Policy Networks: Resolving Collective Environmental ... - CiteSeerX

Policy Networks: Resolving Collective Environmental Problems in Estuaries *

John T. Scholz Brad Kile Ramiro Berardo

The Florida State University Department of Political Science

* An earlier draft of this paper was presented at the 2003 Midwest Political Science Association Annual Meeting, Chicago, Illinois.

In structured policy arenas, institutional rules, norms and established functional processes combine to shape a stable political environment with established and accepted procedures for addressing the collective needs of participants. Even in the most structured policy arenas, however, the complexity and fragmentation of formal policy processes provide an important role for informal policy networks that can facilitate agreements and coordinate activities among policy participants (Heclo 1978). Policy networks in this environment are established, expanded, maintained, or disbanded within the formal institutional structure, which determines the incentives for participants to engage in these networks. The analysis presented here focuses on networks in an entirely different policy context in which no structured, authoritative process exists to address collective problems. In arenas lacking institutional structures, policy networks may provide the only form of decision-making process to resolve these problems. Policy networks in this environment have the potential to facilitate successful management of second-order collective action problems by coordinating stakeholder activities intended to create new institutional forms and to directly resolve collective problems. Our empirical focus is on estuaries, the place where rivers meet oceans. The governance of estuary resources imposes the kinds of collective action problems common to the management of all natural resources, particularly since authority over these resources is fragmented among numerous local governments, special management districts, and specialized state and federal agencies. Policy networks located within the estuary may provide the only mechanism for integrating and

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coordinating the actions of multiple government agencies and other stakeholders trying to cope with the bewildering array of fragmented, overlapping authorities. Previous research by Schneider et al. (2003) described these problems in greater detail, and also demonstrated that the federal National Estuary Program (NEP) successfully increased the size and coordinating capacity of networks in NEP estuaries. We extend that study in three ways. First, we broaden the exclusive focus on the NEP in that paper to investigate the impact of other factors on the size of policy networks in these estuaries. In particular, we view networks from the individual’s perspective, and consider the motivation provided by the policy, individual, and organizational context in which the active policymakers are embedded. Second, we analyze the impact of networks on levels of cooperation and agreement. Thus we are concerned with both the motivation for expanding policy networks and the extent to which expanded networks contribute to the resolution of collective problems in relatively unstructured policy environments. Third, we analyze the second wave data from the same panel of policy stakeholders. This extended data set includes a much more accurate measure of network size, and also allows us to resolve some problems of causal direction by using first-wave measures to predict second-wave dependent variables. This paper proceeds as follows. First, we discuss a framework for ana lyzing policy networks and the incentives that guide individual participation in networks. We present several hypotheses regarding the relationship between the size of the network, agreement among stakeholders, and cooperation. We then discuss the design of our research, including the two-staged survey process that allows us to control for mutual causation among key factors. Next, we present findings from three models that

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examine the factors that influence network size, agreement among stakeholders, and cooperation. Finally, we conclude with a discussion of the dynamics of policy networks in resolving collective environmental problems in estuaries.

Networks and Collective Action Problems in Estuaries Following Heclo (1978) as interpreted by Schneider et al (2003; 143), “the networks we study are built around the routine, deliberate contacts among people who regard each other as knowledgeable about public policy issues in order to work out options, debate evidence, and discover alternative options-- though rarely in any controlled, well-organized way.” We focus on the ability of networks to resolve the collective action problems confronting stakeholders in a given estuary, as described in greater detail in Schneider et al (2003). The basic problem is that fragmented authorities lead to policies that interact in ways that diminish the overall environmental gains in the estuary. Cooperation across multiple authorities could lead to joint optimization of policies, but such cooperation imposes the well-known dilemmas of collective action. We explore the role of informal policy networks in resolving these dilemmas in the unstructured political arenas of estuaries. Successful intergovernmental collaboration among agencies with no formal means of coordinating policies, for example, depends on the development of an effective implementing network—“a set of individuals who design or operate the machinery that makes for smooth day-to-day operations within the constraints set by the various partner agencies” (Bardach 1998). Government agencies are only part of the policy networks we study in estuaries, but we

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are particularly interested in the ability of these networks to expand the levels of cooperation among fragmented policy authorities.

Networks and Policy Agreement Since our focus is on collective action problems in unstructured policy arenas, we begin with Putnam’s perspective that “networks facilitate coordination and communication… and thus allow dilemmas of collective choice to be resolved” (1995: 67). In this perspective, networks in a given community form part of its social capital. An extensive system of personal networks can enhance the stability, multiplicity, and directness of personal relationships throughout the community. In longstanding policy networks such as those identified as “advocacy coalitions”, shared discussions and experiences over time can lead to the development of a shared vision about core policy beliefs and shared policy goals ( Zafonte and Sabatier 1998). Although Taylor and Singleton (1993) do not directly consider networks, they argue that the personal relationships and shared values induced by networks play a critical role in lowering the transaction costs involved in resolving a community’s collective action problems without the imposition of central authority. To the extent that the policy networks we observe in estuaries function to create a community of shared beliefs, more extensive networks should lead to broader agreements about the problems facing an estuary and the potential policy solutions necessary to resolve those problems, and therefore to greater coordination of policies within the estuary.

