Unions Associations Organizational Benefits Social Capital Capacity Model Statistics
Notes: * p < .10, ** p < .05, *** p < .01 one-tailed t-test, except R2 values for F-test of model fit. a Dependent variable is the relevant collaboration score (sq root values used to meet linearity assumptions). Cell entries are standardized values with absolute t-values in parentheses.
The first set of entries consider the role of organizational benefits for which we
hypothesized that increased resource benefits (H1a) and increased problem-solving
benefits (H1b) would contribute to greater collaboration. The hypothesis is supported for
resource benefits as indicated by the magnitude of the coefficients for resource benefits,
which we measure as the dependence on the relevant organizations. One consideration
that we could not address is that as organizations collaborate they become more
The problem-solving benefit hypothesis is supported for collaboration with the Public
Employment Service (although at a lower statistical significance) and for collaboration
with third-party providers. We fail to detect an influence of problem solving benefits on
the degree of collaboration with neighbor municipalities, unemployment funds/unions, or
employer associations. These findings suggest that municipalities with difficult task
environments collaborate more with the PES and third-party providers such as training
institutes, but not with other potential collaborators.
This makes sense because localities and clienteles with more social problems and
difficult labor markets are likely to generate more cases in which both the municipality
and the PES are involved (e.g. long term unemployed insured workers loosing eligibility
for unemployment benefits to be replaced by public assistance, more health problems
leading to municipal sickness allowances for insured unemployed workers.) By the same
token, it might be tempting for municipalities with a problematic task environment to ask
for help from third-party providers that might have specialized in that kind of clientele.
Following one of the major findings of the collaboration literature, we also
hypothesized that organizations are more likely to collaborate with other organizations
that they trust (H2). The social capital entry in Table 2 gets at this in examining the
influence of trust, measured as perception that other organizations fulfill their
commitments, on extent of collaboration. The hypothesis is supported for municipal
collaboration with each of the potential collaborators. However, this relationship is likely
overstated for two methodological reasons. One, as discussed above, is that we have no
measure of trust when collaborative relationships do not exist. Trust is presumably
greater when collaborations exist than when they do not, leading to a stronger effect than
would otherwise be the case. 9 The second related consideration is that it is reasonable to
assume that increased collaboration leads to greater trust (see Isett and Provan 2005),
suggesting that some of the strength of the observed relationship is due to the reciprocal
relationship. Given these caveats, it is surprising that the trust relationship with neighbor
municipalities with whom collaboration is more extensive is relatively weak when
compared with the Public Employment Service in particular.
We hypothesized that increased capacity of municipal employment services would
lessen incentives to collaborate (H3). As shown in the bottom of Table 2, we address
three aspects of capacity: municipal size, resources (revenue base), and whether a
municipality is part of a inter-municipal employment center or not. Other than municipal
collaboration with employer associations, we fail to detect an influence of size on
collaboration. As expected, municipalities with greater resources collaborate less with
other municipalities. But, they also tend to collaborate more with employer associations.
This makes sense as larger and wealthier municipalities may have stronger administrative
capacity for cooperation with employers associations, and employers in larger
municipalities are likely to be better organized.
Taken together, these results about municipal size and resources suggest that their
influences are not as strong as presumed. These findings are consistent with those of
Weiss (1987) in studying school-district collaboration. She found that resource capacity
was much more frequently mentioned as a rhetorical argumentation by school-districts
than evidenced in actual collaboration. The findings for whether a municipality is part of
a municipal center make sense in that they reflect the nature of this form of collaboration
and of the role of the centers. By definition, municipalities that are part of such centers
have greater degrees of collaboration with neighbor municipalities as indicated by the
coefficient for the neighbor municipality collaboration. The greater cooperation that
municipalities, which are members of inter-municipal centers, have with the Public
Employment Service and third parties might due to the greater administrative capacity of
centers for establishing that kind of collaboration. The PES and third party organizations
might also perceive inter-municipal centers as more attractive partners for collaboration
than smaller units of single municipalities that have smaller production scales.
We noted in the introduction that it is often taken as a truism that collaboration fosters
better outcomes. We assess this by considering municipal employment managers’
perceptions of employment outcomes and how those differ for varied levels of municipal
collaboration. We theorized above (H4) that benefits of collaboration are not automatic
since they require “healthy” relationships comprised of active involvement of managers
and supportive interactions. A key caveat to this discussion, noted in our discussion of
data and measures, is that our outcome measures are perceived outcomes by the managers
of municipal employment services rather than actual outcomes. Our measure of
perceived outcomes is an index based on ratings of three items: success in getting clients
to search for jobs, availability for work, and entering ordinary employment. Some 85
percent of the respondents to each of three items (on a scale of 1 to 10) indicated a score
Table 3 reports regression models that explain perceived outcomes as a function
of collaboration with each type of organizations and different contextual considerations.
