Economic Development Networks Among Local Governments

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Economic Development NetworksAmong Local GovernmentsYoungmi Leea

a University of Michigan, USAPublished online: 25 Mar 2014.

To cite this article: Youngmi Lee (2011) Economic Development Networks AmongLocal Governments, International Review of Public Administration, 16:1, 113-134, DOI:10.1080/12264431.2011.10805188

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© International Review of Public Administration2011, Vol. 16, No. 1

113

ECONOMIC DEVELOPMENT NETWORKS AMONGLOCAL GOVERNMENTS:

THE STRUCTURE OF COLLABORATION NETWORKSIN THE TAMPA BAY METROPOLITAN AREA

YOUNGMI LEEUniversity of Michigan, USA

Since the local governmental system has been fragmented, competitionamong local jurisdictions to retain existing business and attract companieslocated in other cities has increased. In the competitive environment, on theother hand, local actors attempt to collaborate with others in order toefficiently secure scarce resources and to maximize their own intereststhrough collaboration. While previous research has normatively discussedcollaboration or focused on the factors influencing policy networks amonglocal jurisdictions, there have been few studies empirically investigatingpolicy actors’ behavior in the collaborative network sphere. This studytested network hypotheses regarding coordination and cooperationdepending on the risk preference of local governments: how institutionalactors make strategic decisions depending on the situation that they face, inorder to resolve collective action problems. This study found that whilelocal governments coordinate with other governments for simpleinformation exchange, they are likely to commit to partnerships throughcreating tightly-clustered network structures to reduce uncertainty and riskof defection in contracts. In this study of the Tampa Bay metropolitan area,in particular, cooperative network structures are more likely thancoordinating network structures to occur in the economic developmentpolicy arena. In addition, this study found that local jurisdictions are morelikely to collaborate with those that belong to the same county.

Key Words: local economic development, social network analysis,Exponential Random Graph Model (ERGM), coordinationand cooperation

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INTRODUCTION

The local economic development policy arena is typically described as a competitiveenvironment. In particular, the more fragmented the relationship among localjurisdictions, the more serious the competition among individual local governments toretain existing business and attract companies located in other cities. However, anextremely fragmented competitive environment can cause local governments to fail toreach the optimal level of community economic development. Therefore, localgovernments began to engage in local economic development through collaboration withother governments. Collaboration among local governments is more complex anduncertain due to the desire to maintain local autonomy, distrust among local jurisdictions,conflicts of interest among potential participants, or imbalances in power and resourceendowment (Visser 2002). Some public policy studies emphasize the crucial role ofpolicy networks in successfully achieving collaboration among decentralized self-interested actors (Provan and Milward 1995; Meier and O’Toole 2002; Schneider et al.2003). However, these studies focus on the impact of networks in the policy decision-making process at the macro level, rather than the motivations of network participants toshape the network structures at the micro level. This study attempts to fill this lacuna byconnecting the motivations or preferences of network participants with observed networkstructures embedded in the economic development policy arena.

In addition, although much literature has investigated management difficulties withsocial networks, especially regarding network effectiveness (Provan and Milward 1995;Meier and O’Toole 2002), scholars have only a limited understanding of how self-organizing policy networks emerge in the first place. Extant research in publicadministration and management pays little attention to network structure in itself, or tothe policy preferences of various network actors who affect the overall structure ofnetwork relationships. In recent years, research concerning the motivation of policyactors that would explain observed network structures has advanced thanks to thedevelopment of sophisticated theoretical models and analytic tools in network policystudies. For instance, Berardo and Scholz (2010) examined networks in ten estuaries andfound that policy actors’ choice of partners depends on whether the policy actors preferto address a need for coordination or for credible commitments. In other words, networkactors prefer ties that link them to popular actors with a strong coordinating role in thepolicy arena, but they also seek to forge a more closely connected relationships such asmutual ties and closed triads, which are better suited to solving problems of cooperation.Likewise, Feiock et al. (2010) argue that the network structures that policy actors createcritically rely on the actors’ attitude toward information bridging, cooperative norms, andthe risk involved in cooperative situations.

