JPART 17:651–672
Explaining Cooperation: How Resource Interdependence, Goal Congruence, and Trust Affect Joint Actions in Policy Implementation Martin Lundin Uppsala University
ABSTRACT A perennial problem when political decisions are to be implemented is how to make authorities work together. Previous research shows that resource interdependence, goal congruence, and mutual trust increase interorganizational cooperation. In this article, it is argued that interaction effects must also be considered in order to fully understand how these variables affect cooperation. The study is based on 203 dyads of Swedish Public Employment Service offices and municipalities in 2003. I find that mutual trust is necessary if goal congruence is to increase cooperation between these agencies. Furthermore, mutual trust only has a positive effect if organizations have similar objectives. However, trust is not required for resource interdependence to affect cooperation, and the effect of trust is not dependent on the organizations’ mutual dependence. The results imply that trust and goal congruence must exist simultaneously in order to promote joint actions. Thus, if a management strategy aimed at increasing cooperation only focuses on the organizations’ objectives or the level of trust between them, it will fail. An important lesson for future research is that including interaction terms in the analysis improves our understanding of interorganizational cooperation.
To understand what is going on when public policies are carried out at local level, interorganizational relationships have to been taken into account (see, e.g., Bardach 1998; Hjern and Porter 1981; O’Toole and Montjoy 1984). Many challenges facing modern societies—such as fighting poverty or reducing unemployment—are difficult to manage within a single public authority. The virtues of interorganizational cooperation have therefore been emphasized in implementation research ever since the pioneering study of Pressman and Wildavsky (1984) of a public labor market program in Oakland in the I would like to thank Jo¨rgen Hermansson, Bjo¨rn Lindberg, Andreas Lindemann, Karl-Oskar Lindgren, Daniela Lundin, ¨ berg, and Soren Winter for comments on earlier drafts of this article. Several participants at the seminars held PerOla O at the XIV Nordic Political Science Association Conference, Reykjavik, August 11–13, 2005, and at the Department of Government, Uppsala University, September 26, 2005, contributed to this research as well. I would also like to thank Carolyn Heinrich and the two anonymous referees for very useful suggestions. Address correspondence to the author at
[email protected]. doi:10.1093/jopart/mul025 Advance Access publication on January 4, 2007 ª The Author 2007. Published by Oxford University Press on behalf of the Journal of Public Administration Research and Theory, Inc. All rights reserved. For permissions, please e-mail:
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1960s. O’Toole (2003, 237) concludes that ‘‘the topic of interorganizational relations will remain important for administrators tasked with helping to make policy implementation succeed. Accordingly, it is critical to understand how to make sense of such institutional settings for improving prospects for implementation success.’’ This article contributes to the discourse by presenting evidence on how resource interdependence, goal congruence, and trust affect cooperation between agencies in local policy implementation. According to research on interorganizational relationships, mutual resource dependence and congruent goals are among the most important antecedents to cooperative behavior. In a situation of mutual dependence, organizations will cooperate in order to exchange resources that make it possible to achieve organizational goals, whereas shared interests and a similar commitment to a policy make it easier to get along and can generate joint struggles (O’Toole 2003, 239–42). Another important finding is that mutual trust increases cooperation (Smith, Carroll, and Ashford 1995, 10–1) since it ‘‘facilitates interpersonal acceptance and openness of expression’’ (Zand 1972, 229). In previous research, the direct additive effects of mutual resource dependence, congruent goals, and trust have been examined and corroborated. However, it has not been studied whether the effects of resource interdependence and goal congruence are different in situations characterized by high levels of trust compared to situations when agencies do not trust each other. In this article, it is examined whether a credible commitment, that is, mutual trust, is a necessary condition for goal congruence and mutual resource dependence to affect cooperation. Moreover, the effect of trust is studied when the levels of resource interdependence and goal congruence vary. It is hard to see why trust should increase cooperation if actors are not interdependent or have similar objectives. In sum, interaction effects might explain levels of cooperation better than the additive effects suggested in previous research. The study focuses on the relationships between agencies operating at a local level but representing different tiers of government. Policy implementation often involves an intergovernmental dimension (Jennings and Ewalt 1998; Radin 2003; Thomas 1979), and ‘‘so long as there have been systems of dividing decisions and functions of governments there have been attempts to organize and manage them’’ (Agranoff 2004, 443). Consequently, studies such as this one are of great interest. I make use of new large-N data on the Swedish active labor market policy and examine dyads of Public Employment Service (PES) offices and municipal labor market agencies. The Swedish PES offices are central government agencies carrying out labor market policies at a local level. However, in recent years, a parallel local government system has evolved for labor market activities since municipalities now take an active part as well (Hjertner Thore´n 2005; Lundin 2005; Lundin and Skedinger 2006; Salonen and Ulmestig 2004). This article takes a closer look at the interaction of the two tiers of government. The empirical results reveal that the effect of goal congruence is dependent on mutual trust. If organizations do not trust each other, similar priorities do not matter. In addition, if the authorities’ objectives diverge to a large extent, trust does not increase cooperation. However, the results do not support the idea that an interaction term between resource interdependence and trust should be included in models trying to explain collaborative behavior. The remainder of this article is organized as follows: In the next section, theory and hypotheses are presented. The Swedish labor market case is then briefly discussed. In the
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two following sections, data and measures are described. The results are then reported, and lastly, in a concluding section, the findings are summarized and their implications are discussed. EXPLAINING COOPERATION: INTERDEPENDENCE, GOAL CONGRUENCE, AND TRUST
Even in the most well-designed political system, authorities’ responsibilities will overlap. Accordingly, development and implementation of public policy frequently ‘‘demands multilateral cooperation, blurs or eliminates traditional boundaries and jurisdictions, and needs the deployment of many actors’’ (Alexander 1995, xvi). Diverging organizational goals and operational routines thus make policy implementation difficult (see, e.g., O’Toole 2003). A primary task of public sector management is to get the various agencies to cooperate when policies are carried out.1 Thus, detecting antecedents to cooperation is of imperative interest. In line with previous interorganizational research, cooperation in the public sector is defined as the interactions among actors aiming at solving public problems by working together rather than by working separately (cf. Smith, Carroll, and Ashford 1995, 10). Collaboration is used synonymously to cooperation throughout the article. Resource Interdependence
There is an extensive and heterogeneous literature on collaboration between organizations (Oliver and Ebers 1998). However, exchange theory provides a foundation for explaining cooperation (Blau 1964; Levine and White 1961; Scharpf 1978). In this framework, cooperation is a consequence of resource interdependence. Organizations are assumed to be rational actors making conscious and intentional decisions in order to achieve their objectives. The motivation for working together is the need to overcome a lack of resources. An organization will avoid interactions with others if the benefits of cooperation do not exceed the costs since cooperation is complicated, is costly, and involves a loss of autonomy. But if organization A needs resources from organization B and organization B needs resources from organization A, there is a good chance that cooperation will take place. Financial resources are, of course, of significant interest, but staff, premises, information, legitimacy, and legal authority are examples of other significant resources that can be obtained from other organizations. Empirical evidence support the idea that mutual resource dependence increases cooperation. Levine and White (1961) indicated that health-related organizations highly dependent on resources from the local health system interacted more and had fewer disagreement with local agencies than organizations less resource dependent. In a quantitative longitudinal study of relationships among child care and health organizations in Texas, Van de Ven and Walker (1984) found that the need for resources was the most important variable stimulating coordination of activities. More recent evidence exists as well. As an example, Gulati and Gargiulo (1999) examine alliance formation using longitudinal data from a sample of American, European, and Japanese organizations; the effect of interdependence on alliance formation is positive and statistically significant. 1 Note that cooperation involves costs and that implementation performance is not automatically improved by interorganizational cooperation. It is an empirical question whether the positive aspects outweigh the negative. Results in Jennings and Ewalt (1998) demonstrate that cooperation improves some aspects of policy performance, whereas there is no effect on other aspects. The effects of cooperation are not examined in this article.