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Networks and Cooperation Although networks may ultimately contribute to the resolution of estuary problems by encouraging widespread agreement, we argue that widespread agreement is not a necessary condition for some agencies to collaborate on limited projects of mutual interest. Indeed, as Bardach (1998) warns, attempts to resolve all differences and gain full agreement on policy may divert efforts away from more concrete goals of coordinating some aspects of policy among some agencies. Lubell (2002) found that consensual institutions are more successful in creating higher levels of policy agreement in estuaries than in producing any substantial change in the levels of cooperation. He argues that: “if process is the product, then consensual institutions may actually do more harm than good by creating perceptions of progress in the absence of any real change, thereby reducing the expressed political demand for the desired policy without addressing the environmental and social problems that generated the demand” (Lubell 2002: 1).

As Bardach observes “visions are easy to share. Costs are harder” (1998: 242). In short, agreement without cooperation and cooperation without agreement are both possible. Thus collaboration on a limited set of actions requiring little agreement may at least in the short run enhance cooperation and coordinated policy outcomes more than attempts to establish broad agreements. Networks may help agencies “muddle through” (Lindblom 1959) to more coordinated actions in an estuary even without full agreement on the overall goals for the estuary.

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The contractual perspective explains how networks can contribute to this more limited form of cooperation that does not depend on creating broader agreements. In this perspective, the benefits of networks flow from their ability to allow individuals and the organizations they work for to make mutually beneficial exchanges or “contracts” for collaborative projects that otherwise would not take place (Granovetter 1985, Rauch and Casella 2001). Agreement on goals is not necessary for such collaboration, although agreement can be helpful in ensuring the credibility of commitments that is necessary for all partners of the contract. Networks enhance the likelihood and scope of policy agreements by increasing available information about potential agreements and enhancing the credibility of commitments to fulfill the agreements.1 By spanning organizational boundaries in fragmented policy arenas, networks provide information about the myriad details of organizational decision-making as well as potential implementation problems in each organization, which allows stakeholders to develop previously unexplored opportunities for collaboration. Furthermore, networks increase the credibility of commitments not only by providing a richer information base about potential contract partners, but also by transforming short-term interactions into repeated games in which a reputation for reciprocity and trustworthiness can potentially mitigate the problem of opportunism involved in single exchanges (Rauch and Casella 2001). The size of an organization’s set of contacts is directly proportional to the gain in information and credibility, and hence the expected level of cooperation. More policy contacts lead to greater information about potential opportunities for mutually

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These dimensions of network advantages are similar to Knoke's (1990) communication networks and resource networks.

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advantageous collaboration (Granovetter 1974; Lee 1969). Perhaps more importantly, expanded contacts can also enhance the credibility of commitments that are necessary to develop and maintain coordinated projects. The larger contact set provides a larger source of potential punishments for both the agency and the agreement partner if an agreement is broken. An organization with a larger contact set has more to lose if it breaks an agreement, at least to the extent that breaking agreements with one organization will diminish the possibilities of cooperation with the other organizations. Furthermore, the agreement partner may also face greater punishment to the extent that the organization’s contacts will share in punishing the defector. Thus larger network size increases both the trustworthiness and the willing ness to trust potential partners for collaboration.

Network Dynamics and the Contact Set Network benefits accruing to the active organizations and the policy community alike are constrained by the costs of developing and maintaining policy contacts, which the contract perspective refers to as transaction costs of developing potential collaboration and agreements (Lubell et al 2002). Time and effort are required to locate, cultivate, and maintain contacts outside the organization, and it is uncertain which if any contacts will ultimately be useful. The costs of network development within estuaries are exacerbated by the structure of U.S. federalism, which creates vertical fragmentation between levels of the federal system and horizontal fragmentation between local jurisdictions in a specific geographic location like an estuary. In particular, this fragmentation has limited the role

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of estuary-specific scientific knowledge in policymaking since local research scientists are more closely linked with professional associations and national funding agencies than with specialized local agencies. Furthermore, the highly adversarial character of traditional environmental policy has created a legacy of conflicting environmental and developmental advocacy coalitions, where the tight-knit networks and shared beliefsystems within each coalition inhibit cooperation between coalitions (Sabatier and Jenkins -Smith 1993, Zafonte and Sabatier 1998). The individuals involved in policy networks outside their organization ultimately must make the decision between developing and maintaining these policy contacts or spending the same time and energy on other aspects of their job. Both time and resources constrain the extent of the contact set developed by network participants. Further, as the size of an individual’s contact set increases, the value of adding each new contact to the network can be expected to decline marginally. Given the importance of the individual’s motivation in developing networks, we focus in this paper on the size of a participant’s contact set as our measure of network size. Although there are many alternative ways of measuring network size and expansiveness (see Scott, 1991), we believe that this empirically convenient measure will capture most of the characteristics of networks important to our current analysis. The hypotheses to be developed next focus specifically on the participant’s contact set as our measure of network size. In order to understand the factors that increase the size of the contact set, we will focus on the policy, individual, and organizational contexts that shape the motivations to develop this set of policy contacts. In order to explore the impact of network size on levels of agreement and cooperation, we focus on

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the impact of network size and the same contextual factors on levels of policy agreement perceived by the participant in the estuary as well as the extent of collaboration in joint projects by the participant’s organization. The social capital perspective on netwo rks predicts that network size would have equal influence on agreement and cooperation. The more instrumental contractual perspective, on the other hand, would predict that network size would primarily impact cooperation.

