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When Does Cooperation Improve Public Policy Implementation?

When Does Cooperation Improve Public Policy Implementation?

Martin Lundin

Interorganizational cooperation is often considered valuable in the public sector. However, in this article it is suggested that the impact of cooperation on public policy implementation is dependent on the type of policy being carried out. It is argued that complex policies are more effectively put into practice if agencies cooperate a lot, whereas less difficult tasks are handled just as well without interorganizational cooperation. Thus, two policies within the Swedish active labor market policy are examined. The empirical test focuses on the cooperation between Public Employment Service offices and municipal labor market administrations. In agreement with the hypothesis, the findings suggest that policy matters. The implementation of one of the policies—the complex policy—is enhanced if cooperation between agencies increase. On the other hand, cooperation does not improve implementa- tion of the less complex task. The study is based on quantitative data.


KEY WORDS: implementation, interorganizational cooperation, policy, active labor market policy, Sweden

Relationships between authorities impinge on policy implementation (O’Toole, 2003) and it is often emphasized that public policy can be carried out better if cooperation increases among agencies. Some scholars claim that interorganizational cooperation is inherently good (e.g., Hudson, Hardy, Henwood, & Wistow, 1999; Jones, Thomas, & Rudd, 2004), although empirical evidence suggests that coopera- tion only sometimes enhances performance (Jennings & Ewalt, 1998). The purpose of this article is to enrich our understanding of policy implementation by examining when a cooperative strategy actually makes implementation output better. More precisely, it is argued that the effects of cooperation vary with the complexity of the policy carried out.

An agency that cooperates with others can make use of additional resources, such as expertise and information. Activities can hopefully also be better co- ordinated. This suggests that interorganizational cooperation improves an agency’s ability to put policy into practice. On the other hand, it is difficult to work across organizational boundaries. For example, the collaborating authorities have to devote a lot of time and other resources to establishing and maintaining a productive relationship. Thus, we cannot be sure that cooperation improves implementation in

The Policy Studies Journal, Vol. 35, No. 4, 2007


0190-292X © 2007 The Policy Studies Journal Published by Blackwell Publishing. Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford, OX4 2DQ.



every situation. In this article, it is suggested that task complexity is a key factor that explains why the effect of cooperation on implementation output will be greater in certain situations. Basically, the benefits of cooperation are likely to more than offset the costs if the task is complex. In contrast, when the policy is not complex co- operation adds very little value, but still involves some costs. To my knowledge, this idea has previously not been thoroughly discussed and empirically evaluated in implementation research.

The empirical focus is relations between different units of government. More precisely, the analysis concerns Public Employment Service (PES) offices and their relationship to local governments (municipalities) in the Swedish active labor market policy (ALMP). The PES is the main local labor market actor. But in recent years, municipalities have become an increasingly important factor (Lundin & Skedinger, 2006; Salonen & Ulmestig, 2004). The Swedish government encourages cooperation between these actors. But will implementation of labor market activities actually be improved if the PES cooperates a lot with the municipality? Is it possible to anticipate a positive effect on a broad range of policies, or is it only certain labor market activities that are affected positively?

Two policies are examined. One of the policies—activities for unemployed youth—is not that complex. The other—activities for individuals with especially long spells of unemployment—is more intricate. Thus, I expect cooperation to be a more efficient strategy in the latter case. The quantitative analysis is based on recent data consisting of information from several sources. The findings indicate that coopera- tion improves the implementation of activities for individuals unemployed for an especially long time. On the other hand, cooperation does not enhance the imple- mentation of the youth policy. This indicates that complex tasks can be carried out better if a cooperative approach is employed, but that it is not reasonable to assume that interorganizational cooperation will always have a positive impact on how policies are implemented.

The rest of the article is structured as follows. First, the theoretical discourse is outlined. An introduction of the research setting comes next, followed by a section that discusses methodological issues and measures. Empirical findings are reported. I conclude by summing up the results and discussing their possible implications.

Implementation and Interorganizational Cooperation

Local practices are not always the same as the intentions stated in official docu- ments endorsed by politicians. In addition, performance frequently varies from one local context to another. As a result, it is wise not to assume that the study of statutes, government bills, and regulations will be enough to understand what political deci- sions imply “in the real world.”1

An implementation problem occurs when a political decision is not carried out in accordance with what the decision maker wants. We assume that local practice should be in line with the elected officials’ intentions, that is, agents should follow the principal’s instructions. This perspective is easy to endorse, based on normative democratic theory. In a modern democracy, citizens freely elect representatives who

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can be held to account at the ballot box. Politicians cannot implement a policy all by themselves; they have to rely on a civil service to do this. But the citizens cannot replace the bureaucracy by casting their vote. Consequently, a prerequisite for satis- factory democracy is that politicians control and govern a civil service that respects their decisions (Sannerstedt, 2001).

There are arguments in favor of another point of view, which argues that the local civil service is more receptive to local desires and needs. Discrepancies between a decision and its implementation might therefore in practice mean a “better” policy and a greater responsiveness to citizens’ wishes. Depending on which normative starting point is assumed, variation in—or insufficient—implementation perfor- mance is thus not always necessarily a disadvantage (deLeon, 1999). Nonetheless, most scholars would concur that it is troublesome if there is a gap between the law and practice (Keiser & Soss, 1998). In this article, I focus on the top-down aspect of implementation, that is, implementation as compliance. It is, however, necessary to acknowledge that other angles of approach are just as relevant in implementation research.

Certain political decisions are quite easy to carry out and can be managed almost exclusively by a unitary public administration. A change in a tax rate or the level of a general welfare benefit is virtually self-implemented. But implementation is normally more complicated and involves several participants. Hence, an important component in almost every contemporary framework explaining implementation success and failure has to do with how interorganizational relationships are managed (e.g., Bardach, 1998; Goggin, Bowman, Lester, & O’Toole, 1990; Hjern & Porter, 1981; O’Toole, 2003; O’Toole & Montjoy, 1984; Pressman & Wildavsky, 1984; Winter, 2003a).

An agency assigned the task of carrying out a political decision may use different techniques to implement a policy. For instance, the agency may try to cooperate as much as possible with other organizations. Cooperation (collaboration)—that is, all the interactions among organizations aimed at solving public problems by working together (cf. Smith, Carroll, & Ashford, 1995)—is one of the golden words in public- sector management. Through partnerships and other collaborative endeavors, public- sector performance is often considered to be improved. All else being equal, one would expect a public agency with access to significant resources to carry out a policy or program better than an agency which lacks resources (e.g., Keiser & Soss, 1998; Meier & McFarlane, 1996). Organizations possess resources. An agency carrying out a political decision may thus enhance its own capacity by collaborating with other organizations (e.g., Jennings, 1994). The surrounding organizations may contribute with information, and they could have staff, knowledge, money, and premises, making the business of putting ideas into practice easier. Cooperation can also mean that resource-consuming and conflicting activities that may result in socially perverse outcomes are avoided (Hooghe & Marks, 2003). Thus, it is easy to see why inter- organizational cooperation is often assumed to improve implementation.