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Goal Congruence
Besides mutual dependence, goal congruence may boost collaboration. Given the assumption that organizations try to reach certain goals, we should not only focus on the need for external resources but also investigate the extent to which the organizations’ goals are similar. A shared interest can be a powerful facilitator of cooperation, whereas diverging objectives may decrease cooperation (O’Toole 2003, 239). Empirical results confirm that objectives are important. For example, Levine and White (1961) indicated that agreement on goals is an important aspect of interorganizational relationships. Compatibility of goals is also found to be positively associated with cooperation in the study of Schmidt and Kochan (1977) of community organizations and local offices of the US Training and Employment Service. O’Toole (1983) indicated that perceived common interest increases cooperation among local actors in a study of interorganizational implementation of labor market policies in Sweden and the Federal Republic of Germany. Moreover, Luo (2001) finds positive effects of goal congruity on personal attachment in international cooperative ventures. Personal attachment is defined as ‘‘the degree to which boundary spanners (resident board members and senior venture managers) from each party are socially bound through having developed personal relationships and interpersonal learning’’ (Luo 2001, 178). Luo’s operationalization includes the extent to which information and skills have been transferred between the partners. Personal attachment can be regarded as a form of cooperation or, at least, as a concept closely related to cooperation. Trust
Trust is a key word in the social sciences. Scholars like Elster (1989), Ostrom (1998), Putnam (2000), and Rothstein (2000) accentuate trust as a mechanism for overcoming social dilemmas. In research on policy implementation, Bardach (1998) stresses the importance of mutual trust in order to make agencies work together. There are various definitions of and perspectives on trust. Scharpf (1997, 137) uses the concept of weak trust.2 By weak trust, Scharpf means the expectation that another actor’s communicated preferences are honest rather than misleading and the expectation that the actor will stick to a commitment as long as the conditions under which it was entered are not altered dra¨ berg 2005). In this article, I use trust in the same sense matically (see also, Svensson and O as Scharpf. There is an agreement among organization theory scholars that trust is an important antecedent to cooperation. In an experimental analysis using data on business executives, Zand (1972) shows that trust enhances cooperation. Muthusamy and White (2005) focus on alliances between business firms in the United States from 1994 to 1998 and find that trust 2 Trust, goal congruence, and resource interdependence are treated as separate variables in the present study. It is likely that goal congruence increases trust, but mutual trust may exist even when objectives diverge. In addition, congruent goals will not automatically imply high trust since such a definition of trust is reliant on an unrealistic assumption of perfect information about preferences. Moreover, in a situation without mutual resource dependence actor A can still think that actor B (and vice versa) has honest intents and will stick to an agreement. Some scholars, for example, Hardin (2002), seems to collapse resource interdependence, congruent goals, and trust: actor A trusts actor B when it is in actor B’s interest to fulfill actor A’s expectation. This position is, in my view, not as beneficial as treating resource interdependence, goal congruence, and trust as separate variables (for a good discussion, see Rothstein 2000, 484–8).
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has a positive effect on transfer of knowledge between partners. The analysis of Zaheer and Venkatraman (1995) of insurance agencies also indicate a positive relationship between mutual trust and cooperation. In an overview article, Smith, Carroll, and Ashford (1995, 10–1) note that ‘‘although research has identified many determinants of cooperation, virtually all scholars have agreed that one especially immediate antecedent is trust.’’ Hypotheses
I suggest that we need to be more specific about under what conditions resource interdependence, goal congruence, and trust affect interorganizational cooperation. The idea is that interaction effects should be considered in order to fully understand the relationships. Previous empirical research seems to have overlooked the possibility of interaction terms. The literature implicitly assumes that the effects of resource interdependence and goal congruence are the same when trust is low as when trust is high. How reasonable is this assumption? Why should an organization choose a collaborative strategy in a situation of resource interdependence if it cannot rely on the other organization’s commitment to the relationship? Blau (1964, 98) describes trust as an important condition for the exchange of resources to occur: ‘‘since social exchange requires trusting others to reciprocate, the initial problem is to prove oneself trustworthy.’’ Thus, resource interdependence is only excepted to be a relevant factor when there is mutual trust. Below, the first hypothesis is outlined. H1 The effect of resource interdependence on interorganizational cooperation is dependent on the level of trust; a positive effect is expected only when organizations trust each other.
Furthermore, objectives cannot be communicated in a credible way when one does not trust the other. When communication is not regarded as reliable, how can organizations be sure that they have congruent goals? Hence, I argue that trust must exist in order to make objectives a relevant factor. H2 The effect of congruent (or diverging) objectives on interorganizational cooperation is dependent on the level of trust. How similar or different their priorities are is only expected to affect cooperation when organizations trust each other.
How about trust then? Could we expect trust to affect cooperation regardless of the level of resource interdependence and goal congruence? I argue that it is unlikely for organizations to work together only because they trust one another. Mutual trust is not a sufficient reason. Trust can make cooperation easier, but it is not something that boost cooperation if there are no other reasons. The effect of trust ought to be dependent on the similarities between organizations’ goals and the level of resource interdependence. This suggests two additional hypotheses. H3 The effect of trust on interorganizational cooperation is dependent on the level of resource interdependence; the effect increases as the level of interdependence increases. H4 The effect of trust on interorganizational cooperation is dependent on the level of goal congruence; the effect increases as objectives become more similar.
