A Longitudinal Network Analysis of Intergovernmental Collaboration for Local Economic Development

Abstract

Fragmented jurisdictions in a metropolitan area have found collaborating with others to be not only normatively appealing but also practically beneficial for reducing negative externalities from intense development competition. However, collaboration among communities could involve collective action dilemmas. As an alternative governance mechanism, jurisdictions strategically establish informal networks with others, depending on the conditions where jurisdictions are involved. This study focuses on the strategic behavior of individual jurisdictions for their economic growth: how autonomous jurisdictions have selected collaborative partners and how the collaboration has evolved over time. We apply an institutional collective action framework and a longitudinal network analysis using Simulation Investigation for Empirical Network Analysis (SIENA). The results indicate that, although there is strong evidence that jurisdictions create mutual and clustered relations with others in collaborative networks, there is relatively less for making ties to a popular actor. In addition, jurisdictions are more likely to collaborate with others who share similar political and socio-economic characteristics.

Keywords

interorganizational collaboration local economic development policy network evolution SIENA (Simulation Investigation for Empirical Network Analysis)

Introduction

Since the 1970s, federal and state governments have delegated much authority to local governments, but without giving them financial support. To secure financial sources for local economic growth, local governments have competed with each other to attract new businesses or to retain existing firms in their communities (Lee 2010). In this highly competitive environment, each community has pursued individual economic benefits through competition with both adjacent and remote communities for jobs and growth, using taxes, spending, zoning, and other regulatory provisions strategically. Intense developmental competition has often produced problems, such as negative externalities and economic spillovers, which are simply too difficult for independent local governments to address individually (O’Toole 2000). In response to these difficulties, local governments have recognized that collaborative efforts provide a way to confront the dilemma from competition and fragmentation and have brought up various solutions based on a “regionalism” approach over the past few decades. In fact, there has been a substantial achievement in terms of creating collaborative institutions, such as intergovernmental agreements (LeRoux, Brandenburger, and Padney 2010; Post 2002), special districts (McCabe 2004), and regional organizations (Olberding 2002).

Despite substantial volumes of academic and practical studies of interlocal collaboration,¹ it still remains unclear how and to what extent the seemingly independent local entities actually collaborate with one another in providing public services. How can the fragmented jurisdictions in the competitive environment self-organize to resolve problems and conflicts that arise from competition between jurisdictions to attract and retain businesses for their communities? Can such dilemmas be resolved while maintaining the autonomy of individual jurisdictions? What conditions lead decentralized local jurisdictions to try using collaborative governance to deal with economic development issues?

Although extensive studies in the economic development literature have revealed that collaboration between local governments becomes not only normatively appealing but also a viable and sustainable strategy for economic development, there has been little answered at the micro level about how self-interest (competition motivation) can be compatible with benefits for the whole society (outcome from collaboration). In this sense, networked relationships between local governments provide an interesting setting to answer this question. Network relationships for economic development between local governments are considered to be a type of collaborative governance mechanism that yields more flexibility and local autonomy. This allows us to focus on connecting the motivation of individual local jurisdictions who have a great deal of autonomy to the overall configuration of the network. Although there has been a growing literature in several policy arenas showing that network relations play crucial roles in mediating actions between decentralized actors (Agranoff and McGuire 2003; Lubell et al. 2002; Meier and O’Toole 2002; Provan and Milward 1995), they have rarely tried to provide a microlevel explanation, especially using longitudinal information, of how each local government makes rational decisions about its collaboration partners.

Hence we focus on the motivations that influence the networking behavior of jurisdictions: What motivates local jurisdictions to create, maintain, or withdraw from network ties with others over time, given their preferences and the situations they confront? This can be viewed as the process of overcoming different collective action problems, which applies to network relationships between local governments as well. To address the aforementioned research questions, we employ the institutional collective action (ICA) framework, especially focusing on explaining how transaction costs affect partner selection in collaborative networks, to explore alternatives for resolving collective problems by means of informal policy networks that exist among local jurisdictions. In addition, we investigate the dynamic networks between local jurisdictions in the economic development policy arena using stochastic actor-oriented models (SAOMs; Lubell et al. 2012). We assume that how interjurisdictional relationships evolve over time depends on what situations the jurisdictions confront. In this methodological approach, we assume that local governments are supposed to choose ties in ways that maximize their utility from the state of the network and even change their attributes based on network positions (Lubell et al. 2012).