Estuary Policy Context Contextua l characteristics of a policy arena are expected to influence network size, the perceived level of agreement and cooperation. When participants perceive the policy arena as containing characteristics that will yield a higher return on their cost investment, they will be more likely to expand the size of their network. For example, when an individual perceives that the policy arena is effective in handling problems they will be more likely to invest their time in developing a network because of the higher potential for a gain. By contrast, individuals that perceive the policy arena as ineffective will be discouraged from investing time and resources when the return-on-investment is dubious. Thus, we expect that participants with more favorable perceptions of the policy arena will engage in expanded networks.

National Estuary Program The National Estuary Program (NEP) has played a key role providing a venue for estuary stakeholders to formulate policy. John (1994: 268) claims that civic environmentalism, characterized by the strong role of collaborative practices for the

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solution of environmental problems, must rely heavily on the facilitation role played by the higher level of government. Consistent with this argument the NEP, which depends on the Environmental Protection Agency, offers select estuaries resources aimed at strengthening contacts and facilitating cooperation and agreement among the stakeholders. We test this hypothesis by exploring how the membership of the estuary in the NEP (that has been launched by the federal government) influences network size and facilitates the emergence of cooperation and perceived levels of agreement. Lubell (2002: 2) argues that “according to the political contracting perspective, the consensual governing style of the NEP reduces the transaction costs of cooperation among stakeholders embroiled in watershed-scale environmental conflicts, and thus should produce changes in behavior.” Hence, we expect a positive relationship between NEP membership and network size, cooperation and perceived levels of agreement.

Trust Trust that other policy participants will fulfill their obligations in the estuary is expected to have a positive relationship on network size, perceived agreement, and cooperation. The importance of trust in creating collaborative behavior is a central focus in the literature concerned with the positive features of creating higher amounts of social capital. As Ostrom observes (1990: 183) “when individuals who have high discount rates and little mutual trust act independently, without the capacity to communicate, to enter into binding agreements, and to arrange for monitoring and enforcing mechanisms, they are not likely to choose jointly beneficial strategies unless such strategies happen to be their dominant strategies.” Thus, trust should be a central

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feature in serving as an incentive to expand policy contacts as well as to produce cooperation and higher levels of perceived agreement. This is one of the main propositions of the transactions costs perspective. In Scholz and Lubell’s words (1998: 399) “…trust enhances social capital by reducing the costs of maintaining cooperation” (italics added).

Problem Severity The severity of the environmental problems faced by stakeholders in the estuary should be positively related to network size, perceived levels of agreement and to the level of cooperation reached by the network participants. When stakeholders face a more severe problem in the use or distribution of the resource in the estuary, they are expected to use every tool available to them to overcome the problem.

Fairness The willingness of an individual to commit to expanded networks and cooperative activities or perceive high levels of agreement among the stakeholders is expected to be e xtremely low if he or she does not consider that the process is fair. Following the contractual approach, it is not just the quantity of a contact set can influence the tendency of an individual to engage in cooperative behavior or reach more agreement with others. The quality in the relationship with others is also an important feature that can provoke changes in the way an individual interacts with other stakeholders. Schneider et al (2003: 145) argue that factors such as the frequency of interactions, the perceived fairness of the local policy decision process, and a belief in

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the efficacy of the policies help reduce the transaction costs of political contracting, which make expanded networks, cooperation and agreement even more profitable for individua ls engaged in interactive relationships. Thus, we expect to observe a positive relationship between evaluated fairness of the policy process in the estuary and network size, cooperation, and perceived levels of agreement.

Teamwork In this analysis we a lso examine the level of teamwork between stakeholders because we expect higher levels of perceived teamwork will lead to expanded networks, as well as increased agreement and cooperation in those estuaries in which this kind of collaborative behavior among participants has already taken place. As Ahn et al (2002: 15) observe an individual’s belief about the behavior type of others “plays a crucial role, along with the individual’s own preference type, in his decision making.” Consequently, we expect that more collaboration in the past leads -ceteris paribus- to more cooperation. On the other hand, there is not a clear theoretical reason to link previous level of teamwork and perceived levels of agreement in the estuary. Hence, we do not expect to find evidence supporting a relationship between these two factors.

Domination by Experts Perceptions regarding domination in the policy process by economic interests and experts may also influence network dynamics in estuaries. We expect that individuals who perceive dominance in the process will respond by constraining their network. Those policy processes in which economic interests are perceived to be more

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powerful or where experts in technical issues are viewed as having more influence in the decision-making process will lead individuals to withdraw from the policy process. Further, the preeminence of technical direction given by experts in the process could be a disincentive to participate for individuals that do not have the background to engage in technical arguments. If this is the case and the respondents identify this characteristic in the policy arena, then the perception of level of agreement and the tendency to engage in cooperative activities should be reduced. Hence, as perceptions regarding the undue domination of economic interests and experts increase, we expect that network size, the perceived level of agreement, and cooperation will decrease.

Policy Changes Another hypothesized relationship we examine links the policy changes that took place in the estuary and network size, perceived agreement and cooperation. A favorable assessment of changes within the policy arena is expected to lead to deeper engagement in collaborative activities. It follows that those who perceive policy changes favorably will be likely to engage in expanded networks, cooperative arrangements, and have a positive perception of agreement among stakeholders in the estuary.