But there are a number of prerequisites. First, the potential partner must have additional resources that can be of use to the focal agency. Second, the partner must be willing to share resources. Third, there are always costs associated with

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collaboration (e.g., Schermerhorn, 1975; Van de Ven, 1976; Weiss, 1987). Although a potential partner is willing to share valuable resources with the focal agency, coop- eration may be a complicated process. To at least some extent, all organizations have different agendas and various routines to handle things. As a consequence, interor- ganizational cooperation may be a complicated process involving bargaining, and therefore call for considerable time and other resources on the part of the agents concerned. Another cost to be considered is that the principal’s objectives—for example, central government intentions—may be put on one side when the local agents concerned are trying to reach their best joint solution. In other words, instead of pursuing central government directives, local actors may work for other goals they can agree on. In sum, the costs of cooperating might well outweigh the benefits and we should not assume that more cooperation implies that political intentions are always realized to a greater extent.

Cooperation can, of course, have important implications for other aspects than implementation output. For instance, a public program might become more effective if authorities cooperate, even if implementation is not enhanced.2 Cooperation might also imply that a decision becomes more legitimate in the eyes of the target group for a certain policy. But this study is limited to the question of how to make a political decision come true in accordance with officials’ intentions.

Jennings and Ewalt (1998) note that there is only anecdotal evidence that coordination—which in practice is measured as the level of cooperation—actually improves public services. Jennings and Ewalt examine the accomplishment of policy goals in employment and training services in the United States. In a prior article, Jennings (1994) indicated positive effects of coordination on the administrators’ subjective perceptions of performance. But in the study from 1998, objective outcome measures were employed as dependent variables. The analysis shows that coordination has a limited positive effect; most of the indicators were unaffected by the level of coordination. The findings suggest that although interorganizational cooperation may sometimes be a good strategy, we cannot expect it always to improve performance. There is some indication that there is a positive effect in the long run, but the evidence is not decisive. On the other hand, Hudson et al. (1999, p. 238) note that “while recognizing that there are other positions, this article takes the normative position that collaboration is generally a ‘good thing’—a stance which is consistent with the rather long history of collaboration in organization theory and public administration.” In many ways this is certainly true, but if we want to under- stand public administration and public policy implementation it must be better to improve theory on what the balance of benefits and costs would look like in various situations. This can tell us when cooperation actually is a good strategy.

There seems to be a lack of research on these matters. Pressman and Wildavsky (1984) suggest that the number of actors involved in an interorganizational setting determines whether implementation will be successful; more actors mean greater probability of failure. O’Toole and Montjoy (1984) refine this argument and hold that a large number of actors makes implementation worse in cases of reciprocal inter- dependence (when actors poses contingencies for each other) and in sequential interdependence (when the output of one actor is the input of another). Contrarily,

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in cases of pooled interdependence (when actors contribute to a task without dealing with each other) and in sequential competitive interdependence (when there are actors competing to do the same thing in the implementation process) a larger number of actors improves policy output. These scholars focus on the effects of the organizational setting per se, not the extent to which actors work together, which is the focus in this article. The question here is rather whether a lot of cooperation generates a better payoff, given certain tasks. More specifically, is a high degree of cooperation especially rewarding for certain policy types?

The discussion is restricted to one policy dimension that deserves particular attention: task complexity. Task complexity is defined as the product of the scope of actions and the intensity required to carry out a policy. By scope I mean the extent to which many areas of expertise are required to fulfill the goals or, put another way, “the degree to which tasks are variable and require a multidisciplinary or multidi- mensional approach” (Alter & Hage, 1993, p. 117). Intensity is a matter of how much work has to be done in order to realize ambitions; a task that requires many man- hours on the part of an agency is considered intense. A possible objection is that complexity is only about the scope. However, I think that we should consider intensity as well because the problems that a policy which has a great scope implies are multiplied if intense actions also are required. Thus, in this article a policy that has large scope and involves intense actions is considered complex. In research on interfirm partnerships, it is quite common to talk about joint task complexity as a function of the scope and depth (intensity) of interactions between firms (White, 2005). This definition parallels the one used in the present study. Most organization theory scholars define complexity in a related vein, although there are alternatives on the same theme (see, e.g., Alter & Hage, 1993, pp. 116–22).

Task complexity is an incentive for cooperation (cf. Alter & Hage, 1993). A need for external resources boosts interorganizational cooperation (e.g., Van de Ven & Walker, 1984; Weiss, 1987) and complex tasks may demand resources that cannot be found within a single agency. In this perspective, actors cooperate when cooperation is needed, otherwise they do not. But the extent of cooperation between two agen- cies, A and B, is certainly not only a direct function of the task complexity of policy X that A implements. Cooperation is a consequence of the agencies’ perceived total value of working together, which may be something rather different from the actual value that cooperation adds to the task of realizing policy X in accordance with official intentions. First, the agencies do not have perfect information and cannot take in all benefits and costs of cooperation: A does not know exactly what greater value cooperation actually implies. Second, A cannot set cooperation precisely at the optimal level on its own. If B does not want cooperation to increase, A cannot do much about this. Third, the incentives and disincentives for A to cooperate with B are numerous.3 External pressure for cooperation is one example. Political representa- tives or other bureaucratic levels may exhort A to collaborate with B. In some cases, a legal mandate may require A to cooperate with B. Norms and values in both organizations are other things that determine cooperation. Moreover, working together with B can make it easier for A to accomplish other goals than those related to policy X. Accordingly, although the net benefit of cooperation for A can be

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positive, this does not mean that policy X becomes better implemented. In sum, it is too restrictive to assume that task complexity impinges only on cooperation levels. It can also tell us something about the effect on policy output. Whereas the other factors mentioned above affect cooperation, it is only the cost–benefit analysis related to the policy being implemented that determines the effect on implementation in the specific case.

So the question is now why we should assume that the effect of cooperation would be greater as policy complexity increases. In a less complex policy, it is likely that the benefits of cooperation would be quite low. In these situations, the agency’s capacity is not going to increase that much by using other organizations’ resources because the most important resources are already available within the agency. Organizations are formed to perform certain tasks and in a less complex policy the most important resources are already available. This means that increasing levels of cooperation do not add that much value. As complexity rises, cooperation provides more benefits. The agency needs more expertise, information, money, premises, and so on. Through cooperation these resources can be attained.