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INTERGOVERNMETAL COOPERATION IN LOCAL POLICY IMPLEMENTATION: THE SWEDISH LABOR MARKET CASE
The hypotheses outlined above are examined using data on dyads of central and local government agencies carrying out the active labor market policy in Sweden. Unemployment is a major problem in most countries, and governments use active programs to reduce the number of persons without a job. Active labor market policy refers to ‘‘measures to raise employment that are directly targeted at the unemployed’’ (Calmfors, Forslund, and Hemstro¨m 2002, 5). This includes job-brokering activities and labor market programs, such as labor market training and subsidized employment. In Sweden, the National Labor Market Administration (Arbetsmarknadsverket), a national government authority, carries out political decisions at local level through local PES offices (arbetsfo¨rmedlingar). There is a PES in nearly all municipalities, and in larger cities, there is often more than one office. Municipalities are involved in implementation of labor market activities as well, although overall responsibility is retained at the national level and the PES remain the most important local actor (Hjertner Thore´n 2005; Lundin 2005; Lundin and Skedinger 2006; Salonen and Ulmestig 2004). It is estimated that in 1999 around 80,000 persons were activated in labor market programs arranged by the municipalities. This accounts for about 40% of the participants in all the National Labor Market Administration’s active measures (Lundin and Skedinger 2006). In addition, municipalities organize labor market programs (active social policy) for persons who are not ‘‘job ready’’ and live on social welfare benefits. Approximately 12,000 individuals were activated in such programs at any given point in time in 2002 (Salonen and Ulmestig 2004). A majority of municipalities have special labor market administrations, whereas others incorporate labor market activities in their social service administration (Lundin 2005). The empirical analysis focuses on the relationship between the PES offices and the municipalities’ labor market administrations. Besides unemployment being one of the largest problems in modern welfare states, the current research setting is suitable for at least five reasons. First, the high information requirements imply that cooperating across organizational boundaries becomes a viable strategy in labor market activities (O’Toole 1983). Second, similar dyads of central-local government agencies are analyzed, and the same political decisions are implemented around Sweden. This means that it is possible to hold a lot of characteristics constant when testing the hypotheses of interest. Third, cooperation varies around Sweden, which results in variation in the dependent variable that can be explored. Fourth, new and suitable data are available and reliable. Fifth, quantitative studies on implementation and cooperation between agencies have mainly been carried out on data from the United States. Evidence from a unitary European context is therefore of interest. In the late 1970s and the beginning of the 1980s, research was conducted on diverse aspects of interorganizational implementation in the realm of active labor market training in Sweden and West Germany. For instance, Hanf, Hjern, and Porter (1978) showed that implementation of programs was dependent on interorganizational relationships to a considerable extent, and O’Toole (1983) indicated that common interest in the locale was an important factor stimulating interorganizational cooperation. PES offices specializing in labor market training were key actors in these studies. Municipalities were involved as well, although labor market training usually does not engage municipalities to any great extent. These days, local governments have a much more profound role in the active labor
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market policy in Sweden than in the 1970s and the 1980s. This makes it interesting to study the relationship between PES offices and municipalities making use of contemporary data. DATA
Cross-sectional data on dyads of PES offices and municipalities in 2003 have been collected. A questionnaire is the main data source, but administrative data from the National Labor Market Administration and official municipal statistics from the database KFAKTA03 supplement the survey. Postal questionnaires were distributed to the managers of all Swedish PES offices in February 2004. The response rate was good: 268 managers replied, which implies a response rate of 75%. Dyadic data require information from both sides of the dyad. Thus, questionnaires were also distributed to managers of municipal labor market administrations and municipal politicians with responsibility for labor market activities. The response rates were even better in these cases: 85% of the managers (246 persons) and 84% of the politicians (245 persons) replied. In Stockholm, Sweden’s largest municipality, labor market operations are handled in 18 different offices organized geographically (kommundelar). The managers of these offices answered the Stockholm questionnaires. The response rate was somewhat lower here: 56% (10 persons). The nonresponses are probably not that problematic since the respondents resemble the population on characteristics such as location, size, and unemployment rate in all groups. The PES managers’ answers form one part of the dyad. However, either the managers’ or the politicians’ answers could make out the other side. It is reasonable to assume managers to have more accurate information about operations. Thus, the first choice was to make use of information from this group. The local politicians’ responses were employed when the managers’ answers were missing to maximize the number of dyads.3 Even though the response rates are very high, the number of observations that can be used decreases in the dyadic data. If one party in an agency pair is missing, it is not possible to use that information. Internal missing data on some variables also reduce the number of useable observations. In the end, the analysis was conducted on 203 cases. Like most other researchers investigating interorganizational ties, I have to rely on reports from key informants. Managers and politicians were instructed to give generalized information about their organization. The assumption that the responses from one person represent the whole organization can be questioned. However, it is reasonable to assume that the respondents in this case are well informed and that data are trustworthy. MEASURES
In this section, operationalizations of cooperation, interdependence, goal congruence, trust, and control variables are described. Table 1 lists descriptive statistics for each of the measures.
3 As a test, all analyses in the article have been performed including a dummy variable measuring whether the municipal response comes from a manager or politician. Furthermore, the analyses have been carried out using a sample solely consisting of responses from the managers. These additional tests did not alter the conclusions.
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Table 1 Descriptive Statistics
Variables
Mean/Proportion
SD
Cooperation Non-Nordic clients Long-term clients Goal congruence Trust Municipality type Big cities Suburbs Larger cities Middle-sized cities Industry Rural Sparsely populated Other larger Other smaller Unemployment Non-Swedish citizens Population change Work rehabilitation PES PES size PES cooperative orientation PES finances Mun. LMP spending Left parties Mun. cooperative orientation Mun. tax base State subsidy Equal terms
3.60 7.28 11.65 7.43 0.45
1.27 6.73 4.95 0.97
0.00 1.17 0.11 4.38
5.00 61.96 30.04 9.85
1.27 2.78 2.33
1.10 0.00 93.59
8.20 36.25 105.99
16.51 5.96 0.34 1.97 10.86 4.54 18.46 5.32
2.00 0.50 0.00 0.04 11.11 0.00 99.29 15.70
105.00 18.00 3.01 13.19 82.86 18.00 250.58 22.14
0.07 0.10 0.12 0.14 0.19 0.09 0.09 0.11 0.09 3.31 2.21 99.84 0.01 17.84 12.36 0.66 2.99 47.65 4.77 124.00 6.77 0.51
Minimum
Maximum
Note: Proportions given in italics. Mun., municipality; LMP, labor market program.