Economic development policy is a particularly ideal setting for investigating how thepreferences of policy actors and their micro decisions shape the overall structure of

114 Economic Development Networks among Local Governments: Vol. 16, No. 1The Structure of Collaboration Networks in the Tampa Bay Metropolitan Area

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collaborative networks. In the process of promoting economic development projectsthrough collaboration, local governments may face problems that differ from those thatarise in other policy arenas. Interjurisdictional cooperation provides substantial benefits ifit induces new development, but simultaneously, it also poses greater risks thancooperation in most other policy arenas when the efforts are proven to be futile. In thissense, the arena of economic development policy is the best setting to explorecollaboration among competitors under uncertainty.

This study extends the previous literature on local policy networks in two ways. First,this study focuses on the dynamic policy arena of economic development, whichcombines tremendous potential gains from information sharing and cooperation withgreat risk of opportunism among competitors. This arena allows me to extend theliterature (Scholz, Berardo and Kile 2008; Shrestha 2009; Berardo and Scholz 2010;Feiock and Scholz 2010) that focuses on policy networks as collaborative institutionsthat facilitate efficient information transmission related to coordination problems, and ontrustworthiness associated with cooperation problems. Coordination and cooperationimpose different incentives for policy actors to seek collaborating partners. According toBurt’s argument (2005), the decision to be connected with central coordinators or policyentrepreneurs who bridge structural holes may result in the benefits of coordinatedactivities. However, if the gains from coordination make network participants morevulnerable to risks of defection, they may be better off by building a tightly-clusterednetwork structure that can ensure credible commitments to collaboration (Putnam 1995;Coleman 1988). This study investigates how local governments balance these contrastingvalues when choosing from whom to seek advice and with whom to share informationregarding economic development activities.

Second, by utilizing the ERGM (Exponential Random Graph Model), advancedstatistical techniques for social network analysis, this study tests hypothesis for whethercoordination or cooperation provides a more convincing explanation for observedpatterns of network relations among local governments. In addition, this study maps thestructures of network relationships among local jurisdictions by employing UCINET.

ECONOMIC DEVELOPMENT POLICY NETWORKS

Following Berardo and Scholz (2010), this study distinguishes between two types ofcollective action problems, which are related implicitly to two different network researchtraditions: cooperation and coordination. In both problems, actors can make choices thatleave them better off than the expected Nash equilibrium.

In pure coordination games, where there is low risk, the problem confronted by actorsis simply how to obtain information about what choice and strategies are availablearound the policy domain. In doing so, local governments can complement information

April 2011 Youngmi Lee 115

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limitation through forming relationships with other county or city governments topromote regional economy. Burt (2005) and Granovetter (1973) have emphasized theimportance of efficiency of information flows and “weak-tie” network structures inovercoming coordination difficulties.

Once the most straightforward coordination problems are resolved, more substantiveinformation sharing and coordination, such as business recruitment efforts, involve muchhigher levels of risk. Trust and the ability to make credible commitments take centerstage in creating and maintaining development interactions. Risk arising from conflictsof interest is the distinguishing feature between coordination and cooperation problemsthat leads to different network strategies (Berardo and Scholz 2010). Interactions amonglocal governments can provide an added sense of security through trust, commitment,and social capital even when the future is filled with uncertainty (Coleman 1988; Putnam2000). Local actors pursing potential gains seek to forge specific patterns of relations,which is a “strong-tie” in this particular situation.

These networks are the result of the purposive actions of policy actors. Studies ofdifferent inter-organizational settings show that trust, mutuality, reputation, or repeatedinteraction that are manifestations of networked relationships help control uncertaintiesand facilitate collective action (Granovettor 1985; Gulati and Gargiulo 1999; Lubell et.al. 2002).

COORDINATION AND COOPERATION IN ECONOMICDEVELOPMENT COLLABORATION

Coordination and cooperation, two different types of network structures that havebeen frequently discussed in policy network research, represent critical collective actiondilemmas in the local economic development policy arena (Berardo and Scholz 2010). Ifthe important role of policy networks is to deliver information, policy actors prefernetwork structures that can simply spread the ideas and knowledge. From an individual’sstandpoint, this could be done efficiently by sharing the information and then mimickingwhen information turns out to be useful. Therefore, more information about the strategiesof others generally increases the likelihood that more actors will benefit by adopting themost common approach to common problems (Berardo and Scholz 2010; Feiock et al.2010). This mechanism may be particularly essential when there is no central authoritythat can provide certainty around information sharing and decision making (Feiock et al.2010). The virtue of a network structure promoting coordination is well documented in“strength of weak-ties” literature. From an individual actor’s perspective, it is morerational to contact with a person who already possesses reliable information. In general,this leads to the emergence of central actors who play a coordinating role.