For each model, the dependent variable is the index of perceived outcomes. This is a
well-behaved continuous measure for which higher scores indicate better perceived
outcomes. The models have been estimated using Ordinary Least Squares regression
with appropriate transformations of relevant variables to meet assumptions of linear
relationships. Appropriate visual inspections and statistical tests were conducted to
verify that OLS regression assumptions were met for these models. The cell entries are
the standardized coefficients. The column headings indicate the relevant municipal
collaborator for the model that is entered as an independent variable either alone or as
part of an interaction term as shown under the collaboration influence row heading. The
interaction terms are explained below. Put differently, the models differ with respect to
the municipal collaborating organization that is considered in explaining variation in
perceived outcomes but they are the same with respect to other contextual factors entered
Table 3. Explaining Variation in Perceived Outcomes Regression Models for Perceived Outcomes involving each collaborator a Explanatory Factors Employment Neighbor Unemployment Employer Municipalities Funds/Unions Associations Collaboration Influences Without interaction b Context f Model Statistics
Notes: * p < .10, ** p < .05, *** p < .01 one-tailed t-test, except R2 values for F-test of model fit. a Dependent variable is an index of perceived extent to which the municipality has succeeded in getting clients to search for jobs, to be available for work, and to enter ordinary employment. Cell entries are standardized values with absolute t-values in parentheses. The relevant collaborator for each model is shown as the column heading for the model. b The relevant collaboration variable is the column heading (sq root values). c Separate modeling that includes the product of the relevant collaboration score (sq root) times the degree of direct manager involvement in the collaboration. d Separate modeling that includes the product of the relevant collaboration score (sq root) times the degree of manager direct involvement in the collaboration times an index of the character of the relationship e Change in adjusted R2 between the model without interaction terms and the model with the 3-way interaction. f Context explanatory coefficients are reported for the models involving the collaboration interaction term. g Extent to which the manager respondent indicated that caseworkers should focus on the goal of getting clients into jobs more quickly rather than improving their employability in the long run.
The number of observations is reduced in these models because only 72 percent
of the respondents provided responses for the items comprising the index of perceived
outcomes. Non-respondents for the perceived outcome measures are from municipalities
that have lower collaboration scores with other partners.11 This suggests that the impacts
of collaboration are likely to be overstated by these results, which reinforces the point we
make below about limited impacts. Evidence for this is provided by observing that the
impact of collaboration on perceived outcomes is reduced when substituting mean values
for missing values of the index of perceived outcomes in the regression models. We
report the models without mean-value substitution given the arbitrariness of that
These models suggest that the various forms of municipal collaboration account for
relatively little variation in perceived outcomes. A separate regression model containing
only the collaboration variables explains only 2 percent of the variation in perceived
outcomes.12 As shown in the first row under collaboration influences (without
interactions), there is no detectable influence of collaboration on perceived outcomes
when considering collaboration with the Public Employment Service and relatively
limited influence when considering collaboration with unemployment funds/unions and
employer associations (as gauged by the magnitude of the standardized coefficients).
These findings are contrary to the general presumption that collaboration leads to
We theorized, however, that the health of the collaborative arrangement needs to be
taken into account (H4). This is reflected by the coefficients for the interaction terms that
are reported as the second and third entries under the heading, collaboration influences.
Each of these involves an interaction term that was entered into a separate model
explaining variation in perceived outcomes while controlling for the designated
contextual factors. The first interaction term is the interaction of the municipal
collaboration score with a given actor and the frequency with which the municipal
manager has direct contact with the collaborating entity. The second interaction term
also includes the measure of the character of the collaborative relationship. These factors
are scored so that higher scores indicate “healthier” relationships. The entry for change
in R2 shows the change in adjusted-R2 from the model with the basic collaboration score
to the model involving the three-way interaction.