Cooperation
The dependent variable cooperation is measured using five 0/1-indicators obtained from the PES questionnaire. The managers were asked if the PES office and the municipality have established cooperative groups where (a) caseworkers from the two organizations collaborate and (b) managers from the two organizations collaborate. Moreover, the managers indicated whether (c) caseworkers contact each other on a daily basis or more seldom. They also provided information about whether the PES office and the municipality have formal collaborative contracts concerning two policies: (d) actions to prevent long-term unemployment among young people and (e) a program called the ‘‘Activity Guarantee’’ where the target group is individuals who have been unemployed for a considerable time period. In both policies, the Swedish government encourages collaboration between PES offices and municipalities, but the authorities are not compelled to sign a collaborative contract.4 For more information about the Activity Guarantee, see Forslund, Fro¨berg, and Lindqvist (2004). The youth policy is described and evaluated in Carling and Larsson (2005).
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Table 2 Principal Component Factor Analysis: Cooperation
Item
Factor Loadings
(a) Joint collaborative groups at the caseworker level (b) Joint collaborative groups at the manager level (c) Daily contacts between caseworkers (d) Youth policy collaborative contract (e) Activity Guarantee collaborative contract
0.74 0.70 0.43 0.56 0.53
Note: Entries are factor loadings in a principal component analysis. The retention of factors is based on the Kaiser criterion (i.e., eigenvalues greater than 1.00). The eigenvalue for the first dimension is 1.82. This is the only dimension where the eigenvalue exceeds 1.00. The factor explains 36.3% of the variance in the variables. Bartlett’s test of sphericity indicates that we can reject the hypothesis that the correlation matrix is an identity matrix at the .05 level. Kaiser-Meyer-Olkin measure of sampling adequacy is .65 (.50 or lower is usually considered unacceptable).
In table 2, a factor analysis is presented. All items load positively on the first dimension. Since the items form a single dimension, a collaboration index is constructed ranging from zero to five. A score of zero implies that none of the activities (a)–(e) are taking place, whereas five means that the organizations have collaborative groups at the caseworker and manager levels, they contact each other on a daily basis, and they have signed cooperation contracts concerning the youth policy and the Activity Guarantee. I use a simple additive index in order to make interpretation easy. All analyses in the article have been performed using the factor scores obtained from the factor analysis as well, and the conclusions remain the same. Cooperation has been measured in various ways in prior research. Sometimes a single item is employed; other times indices are used. Measures vary depending on the context. The frequency of communication, how often clients are transferred between organizations, how much support a focal organization receives from another organization, the use of various forms of coordination techniques such as interagency committees and task forces are examples of ways to assess to what extent organizations work together (see, e.g., Alter and Hage 1993; Jennings and Ewalt 1998; Schmidt and Kochan 1977; Van de Ven and Walker 1984; Wholey and Huonker 1993). The items in table 2 are similar to the measures used in other settings, although I am not aware of any study using exactly the same approach. Interdependence
There are at least two approaches available to assess interdependence, none of them perfect. A common approach is to ask respondents directly how dependent they are on each other (see, e.g., Provan and Skinner 1989). A potential drawback is the risk that the responses are a consequence of the level of cooperation rather than a cause. Another problem is that respondents may have trouble interpreting survey questions and response alternatives. This leads to problems with both validity and reliability. Instead of assessing resource interdependence directly, it is possible to look for situations in which it is realistic to assume that organizations are mutually resource dependent. This approach was employed by Gulati and Gargiulo (1999) studying alliances in three industries in a sample of American, European, and Japanese organizations. Gulati and Gargiulo assume that firms benefit from cooperation between niches since they have
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complementary resources. Moreover, firms that are located in different regions are understood to be interdependent; a firm needs a partner in another market. The latter method depends on the critical assumption that it is possible to identify circumstances when organizations are mutually dependent. I argue that such variables are available in the present research setting. My claim is that a main determinant of resource interdependence is the composition of the unemployed in the locale. At the heart of the argument is that there is a net mutual benefit from collaboration when the clientele consists of many individuals in need of assistance both from the PES and from the municipality. If a large part of the unemployed face some additional hindrance besides being unemployed, the authorities are likely to need one another. Difficulties in speaking and understanding the Swedish language, various work disabilities, and drug addiction problems are examples of problems that intertwine with difficulties in finding a job. Municipalities implement and fund social assistance, policies aiming at giving support to those with a handicap, and the introduction of immigrants into the Swedish society. Municipal resources, such as expertise and information, are important for the PES to obtain when many of the unemployed have problems lying within the jurisdiction of the local governments. At the same time, the PES offices’ knowledge in labor market issues and, above all, their financial resources are of municipal interest. Thus, mutual dependence is likely to be high when a large part of the unemployed have multiple problems. I cannot observe when several clients are in need of both authorities’ actions, but there are rather good indicators available. Two variables based on register data from the National Labor Market Administration are employed. Long-term clients, measured as the percentage of clients registered with the PES as openly unemployed for more than six consecutive months in 2003, is one of the indicators (this is the official definition of long-term unemployment in Sweden). The clients should not have participated in a labor market program during this time period. Among these long-term unemployed, I expect to find a relatively large share of persons with multiple problems. The second variable, nonNordic clients, is the percentage of the unemployed clients without a Nordic citizenship that are registered with the PES in 2003.5 It is reasonable to expect that a relatively large proportion of these clients are in need of support from both agencies. For example, according to the National Board of Health and Welfare (2004), 11% of those born in countries other than Sweden, 18 years or older, received municipal welfare benefits in 2003. This can be compared with the 2% among citizens born in Sweden. Moreover, municipalities are responsible for introduction of immigrants into the Swedish society, including teaching the Swedish language. I believe that long-term clients and non-Nordic clients are reasonable indicators of resource interdependence, but at least two caveats should be mentioned. First, the variables provide no direct evidence of dependence on each other’s resources. Thus, I cannot be completely sure that the indicators measure something else other than interdependence. Second, even if we accept the measures as valid indicators, one problem remains: the agencies may be mutually dependent for other reasons. This implies that the empirical study is limited to a certain kind of resource interdependence and that conclusions should be made with some reservations. 5 A large share of the immigrants are coming to Sweden from the neighbor countries. Nordic immigrants are likely to have fewer boundary-spanning problems than other immigrants since language and cultural barriers are smaller, for example.