The costs must also be considered. As complexity increases, the costs of coop- eration are also likely to increase. For instance, the risk of disputes and impediments becomes greater. But while the benefits of cooperation are very low given little complexity, there will always be costs. In fact, many costs are independent of the level of complexity. Cooperation is a dynamic process and decisions and activities within a policy are not independent of each other. Given that actors agree on some basic things, the additional problems that arise with increasing complexity will not be that high. In another context—interfirm alliances—White (2005) considers the costs asso- ciated with cooperation. Even though he claims that costs are higher given a complex task, he repeatedly notes that the benefits are often likely to be even greater. He claims that “additional costs of a more complex interface may be more than offset by the benefits possible from more extensive interaction” (p. 1388) and if the partners have relatively similar objectives and values, “the additional marginal cooperation costs resulting from greater scope or depth-related coordination costs should be more than offset by increased benefits” (p. 1395).

In sum, it seems likely that in a situation characterized by low task complexity there will be very little (or no) benefit from cooperation, but some costs. These costs increase somewhat as complexity increases, but the increase will be lower than the increase in benefits. If complexity is high, the large benefits will prevail over the costs. Cooperation is therefore a more value-adding activity given complex tasks. Accordingly, we should expect the effect of cooperation on policy output to be greater given complex tasks. These arguments are perhaps not brand new, but to my knowledge they have not been discussed and evaluated empirically in the context of local policy implementation.

Intergovernmental Cooperation in Swedish Active Labor Market Policy

To discern whether complexity is an important variable, we need to know the effects of cooperation on the implementation of at least two policies: one complex

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task and another less complex task. Swedish ALMP provides rather good opportu- nities to examine this. Unemployment is a huge problem in most OECD countries (Martin & Grubb, 2001). Job brokering activities and labor market programs are examples of ALMPs a government can use to reduce these problems. In Sweden, the National Labor Market Administration (Arbetsmarknadsverket) implements ALMPs. Local PES offices (Arbetsförmedlingar) carry out most of the programs in practice. There is a PES in nearly all cities, and in larger cities there is usually more than one office.

Although the National Labor Market Administration is clearly the most impor- tant authority managing ALMPs in Sweden, other actors are involved. One feature in recent years has been more involvement on the part of local governments in the implementation of ALMPs (e.g., Lundin, 2007; Lundin & Skedinger, 2006; Salonen & Ulmestig, 2004). The municipalities take an active part by, for example, organizing labor market programs. About 40 percent of the clients involved in programs admin- istered by the National Labor Market Administration are participating in activities in which the municipalities are involved (Lundin & Skedinger, 2006). A lot of the municipal activities are targeted at social welfare benefit recipients (Salonen & Ulmestig, 2004) and at unemployed youth (Carling & Larsson, 2005). The content of the activities varies, but job-search assistance and work practice are quite common.

The municipalities have a premier position within the Swedish political system. For example, they have the constitutional right of self-government; they are comparably large; they can decide on their own organization; and their incomes come mostly from a proportional income tax they can set freely (see, e.g., Bäck, 2003; Gustafsson, 1999). The municipalities provide most of the services of the Swedish welfare state. For instance, they supply day care, care of the elderly, social welfare services, introduction of immigrants into Swedish society, and primary education. Several of these policy areas—the most obvious example being social welfare services—clearly intertwine with ALMPs. This suggests that collaboration with the municipality could be beneficial for the PES.

In the analysis, effects of cooperation between the PES and the municipality on implementation of ALMPs are studied. The study concerns two policies. One of the policies is more complex. The positive effect of cooperation is anticipated to be greater in this policy.

In August 2000, the Swedish government launched a new labor market program called the Activity Guarantee (Aktivitetsgarantin). The target group was individuals who had been unemployed for a considerable time period. By means of intense job-search assistance and close monitoring, persons who were, or who risked being, long-term unemployed were to be given a place in the program. All traditional ALMPs could be used within the Activity Guarantee. Accordingly, the content of the activities varies to a large extent. The political ambition was that jobseekers should be enrolled in the program after 27 months of unbroken PES registration, at the latest. What distinguishes the Activity Guarantee from other labor market programs is its intensity. All activities are assumed to be full-time activities and the participants should meet their personal PES supervisor on a regular basis, and more frequently than before they entered the program. Since the persons participating in the

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guarantee are “hard cases” and often have multiple problems, the government calls for intense treatment and encourages PES offices to collaborate with other local actors. The local governments are, in practice, the PES offices’ most common coop- eration partners (Forslund, Fröberg, & Lindqvist, 2004).

In 1994, the Swedish government declared that no young person should remain unemployed for more than 100 days. The government regarded long-term unem- ployment as devastating for future labor market prospects. Young people are defined as long-term unemployed if they are unemployed for more than 100 days. Thus, the ambition was that every individual of below 25 years of age should be offered a labor market program if they were unable to find a job within three months of registration at the local PES. The central government encouraged the municipalities to take part in actions to reduce long-term unemployment among young people in different ways. For instance, in 1995 the government introduced the Municipal Youth Program (Kommunala ungdomsprogrammet) and three years later the UVG-guarantee (Ung- domsgarantin). The PES offices are expected to collaborate with local governments in both programs (Carling & Larsson, 2005).

Recall that task complexity is defined in terms of the scope and intensity of a policy. The Activity Guarantee is more intense and has greater scope than the youth policy. Thus, it is a good representative of a complex policy (and this applies even if complexity is defined only in terms of the scope). First, consider the client groups. In the Activity Guarantee, the clients are comparatively old and they are obviously not particularly attractive on the labor market because they have been looking for a job for such a long time. It is also reasonable to assume that a relatively large part of the clients have multiple problems because the program is directed at those with the weakest position on the labor market. In comparison, youth clients have not been unemployed for such a long time. Consequently, they are probably more motivated, more homogenous, and easier to handle. Accordingly, the treatment of clients in the Activity Guarantee needs more areas of expertise and more intense activities.

Second, although various youth activities are probably not so simple to imple- ment, the Activity Guarantee is more complex. The activities are supposed to be rigorous and carried out in small groups. Contacts between clients and personal supervisor are also expected to be more frequent than is generally the case. In short, these activities are more intense. Moreover, the government’s intention was that the program should be flexible and contain various activities suited for the individual client. This means that the Activity Guarantee is intended to have greater scope than other ALMPs. On balance, it is safe to conclude that the Activity Guarantee is much more complex than the youth policy, even though some youth activities can also be said to contain complexities. Thus, I predict a greater effect of cooperation on imple- mentation output in the Activity Guarantee.