Individual Context The individual preferences and career circumstances of the individual can alter the motivation to expand their network. For example, an individual who has spent more time in a given job and has lived longer in the estuary is expected to have more past

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contacts than more recent arrivals, and hence will be more likely to have a larger network. In addition, the individual’s personal concern for environmental conditions is likely to alter the size of the contact set. Public policies relating to shared natural resources are frequently framed along the dimension from strong environmental protection interests to pro-development interests. For example, the debate over the use of waterfront property often pits developers who emphasize economic interests against environmentalists who emphasize the need to preserve and protect natural resources. Individuals who value development (or some other outcome) above protection of the environment have considerably less incentive to invest personally in an estuary policy arena that inevitably emphasizes the appropriate management of natural resources. These individuals are more likely to focus their attention on policy arenas that emphasize economic development. This unbalanced incentive structure serves to motivate those with greater environmental concerns to develop enhanced networks within the estuary policy arena we are studying. We expect that the core environmental concerns of a participant will influence network size in such a way that participants with high concern for environmental policies will have an expanded network. In turn, we also expect a positive relationship between environmental concerns and perceived levels of agreement and cooperation.

Organizational Context The type of organization a network participant represents is also expected to be a key factor influencing network size. Networking activities of an individual are

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constrained by the mission and resources of his or her organization, and the overall motivation of the organization will undoubtedly affect the incentives provided to encourage individuals to expand their network. We argue that participants desiring change in public policy will be those most likely to pursue an expanded network, since they will likely derive the highest potential benefit from an active policy network. In unstructured political arenas where the absence of clear authority ensures that the status quo is difficult to change, organizations with greater incentive to seek changes are more likely to encourage expanded policy networks and seek opportunities for mutually advantageous collaboration. Thus, we expect that participants working for business interests will have smaller networks because they are likely to prefer a stable, unchanged policy environment. The fragmentation and resultant number of veto points that could limit cooperation provides opportunities for those groups most closely associated with existing agencies to exert their influence within the existing institutional framework. We also expect the same dynamic to be in place for perceived agreement and cooperation.

II. Research Design The data used in this paper were collected through a two -wave telephone survey of stakeholders in 22 estuaries in the U.S.2 The survey was part of an assessment of the National Estuary Program (NEP) reported in Schneider et al (2003). Twelve of the 22 estuaries were selected because they were part of the NEP, and the remaining 10 were selected from the approximately 100 major estuaries in the U.S. to match the

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A mail survey was used for part of the sample in wave 1, as explained in Schneider et al 2003.

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regional distribution and physical characteristics (watershed condition; agricultural runoff potential; per capita income; population density) of the NEP estuaries. The resultant list of estuaries is included in Appendix B. The panel survey consisted of two waves. The first was conducted from March to July 1999, and the second wave from March to June 2001. The analysis is based on a total of 479 individuals that responded to both waves of the survey3. We analyze three models to estimate the factors that influence the size of an individual’s policy network, the level of perceived agreement, and the level of cooperation. Next we provide a description of the three dependent variables estimated in our models. A complete description of all indicators used in this analysis is provided in Appendix A.

Measuring Network Size The size of a respondent’s network was measured as the total number of contacts identified in the second-wave survey. In the first wave, respondents identified up to three individuals or organizations whom they had contacted regularly on issues related to policies relevant to their estuary. A list of organizational affiliations for major contacts from all respondents in each estuary was compiled for that specific estuary. Respondents in the second wave were asked to indicate which organizations on this list they contacted regularly. Additionally, respondents were asked to write-in any additional contacts. Our measure is a count of all the contacts checked or listed by the respondent, which ranges from 1 to 57 in our sample.

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For more information regarding the techniques used to identify the respondents, the type of questionnaire used, and the rate of responses obtained, see Schneider et al 2003.

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We believe that this two-step process is necessary to ensure a relatively accurate count of network size, since without the list respondents would have to rely fully on memory to complete the task. The checklist minimizes this problem. The checklist approach also minimizes the problem that busier or less thorough respondents might list proportionately fewer of their contacts if asked to list them, since the task of checking from a list is simple enough that busier respondents are unlikely to differ significantly from others. As an additional precaution agains t this potential threat to validity, we include in our analyses a measure of the time taken to complete the survey. The relationship between size and completion time was not significant.

Measuring Agreement Respondents assessed the level of agreement among stakeholders on several issues related to problems in the estuary. A summary variable was constructed that averaged the responses to issues including causes of estuary problems, severity of estuary problems, the amount and type of research needed, the best policy tools to address problems, the economic consequences of estuary policies, and the environmental consequences of estuary policies.

Measuring Cooperation Our measure of cooperation identifies the collaborative actions that an organization takes with one or more other stakeholders in the partnership. Respondents were asked to identify which of the following collaborative activities relating to estuary policies the organization they work for had participated: provision of information to

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another organization; sharing of personnel; collaboration on joint research projects with other stakeholders; collaboration on joint grant/funding proposals; creation of an interagency taskforce; signing of memorandums of understanding; and, sharing or permitting regulatory activities. In the models we estimate, we use an indicator measuring the proportion of collaborative activities identified by the respondent. In this sense, we do not try to measure cooperation in the estuary as a whole, rather we focus on the activities of the stakeholder in relation to other participants in the policy arena.