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Table 3 How Similar Various Objectives Are Ranked by the PES Offices and the Municipalities (0 5 Minimum Congruence, 12 5 Maximum Congruence)
Objective
Congruence
Ensuring that there are labor market programs for young people under 25 years Reducing unemployment Ensuring that there are labor market programs for groups of unemployed with severe problems in the labor market Improving matching between available jobs and unemployed persons Taking clients’ own requests and needs into account Improving municipal services for the local population Monitoring clients Shifting people from subsidized to unsubsidized jobs Following central government rules and guidelines Activating unemployed persons living on social assistance in labor market programs Increasing or maintaining the local population Attaining the quantitative goals of the National Labor Market Administration Reducing expenditure for social assistance
10.56 9.85 8.90 8.70 7.92 7.70 7.45 7.30 6.56 6.36 5.75 5.27 3.94
Goal Congruence
A direct question is often used to assess goal congruence: ‘‘to what extent are the goals of the other organization compatible with the goals of your organization?’’ (see, e.g., Schmidt and Kochan 1977). However, to reassure that answers are exogenous, I believe it is more appropriate to let the respondents estimate the importance of certain goals and compare the answers of the actors within each dyad. The questionnaire respondents were asked to grade, on a scale from one to five, the importance of 13 different objectives. The ratings were transferred into a ranking of objectives for each agency. This approached is preferred since a ranking is more comparable between respondents. The ranking was inverted so that the most important goal received a value of 13, whereas the least important goal was given the value of 1. If two objectives were given the same rating by a respondent, they got the same ranking. The congruence between the PES office’s and the municipality’s ranking in each dyad was then calculated.6 In table 3, objectives and averages are presented. The minimum conceivable value is zero, which implies very different priorities. The maximum is 12, that is, both agencies in all dyads rank the objective in the same way. The agencies agree most on the importance of arranging labor market programs for unemployed youth and the overall goal of reducing unemployment. Disagreement is highest concerning the importance of decreasing social assistance expenditures and the quantitative goals of the National Labor Market Administration. The overall goal congruence is the average score of the 13 objectives in table 3. Descriptive statistics are reported in table 1. The selected goals are chosen because they are highly relevant in the active labor market policy in general, especially for the relationship between PES offices and municipalities. Objectives of importance in some relationships are probably missing, but I find it qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The congruence between the agencies’ ranking of a certain objective is given by ðPESranki Mun:ranki Þ2 : PESranki is the PES office’s ranking of the objective from 1–13 in each dyad (13 is the most important objective, 1 is the least important objective), whereas Mun:ranki is the corresponding municipality’s ranking of the same objective. 6
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more reliable and more interesting to focus on certain goals rather than asking a general question on goal similarity.
Trust
Two survey questions were asked in order to cover trust. The purpose of the first question was to measure perceptions of the other actor’s intents: ‘‘To what extent do you assume that the PES office (municipality) reveals true motives to you?’’ The second question is aimed at capturing the agencies’ assumptions about whether the other agency will actually keep promises: ‘‘To what extent do you assume that the PES office (municipality) performs in line with its promises?’’ There were four possible response alternatives: ‘‘completely,’’ ‘‘mostly,’’ ‘‘partly,’’ and ‘‘not at all.’’ Trust is coded as a dichotomous variable. A relationship is characterized by mutual trust when both actors respond that they believe the other actor have honest intents and will keep their promises all or most of the time (i.e., the alternatives completely and mostly), otherwise the level of trust is low. Sometimes researchers instead employ a battery of items (e.g., using a Likert-type scale) to measure trust. Some researchers also distinguish between diverse forms of mutual trust. It is not possible for me to use such measures since I have no access to that kind of data. Trust is a somewhat problematic variable to deal with. It could be argued that cooperation affects trust rather than the other way around. Thus, conclusions should be made with some reservations. The problem of causal order between trust and other variables is a recurrent problem in the social sciences. Studies trying to establish time order between trust and cooperation using, for example, panel data would be important contributions to the research field. However, in the literature on interorganizational relationships, one usually treats trust as an independent variable and cooperation as the dependent variable.
Control Variables
The research setting makes it possible to hold characteristics of the policy and the authorities that do not vary around Sweden constant. But control variables should be added to account for variations among dyads that could affect both the level of cooperation and some of the main explanatory variables. The local context is important to hold constant for at least two reasons. First, contextual pressure is stronger in some places than in others. Evidence reveals that when the times are turbulent and there is external pressure, organizations tend to join together (Schermerhorn 1975). Contextual pressure can also affect the main explanatory variables. Second, alternative collaborative partners and/or external resources are more readily available in some areas than in others. This can affect the relationship between the PES and the municipality. I have added four control variables to account for important variation among local contexts. All four of them are based on data from official statistical sources. A set of dummy variables are included to determine the type of municipality. The context is likely to differ among big cities, small cities, rural areas, and so on but be quite alike within these groups. For instance, the number of potential partners is expected to be larger in metropolitan areas than in sparsely populated municipalities. Municipality type is based on a classification made by the Swedish Association of Local Authorities. The scheme
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consists of nine categories covering, for example, big cities, industry municipalities, and rural areas.7 The unemployment rate in percent in 2003 is another control variable. I also incorporate the population change between 2000 and 2003, measured as an index where the local population in 2000 is the base. High unemployment and/or decreasing population may imply external pressure. Moreover, additional resources from, for example, the Swedish Agency for Economic and Regional Growth or the European Union often become available when the population decreases or when unemployment is high. The last context variable is non-Swedish citizens, which is the percentage of inhabitants having foreign citizenship (in 2001, since I do not have data from 2003). The variable non-Swedish citizens is added mainly because I want to make sure that one of the key explanatory variables, non-Nordic clients, only account for the share of unemployed clients without Nordic citizenship. I expect a relatively large share with multiple boundaryspanning problems in this group. There are several features of the authorities that are important to take into account. As previously noted, labor market policy is a national government responsibility in Sweden. Municipalities can involve themselves when they find it appropriate. Some municipalities are therefore more active in the labor market policy than others. The level of cooperation and the main explanatory variables could be affected by municipal engagement. The agencies may, for example, trust each other as a consequence of municipal activity in the field. The municipalities’ spending on labor market policies, excluding state subsidies, in 100 crowns per inhabitant in 2003 is employed as an indicator of municipal engagement (mun. LMP spending). What is more, a control variable for the percentage of seats in the local council held by socialist parties is incorporated (left parties).8 The active labor market policy is to a large extent a socialist project, and it is reasonable to assume that socialist municipalities are more involved in labor market programs. Both variables are based on official statistics. Some PES offices and/or municipalities may, for some reason, be more open to cooperation with others (cf. Alexander 1995, 16). This general orientation may affect the relationship between the PES and the municipality. How can I make sure that it is not a general openness toward collaboration that drives the results? The coping strategy is to include variables measuring the level of communication between the PES and the Regional Social Insurance offices (PES cooperative orientation) and between the municipalities and the Regional Social Insurance offices (mun. cooperative orientation). The Social Insurance offices (Fo¨rsa¨kringskassan) are common partners to the PES offices Big cities (reference category in the regression analyses): Municipalities with more than 200,000 inhabitants. Suburbs: More than 50% of the employed in the municipality travel to a big city to get to their job. Larger cities: More than 50,000 inhabitants in the municipality, and less than 40% employed in the industry sector. Middle-sized cities: 20,000–50,000 inhabitants, with less than 40% employed in the industry sector, and more than 70% living in densely populated areas. Industry: Municipalities that are not sparsely populated, with more than 40% employed in industry. Rural: Municipalities that are not sparsely populated, where more than 6.4% are employed in agriculture and forestry and where more than 70% are living in densely populated areas. Sparsely populated: Municipalities with less than five inhabitants per square kilometer and with less than 20,000 inhabitants. Other larger: Other municipalities with 15,000–50,000 inhabitants. Other smaller: Other municipalities with less than 15,000 inhabitants. This classification is also used in, for example, Dahlberg and Johansson (2002). Data source: KFAKTA03. 8 As an robustness check, I have used an alternative operationalization of left parties. According to the alternative assessment, a municipality is considered socialist if the chairman of the executive board represents the Social Democrats. Results are not substantially altered. 7