The selected research setting is suitable for several reasons. First, unemployment is a large societal problem and a lot of resources are allocated to diminishing the unemployment rate all around world. Studies that can bring some clarity to what is going on when ALMPs are put into practice are therefore important. Second, this study is focused on the relationships between the same types of actors who imple- ment the same policies around Sweden. This implies that features of the agencies and

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the policies that do not vary can be held constant. From a methodological point of view this is very beneficial. Third, Swedish ALMPs can be seen as a “critical case,” meaning that here, if anywhere, we ought to expect that cooperation will improve implementation on a broad range of activities. O’Toole (1983) claims that there are very high information and expertise requirements in ALMPs. Interorganizational cooperation is therefore likely to be a good strategy for gathering the necessary resources. Furthermore, there are obvious connections between the responsibilities of the PES offices and the municipalities. This means that we have good reasons for believing that cooperation is a good strategy when it comes to both policies.

Method and Measures

Whereas quantitative approaches have become increasingly common and more sophisticated in the implementation literature from the United States (e.g., Brehm & Gates, 1997; Daley & Layton, 2004; Meier & McFarlane, 1996), studies based on large-N data in the European context are rare. This study is based on European quantitative data and is therefore a significant contribution.4 The PES offices are the unit of analysis and cross-section data covering 2003 is used. Questionnaires were distributed to the chief managers of all PES offices in February 2004: 268 managers answered the questionnaire, which implies a response rate of 75 percent. An analysis of the nonresponses showed no noticeable difference between respondents and nonrespondents on background characteristics. Register data from the National Labor Market Administration supplement the survey. Lastly, official statistics, in the form of municipal characteristics, are taken from the KFAKTA03 database. Variables are discussed below (see also the Appendix).


In the questionnaire, the PES managers were asked whether the PES and the municipality had set up regular cooperative groups in which (i) caseworkers from the two authorities work together and (ii) the managers collaborate. The managers also indicated whether (iii) caseworkers contact each other on a daily basis or more seldom. In addition, they provided information about whether the agencies had formal collaborative contracts concerning (iv) the youth policy and (v) the Activity Guarantee.

Various factor analyses show that all items load high on a single dimension (see Lundin, in press). Thus, an index was constructed. It is a simple additive index: A value of zero implies that none of the above mentioned ways of cooperating was used, whereas agencies that use all five activities got a score of five. To make interpretation easier, the factor scores obtained from the analysis are not utilized in the article. But the analyses have been carried out based on the factor scores as well, and the conclusions are unchanged.

One problem with the index is that two of the items are directly associated with the youth policy and the Activity Guarantee, respectively. The other three items are more general. I have therefore decided to test the robustness of the results. I use the

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complete index with all five indicators, but also an alternative index ranging from zero to three in which the collaborative contracts in (iv) and (v) are excluded. In addition, the contracts are employed as indicators of cooperation. Summary statistics are reported in Table 1.

Implementation Performance

Three measures of implementation performance are employed in the article. I consider enrollment in programs among youth (Y-Enrollment) and enrollment in the Activity Guarantee (AG-Enrollment). I also study whether the PES arranges full-time activities within the Activity Guarantee (AG Activity). Obviously, there are many other aspects of the performance that ideally should be taken into account. The quality of the programs is not directly assessed in this way and in the worst case the measures capture symbolic rather than real implementation. But the performance measures have certain appealing features. The variables concern vital parts of the policies, the elected representatives’ directives are also relatively clear, and quanti- tative data are available and reliable. These three factors make them suitable depen- dent variables and they are definitely important things to consider if we want to know how government ambitions have been realized.

Two variables address the implementation of the Activity Guarantee. First, unemployed persons are supposed to be enrolled in the Activity Guarantee after 27 months of unbroken PES registration at the latest. I calculate how large a propor- tion of the individuals with 835 days (27.5 months) or more of PES registration was activated in the Activity Guarantee on four occasions (February 15, May, August, and November). The average value indicates how well the agency manages to enroll clients. The reason for not using precisely 27 months is to avoid problems that a delay in registration might entail. This variable is named AG-Enrollment and a higher score implies better implementation. Data come from the National Labor Market Administration.

Second, the activities should be full-time for all participants in the Activity Guarantee. In the questionnaire, the managers were asked whether the PES arranges full-time activities for all (or almost all) of the participants in the Activity Guarantee. The information from the questionnaire is used as a second measurement of the implementation of the program. This variable can take on two values (0 = no, 1 = yes) and a score of one thus implies better implementation. The variable is referred to as AG Activity.

Table 1. Cooperation between PES Offices and Municipalities, Various Indicators

Mean/ Proportion Standard Deviation Minimum Maximum

Cooperation, five items 3.50 1.36 0.00 5.00 Cooperation, three items 1.96 0.96 0.00 3.00 Youth contract 0.77 — — — AG contract 0.80 — — —

PES, Public Employment Service.

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The central government ambition for youth unemployment is clear-cut: if indi- viduals under the age of 25 have not found a job after 100 days of unemployment the PES should activate them by applying labor market programs. Using data from the National Labor Market Administration administrative system, I investigated the stock of persons of 20–25 years of age on four occasions (February 15, May, August, and November) in 2003 for each PES office. I counted the number of individuals unemployed for 110 days or more in sequence, without participating in a program, directly before the point of measurement. By setting the point of calculation to 110 days instead of 100, I made sure that the results were not affected by a possible delay in registration. To standardize the measure, I divided the values by the number of youth clients registered at the PES for 110 days on the same occasions. In order to make interpretation easier, the computed values were subtracted from one. The average of the four points of measurement was calculated. This gives the variable Y-Enrollment. A higher value means better output.

Table 2 describes how the offices have succeeded in implementing the youth policy and the Activity Guarantee. There are youth who do not participate in programs but who should, according to the government’s intentions. The Activity Guarantee has not been fully implemented either because around half of the target group is not engaged in activities and only 7 in 10 managers say that they arrange full-time activities. Note that there is a lot of variation in implementation performance.

Why should we expect that cooperating with the municipality makes it easier for the PES to improve the implementation aspects mentioned above? There are many possible reasons and I shall briefly mention two: First, if the municipality can con- tribute with money, staff, and premises it becomes easier for the PES to set up the neccessary activities in order to enroll clients and make sure that the actions really are conducted on a full-time basis. Second, if the municipality helps the PES to find work places where participants can participate in work practice and so on, the PES can devote more time to making sure that the target group is reached and that programs are conducted full-time.