The Orlando metropolitan area to be examined in this study provides an interesting setting for probing the dynamic partner-selection process in two ways. First, the Orlando metropolitan area is one of the fastest growing metropolitan areas in the nation,² which implies that many active economic development projects induce people to move into the area. This involves active interactions including formal and informal networks at the different levels of government and across the sectors. Second, the years 2006–2010 serve as an excellent laboratory to examine how the economic recession has influenced local economic development. Orlando experienced an economic recession in 2007–2009, as other areas in the nation did. However, it was engaged in various activities to overcome economic hardship as quickly as possible, although its impact of was somewhat asymmetric to cities and counties within the metropolitan area.

Local jurisdictions in this area begin to work together to overcome economic recession and achieve economic growth. Some efforts can be described as large-scale, multilateral, and region-wide projects, whereas others are based on bilateral discussion on small-scale projects. Formation of the Congress of Regional Leaders, representing Central Florida’s counties, school boards and municipalities, is a great example of multilateral effort to work together for a common goal. Commuter rail project is also a regional endeavor, which is expected to ease traffic congestion, to preserve the environment, and to create a multimodal transportation network that will serve the entire Central Florida region. On the other hand, Apopka, Ocoee, and Winter Garden in Orange County have a long history of exercising independent collaborative strategies to induce small business and support the tourism industry. This collaboration strategy based on bilateral relationship includes the operation of business assistance center, implementation of small business incubator program, and co-promotion or co-marketing through regional agencies.

Interjurisdictional Collaboration and ICA

Interjurisdictional relationships in metropolitan areas have probably been most commonly depicted as a prisoners’ dilemma game, especially in the area of economic development (Bowman 1988; Grady 1987; Rubin and Rubin 1987). Although intuitively appealing, a prisoners’ dilemma game is not plausible even for economic development, in that it simply neglects situations where institutional mechanisms provide a ground for actors to use cooperative strategies and where embedded social relations often alter the environments of social interaction. In fact, extensive volumes of academic and practical studies in economics and political science³ suggest that the prisoners’ dilemma depicts only one type of social interaction, where individual motivations conflict with socially desirable outcomes, among many possible variations. We have also witnessed that local governments have sought to find more effective win–win strategies to reduce negative economic externalities in the competitive environment. However, collaborative governance strategies can be successfully implemented only when competitive perceptions and motivations are overcome (Gordon 2007).

Then the question is, how does a fragmented system of local governments forge various types of self-organizing institutions that operate as if they were a single integrated system? One way to answer this question is to consider voluntary self-organizing systems as an “institutional arrangement” (Ostrom 1998). Institutional arrangements are, in fact, outcomes of interactions between local governments and, at the same time, they essentially provide norms and rules that constrain interactions among themselves for the production and provision of public goods.

However, various modes of collaborative strategy turn out to be vulnerable to the collective action problem. Collective action occurs when the coordination and cooperation of two or more local governments help them achieve a desirable collective outcome. Collective action begins with the recognition of interdependence among local governments, where the fate of one community depends heavily on the actions of others and vice versa, which leads to a strategic interaction.

The theory of ICA lays out diverse subcollective action situations in the local jurisdictions. It posits that many problems of interaction between local entities can be viewed as transaction costs of exchange. Therefore, depending on the conditions that local jurisdictions confront, they strategically correspond to different problems of collective action. An interjurisdictional collaborative network is one strategy. A networked relationship, which is, by itself, an important form of collaboration, also plays an important role in reducing various types of transaction cost and fortifying collaboration. Sturdy collaboration between actors may be secured by networked relationships, which can help reduce the transaction costs that occur in the exchanging process (Maser 1998; Heckathorn and Maser 1987).