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(Lundin 2005, 15) and to the municipalities (Swedish Association of Local Authorities 1999, 23). The idea is that by including these two controls, factors that impinge on the authorities’ relationship both to the Social Insurance offices and to each other can be held constant. The measures are based on questionnaire data. The respondents were asked how often they communicate with the Social Insurance office. The ordinal response alternatives were recoded into a continuous variable. Various ways of doing this was tested, without any substantial change in the results. The following operationalization is used: ‘‘daily contacts’’ is set to 18 times per month, ‘‘at least once a week’’ is set to eight times per month, ‘‘at least once a month’’ is set to two times per month, ‘‘at least once a year’’ is set to every second month, and ‘‘more seldom or never’’ equals zero times per month. The agencies’ own resources need to be taken into account. An agency having a lot of financial resources is probably not that dependent on external resources, all else equal. Two variables based on official figures for 2003 are included to control for the municipalities’ financial strength: tax incomes (mun. tax base) in 1,000 crowns per inhabitant and state subsidies (state subsidy) in 1,000 crowns per inhabitant. Data to calculate the PES offices’ financial resources (PES finances) come from the questionnaire. The available benefits to clients participating in active programs in 1,000 crowns per week, divided by the number of clients per week in 2003, is employed as an indicator of the PES office’s financial strength. The size of the PES (PES size), operationalized as the number of caseworkers at the office, is an additional control variable. The reason for including this variable is primarily that the dependent variable is not relative to office size. One of the items in the cooperation index is about frequency of communication: Do the authorities have daily contacts? A PES with around 100 employees is more likely to have daily contacts than an office with only a couple of caseworkers. Thus, cooperation is somewhat biased toward large agencies. By including size, this deficiency is taken into account. I do not have reliable data on the size of the municipal labor market administration. This does not, however, impose a big problem. Several of the already mentioned control variables are likely to account for this possible effect. Large administrations are, for example, expected to be found in large cities, in municipalities governed by left parties, and in municipalities spending a lot on labor market programs. As an additional test, I have included the number of inhabitants in the municipality as a proxy of the size of the labor market administration; the results are not substantially changed. Some PES offices are specialized on work disabilities, and they have rather different objectives than the ordinary offices. The results may, in some way or another, be affected by this. Thus, I include a dummy variable called work rehabilitation PES that takes on a value of one if the PES is specialized on work disabilities, otherwise its value is zero. Lastly, it is possible that the level of cooperation increases if the actors perceive the relationship to be on equal terms. If one of them think that the other is trying to dominate them, the incentives to collaborate may decrease. A dummy variable (equal terms) is constructed from the questionnaire responses as a measure. If both parties declare the relationship to be on equal terms, the variable gets a value of one, otherwise zero.9 9 The following question was asked: ‘‘How are the terms of your organization’s interaction with the PES office (municipality) set?’’ The response alternatives were as follows: ‘‘completely on the municipality’s terms,’’ ‘‘mostly on the municipality’s terms,’’ ‘‘equal terms,’’ ‘‘mostly on the PES office’s terms,’’ and ‘‘completely on the PES office’s terms.’’
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FINDINGS
Ordinary least squares regression analysis is used to evaluate the hypotheses. Table 4 reports the results from six different model specifications, with more controls added successively. In models 1–3, interaction terms are left out; the purpose is to demonstrate how the effects would look like if the interactions are not taken into account. Models 4–6 include interaction terms. In models 1 and 4, environmental characteristics are used as control variables. Agency features, except finances and whether the relationship is set on equal terms, are added in models 2 and 5. Models 3 and 6 include all control variables. Internal missing values, especially concerning PES finances, imply a decrease of useable observations in the specifications including all controls. Diagnostic plots and the BreuschPagan test indicated problems of heteroscedasticity. For this reason, robust standard errors are used in all specifications. The coefficients in models 1–3 are rather similar, and interpretation can therefore be focused solely on the coefficients presented in model 3. Long-term clients, non-Nordic clients, goal congruence, and trust affect cooperation along the lines of previous research; the signs of the b-coefficients are expected, and the effects are statistically significant at conventional levels. In a dyad where the authorities trust each other, compared to an identical dyad without mutual trust, the predicted number of collaborative activities will be about 0.45 higher, all else equal. If goal congruence improves by one unit, the average level of cooperation changes by roughly 0.23 units, holding everything else constant. An increase of one percentage point of clients being long-term unemployed or being nonNordic citizens increases cooperation by approximately 0.05 units on average, all else equal. To get a better indication of the joint impact of the main independent variables, it is possible to compare two dyads. In the first dyad, the agencies put mutual trust in each other, goals are rather similar (goal congruence held at the third quartile), and interdependence is relatively high (long-term clients and non-Nordic clients held at the third quartile). In the second dyad, the agencies do not trust each other, goals diverge (goal congruence held at the first quartile), and mutual dependence is low (long-term clients and non-Nordic clients held at the first quartile). In all other respects, the dyads are identical. The predicted level of cooperation will, on average, be around 1.36 units higher in the first dyad. This is a relatively large difference, given the scale of the dependent variable (0–5). Models 4–6 contain the relevant information to evaluate the four hypotheses. However, it is not possible to assess the details directly from table 4 since interaction effects are estimates conditional on the values of the other interaction variables. The results in table 4 can only provide a rough indication. The b-coefficients of long-term clients and non-Nordic clients in models 4–6 are more or less unchanged in comparison with models 1–3, and the interaction terms that include these two variables are small and statistically insignificant. This indicates that there is no interaction between long-term clients and nonNordic clients, on the one hand, and trust, on the other hand. Thus, there is no support for hypotheses 1 and 3. However, the effects of trust and goal congruence changes dramatically. Moreover, the interaction term of trust and goal congruence is positive, large, and statistically significant at conventional levels. This suggests that there are interaction effects in accordance with hypotheses 2 and 4. As noted above, interaction effects are conditional effects, and the analysis should not stop at this point. Allison (1977), Braumoeller (2004), and Friedrich (1982) thoroughly
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Table 4 OLS Regression Analysis with Cooperation as Dependent Variable, Six Model Specifications (Robust Standard Errors in Parentheses)
1 Non-Nordic clients Long-term clients Goal congruence Trust Trust non-Nordic clients Trust long-term clients Trust goal congruence Unemployment Non-Swedish citizens Population change Work rehabilitation PES PES size PES cooperative orientation PES finances Mun. LMP spending Left parties Mun. cooperative orientation Mun. tax base State subsidy Equal terms Constant Municipality type, dummies Standard error of regression Number of observations
0.04 0.04 0.25 0.40
(0.02)** (0.02)* (0.09)*** (0.17)**
0.12 (0.09) 0.03 (0.02)* 0.10 (0.06)
2 0.05 0.04 0.29 0.37
(0.02)*** (0.02)* (0.09)*** (0.15)**
0.05 0.05 0.23 0.45
0.02 0.04 0.04 1.68 0.02 0.06
(0.09) (0.02)* (0.07) (0.54)*** (0.01)** (0.02)***
0.01 0.03 0.05 1.22 0.02 0.05 0.46 0.01 0.01 0.01 0.01 0.05 0.26 3.93
0.03 (0.04) 0.02 (0.01)* 0.03 (0.02)
10.48 (6.55) Yes 1.19 203
3
6.90 (7.06) Yes 1.08 202
Mun., municipality; LMP, labor market program; OLS, ordinary least squares. *p , .10, **p , .05, ***p , .01.