Control Variables

Unfortunately, the implementation research discourse has not been able to develop a generally accepted theory that pinpoints the precise variables to include when explaining implementation. As O’Toole (2004, p. 310) puts it, “Theories about policy implementation have been almost embarrassingly plentiful, yet theoretical consensus is not on the horizon. . . . After hundreds of empirical studies, validated

Table 2. Implementation of the Youth Policy and the Activity Guarantee

Mean/Proportion Standard Deviation Minimum Maximum

Y-Enrollment 0.66 0.14 0.10 0.95 AG-Enrollment 0.49 0.16 0.00 0.97 AG Activity 0.71 — — —

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findings are relatively scarce.” But there are some broad categories of factors that merit attention. External and internal characteristics of an agency carrying out public policies may influence implementation (Gill & Meier, 2001; Keiser & Soss, 1998; Winter, 2003a).

External characteristics that should be taken into account are the client group treated and the implementation environment (Winter, 2003a). A demanding clientele and a difficult labor market situation might affect implementation positively or negatively. On the one hand, the task becomes more challenging, which may make implementation more difficult. On the other hand, the incentives to arrange appro- priate programs are probably stronger because it is less likely that clients can manage on their own. There should also be greater demand for services when the labor market situation is problematical.

Three variables that describe clients’ characteristics are used as control variables. Some offices are responsible for vocational rehabilitation of unemployed individuals. These agencies’ clientele is quite different from the client group of the standard PES, and therefore a dummy variable for rehabilitation PES is incorporated in the analyses. The other two controls are the proportion of long-term unemployed clients and the proportion of clients who are not Nordic citizens (Non-Nordic clients). By including these variables, I am able to hold important clientele characteristics constant.

The characteristics of the PES offices’ local context are measured by the follow- ing variables. The municipal unemployment rate, including participants in active labor market measures, provides an assessment of the local labor market situation. The size of the local population is added as a control in order to address other socioeconomic factors; the local labor market is very different in a large, urban area in comparison with a thinly populated municipality with a small population. A dummy variable (socialist government) indicating the presence of a Social Demo- cratic government or a nonsocialist government captures the local political context. This political variable’s importance for implementation has been stressed in earlier studies (Keiser & Soss, 1998). Leftist governments are usually more positive toward governmental interventions such as labor market programs (Korpi, 2006). Lundin (2007) demonstrates that left-wing local governments in Sweden spend more money on labor market activities than right-wing governments, at least if the local entity is not tiny. It is reasonable to expect that a PES office operating in a context in which socialist values are strong have an easier task implementing policy ambi- tions, all else being equal. This is a strong motivation for controlling for local politi- cal circumstances.

Performance could also vary as a result of factors internal to an agency. Keeping external circumstances constant, agencies which have employees who are more willing to carry out a policy and who have a high capacity to implement decisions will, on average, perform better (Sannerstedt, 2001).

There is solid evidence that the incentives, preferences, and attitudes of bureau- crats often affect implementation (Brehm & Gates, 1997). Implementation is likely to be improved if an agency gives a policy high priority. The questionnaire supplies data on the PES managers’ attitudes toward different labor market goals. Among 13 labor market objectives, the managers rated the importance of arranging programs

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for unemployed young people and for unemployed individuals with severe problems on the labor market. From this rating, I have constructed a ranking of the objectives of each PES office. The rating is composed so that the goal that receives the highest priority gets a score of 13; the other goals are arranged in descending order so that the least important objective gets a score of one. Thus, the variables priority of youth clients and priority of difficult clients assess how important the two relevant client groups are to the PES offices.

It is difficult to obtain good indicators of an agency’s capacity. But staff and financial resources are the main factors influencing capacity. If the agencies do not have these resources they will certainly run into difficulties when policies are imple- mented (Gill & Meier, 2001; Sannerstedt, 2001). The National Labor Market Board allocates resources so that each agency receives an amount reflecting the local labor market situation and the client group (Nyberg & Skedinger, 1998). Thus, if resources are allocated adequately, capacity should not vary because of staff and financial resources. Nevertheless, in the event that resources are distributed poorly, the number of clients per staff member and the amount of financial resources per client reserved for benefits for participants in programs are used as control variables.

Several organizational factors could shape the agency’s capacity (Winter, 2003a). Identifying the important organizational characteristics is difficult, and the capacity may impinge on various variables we cannot observe. In this article, organizational size is employed as a control variable. This is primarily motivated by the fact that large agencies have greater opportunities to cooperate with other organizations. For instance, a large staff increases the chance that an agency will communicate with the municipality on a daily basis. Another reason for including size is that this variable could be correlated with many organizational aspects that may affect implementa- tion. The logarithm of the number of employees at the PES is utilized as a measure of organizational size. I use the logarithm because it is reasonable to assume that a one-unit change in the number of employees is more important when staff size is small than when it is large.


In the following section, I examine whether the degree of cooperation can account for the differences in implementation performance depicted in Table 2. The empirical results are reported in two subsections.

Effects of Cooperation: The Activity Guarantee

Estimates from ordinary least squares (OLS) regression models are presented in Table 3. The dependent variable is AG-Enrollment. The number of observations is between 201 and 212, although the number of questionnaire respondents was 268. Some agencies do not handle the Activity Guarantee and there are also internal missing values for some variables. Thus, the number of valid cases is reduced.

In Model 1, the index that includes all five indicators of collaboration is used as the main independent variable. The results indicate a positive effect of cooperation

Lundin: Cooperation and Public Policy Implementation 641



on implementation. When controlling for internal and external characteristics, the share of clients engaged in the Activity Guarantee increases by roughly 3 percentage points, on average, if cooperation increases by one step. The effect is significant at the 0.01 level. The impact is substantial but not huge. An increase in cooperation by one step is quite a large increase (almost a standard deviation) and this yields an increase of clients enrolled in the program by a moderate 3 percentage points. But it is not reasonable to expect that a lot of cooperation between the PES and the municipality should imply a dramatic difference. And cooperation seems not unimportant: going from the sample minimum to the maximum increases in enrollment by 15 percent- age points, on average.

If the cooperative contracts are excluded from the collaboration index (see Model 2) the effect becomes a little bit lower. But it is still of importance (0.027) and it is significant at the 0.05 level. Model 3 indicates that agencies having a formal coop- erative contract, on average, manage to engage about 9 percentage points more of the target group, all else being equal. The effect is statistically significant at the 0.01 level.