The theory of ICA has illuminated two important but distinct situations in which actors should respond differently: coordination and cooperation (Feiock, Lee, and Park 2012; Feiock and Scholz 2010). These are associated with the transaction cost that each collective action situation poses differently. Individual actors may prefer to transmit information and innovative ideas in the most efficient way, which eventually leads to the emergence of a popular actor or coordinator who bridges the information gap (structural holes) (Burt 2005; Feiock, Lee, and Park 2012). In contrast, since economic development collaboration may provide various opportunities to exploit potential partners, actors prefer to forge relationships that can directly monitor others’ activities. The exchange of more critical information, trustworthiness, and credible commitments become more important when selecting network partners (Berardo and Scholz 2010; Coleman 1988). Ultimately, tightly clustered networks evolve that can maintain credible commitments to collective solutions (Feiock, Lee, and Park 2012; Putnam 1995).

Therefore, it is necessary to account for the motivations of actors, whether they are to share information efficiently or to ensure the credibility of commitments, when examining the interjurisdictional collaborative networks in the economic development policy arena. Depending on whether the problem is one of coordination or cooperation, ICA can be achieved through a more appropriate network structure, efficiently sharing information or building trust or reputation between local actors (Feiock, Lee, and Park 2012; Feiock and Scholz 2010).

Partner Selection as Interjurisdictional Collaboration

The transaction cost explanation, on which the theory of ICA largely depends, suggests that various collective action situations can be resolved by means of numerous alternative institutions because the nature of problems imposes different types of transaction costs. However, this approach has suffered from much criticism, mainly because of its overexploitation of concepts and difficulty of measurement. It is only recently that some scholars have attempted to differentiate the types of transaction costs in collective action situations and to explain how the efforts to reduce a certain type of transaction cost from an individual standpoint can lead to a particular network structure. We also extend the literature by explaining how rational actors’ decisions to reduce transaction costs in collective action situations can be understood as a partner-selection process in building network relationships. Furthermore, longitudinal information on local governments’ decisions allows us to analyze the dynamic process of partner selection at the micro level.

Local jurisdictions search for collaborative partners to secure resources that they need for local economic development and choose partners from among many potential actors to reduce transaction costs and to maximize their selective benefits through collective action.⁴

The ICA framework proposes that the emergence of specific network structures can make it easier to resolve collective action dilemmas that can occur in the collaborative process among individual actors who have their own preferences and pursue their own selective benefits. For example, the existence of an organization that coordinates the network or bridges a “structural hole” can help efficiently acquire information and make resources available. On the other hand, if there has been no previous interaction or given high uncertainty, actors prefer to prevent any opportunism of others and to form reciprocal or closely clustered relations with others. These networking strategies can improve the welfare of an entire society as well as that of individual actors, that is, efficiently and effectively spreading innovative information or accumulating trust among actors in the society.

The interorganizational networks literature emphasizes the embeddedness or the similarity between organizations that enables them to efficiently access information or resources and to minimize the transaction costs. The transaction costs explanation argues that having many heterogeneous actors in the potential collaboration increases transaction costs, such as information, commitment, bargaining, monitoring, and enforcement costs (Williamson 1985). In a similar vein, the homophily theory suggests that similarity in actors’ characteristics can mitigate the costs that occur in exchange (Feiock and Scholz 2010; Lubell 2007) and can furthermore help maintain a stable collaboration among actors (Gulati and Gargiulo 1999; Podolny 1994). Based on the transaction costs explanation and the theory of homophily, the next sections advance hypotheses about the network structure of collaboration linking the motivations of network actors.

Hypotheses

Coordination

When local jurisdictions are involved in collaboration situations which aim to access innovative information or critical resources, they are likely to create efficient and effective ways to share information with others. Through weak-ties, local governments may efficiently acquire information for local economic development at minimal cost to maintain or secure a need to closely monitor the relationships. The network structure related to coordination between local jurisdictions is captured by the concept of “popularity” and is empirically measured by “in-star.” We assume that actors prefer to create a tie with a popular actor so that they can efficiently exchange information as part of a strategy to promote economic development.