However, the benefits from collaboration for economic development can be


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accomplished only when specific actions are taken rather than merely exchanging “softtalk” among potential cooperators. On a practical level, the uncertainty surroundingdefection makes cooperation difficult to establish and sustain. Therefore, aftercoordination problems with relatively low risk are overcome in a policy arena, remainingopportunities for coordinated action involve increasingly higher levels of risk (Berardoand Scholz 2010; Feiock et al. 2010). In other words, while successful cooperation mayguarantee desirable outcomes, participants’ efforts to achieve cooperation would beeasily wasted unless others demonstrate the same level of commitment to collaboration.In particular, interlocal collaboration provides multiple opportunities to exploit otheractors by deviating from cooperative norms. As the risks regarding the collectiveincentives become prominent, the role of credible commitments would be moreimportant than that of information in selecting network partners (Berardo and Scholz2010; Feiock et al. 2010). Therefore, policy actors prefer mechanisms that enable them tomore closely monitor others’ decisions. The relevant network literature highlighting the“strength of strong ties” emphasizes matters of trust, commitment, assurance, and socialcapital as relevant ingredients to overcome collective action problems (Coleman 1988;Putnam 1993, 1995).

Based on the discussion above, this study develops general network hypothesesregarding coordination and cooperation for collaborative economic development policy,which will be further discussed in the next section.

HYPOTHESES

This section builds up some working hypotheses that connect policy preferences ofnetwork participants with the network structures they forge. In general, we expect thatthe network structure of local governments for collaborative economic developmentprovides alternative governance mechanisms to overcome collective action problems.This study focuses on two main functions of network structures: efficient transmission ofinformation and establishment of trustworthiness. Further, we argue that two seeminglycontrasting network structures are, in fact, not incompatible. Rather, we anticipate thatboth network structures play distinctive but equally important roles in addressingtransaction costs related to each collective action problem. While an information-bridging network solves coordination problems in relatively low-risk situations, a tightly-clustered network that promotes credible commitment addresses cooperation problems inrelatively high-risk situations.


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Coordination Hypothesis

Popularity

More information about what other actors choose leads to a situation where moreactors will benefit from adopting the most common approach to common problems(Berardo and Scholz 2010). In this situation, once an actor is informed of who is the mostpopular network actor in the organization or the network group, he or she is willing tocreate a channel to this popular actor, which is the most efficient way of gathering usefuland reliable information. As an actor creates relations with many other actors, collectedinformation can be more obvious, but redundant. Assuming that creating andmaintaining additional relations with other actors is costly, connecting with as manyparticipants as possible is considered to be inefficient. Therefore, connecting with a keypopular actor rather than making ties with many other actors can be more efficient from acost-benefit perspective. In Figure 1, actor E can efficiently get information throughcreating a relationship with actor A, a popular actor who has plentiful information. Inaddition, the existence of popular actors may be particularly essential when there is nocentral authority but there is still a need for coordination of information flow. From thewhole network perspective, a “star” network representing a situation where only onecentral actor is connected with other actors provides the most efficient network structurefor information transmission by distributing valuable information (Burt 2005).

(H1) When network actors want to address information transmissionefficiently through a coordinator, they are more likely to rely on popularactors. This is measured by “in-stars.”


Figure 1. Popularity and Bridging

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Bridging

In addition to a “star” network, representing a situation where only one central actoris connected with other actors, actors who bridge structural holes also enable others togain useful information and take advantage of innovation that are not available amonghighly homogeneous network participants. In particular, when initiating new ties withothers is costly and network actors have existing relations with someone who plays abrokerage role, they are willing to exploit the focal actor’s position in order to exchangeinformation rather than investing in creating many new relations. In Figure 1, actor E cancapture information that flows from actor D to actor A, through making a relation withactor C, a broker who bridges between actors A and D. In this sense, a bridging structurealso provides a mechanism to efficiently transmit information about what others do andknow (Burt 2005).