Table 3. OLS Regression Analysis with AG-Enrollment as Dependent Variable (Standard Errors in Parentheses)

1 2 3

Cooperation, five items 0.030*** (0.010)

Cooperation, three items 0.027** (0.012)

AG contract 0.090*** (0.026)

Rehabilitation -0.091 -0.092 -0.081 (0.083) (0.074) (0.073)

Long-term unemployed clients 0.481* 0.482* 0.541** (0.256) (0.247) (0.245)

Non-Nordic clients -0.432** -0.348* -0.375** (0.183) (0.179) (0.176)

Unemployment -0.021*** -0.020*** -0.021*** (0.005) (0.005) (0.005)

Local population 0.000 0.000 0.000 (0.000) (0.000) (0.000)

Socialist government -0.007 -0.003 -0.008 (0.023) (0.022) (0.022)

Priority of difficult clients 0.004 0.003 0.004 (0.004) (0.004) (0.004)

Clients per staff member -0.012* -0.013* -0.010 (0.007) (0.007) (0.008)

Financial resources 0.046 0.040 0.054 (0.035) (0.034) (0.034)

Organizational size -0.049** -0.043** -0.032** (0.017) (0.017) (0.016)

Constant 0.593*** 0.635*** 0.553*** (0.083) (0.081) (0.083)

Adjusted R2 0.180 0.152 0.187 Standard error of regression 0.139 0.139 0.137 Number of observations 201 212 208

*p < 0.10; **p < 0.05; ***p < 0.01.

642 Policy Studies Journal, 35:4



The contract seems to account for a noticeable part of the overall effect, which is not surprising because the contract is linked directly to the Activity Guarantee. To sum up, cooperation has a positive effect on implementation no matter how the variable is operationalized.5

Control variables are only commented upon briefly. Client group characteristics and the labor market situation affect implementation. Moreover, large organizations perform worse, while agencies having few clients per staff member achieve objec- tives to a greater extent.

The level of activation within the Activity Guarantee is a dichotomous variable. The estimation method when using AG Activity as the dependent variable is there- fore binary logit. Results are reported in Table 4. The logit model yields information about whether the relationship is positive or negative and if it is statistically signifi- cant but the coefficients are not readily interpretable. Thus, predicted probabilities, based on logit coefficients, are reported in Figure 1.

Table 4. Binary Logit Regression Analysis with AG Activity as Dependent Variable (Standard Errors in Parentheses)

1 2 3

Cooperation, five items 0.345** (0.175)

Cooperation, three items 0.241 (0.223)

AG contract 1.539*** (0.470)

Rehabilitation -0.205 -0.998 -1.289 (1.536) (1.355) (1.388)

Long-term unemployed clients -3.468 -2.068 -2.210 (4.591) (4.422) (4.594)

Non-Nordic clients 3.170 3.610 3.701 (4.250) (4.176) (4.405)

Unemployment 0.450*** 0.447*** 0.464*** (0.137) (0.134) (0.139)

Local population -0.003* -0.004** -0.004** (0.002) (0.002) (0.002)

Socialist government -0.276 -0.225 -0.229 (0.413) (0.403) (0.418)

Priority of difficult clients 0.146** 0.151** 0.191** (0.073) (0.070) (0.075)

Clients per staff member -0.097 -0.094 -0.085 (0.122) (0.119) (0.129)

Financial resources 0.081 -0.021 0.167 (0.612) (0.583) (0.620)

Organizational size -0.664** -0.556* -0.472 (0.330) (0.326) (0.313)

Constant -1.514 -1.152 -2.821* (1.433) (1.389) (1.540)

Pseudo R2 (McFaddens R2) 0.185 0.187 0.217 Log likelihood -98.037 -102.276 -96.456 Number of observations 197 208 203

*p < 0.10; **p < 0.05; ***p < 0.01.

Lundin: Cooperation and Public Policy Implementation 643



Table 4 indicates that cooperation increases the probability of arranging full-time activities within the Activity Guarantee. But when the cooperative contract between the PES and the municipality is excluded from the index (see Model 2), the effect is statistically insignificant. The findings are very robust to model specification.

Figure 1 displays the probability of arranging full-time activities as the level of cooperation changes. In this graph, logit estimates from Model 1 have been used. The probability varies between approximately 0.40 and 0.80 depending on how much the agencies collaborate. This is a large difference. The effect is, of course, even stronger if the contract is employed as the indicator of cooperation, but weaker and insignifi- cant at all conventional levels if the contract is excluded from the model specification.

A few notes on control variables are appropriate. The probability of arranging full-time activities within the Activity Guarantee increases when the PES office prioritize clients with an especially difficult situation on the labor market and when unemployment is high. Large organizations and agencies located in large cities have a lower probability of arranging full-time activities. Note that it seems that the control variables do not affect the variables AG Activity and AG-Enrollment in the same manner.

The findings presented above clearly indicate that cooperation between PES offices and municipal labor market administrations improves the PES offices’ imple- mentation of the Activity Guarantee. There are, however, two methodological chal- lenges to the results. First, there might be some important control variables that are left out from the model specification. The capacity of the PES offices is probably the most difficult thing to assess in a quantitative study. I have tried to control for as many variables as possible and the problems are probably not that big. Although I recommend some caution, it seems we can be quite confident in the conclusions.

0 0 .2

0 .4

0 .6

0 .8


P r(

fu ll

tim e

a ct

iv ity


0 1 2 3 4 5


Figure 1. Predicted Probabilities of Organizing Full-Time Activities within the Activity Guarantee as the Level of Cooperation Changes (Continuous Control Variables Held at Mean Values and Discrete

Control Variables Held at Mode Values).

644 Policy Studies Journal, 35:4



Second, the time order between cooperation and implementation might be reversed. For example, if a PES office arranges full-time activities for clients in the Activity Guarantee, the need for external assistance may increase and/or munici- palities may be more interested in collaborating with the PES. In turn, this will lead to more cooperation between the authorities. This is certainly a possibility, although the assumed time order is probably more likely. But this problem should not be ignored. One way to test causal order is to check the timing. Cross-section data are limited in providing evidence of time precedence, whereas panel data have advan- tages (Finkel, 1995). Some panel data are available for a smaller number of cases (137 agencies). I have information about whether the PES arranged full-time activities for the clients in the Activity Guarantee in 2001 and 2003. I also know if a collaboration contract existed in 2001 and 2003.6 These data can be utilized to estimate a two-wave cross-lagged effect model, which is a test of the time order between variables. The basic idea is to predict each variable (AG-Enrollment and AG Contract) in 2003 by its previous value in 2001 (the lagged dependent variable), as well as the value of the other variable in 2001.7

The cross-lagged model cannot be regarded as a definite solution to the time- order problem, although it is a useable test. Data exist only for a subsample of agencies and only some of the important dependent and independent variables are available. I also have to assume that the causal lags are about two years. That is, signing a contract in 2001 affects implementation approximately in 2003. It is rea- sonable to assume that there is some time lag, although two years is perhaps on the high side. To save space, a table or a figure reporting the results is not presented in the article. But the test indicates that having a collaborative contract in 2001 signifi- cantly (at the 0.05 level) increases the propensity to organize full-time activities in 2003. For instance, having a cooperation contract in 2001 increases the probability of arranging full-time activities in 2003 by 0.24, given that full-time activities were not arranged in 2001. On the other hand, the effect of arranging full-time activities in 2001 on the propensity to have a collaborative contract in 2003 is not statistically significant (although there is a small positive coefficient). This speaks clearly in favor of the time order assumed in the analyses. That is, cooperation precedes implementation.