(0.02)*** (0.02)** (0.09)*** (0.16)***
(0.10) (0.02)* (0.08) (0.36)*** (0.01)*** (0.02)*** (0.39) (0.04) (0.01) (0.02) (0.01) (0.02)*** (0.18) (8.53) Yes 1.03 187
4 0.03 0.04 0.12 1.82 0.00 0.00 0.30 0.12 0.03 0.09
(0.02)* (0.03) (0.13) (1.43) (0.02) (0.04) (0.18)* (0.09) (0.02) (0.06)
5 0.05 0.05 0.12 1.95 0.00 0.02 0.35 0.02 0.04 0.04 1.87 0.02 0.05
(0.02)*** (0.03)* (0.12) (1.30) (0.02) (0.03) (0.15)** (0.09) (0.02)** (0.07) (0.64)*** (0.01)*** (0.02)***
0.03 (0.04) 0.02 (0.01)* 0.03 (0.02)*
8.96 (6.75) Yes 1.19 203
5.08 (7.42) Yes 1.08 202
6 0.04 0.06 0.09 1.65 0.00 0.01 0.30 0.01 0.03 0.04 1.27 0.02 0.04 0.46 0.00 0.01 0.01 0.01 0.04 0.27 3.05
(0.02)** (0.02)** (0.12) (1.27) (0.02) (0.03) (0.15)** (0.10) (0.02) (0.08) (0.39)*** (0.01)*** (0.02)*** (0.37) (0.04) (0.01) (0.02) (0.01) (0.02)*** (0.18) (8.85) Yes 1.02 187
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Table 5 Conditional Effects: The Effects of Non-Nordic Clients, Long-Term Clients, and Goal Congruence as the Level of Trust Changes (Robust Standard Errors in Parentheses)
Trust Non-Nordic clients Long-term clients Goal congruence
Low
High
0.04 (0.02)* 0.06 (0.03)* 0.09 (0.12)
0.05 (0.02)* 0.04 (0.03) 0.39 (0.10)**
Note: The conditional effects are based on the estimates from model 6 in table 4. *p , .05, **p , .01.
discuss the commonly made mistakes when interaction variables are included in regression analysis. The main thrust of their argument is that the b-coefficient and the standard error of a variable included in an interaction term depend on the value of the other interaction variable. It is necessary to calculate the impacts of long-term clients, non-Nordic clients, and goal congruence when trust is low and high and to compute the effects of trust at various levels of long-term clients, non-Nordic clients, and goal congruence to find out the details. Tables 5 and 6 report conditional effects based on the coefficients in model 6 in table 4. The results stay about the same if the coefficients from model 4 or 5 are used instead.10 In table 5, we can see that the effect of non-Nordic clients is almost the same when trust is high and low: a one percentage point increase of unemployed clients from outside the Nordic countries yields, on average, an increase of cooperation by roughly 0.05 units in both cases (significant at the .05 level). The effect is marginally larger when trust is high, but the difference is negligible and not robust to model specification. A similar story applies to long-term clients, but in this case, the b-coefficient is a bit larger when trust is low and insignificant when trust is high. This is opposite to what hypothesis 1 suggests. But the results are not robust to model specification. In some specifications, the variable long-term clients has a significant and positive effect of about the same magnitude when trust is high as when it is low. In sum, the effect of resource interdependence does not rely on mutual trust, and hypothesis 1 is therefore rejected. However, table 5 shows that hypothesis 2 is strongly supported. The impact of goal congruence is statistically insignificant at any conventional level in cases of low trust. But when trust is high, the effect is significant at least at the .01 level and the size of the b-coefficient is more than three times as large as when trust is low. Thus, the general effect of goal congruence of 0.23 in model 3 hides that the impact is much stronger when organizations trust each other and is insignificant when they do not. Given mutual trust, cooperation increases by 0.39 units, on average, when goal congruence increases by one unit, all else equal. To see how trust affects cooperation, it is necessary to turn to table 6. The table consists of two panels. In panel A, goal congruence is set to 8.15. This is the third quartile of the variable’s distribution, and it is (arbitrarily) chosen to reflect a situation where the agencies’ objectives are rather similar. In panel B, goal congruence is set to 6.77, which is
See Allison (1977), Braumoeller (2004), and Friedrich (1982) for details on how to calculate conditional effects and significance in interaction models.