(H2) When network actors want to address information transmissionefficiently through brokerage, they are more likely to count on bridgingactors. This is measured by “2-paths.”


Figure 2. Reciprocity and Transitivity

Cooperation Hypothesis

Reciprocity

Reciprocity is the most fundamental norm in most collective action situations(Ostrom 1998; Thurmaier and Wood 2002). Putnam (1995) emphasized the importance

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of reciprocal ties of mutual cooperation in the development of social capital. Mutualexchanges create the shadow of the future in which defection by one actor can bepunished by future defection (Axelrod 1984), thus providing a mutual deterrence inwhich credible commitments can develop. In practice, actors receiving information fromothers are more likely to provide information or resources in return. When cooperationwith competitors is uncertain, exercising a tit-for-tat strategy based on reciprocity helpsnetwork participants overcome the credible commitment problem. In Figure 2, ascreating a relation from City A toward City C, City A can deter City C from breakingaway from the contract between cities A and C. This principle still holds when networkparticipants are involved in informal relationships, including discussion and adviceexchange, by preventing potential opportunism and the problem of free riders.

(H3) When network actors want to build up network structures thatpromote trustworthiness and credible commitments, they are more likely toforge reciprocal relations. This is measured by “reciprocal ties.”

Bonding

Developing working rules to guarantee credible commitment extends from the dyadiclevel to a social group dimension. When shirking is a potential problem in the delivery ofcollective outcomes, the possibility of shirking imposes costs on those who have alreadyinvested their efforts. A tightly-clustered network structure is advantageous in reducingthe transaction costs of enforcing and monitoring the relational obligations, since anyactions taken or not taken by a locality are made public (Feiock and Scholz 2010).

From a social capital perspective, denser and “closed” forms of networks reduce thecost of controlling activities effectively through overlapping information that circulates inthe network about each actor’s behavior (Putnam 1995; Coleman 1988). This provides avery efficient monitoring mechanism to overcome the collective action dilemma. Thistype of credible threat of detection and punishment of non-cooperative activity, in turn,should increase the chances of cooperation between network participants. Beyond dyadic(mutual social) relationships, we expect that network actors will forge a tightly-clusterednetwork structure to enhance credible commitment at a triad or even whole network level(see Figure 2).

In particular, the transitive triad serves as the most basic network structure thatcaptures the merits of a tightly-clustered network, since transitivity in a relationship mayindicate social trust being built in a social context. Network actors tend to reach otherpartners whose trustworthiness has been scrutinized by others. A typical example of thisprinciple is to become friends with people whose friends are already yours. In doing so,more generalized clusters beyond the dyadic level come to exist. In this sense, thistransitive triad constructs the most basic level of tightly-clustered network.


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(H4) When network actors want to construct network structures that fortifytrustworthiness and credible commitments beyond the dyadic level, theactors are more likely to establish tightly-clustered structures based onsocial bonding. This is measured by “transitivity.”

Other Network Hypotheses

Activity (Expansiveness) and Constraint

From a policy actor’s perspective, network relationships tend to grow for manyreasons: most of all, in terms of the value of information coming from networkrelationships; policy actors are more sensitive to a situation where they could be misledby wrong or irrelevant information. Therefore, they try to maintain multiple informationsources, even if creating the additional relationships involves additional effort but offersonly redundant information. This suggests that actors do not seek globally and locallypopular actors or intermediary brokers, but independently seek information and verify itsquality. Therefore, the activity (expansiveness) of network participants has twocontrasting aspects: information verification and self-constraint.

Network participants look for reliable and valuable information. This goal may oftenbe achieved by simply connecting to coordinators or brokerage actors. However, whenthe consequence of being misled by wrong information is significant, actors prefer toclosely monitor the quality and magnitude of information. This motivation leads them toexpand their network links in order to verify the usefulness of received information. Thisinformation verification process can be particularly important in dealings with potentialcompetitors when pursuing collaborative economic development.