Effects of Cooperation: The Youth Policy

In 2003, the Activity Guarantee was a more complex task than the youth policy. Cooperation is therefore expected to be a less successful strategy when it comes to youth. The findings are reported in Table 5. The table shows estimates from OLS regression models. Robust standard errors are reported, due to heteroskedasticity.

The regression coefficients of the cooperation indicators are very close to zero regardless of how cooperation is operationalized in Models 1–3. The effect is also statistically insignificant. The only reasonable conclusion is thus that cooperation does not affect implementation. Instead, traditional factors highlighted in the literature appear to account for the variation in performance. More clients per staff member and a larger share of clients with an especially difficult situation on the labor

Lundin: Cooperation and Public Policy Implementation 645



market make implementation worse. An increase of financial resources and a chal- lenging local labor market situation result in a larger share of young unemployed clients enrolled in labor market programs.

To test the robustness of the findings, numerous diagnostic tests have been carried out. I have also specified the model in many ways using various sets of control variables. The results are robust and there is no indication whatsoever that collaboration improves the implementation of the youth policy. I have no data to examine time order but it is reasonable to assume that cooperation is causal prior to implementation since the analysis in the previous section provided some evidence of this.

To summarize, cooperation improves the implementation of the Activity Guar- antee but not the youth policy. Is this really a consequence of task complexity or does something else explain the findings? The research setting implies that many poten- tial explanations are taken into account: for example, the policy area, the actors involved, and the local context. This means that the explanation has to do with the

Table 5. OLS Regression Analysis with Y-Enrollment as Dependent Variable (Robust Standard Errors in Parentheses)

1 2 3

Cooperation, five items -0.004 (0.008)

Cooperation, three items -0.004 (0.011)

Youth contract -0.004 (0.018)

Rehabilitation -0.037 -0.124 -0.033 (0.081) (0.097) (0.080)

Long-term unemployed clients -0.599** -0.554** -0.598*** (0.235) (0.229) (0.222)

Non-Nordic clients -0.516*** -0.560*** -0.529*** (0.170) (0.175) (0.171)

Unemployment 0.019*** 0.018*** 0.019*** (0.004) (0.004) (0.004)

Local population -0.000 -0.000 -0.000 (0.000) (0.000) (0.000)

Socialist government 0.014 0.010 0.012 (0.016) (0.015) (0.016)

Priority of youth clients 0.004 0.003 0.004 (0.006) (0.005) (0.006)

Clients per staff member -0.023*** -0.023*** -0.023*** (0.006) (0.006) (0.006)

Financial resources 0.044* 0.048* 0.046* (0.026) (0.026) (0.026)

Organizational size -0.018 -0.018 -0.022* (0.013) (0.013) (0.012)

Constant 0.758*** 0.766** 0.762** (0.092) (0.090) (0.092)

R2 0.397 0.405 0.407 Standard error of regression 0.110 0.110 0.109 Number of observations 211 222 216

*p < 0.10; **p < 0.05; ***p < 0.01.

646 Policy Studies Journal, 35:4



policies. A hypothesis competing with complexity is the agencies’ interests in the two policies. The municipalities may, for some reason, pay less attention to the youth or the agencies’ objectives concerning youth may diverge to a large extent. But quantitative evidence indicates that the target groups of both policies receive equally and very high priority from both authorities (Lundin, 2007). Thus, task complexity is a more promising explanation.


Interorganizational cooperation is an important factor to consider in policy implementation. Contacts between various public authorities are inescapable in con- temporary democratic states. Practitioners and scholars are therefore interested in understanding how interorganizational relationships impinge on the delivery of political decisions. But when does cooperation actually improve implementation? Research thus far has not provided a satisfying answer to this question. Part of the answer is offered in this study: The impact of cooperation increases with task complexity.

Collaborative efforts of PES offices and municipal labor market administrations in Sweden have been examined. The findings indicate that cooperation improves implementation of measures directed toward persons who have been unemployed for a very long time. The implementation of measures directed toward youth is not affected. The striking difference between these policies, it is argued, is that the program for clients with an especially difficult situation on the labor market (the Activity Guarantee) is much more complex. Three questions emerge as a conse- quence of the analysis: How reliable are the findings? Could they be generalized? And what are the implications?

The empirical results have been subjected to numerous checks and the findings appear to be robust. It seems that we can be quite confident of the results. A couple of minor caveats should be reported: Data are cross-sectional, which implies some restriction when it comes to causal statements. The analysis is also limited to a couple of measures of implementation performance and to two policies.

It is always difficult to generalize the results from a study of a certain policy area to other contexts. But the present research setting provides rather good opportuni- ties. I have argued that the case comes near to being a “critical case.” Labor market policies require a lot of resources, such as information, to be implemented effectively. The PES offices and the municipalities hold important resources, there are clear connections between the authorities, and they share the overall goal of reducing unemployment. Thus, if we do not find positive effects of cooperation on the imple- mentation of both complex and less complex policies within this policy area, we are not likely to find positive effects when the demand for resources is less obvious and the ties between the actors are less clear.

The main conclusion of this study is that interorganizational cooperation is a reasonable strategy to improve policy implementation—but only under certain cir- cumstances. This conclusion is important for central discourses in political science, such as implementation research and research on multilevel governance. There is a

Lundin: Cooperation and Public Policy Implementation 647



general positive attitude toward cooperation. The findings suggest that it is appro- priate to be more careful. Decision makers in the public sector should not always stress the importance of cooperation, but rather reflect on the necessity to collaborate as much as possible across organizational boundaries. Many public concerns could probably be handled quite well without extensive cooperation. This does not, however, mean that interorganizational cooperation should be completely avoided.

The idea of solving public problems by means of partnerships of actors is popular both in “real life politics” and among academic scholars. For example, several researchers taking the “governance” discourse as a point of departure praise the partnership model.8 This model, based on the idea that several local actors should collaborate in order to improve the delivery of public policy, has had a pervasive breakthrough in recent years. The findings of this study suggest that we should not be too eager to praise the partnership model. Cooperation might be valuable for other reasons than those investigated here. But for the sake of clarity, it is important to develop and test theories about when interorganizational cooperation improves implementation. This is better than stipulating that cooperation is a “good thing” in general.