III. Analysis of Results The first column of results in Table 1 presents the coefficients and standard errors of the negative binomial regression used to estimate the factors that influence network size.4 Because the dependent variable in this model, size of the contact set, is a count variable it is necessary to move from a linear regression model in order to minimize the risk of generating estimates that are inefficient, inconsistent, and biased (Long 2001). Therefore, we turn to the Poisson regression model that accounts for the count dependent variable where the probability of a count is determined by the Poisson distribution and the mean of the distribution is a function of the independent variables (Long 2001). The basic Poisson model constrains the conditional mean of the outcome to equal the conditional variance. We performed a statistical test and determined that our data produce a violation of this constraint. This indicates that the appropriate econometric model is the negative binomial, which is a form of Poisson model that does

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The dependent variable is a count variable of the number of contacts identified by survey respondents, making the negative binomial the appropriate model specification. See Long (2001).

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not impose this constraint. 5 The second and third columns present the results of OLS regression models for estimating impacts of the same factors on perceived agreement and cooperation.

Network Size The results demonstrate that the policy, individual, and organizational contexts all influence network size in the estuary as expected, although several of the factors we expected to increase the number of contacts did not have significant effects. Beginning with the policy context, we reconfirm the findings of Schneider et al. (2003) that the National Estuary Program significantly increases network size. Being in an NEP estuary inc reases the expected number of contacts by 12.7 percent, holding all other factors constant. The additional funding, energy, and enthusiasm provided by the NEP appears to justify the added time and effort required to increase network size. On the other hand, none of the other policy factors had significant effects. The perceived severity of problems did not elicit a broader network. Nor did a favorable policy arena reflecting trust, fairness, teamwork and lack of domination appear to stimulate the size of networks. And finally, positive changes in the perceived support and effectiveness of estuary policy were apparently not important in creating incentives to extend the size of the network. We find these results to be puzzling, since each of these factors was expected to either increase the payoffs or reduce the costs of developing and maintaining policy networks.

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In practice, Long (2001) reports that this is frequently the case, making the negative binomial the appropriate specification for many analyses with count dependent variables.

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The individual’s context had a more consistent impact on network size. Not surprisingly, individuals who reported larger contact sets in the past continue to report larger contact sets two years later in the second-wave survey. Each additional contact reported in the past (ranging from 0 to 3) leads to a 6.2 percent increase in the expected number of contacts reported two years later, holding all other factors constant. In addition, individuals who have spent a longer time in the community tend to have larger networks. The time in the community is more important than the time in a given job position, probably reflecting the fact that participants tend to shift more frequently from job to job within the policy community than they do from estuary to estuary. Job shifts within the estuary do not have the same impact on local contacts as moving to a different estuary, so the years in a given job are less critical than the years in the community in increasing the network size. Relatively stable policy communities appear more likely to maintain the advantages of networks than communities with more rapid changeovers in population.



Of all the political and individual context variables, the ideological orientation of the respondent has by far the largest impact on network size. This factor taps the “prodevelopment v. pro-conservation” and “less government v. more government” perspectives that often serve as the framework shaping the policy process in estuaries. As participants are increasingly concerned with an active policy to protect the environment rather than a minimal policy to encourage economic development, they

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invest much more heavily in expanding their policy networks. Each one percent increase in an individual’s concern towards protecting the environment is expected to increase the network size by .34 percent, holding other factors constant. More dramatically, a change from the lowest concern for the environment to the greatest concern for the environment leads to a full one -third increase in the magnitude of the expected network size, holding other factors constant. Comparing environmental concerns with the other individual and policy context factors, the magnitude of the effect of environmental concerns is nearly three times as great as NEP membership status and more than five times greater than the effect of the number of contacts reported in the past. As noted earlier, this relationship reflects the fact that respondents most concerned with changing water policy are the ones for whom the investment in wider policy networks to influence this policy is most worthwhile, and those most concerned with environmental values and active government are the ones who seek the greatest changes in local water policies. Those who are less concerned about environmental problems appear to be more willing to leave policy with existing institutions. Respondents most concerned with economic development would presumably invest in greater contacts in other policy arenas directly affecting these developments. Many such policy actors are involved in the water policy arena either defensively to prevent adverse economic impacts or have relatively narrow concerns for which broader contacts are less worthwhile. Before turning to the organizational context, we note in passing that the survey response time is not significant. This was included to check the validity of a

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counterargument claiming that differences in the size of networks is simply a reflection of the time respondents are willing to take in listing their contacts. The organizational context provides additional evidence that respondents in business organizations, who are presumably less interested in expanding environmental policies, behave differently in the estuary policy context. Since business was the excluded category of organizational types in our analysis, the positive, significant coefficients for all other types of organizations in Column 1 indicate that businesses systematically invest less in networks than all other organizations involved in estuary policies. Government, research, and environmental organizations all reflect similar levels of investment in policy networks, resulting in networks approximately 44 percent larger than business organizations, holding all other factors constant. As noted earlier, we speculate that businesses have much narrower, more focused interests in very specific agencies, and are more concerned with protecting existing access and veto points for new initiatives than with investigating the alternatives for broader policy agreement and more extensive areas of coordinated policies. Business interests have the economic resources to wield political influence through channels other than cooperative networks. In sum, we find that the dominant motivation for expanding networks comes from a commitment to improve environmental concerns, with prior contacts and the length of time in the a rea also contributing as expected to larger contact sets. On the other hand, perceptions of the characteristics of the policy arena that would presumably enhance cost/benefit incentives for individuals to expand or contract their contact sets had no clear impact.