Although having multiple relationships is generally considered to be advantageousfor monitoring purposes, this is not always the case, since creating a new relationshipentails a great deal of cost in terms of time and resources. Therefore, the tendency toexpand (activity) becomes burdensome, particularly when marginal information comingfrom a new relationship is very small. Therefore, cost consideration acts as a self-constraint for network expansion as well. This implies that activity (expansiveness)represented by out-degrees has two conflicting aspects.

(H6) When network actors want to monitor information more directly andindependently, they are more likely to expand their network links withothers, but only if additional relationships provides new information. Thisis measured by “out-stars.”


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DATA AND METHOD

Data and Variables

The key analytic method employed to test the hypotheses proposed in the previoussection is ERGM (Exponential Random Graph Model). By using statistical approach,this method estimates the statistics of simple, but prominent network structures,including reciprocity, transitivity, in- and out-stars, and so on, which relate to thepreferences of policy actors in observed policy networks in the Tampa metropolitan area.

A total of 22 city and county governments in the four-county (Hernando,Hillsborough, Pasco, and Pinellas) Tampa Bay-St. Petersburg-Clearwater metropolitanarea in Florida were surveyed in 2006–2007. Tampa Bay-St. Petersburg-Clearwaterseems to be an ideal setting for studying complex dynamics of competition, coordination,and cooperation for economic development in that it is a multi-county metropolitan areawith multiple core cities: Tampa Bay, St. Petersburg, and Clearwater. Local jurisdictionsin this area represent a wide variety in their size and other socio-economic composition.This social network analysis, in general, investigates network structures among theselocal governments.

The network survey provided key respondents (mostly top administrators such as citymanagers and city clerks) in each of these governments with an exhaustive list of localgovernments within the metropolitan area and asked them to report with whichgovernments they had interacted (including discussion, advice, information sharing) oneconomic development issues in the last year, and how frequently. This enabled us toconstruct collaborative network configurations for economic development in this area.The specific questions were originally designed to measure the intensity of network linksamong local jurisdictions on a scale ranging from 0–5 (0: no interaction, 1: interaction onan annual basis, 5: interaction on a daily basis). The responses were then collapsed to abinary variable: coded 0 if there was no interaction, 1 if an annual or more frequentinteraction occurred. We made this operational judgment for the following two reasons.First, this cut point is considered to capture the greatest interval. Second, in a morepractical sense, this coding is desirable because the number of null dyads willsignificantly increase otherwise (based on our operationalization, total dyad count is 231;mutual 38; asymmetric 73; null 120, respectively), which makes data convergence moredifficult.

Then, this information was translated into an adjacency matrix of links forparticipating actors, where 1s represent existing interactions and 0s the absence of suchinteractions. In these adjacency matrices, when government A has identified a networklink with government B, row A of column B has a value of “1.” In this sense, the valuesin the matrix represent a directed relationship between two governments, and theadjacency matrix is asymmetric in its structure.


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We then can estimate which of the hypothesized network structures are most desiredby the surveyed local governments. In general, ERGM analysis not only investigates theeffect of network structures, but also takes into account the effect of actor attributes forthe complete test. In this analysis, we want to control for and test whether geographicproximity affects the emergence of a certain hypothesized network structure amongpolicy actors. In this sense, we include a variable that captures whether actors belong tothe same county. While adding other actor-attribute variables that represent other socio-economic statuses and political institutions may enrich our analysis, we decided to focusprimarily on the effect of network structure variables controlling for geographicproximity.

Two models were constructed for the economic development network. The firstconsiders only network structure parameters based on the lower order (Markov) model,which ultimately investigates which network structures are more likely to occur (ModelI). The second model adds a simple attribute variable, whether actors belong to the samecounty, to examine whether the characteristics of actors systemically influence theirnetwork behaviors (Model II). While constructing Model I, we fit several differentmodels, either to explore different parameter combinations if good convergence wasdifficult to achieve, or to find the model that best represented the data (Robin et al. 2007).The convergence t-ratios in ERGM analysis indicate how well the overall networkestimates have converged. In fitting the models, we began with basic network structures,including degrees, reciprocity, transitivity, and in- and out-stars parameters. If thesemodels did not converge, we tried either simpler or more complex combinations. Oncewe finalized the best fit model, we estimated the network structure parameters and testedif these values were statistically significant. Then, we also extended Model I by includingan actor attributes variable, whether actors belong to the same county (Model II),retaining the network structures parameters in Model I.