Martin Lundin, Ph.D., is a researcher at the Institute for Labour Market Policy Evaluation (IFAU) and the Department of Government, Uppsala University.


1. For overviews of implementation research, see, for example, Winter (2003b) and deLeon (1999).

2. Even if a decision is carried out perfectly in line with the politicians’ intentions, that does not entitle one to say that the policy is effective. The decision itself could be based on a causal theory that is not accurate, that is, it is not certain that desired outcomes are achieved by the formula the statute or regulation suggests. This article focuses exclusively on policy outputs, not outcomes.

3. For an overview, see Alexander (1995, chap. 1).

4. The lack of quantitative studies implies at least two problems. First, it is difficult to generalize the findings when studying only one or a couple of cases. Second, it is extremely difficult to control for a large number of potential explanations in case studies; almost inevitably we end up in a situation in which several variables account for the variation in the dependent variable equally well (e.g., Goggin, 1986; Winter, 2003b). Qualitative studies are still important, but right now quantitative research based on European data is probably more needed.

5. To examine the robustness of the results, several diagnostics tests have been carried out. I find no problem of heteroskedasticity or multicollinearity. There are some influential outliers but the results are not substantially altered if outliers are excluded from the regression model. Diagnostic plots did not indicate any nonlinear relationships. There might be some problem of endogeneity regarding two control variables: the share of long-term unemployed clients and the unemployment rate. I have estimated models excluding these variables. Conclusions remain the same. In addition, numerous model specifications including various control variables have been tested. The results are quite robust, although the two cooperation indices turn out to be statistically insignificant in a small number of specifications. The effect of the contract is very robust.

6. Data for 2001 come from a research project at the Institute for Labour Market Policy Evaluation (IFAU); see Forslund et al. (2004).

7. The cross-lagged model is compatible with the “Granger test” for causality employed in time series analysis. Finkel (1995) describes the cross-lagged model and the underlying assumptions.

8. See, for example, Pierre (2000) for an overview of the governance literature.

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Table A1. Description of Variables

AG Activity The PES office arranges full-time activities for all (or almost all) clients participating in the Activity Guarantee. Dummy variable: 0 = no (0.29); 1 = yes (0.71). Based on questionnaire data.

AG-Enrollment Proportion of persons registered for a sequence of 835 days or more at the PES office enrolled in the Activity Guarantee; average of four different points of measurement (15th of February, May, August, and December 2003). Based on register data from the National Labor Market Administration. Mean: 0.49, SD: 0.16, min: 0.00, max: 0.97.

Y-Enrollment Young persons aged 20 to 25 with at least 110 days of open unemployment in sequence, divided by the total number of young people registered at the PES for at least 110 days; average of four different points of measurement (15th of February, May, August and December 2003). In order to make interpretation easier, the computed values were subtracted from one. Based on register data from the National Labour Market Administration. Mean: 0.66, SD: 0.14, min: 0.10, max: 0.95.

Cooperation, five items Index of cooperation. Includes cooperative groups at manager level, cooperative groups at caseworker level, daily communication at caseworker level, cooperative contract concerning the youth policy, and cooperative contract concerning the Activity Guarantee. Based on questionnaire data. Mean: 3.50, SD: 1.36, min: 0.00, max: 5.00

Cooperation, three items Index of cooperation. Includes cooperative groups at manager level, cooperative groups at caseworker level, and daily communication at caseworker level. Based on questionnaire data. Mean: 1.96, SD: 0.96, min: 0.00, max: 3.00.

AG Contract The PES office and the municipality have signed a cooperation concerning the Activity Guarantee. Dummy variable: 0 = no (0.20); 1 = yes (0.80). Based on questionnaire data.

Youth Contract The PES office and the municipality have signed a cooperation contract about youth programs. Dummy variable: 0 = no (0.23); 1 = yes (0.77). Based on questionnaire data.

Rehabilitation office PES office responsible for vocational rehabilitation. Dummy variable: 0 = no (0.96); 1 = yes (0.04). Data obtained from the Internet homepage of AMV, http://www.ams.se.

Long-term unemployed clients Share of clients at the PES office unemployed for six months or more in 2003. Based on register data from the National Labour Market Administration. Mean: 0.11, SD: 0.05, min: 0.00, max: 0.30.

Non-Nordic clients Proportion of clients at the PES office without Nordic citizenship in 2003. Based on register data from the National Labour Market Administration. Mean: 0.07, SD: 0.07, min: 0.01, max: 0.62.

Unemployment Local unemployment rate in percent, including participants in measures, in the municipality where the PES office is located (April 2003). Based on official municipal statistics from KFAKTA03. Mean: 5.47, SD: 2.31, min: 1.90, max: 19.60.

Local population Number of inhabitants in the municipality (in 1,000’s of persons) where the PES office is located (December 2002). Based on official municipal statistics from KFAKTA03. Mean: 95.56, SD: 192.14, min: 2.61, max: 758.15.

Lundin: Cooperation and Public Policy Implementation 651



Table A1. Continued

Socialist government The chairman of the municipal executive board represents the Social Democrats. Dummy variable: 0 = no (0.33); 1 = yes (0.67). Based on official municipal statistics from KFAKTA03.

Priority of difficult clients The PES manager’s rank of the objective “ensuring that there are labour market programmes for groups of unemployed with severe problems in the labour market” among 13 objectives (scale 1–13, where 13 means highest priority). Based on questionnaire data. Mean: 11.16, SD: 2.46, min: 2.00, max: 13.00.

Priority of youth clients The PES manager’s rank of the objective “ensuring that there are labour market programmes for young people under 25” among 13 objectives (scale 1–13, where 13 means highest priority). Based on questionnaire data. Mean: 12.21, SD: 1.85, min: 4.00, max: 13.00.

Clients per staff member The average number of clients (measured in 10 clients) per week per employee at the PES office. Based on register data from the National Labour Market Administration. Mean: 4.96, SD: 1.74, min: 0.26, max: 12.44.

Financial resources The amount of financial resources at the PES office reserved for benefits to clients participating in active measures (in 1,000 Swedish crowns) per week divided by the number of clients per week. Based on questionnaire data. Mean: 0.66, SD: 0.35, min: 0.00, max: 3.01.

Organizational size The logarithm of the number of employees at the PES office. Based on register data from the National Labour Market Administration. Mean: 2.63, SD: 0.78, min: 0.00, max: 4.65.

652 Policy Studies Journal, 35:4

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