The findings for the interaction terms clearly show that taking the frequency of
manager involvement and character of the collaborative arrangement into account makes
a difference in explaining variation in perceived outcomes. These explain an additional 2
to 5 percent of the variation in perceived outcomes, depending on which collaborating
entity is considered. Consideration of the three-way interaction also leads to a detectable
influence for each collaborating organization while that influence was not as apparent
when considering only the collaboration score. Nonetheless, the relative magnitude of
influence and of the explanatory power of these variables is less for the Public
Employment Service and for unemployment funds/unions than for other collaborating
The lesser influence of collaboration with the national Public Employment Service on
perceived outcomes is surprising to advocates of the Danish employment policy of
merging municipalities and local PES offices into local job centers. One interpretation is
that inter-organizational collaborations between organizations that are ‘pooled’—those
that are relatively self-contained and independent––are less likely to improve outcomes.
In contrast, collaboration or more exchanges in inter-organizational relationships that are
either sequential (where one organization is unilaterally dependent on resources or inputs
from another organization) or reciprocal (with mutual dependence) may lead to better
outcomes because resources and benefits can be exchanged (O’Toole and Montjoy 1984,
O’Toole 2003). Municipalities and the PES offices have gradually moved towards
forming two relatively independent, parallel systems that share some similarity to pooled
inter-organizational relations under which relatively self-contained organizations each
address their own clientele. Another interpretation is that much of the municipal
collaboration with the PES is relatively new as an adaptation to the future demands of
merging into job centers. New collaboration with organizations which have not
previously trusted each other very much is likely to imply substantial transaction costs
that may not be compensated by better outcomes in the short run.
The findings for contextual factors are consistent across models as the same scores
entered in each model. Not surprisingly, the perceived outcomes are poorer for
municipalities that have more challenging client mixes (problem extent). Consistent with
the findings in the literature about the importance of managerial factors in service
outcomes (e.g. Boyne 2003 and O’Toole and Meier 2004a), respondents from
municipalities that report emphasizing getting clients into jobs more quickly as a
managerial priority in turn perceived that they were achieving better outcomes.13 We fail
to find influences of municipal size and resources on perceived outcomes.
The lack of influence of size on employment outcomes might be surprising to
advocates of the Danish local government structural reform policy of merging
municipalities in order to increase effectiveness. However, this finding is consistent with
other analyses that failed to document any such consistent results for services in general
and employment services in particular (Arendt 2004, Bengtsson 2004, Groes and Olsen
This research addresses different patterns of collaboration for Danish employment
services. We address collaborative relationships between one type of organization—
municipal employment services—and a range of partners that include the national Public
Employment Service, unions and their affiliated unemployment funds, employer
associations, other municipalities, and third-party providers that include private for profit,
non-profit, and public providers of education, training, case management, and job
placement services. Three key sets of findings emerge from this research.
One is different patterns in collaboration. Municipalities vary considerably in the
extent and forms with which they collaborate with other organizations for employment
services. This suggests that collaboration should not be thought of as a generic activity
with other organizations as the nature of the partners and their prospective roles needs to
be considered. In addition, measures of collaboration need to account for more than just
the extent or frequency of cooperation. The effort that goes into different types of
collaborative arrangements is also important to consider. Our findings show that
considering frequency of collaboration alone would give a different understanding of the
collaborative arrangements than taking into account the types of collaboration.
A second set of findings concerns the factors that explain variation in the extent of
collaboration. As found in prior research, we find organizational benefits (dependence
and problem-solving benefits), social capital, and various aspects of capacity help shape
municipal collaboration with other actors for employment services. However, the role of
these considerations differs somewhat for the organizations with which municipalities
collaborate. Our findings suggest that each collaborative arrangement is induced (or
benefits from) perceptions that there are resource benefits from collaborating. This
makes sense since organizations are more likely to collaborate if they perceive the
collaboration as extending their expertise, personnel, or other capabilities. Yet, problem-
solving benefits only appear to be relevant for collaboration with the Public Employment
Service—although weakly—and to a greater extent with third-party providers such as
private consultants, non-profit organizations, and public training institutes. Collaborating
with third-party providers typically entail contractual relationships that involve a different
collaborative dynamic than for the other organizations. This suggests that municipalities
are very specific in seeking out collaborators that they think will provide specific
A third set of findings concerns how the perceived employment outcomes are affected
by municipal collaboration with other organizations. Municipal employment managers
generally report that employment outcomes for their clients have been relatively good in
the prior year with respect to job search, availability for work, and employment.
However, our modeling of these perceived outcomes suggests that the various forms of
collaboration account for relatively little variation; 2 percent without taking health of the
relationship into account. These findings suggest that the benefits of collaboration per se
are over-stated, at least in the general literature on the subject.