ERGM (Exponential Random Graph Model) Method

For our analyses, the ERGM (p*) program, in the SIENA (Simulation Investigationfor Empirical Network Analysis) package, is used to estimate and test the likelihood ofthe presence of the hypothesized network structures.1 As Snijders et al. (20062) state, theERGM model uses the Metropolis-Hastings algorithm to generate random draws from anexponential random graph distribution, and employs the stochastic approximationalgorithm to estimate the pattern of relationships. In essence, it estimates the probabilitythat the network structures included in the model appear at a greater frequency thanwould be explained by a random graph theory (model) with the same number of localgovernment actors (nodes) and relationships (links). This suggests that localconfigurations in this model construct the global structure (Steglich, 2006).

The distribution of random graphs is simulated from a starting set of parameter


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values, and these estimated parameter values are refined by comparing the distribution ofgraphs with the observed graph (Wasserman and Robins 2005). The estimatedparameters provide the likelihood of the structural effects observed in the network data(Robins et. al. 2006). As mentioned above, the model must not only be conditioned onthe observed networks, but also take into account the potential random relationsdependence of all relationships, and the effect of actor attributes for the robust test.

The program implements a Markov chain Monte Carlo (MCMC) estimation for theERGM model. This simulation algorithm computes the Monte Carlo approximation ofthe maximum likelihood estimates. The program verifies a convergence, whether or notthe average of the statistics calculated for the randomly generated graphs is extremelyclose to the statistics of the observed network. If the convergence diagnostic statistic (t-statistics) for the algorithm is less than 0.1 in absolute value, the parameter is consideredto have a good convergence. The default conditional simulation option was used for themodel estimation.3 A forward selection procedure was followed for the estimation of theparameters: that is, first, network structure parameters were established, then actorattributes were added in Model II. The convergence diagnostics, covariance, andderivative matrices were based on 1,000 iterations, but we added another set of 1,000iterations for final estimation. The t-value provides the significance test of the estimatedparameters.

RESULTS


Table 1. Descriptive Statistics: Links of Actors (from SIENA)

Local Government

Dyadic counts 231

Mutual 38

Asymmetric 73

Null 120

Network Density 0.3225

Average Degree 6.7727

The total number of actors is 22.

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Table 1 reports descriptive statistics of network structures, such as relations (links)among actors (nodes) that consist of Tampa economic development networks. Among231 possible ties in this network, about 52% (total of 120) are null dyads, implying thereis no relationship between two actors. Network density, which is defined as theproportion of ties in a network to the total number of possible ties (Wasserman and Faust1994), is 0.3225 in this case.

As the Tampa-St. Petersburg-Clearwater metropolitan area is one of the fastestgrowing metropolitan areas in Florida, the demand for infrastructure, industrial orcommercial centers, and business promotion for local economic development criticallyincreases. In order to meet these needs, local governments jointly supply the publicgoods and services or exchange ideas or information through collaboration. Figure 3graphically represents these economic development policy networks among city andcounty governments within the Tampa-St. Petersburg-Clearwater metropolitan area. Inthis figure, cities with the same color belong to the same county. County governments aremarked by a square and city governments are marked by a circle. Although the location


Figure 3. Network Structures among Local Governments (from UCINET 6.0)

Names of city and county governments are listed in Appendix A. Square: county government / circle: city governmentSame color represents cities that belong to the same county.

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of each node in this figure is nothing to do with the city’s real geographic location, thispicture indicates that cities are more likely to interact with those that belong to the samecounty. Therefore, in this particular network, the conceptual space in the networkanalysis is very much related to physical space. One exception is that even thoughZephyrhills, San Antonio, and New Port Richey in Pasco County collaborate with eachother, the collaboration does not center around Pasco County that these cities belong to.Instead, Pasco County works with Hernando County, which is located slightly south ofPasco County. However, in general, when selecting collaborating partners to obtainresources and maximize their own interests, local governments are inclined to considergeographically neighboring communities within the same county.