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Table 6 Conditional Effects: The Effects of Trust as Non-Nordic Clients, Long-Term Clients, and Goal Congruence Changes (Robust Standard Errors in Parentheses)
Long-Term Clients Few (8.14)
Many (14.70)
Few (3.28) Many (8.61)
0.70 (0.23)* 0.71 (0.20)*
0.61 (0.18)* 0.61 (0.18)*
Few (3.28) Many (8.61)
0.29 (0.27) 0.29 (0.25)
0.19 (0.23) 0.19 (0.24)
Panel A. Effects when goals are similar (goal congruence 5 8.15) Non-Nordic clients Panel B. Effects when goals diverge (goal congruence 5 6.77) Non-Nordic clients
Note: The third quartile (8.15) of goal congruence represents a situation in which goals are similar, whereas the first quartile (6.77) implies a situation when the agencies’ objectives diverge to a large extent. The first quartile of non-Nordic clients (3.28) and long-term clients (8.14) represents low resource interdependence, and the third quartile of non-Nordic clients (8.61) and long-term clients (14.70) means high resource interdependence. The conditional effects are based on the estimates from model 6 in table 4. *p , .01.
the first quartile. Consequently, panel B shows the effects of trust, given diverging objectives. In both panels, long-term clients and non-Nordic clients vary. In these cases, the third quartile represents high resource interdependence and the first quartile represents low resource interdependence. In order to evaluate hypothesis 3—i.e., that the effect of trust increases when resource interdependence increases—coefficients within each panel should be compared. There is no indication that the impact of trust becomes larger when non-Nordic clients and longterm clients take on high values. The size of the coefficients and the significance levels are about the same within each panel. If anything, the evidence is opposite to hypothesis 3. Hypothesis 3 is therefore rejected. If we instead compare figures between panel A and panel B, hypothesis 4 can be evaluated. That is, the impact of trust is examined at different levels of goal congruence. The findings are conclusive: panel A shows statistically significant and large positive effects of trust when objectives are similar, whereas panel B indicates statistically insignificant and small effects of trust given diverging objectives. This is precisely what hypothesis 4 suggests. Thus, the effects of trust presented in models 1–3 hide the fact that the impact is much larger when goals are similar and that we cannot expect a positive impact if organizations have very different agendas. To examine the robustness of the results, several diagnostic tests have been performed.11 The most important tests can be mentioned. First, numerous model specifications including different controls have been examined—results are robust. Second, I have examined many diagnostic plots looking for nonlinear relationships and outliers. Some outliers are detected, but I find no substantial reason to exclude them. Nonetheless, the models have been reestimated without them. The results remain more or less unchanged. Third, one concern when analyzing dyadic data is possible interdependence across observations: when one actor is present in several dyads, there is a risk of autocorrelation (see, 11
See Fox (1991) for an overview of most of the diagnostic tests presented here.
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e.g., Mizruchi 1989). Some municipalities are included in more than one dyad. Since they are few, interdependence across observations should not be a big problem. As a precaution, regression analysis excluding dyads consisting of cases that appear more than once has been performed. In some specifications, the variable long-term clients turns out to be insignificant (but not in model 6), which implies some uncertainty regarding the effect of this variable. Fourth, I have also tested excluding dyads in which the response from the municipal side of the dyad is provided by a politician. Again, the variable long-term clients comes out insignificant in a couple of specifications. Fifth, tolerance tests for multicollinearity have been carried out. In models 1–3, it is definitely not a problem. However, the diagnostic statistics indicate that multicollinearity is present in models 4–6. This is a usual setback in interaction models. However, I do not worry much since the problem appears to apply mainly to the variables trust and the interaction of trust and goal congruence (the correlation between these variables is as high as .96)—two variables that are statistically significant in all specifications at least at the .10 level. To sum up, the only noteworthy problem indicated by the robustness checks is that the impact of long-term clients is a bit hazardous. Otherwise, the findings clearly demonstrate that mutual resource dependence enhances collaboration regardless of whether the authorities trust each other or not. The findings also imply that the logic of interorganizational cooperation suggested in additive models should be developed: similar goals and mutual trust do promote cooperation but only when they occur simultaneously. CONCLUSION
Implementation of political decisions will continue to rely on joint efforts of various authorities. Governance in these contexts is challenging and knowledge on how cooperation can be explained is of significant interest. In this article, new data on the relationships between Swedish PES offices and municipalities have been utilized in order to evaluate potential explanations of interorganizational cooperation. Prior research suggests that authorities cooperate because they are resource interdependent, have congruent objectives, and trust each other. This explanation holds in the present case as well. Cooperation between PES offices and municipalities is, at least partly, a consequence of a situation where the authorities need one another to solve problems, give priority to similar objectives, and trust one another. However, the argument in the article is that theory should be adjusted to incorporate interaction effects. The results clearly demonstrate that congruent goals do not promote cooperation if authorities do not trust each other. Furthermore, mutual trust cannot enhance cooperation when authorities have very different priorities. That is, mutual trust and goal congruence must exist simultaneously. The empirical evidence demonstrates that including interaction terms improves our understanding of interorganizational cooperation. But the impact of resource interdependence is not dependent on a high level of mutual trust. Moreover, the effect of trust is not reliant on resource interdependence. To understand these results better, more research is required. What are the management implication of the findings? What should public sector managers who would like to increase collaboration do? This is hard to tell. One strategy would be to highlight what agencies have in common and introduce transparent reporting systems between the organizations. By doing this, congruent objectives can perhaps be communicated in a trustworthy way. It is not a feasible strategy to build up trust if
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the authorities’ goals diverge to a large extent or to focus solely on objectives without establishing trust. That is, the strategy must include both objectives and trust. On the other hand, situations of resource interdependence seem, more or less, to enhance cooperation by itself. The hypotheses examined in the article are general, although the empirical tests are focused on labor market policies in the unitary Swedish system. There is no strong reason to assume that the research setting is atypical to other situations of local policy implementation. We can expect the results to hold for other similar situations as well. Nevertheless, some cautionary points should be mentioned. First, the empirical setting implies a rather truncated range on goal congruence by comparison with other types of collaborations. Disagreements might be more severe in other settings, for example, between public agencies and private enterprises. A good way to test the robustness of the results is thus to examine how they would fare under other conditions, in other countries, and in other policy areas. Second, the measures of resource interdependence are far from being perfect since interdependence is not observed directly. The fact that the results do not indicate an interaction effect between trust and mutual resource dependence might be a consequence of the somewhat problematic measures. Consequently, in future research, it would be beneficial if other indicators of resource interdependence are examined. Third, I have taken some license to speculate on causal relationships while operating within the confines of cross-sectional data. Obviously, a longitudinal study would be suitable to test the propositions put forward in this article. In-depth case studies, or quantitative research, focusing on potential causal mechanisms would also be valuable. Even though the mechanisms are logical, they are assumed rather than tested in this particular case. In sum, and as always, work remains to be done. However, this study has moved the research agenda forward and provided insights about interorganizational cooperation when public policies are carried out at local level.
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