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Agreement The results presented in Table 1 for the level of agreement and level of cooperation support the perspective that they are independent phenomena with very different underlying dynamics. The initial discussion emphasized the common argument that local policy networks are one aspect of social capital that can bolster both agreement and cooperation. Our results, however, indicate that network size is not very closely related to agreement. The coefficient for the size of the contact set is not significant and not even in the right direction, suggesting again that the informal networks are not closely related to the more formal policy institutions in the estuary. The perceived level of agreement is influenced by several aspects of the policy context that did not affect network size, which further underscores the possibility that networks are substitutes rather than complements to the more formalized policymaking institutions in the estuary. Trust among stakeholders, perceived fairness in the partnership process, perceived lack of dominance by economic interests groups and experts, and perceived changes favoring estuary policies are all positively related to perceptions about the level of agreement among policymakers in the estuary, even though none of them significantly affect network size. That is, respondents who believe that others will fulfill their promises relating to estuary policy, that the policymaking processes are fair and not dominated by experts or business, and that support for policies is growing are likely to perceive higher levels of agreement, but are unlikely to invest in larger networks. If the formal institutions appear to be functioning reasonably well, there appear to be fewer incentives to invest in personal networks.

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Levels of agreement are affected by several factors that also increase network size, indicating that there are at least some complementarities between the more formal and informal aspects of local policymaking. Estuaries with NEP status and individuals with strong environmental concerns are both related to higher perceived levels of agreement and larger networks. The first variable suggests that the stronger foundation for policies and a greater intensity of effort associated with the NEP at least creates a broader consensus about policies. 6 The second variable suggests that the individual context affects perceptions as well. We cannot with our data test whether this perceived agreement is an accurate portrayal or just biased perceptions about the other stakeholders that are most important to those with greater environmental concerns. The organizational context also suggests some complementary influences, since respondents of research and environmental organizations perceive higher levels of agreement than respondents that belong to the default business organizations. However, the difference between business and government and other organizations were insignificant for agreement, even though the differences were significantly different for network size. Finally, the magnitude of effects provides a similar picture about the different dynamics affecting perceived agreement and networks. Positive policy changes and perceptions of fairness have the greatest magnitude of impact, with a change from the lowest to the highest value (0 and 1 respectively) in the scale producing an increase of 20 percent in perceived level of agreement for policy changes and 14 percent for fairness, holding all other factors constant. The same change in trust and in 6

The 22 estuaries in our study are not sufficient to test whether the average perceived levels of agreement are also higher in NEP estuaries, since regressions with the 22 observations did not generate

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environmental concerns each lead to increases of approximately 9 percent in the perceived level of agreement, while the difference between NEP and non-NEP estuaries is around 4 percent, holding all other factors constant. So the two factors that influence both agreement and network size have less impact on agreement than the factors that uniquely influence network size.

Cooperation Collaborative efforts by participant’s organizations are influenced by a completely different set of factors than those influencing agreement. Most importantly for our focus on networks, the size of the participant’s contact set has the expected strong, positive influence on cooperation. Each additional policy contact a participant adds to their policy set increases by .7 percent the level of cooperation between the participant’s organization and other stakeholders in the estuary, holding all other factors constant. Decreasing the number of contacts from the median value (16) to the minimum observed value (1) leads to an expected decrease in the average level of cooperation by almost one-third, holding all other factors constant. Informal policy networks apparently provide a critically important resource for collaboration even if they do not necessarily provide support for perceived agreement. Like networks, cooperation appears to be little affected by the variables representing the policy context. Only the assessment of past teamwork has a significant impact, increasing the level of cooperation by 16.5 percent as it ranges from zero to one, when holding other factors constant. The perceptions about the organized

significant predictors of average perceived levels of agreement for NEP or other policy context variables.

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policy processes that affected agreement have no significant impact on levels of cooperation. Nor did NEP status have a direct impact on cooperation; the contrast between NEP’s positive contribution to agreement and its lack of impact on cooperation prompted Lubell (2002) to ask whether the NEP breeds “all talk and no action.” Of course, NEP status does have an indirect impact on levels of cooperation through its impact on networks, as has been more thoroughly analyzed in Schneider et al (2003). Furthermore, the individual environmental concerns that influenced both networks and agreement had no significant impact on level of cooperation. This is somewhat less surprising than the lack of influence from the policy context, since the cooperation measure relates to organizational activities less influenced by individual actions than the other two dependent variables. Perhaps the most revealing finding relates to the organizational context, since respondents from government and research organizations identify more cooperative practices conducted by their institutions than respondents from the default business groups. On average they participate in about half of the collaborative activities we measured (listed in the Appendix A), compared to about one third for the environmental, business, and the other miscellaneous organizations involved in estuary policymaking. Cooperation in the context of estuary policies primarily involves the coordination of research and implementation of joint projects for which these other organizations have fewer resources than do government and research organizations. In sum, we find that the level of cooperation is significantly greater for respondents with expanded networks, for estuaries with more teamwork in the past, and for government and research organizations. On the other hand, we do not find that

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cooperation depends on other characteristics of the estuary or estuary policy processes, or on the environmental concerns of the respondents.