Table 2. Estimation Results in the Base Model: Estimates and Standard Errors

Model I Model II

Network Structure Effects

Cooperation reciprocity 0.9231 0.9500(0.5388) (0.5817)

transitive triplets (bonding) 0.1531*** 0.1268**(0.0528) (0.0526)

Cooperation in-2-stars (popularity) 0.1842 0.1849(0.1977) (0.1880)

in-3-stars (popularity) -0.0097 -0.0096(0.0263) (0.0259)

2-path (bridging) -0.1027** -0.1078**(0.0410) (0.0425)

Other Network Effects

out-2-stars (activity) 0.1956* 0.1924*(0.1181) (0.1112)

out-3-stars (activity) -0.0052 -0.0030(0.0124) (0.0114)

Control Effect

Same County 0.5107**(0.2510)

Coefficients from standard SIENA (3.1) ERGM analysis of directed network matrix for each actor inthe Tampa metropolitan area. All statistics converged with t-statistic <0.1 with minimum of 2,000iterations. Numbers in parentheses represent standard errors.All statistics converged with t-statistic <0.1.* significant at the 10% level, ** significant at 5%, *** significant at 1%

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Table 2 reports which network structures in the economic development policy arenaemerge more than what a random graph model suggests, by using the ERGM analysis.We tested network hypotheses regarding coordination and cooperation depending on therisk preference of local governments. Model II is the full model, controlled for the samecounty predictor as a geographic effect. Although we control for the effects of an actorattribute, the results in Model II are quite similar to those in Model I.

In the estimated results in Model II, the bonding structure (transitivity triple),associated with cooperation, has a positive coefficient and is statistically significant,indicating that the this type of network structure appears more frequently in the economicdevelopment policy arena than those explained by a random graph model. That is, thepositive estimate of a bonding structure confirms that local governments establishingcollaboration for the purpose of dealing with local economic development issues aremore likely to prefer tightly closed relations in order to protect loss from defection ofcooperative partners. On the other hand, contrary to the hypothesis for informationbridging (2-path), associated with coordination, this type of network structure is lesslikely to occur than a random graph model suggests. The negative coefficient ofinformation bridging implies that in this model, the emergence of relations to simplyexchange information about economic development policy issues are less probable thanis shown by a random graph model.

In addition, for other network structures, while the estimate of activity for out-2-starsis positive, out-3-stars has a negative coefficient even though it is not significant in thismodel. They may suggest that while local governments are more likely to expand theirnetwork links with others when they want to monitor information more directly andindependently, however, they are not to do so if creating an additional tie is costly.

As shown in the results related to cooperation, “reciprocity” and “bonding” networkstructures have positive results, suggesting that these network mechanisms are preferredby network participants, and thus are more likely to occur. This result directs ourattention to the claim that whereas local governments are likely to coordinate with othergovernments when the risk of defection is low and they simply want to transmitinformation or advice regarding economic development, they are more likely to createclosed relationships with partners under high risk conditions. If actors are confrontedwith a situation where they compete with potential actors in order to secure scarceresources or to increase their own benefits, they are more inclined to be risk-averse.Therefore, they may prefer to serve in the densely connected relationships that maintaincredible commitments to collective solutions (Putnam 1995). Tight-clustered networkstructures can enhance trustworthiness among participants in economic developmentpolicy networks. The institutional mechanism can overcome cooperative action problemsand thus reduce transaction costs of enforcing actors to keep the relational obligationsand continuously monitoring actors’ behaviors (Feiock and Scholz 2010).

Last, the hypothesis on same county is supported by the ERGM statistical analysis.


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The same-county predictor is statistically significant and is positively related to thecreation of relationships among local actors in the network. This makes sense in thateconomic development collaboration is more likely to take place among cities thatbelong to the same county. That is, local governments are more inclined to choose othercities within the same county as collaborative partners for economic development thanthose within other counties.