We show that “healthier” collaborative relationships, which directly involve
managers and foster positive relationships, have stronger perceived outcomes. Indeed, an
additional 2 to 5 percent of the variation in perceived outcomes is explained when taking
health of the collaborative relationship into account. This observation is consistent with
the findings of other research (Provan and Milward 1995, O’Toole and Meier 2004b) that
managerial factors are important in determining the outcomes of collaborative
arrangements. Also important are the extent to which the organization has embraced a
goal of getting clients into jobs and the presence of a supportive task environment for
This research contributes to the empirical understanding of collaborative
arrangements for service delivery. Although the findings are proscribed by attention to
one setting, the results have good face validity and are generally consistent with our
theorizing. This gives us confidence of our choices about conceptualization of
collaboration and our measures. Nonetheless, we recognize a number of caveats to our
findings that arise from our reliance on data from a single source (municipal middle
managers), limitations in our analyses due to missing data, and reliance on perceived
measures of the outcomes of employment services. We have discussed the implications
of these limitations for specific findings. One key consequence is that the effects of
collaboration on perceived outcomes are if anything overstated in these data. This further
reinforces our sense that collaborative benefits are presumed to be greater than they may
Our examination of collaborative relationships in the delivery of employment services
highlights a number of issues that are relevant for future theorizing and empirical
examination of collaborative service delivery. One is greater attention to different
aspects of collaboration. Our findings show that the specifics of collaborative
arrangements—in terms of who is collaborating with whom, the type of collaboration, the
frequency of managerial involvement, and the character of the relationship—need to be
taken into account in order to understand the potential for and limits to collaboration in
service delivery. A second issue is sorting out connections among dependence, trust, and
collaboration with particular attention to the interactions over time. It may be that the
collaboration fosters both greater degrees of trust and dependence, rather than the reverse.
A third issue is addressing the relationship between outcomes and subsequent
collaboration. It also may be that perceived outcomes of collaboration either enhance
(positive outcomes) or detract (negative outcomes) from subsequent collaboration.
Clearly, these relationships cannot be sorted out with cross-sectional data. A final
direction is more refined analyses of the impacts of collaboration for service delivery
outcomes. Simply put, not all collaboration is the same and collaboration itself is not a
1 Data about municipal population size and the extent of municipal resources have been provided
2 The relevant measures are discussed in the text that follows. None of the correlations was
statistically significant at a p-value of .1 or less for age using a two-tailed test. The strongest
correlations involving experience were -.12 for extent of cooperation with neighbor
municipalities (p = .09) and -.16 for trust in employer associations (p = .07).
3 Only two independent-sample t-tests were statistically significant at conventional levels of the
11 that we conducted. Male respondents on average report slightly greater degrees of cooperation
with unions and with employer associations than do female respondents; amounting to an average
difference of .3 on a 5-point scale (p-values < .05). This may be because of stronger gender-
4 Ideally, we would have separate scores for the extent of each form of collaboration with each
potential actor. However, we only have a measure of the extent of overall cooperation with each
5 The measure was obtained from the Danish Institute of Local Government Studies based on rich
Danish register data from Statistics Denmark. The details of the calculation yet made for the
future, larger municipalities can be found in Clausen et al. (2006).
6 The index is a principal component score for the items that make up this dimension of
outcomes. As such, the index is a weighted average of the scores on each item. The Cronbach
reliability coefficient for this is .90.
7 The response options for each organization are: not at all, once a year, once every half year,
once every quarter, once a month, and once a week.
8 The alternative of analyzing the smallest subset of data (128 observations) violates the random
selection of observations and does not address the missing value issue.
9 This is evidenced by the fact that the effect of trust when using mean-value substitution for
missing data is no longer statistically significant for the models of collaboration with employer
10 These are outcomes that are consistent with the national government emphasis in employment
policy in “getting people to work.” These are clearly not the only potential outcomes, or
necessarily the desired municipal outcomes, for employment services. Some employment
programs put more emphasis on improving clients’ longer-term prospects for obtaining
meaningful work. We emphasize the more immediate outcomes here because of their importance
11 The p values are less than .01 for independent sample t-tests comparing means of collaboration
scores for each partner between respondents and non respondents to the perceived outcome
12 The collaboration scores are entered separately in Table 3 rather than in combination. The
latter introduces multi-collinearity problems given that municipalities often collaborate with more
13 While these may seem like self-serving responses, the two questions were far apart in the
questionnaire. As such, it seems unlikely that respondents equated the two responses.
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