IV. Conclusion Our study of policy networks in relatively unstructured environments suggests that they are predominantly instrumental in character. They affect the kinds of collaborative activities that the respondent’s organization engages in, but they have no significant impact on perceived levels of agreement within the policy community. Thus the driving incentive for policy participants to expand their network appears to be more oriented toward getting their organizations involved in collaborative efforts than toward increasing the level of policy consensus and general cooperation within the estuary. Our results suggest that networks are more strongly related to shared interests than to shared goals and concerns. They provide a device for policymakers to “muddle through” resource management in a fragmented authority system, enhancing opportunities to coordinate policies on a project-by-project basis. In this sense, the narrower contract perspective discussed earlier appears to be more relevant for understanding networks than the broader social capital perspective. We were frankly surprised by the lack of impact on agreement, and can only speculate about the reasons why broader networks were not related to higher levels of perceived agreement in our study. One possibility is that the array of networks within estuary policy arenas are relatively underdeveloped, and consequently the difference between more and less extensive networks are not sufficient to impact existing difference. A second possibility is that the limitation to 22 estuaries may have limited

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the possibilities for observing true differences in agreement, or that our measure of network size did not capture some more critical characteristic of network that may indeed have demonstrated impacts on agreement. While both are possibly true, neither appears particularly likely. We conjecture that networks had little impact on agreement precisely because of the unstructured characteristic of the policy arena. Consider the spectrum of arenas ranging from the relatively well-structured, longstanding federal policy context that Heclo (1978) was describing to the fragmented, barely-emergent context of estuary policymaking described in Schneider et al (2003). In relatively formalized settings, networks serve to develop common beliefs and objectives that can provide a basis for consistent policy decisions, or at least for stable advocacy coalitions that battle for controls over well-defined authoritative decision centers. In these settings symbolic or expressive values dominate instrumental functions, and expanded networks would be expected to enhance the level of policy agreement at least among network participants. In unstructured situations, on the other hand, particularly in early stages where the boundaries of the policy community are unstable, shared beliefs and values may be of little use in producing coordinated policy results. In these circumstances, networks provide an early coping mechanism to produce limited cooperation on a project level. Such cooperation would of course be enhanced by broad agreements on the problems and appropriate policy solutions, and networks may eventually contribute to such broader agreements. Given the current status of the policy community concerned with the sustained management of estuary resources, however, it is not surprising to find that networks are more instrumental than expressive, and that they are more directly

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related to limited collaborative efforts and less related to the more expressive aspects of clarifying the beliefs and goals of the policy arena.

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Appendix A: Description of Variables

Network Size Every respondent was asked to identify from a pre-determined list those stakeholders in the estuary with whom they had contact with on a policy related to the estuary. Respondents were also asked to identify additional contacts by writing them in on the survey in space provided. The variable is a total count that measures the number of contacts. It was constructed with data available from the second wave of the survey. An OLS analysis with a logged version of the count variable provided results that are fully consistent with the reported results, and are therefore not presented.

Cooperation The variable is constructed with the following indicators: provision of information to another organization; sharing of personnel; collaboration on joint research projects with other stakeholders; collaboration on joint grant/funding proposals; creation of an interagenc y taskforce; signing of memorandums of understanding; and, sharing or permitting regulatory activities. For each of these indicators, the respondent provided an answer that could take two values: “1” if the activity was performed and “0” if the activity was not performed 7. The final value of the variable –with minimum and maximum values of 0 and 1 respectively- comes from obtaining an average of the responses for these seven indicators. A reliability analysis of these seven indicators

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resulted in an alpha score of .658. The variable was constructed using data from the second wave of interviews.

Agreement Respondents provided an answer ranging from “1” (strong disagreement among stakeholders) to “10” (strong agreement among stakeholders) to indicate their perceptions on agreement among stakeholders on: (a) causes of estuary problems, (b) severity of estuary problems, (c) the amount and type of research needed, (d) the best policy tools to address problems, (e) the economic consequences of estuary policies, (f) the environmental consequences of estuary policies. A summary indicator was created to reflect the proportion of the maximum number of response values possible for all six questions, giving the final indicator used in the analysis a minimum possible value of “0” and a maximum possible value of “1.” A reliability analysis of these seven indicators resulted in an alpha score of .869. The data used to construct the variables were collected during the second wave of surveys.

7

For example, the value of the first indicator listed here is “1” if the respondent provided information to another organization, and “0” if the actor did not provide this information.

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Trust*8 The value for trust is obtained from the answer given by the respondent to the following question: “Thinking about the range of contacts you had with other stakeholders, how much did you completely trust these stakeholders to fulfill promises and obligations made in the context of the partnership?” The indicator used in the analysis reflects the level of reported trust ranging from “0” (“complete distrust”) to 1 (“complete trust”). This variable was created with data from the first wave of surveys.

Problem Severity* Respondents answered the question “How severe are/were the environmental problems affecting the overall health of your estuary?” The indicator reflects the level of reported problems in the estuary ranging from 0 (very severe) to 1 (not severe). The data used to create the variable were collected during the first wave of surveys.