CONCLUSION AND DISCUSSION

This study examines the economic development policy networks among localgovernments within a metropolitan area. The results of ERGM analysis reveal thatcooperative network structures are more likely to occur than coordinating networkstructures in the economic development policy arena. In a competitive environment, toretain existing business and attract companies located in other cities, local actors attemptto collaborate with others in order to efficiently secure scarce resources and to maximizetheir own interests. In the process of selecting appropriate partners, they behave withdiverse collaborative strategies to their partners: coordination and cooperation. Whilelocal governments coordinate with others to simply exchange information or ideas, theyare likely to commit to partners through creating tightly-clustered or mutuallycommitting network structures in order to reduce uncertainty and risk of defection incontracting situations.

This network analysis provides a more systematic understanding of how localgovernments shape economic development collaboration with others, depending ondifferences in incentives and risk aversion. Making ties with new partners, maintainingthe networks, and monitoring partners’ obligations raise costs as well as benefits.Therefore, policy actors strategically select their collaborative partners so as to minimizecosts and maximize benefits. For example, they tend to exchange information throughcreating links to centralized coordinators without making redundant reciprocalrelationships or tightly-clustered networks. On the other hand, in the situation when theyneed to constrain each other in contracts or are under uncertain environment, actorsprefer to create more reciprocal and closed relationships even though they need to putmuch effort into creating and maintaining those multiple relationships.

This study found notable results associated with the economic development policyarena. When competing with remote communities as well as adjacent ones in order topursue the individual economic benefit, local jurisdictions make their own economicdevelopment plans. However, fierce competition often results in negative externalities.Therefore, local jurisdictions consider collaborating with other local jurisdictions toincrease their own interests through collective action, and, when doing so, determine thelevel of collaboration with partners. While previous research has merely discussed and


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examined the factors influencing policy networks among local jurisdictions in anormative sense, there have been few studies empirically investigating policy actors’behavior at the collaborative network level: how policy actors strategically resolvecollective action problems, depending on the situation that they are confronted with. Inthis sense, this study contributes to a more systematic understanding of the networkpartner selection strategy of individual governments regarding economic developmentpolicy, depending on the nature of collective action.

Since the governmental system in metropolitan areas has become more fragmented,collaboration among local jurisdictions has not been optional but rather an indispensiblestrategy for survival in a competitive environment. Similar issues regarding causalrelationships have occurred in public policy or administration studies: environmentalpolicies, conflicts that arise in the sharing of common resources, service delivery issues,etc. This study of the actor-oriented network model may be broadly applied for thesecases in diverse policy arenas. As next stages, this study extends two projects. The first isto investigate the differences in collaborative networks depending on actors’ preferencesand attributes: appointed vs. elected officials. Some research suggests that electedofficials and administrators participate in the policy-making process in a different waydepending on their own interests or preferences (Frederickson 1997; Feiock 2004; Wood2006). The second area for future research is to extend the collaborative networkboundary to a domain including nonprofit, public, and private organizations. Many localgovernments have collaborated with diverse types of local and national organizations,not bounded only by governmental organizations. Therefore, unless othernongovernmental organizations involved in local economic development projects areconsidered, the current study would have the limitation of depicting only part of theconfiguration of local economic development collaboration. The survey mailed for thisresearch project has allowed me to identify nongovernmental organizations as well. Thiswork may help us more fully understand collaborative networks among diverseorganizations involved in economic development policy.

NOTES

1. SIENA is a statistical program included in Stocnet, a software package for the study ofnetwork analysis. A free copy of the software can be downloaded athttp://stat.gamma.rug.nl/stocnet/. The p* model built in to SIENA is considered the mostpromising class of statistical models for evaluating structural properties of social networksobserved cross-sectionally (Snijders et. al. 2004).

2. The specifications provided by Snijders et. al. (2004) is based on the higher-order dependencestructure of social phenomena and is capable of modeling higher order graph structures (k-stars, k-triangles, and independent 2-paths) controlling for actor attributes and dyadiccovariates (for details, see Snijders et. al. 2004; Robins and Pattison 2005; and Robbins et. al.


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2006).3. The manual advises to use the conditional (default) option for better convergence of the

algorithm. It keeps the total number of ties fixed at the observed value, implying that there isno separate parameter for the density statistics (Snijders et. al. 2005).

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Received: November 3, 2010

Accepted with no revision: January 19, 2011


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Documents

Economic Development Networks Among Local Governments