Fairness* Fairness was constructed using responses to two statements: (1) “overall, the decision-making process in the partnership was fair to all stakeholders”, and (2) “your organization’s/your interests and concerns are/were adequately represented in the partnership.” The indicator reflects the average of the two responses with values 8

Missing values for indicators marked with”*” were completed using multiple imputation facilitated through the software program NORM, version 2.03 (J.L. Schafer 2000). Computational routines used in NORM are derived by techniques explained by J.L. Schafer (1997) in Analysis of Incomplete Multivariate Data (London: Chapman and Hall). The multiple imputation procedure produced five data sets. One data set was selected at random for use in the preliminary analysis in the paper. Once we established final models, another data set was selected from the remaining four to determine if the results changed when

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between 0 (“strongly disagree”) and 1 (“strongly agree”). The variable was constructed with data obtained from the first wave of surveys.

Teamwork* Each respondent in the first wave was asked to agree or disagree in a 10-point scale with the following statement: “there is/was a high level of teamwork between stakeholders in the partnership.” Responses were coded to obtain an indicator with a minimum value of 0 (strongly disagree) and a maximum possible of 1 (strongly agree).

Domination by Experts* Respondents were asked in the first wave whether or not they agreed with two statements: (1) “economic interest groups have/had an undue influence on partnership decisions”, and (2) “the partnership is/was dominated by experts and administrators.” For each question, the respondents were given a 10-pont scale ranging from strongly agree to strongly disagree. The final indicator reflects the average responses measured from 0 to 1.

Core Environmental Concerns* The value of the variable was obtained from the average of the responses to three statements: (1) “preserving the private rights of individual citi zens is more important than protecting the environment”, (2) “in general, government agencies and

using another imputed data set. There were no significant changes in the results when comparing the data produced by the data sets.

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regulations intrude too much on the daily lives of private citizens”, and (3) “how would you describe your policy orientation on estuary issues when tradeoffs between environmental protection and economic development are important?” The possible responses for the first two indicators range on a 10-point scale from “strongly agree” to “strongly disagree.” The response for the third question is also on a 10-point scale and goes from “pro-development” to “pro-environment.” The values were added and an average was obtained. The final indicator reflects the proportion of the possible values with a minimum value of 0 and a maximum possible value of 1. A reliability analysis of these seven indicators resulted in an alpha score of .644. The data were collected in the first wave of observations.

Change in Policy Conditions* The variable is constructed from a set of four questions on the second wave survey tapping the perceived policy changes in the areas of: 1) expenditures on all estuary or watershed protection activities, 2) number of stakeholders participating in all estuary or watershed protection activities, 3) the ability of government policies to protect the estuary or watershed, and 4) the support for estuary policies among agency or administrative leaders in the estuary. For each of these indicators, the respondent is asked to provide an answer on a 5-point scale ranging from “declined significantly” to “increased significantly”. A summary indicator was constructed to reflect the proportion of possible responses, which results in a minimum value of 0 and a maximum value of 1. A reliability analysis of these seven indicators resulted in an alpha score of .648.

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Organization Type The respondents answered the question “Which of the following best describes this organization/your work relevant to the estuary?” The possible responses were: (1) government, (2) environmental group, (3) business group, (4) research organization, and (5) something else. With the responses, we constructed dichotomous variables to represent each organization type.

National Estuary Program Site This is a dichotomous variable equal to 1 when the estuary in which the respondent participates is in the National Estuary Program, and 0 otherwise.

Years in Job The respondents answered the question: “In what year did you begin working for this organization/at your current activity relevant to the estuary?” Based on this response, we calc ulated the years that the respondent has been working in the organization or activity through the year 2001.

Number of Allies Respondents in the first wave provided up to three names of policy contacts that they have in the estuary. Thus, this indicator has a minimum value of 0 (no contacts) and a maximum of 3 (contacts).

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Year as Resident Respondents were asked how many years they have lived in the estuary area.

Response Time Duration The length (in seconds) of the completed telephone interview for the second wave.

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Appendix B: Estuaries and Membership to the National Estuary Program (NEP)

Estuary Name

Albemarle-Pamlico, NC Barnegat Bay, NJ Casco Bay, ME Charlotte Harbor, FL Corpus Christi, TX Delaware Inland Bays, DE Long Island Sound, NY Lower Columbia River, WA/OR Maryland Coastal Bays, MD Mobile Bay, AL New Hampshire Estuaries, NH Tampa Bay, FL Apalachicola Bay Estuary, FL Atchafalaya Bay Estuary, LA Cape Fear River, NC Gray's Harbor Estuary, WA Lower Saint John's River, FL Martha's Vineyard, MA Penobscot Bay, ME Pensacola Bay Estuary, FL Saco Bay, ME St Andrew's Bay Estuary, FL

NEP Status

NEP Members

Non NEP Members

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Table 1 Factors Influencing Size of Policy Contact Set, Agreement, and Cooperation Explanatory Factors

Network Size

Size of Individual’s Policy Contact Set Estuary Policy Context National Estuary Program Site Problem Severity Trust Fairness Teamwork Domination by Experts Changes Supporting Policies Individual Context Number of Contacts in Past Years in Job Years as Resident Environmental Concerns Survey Response Time Organization Context Government Research Environment Other Constant

Notes: * = p