Hypothesis 1: Local jurisdictions are more likely to create a network structure to efficiently transmit development information.

Cooperation

When local jurisdictions are involved in collaboration situations which aim to build closer relationships and trust between local actors, actors in the networks are more likely to forge closed and mutual relationships to prevent their collaborative partners from defecting. As the uncertainty of collaboration increases, local jurisdictions prefer to prevent opportunistic behavior by forging closer relationships and maintaining cooperative relationships with their partners. The network structures related to cooperation between local jurisdictions are “reciprocity” to forge mutual relations and “transitivity” to forge closely clustered relationships. By creating stronger and more trustworthy relationships, they want to avoid deception and misinformation and to maintain cooperative relationships, facilitating the promotion of local economic development.

Hypothesis 2: Local jurisdictions are more likely to create a closed network structure to effectively verify development information by building closer relationships and exploiting redundant information.

The Effect of Homophily: Political and Administrative Institutions

Most research on the effects of homophily has been done by sociologists (Carley 1991; Laumann 1966; Marsden 1988). In the field of public administration and management, scholars recently have examined the influence of shared norms or similar characteristics on the formation of informal and formal relationships. Some studies found that communities with the same form of government are more likely to establish cooperative relationships because appointed and elected officials operating under the same institutional arrangements pursue similar goals and share experiences (Feiock 2004; Feiock et al. 2010). Sharing similarities with each other may reduce transaction costs of collaborative economic development relationships with potential partners (Andrew 2009; Zhang 2007). In this study, we assume that communities with the same form of government are more likely to collaborate with each other. In addition, there is a greater chance that relationships will emerge between the same level of government, that is, city–city or county–county. Governments at each different level implement their unique administrative work and policies. County governments tend to reflect policy directions from federal and state governments and make plans for the local areas (Waugh 1994). City governments have discretionary authority to make economic development plans aligning with the desires of the residents in the cities. Thus, cities more frequently contact and exchange information with adjacent city governments than with county governments, depending on their needs or preferences. Therefore, we assume that jurisdictions at the same governmental level are more likely to collaborate with each other.

Last, cities that belong to the same county are more likely to frequently exchange and jointly work on development projects with each other because they are geographically close to each other and adopt economic guidance or advice from the same county government. Hence, we hypothesize that cities in the same county are more likely to collaborate with each other.

Hypothesis 3a: Local jurisdictions with similar political and administrative institutions are more likely to create ties with each other.

The Effect of Homophily: Socio-Economic Conditions

As with sharing similar political institutions across local jurisdictions, sharing similar socio-economic characteristics also tends to make collaboration more likely. In particular, the similarities in income and education levels across communities result in more links between actors because similarity in median income and education level is likely to reflect similar economic or political agendas and policy preferences (Lee et al. 2012). There is a tendency for jurisdictions to choose similar jurisdictions as potential partners. For consideration of collaborative partners, ICA theory posits that jurisdictions are more likely to establish network ties with jurisdictions that have similar income backgrounds. These arguments are consistent with the homophily argument in the social network literature that similarity (homogeneity) breeds collaboration (Lubell 2007).

Hypothesis 3b: Local jurisdictions with similar socio-economic status are more likely to share similar economic agendas, which leads to ties with each other.

Research Design

Sample and Data

This study focuses on the longitudinal information on economic development collaboration between local governments located in the Orlando Metropolitan Area, especially during two periods in 2006 and in 2010. The Orlando area is one of the fastest growing metropolitan areas in Florida, where the demands for development are explosive, and competition between jurisdictions is quite intense. In addition, the socio-economic conditions or resources across jurisdictions within the Orlando Metropolitan Area are quite diverse, as presented in Tables 1 and 2. The primary industry in the area is tourism, with conventions and entertainment attractions, yet Orlando has tried to diversify its economy to high technology and biotechnology since early 2000. The influx of technology-related companies to the area has made Orlando one of the fastest growing high-technology centers in the nation. The metro area has the county’s largest concentration of modeling, simulation, and training (MS&T) businesses, research centers, and educational facilities. The economic recession in 2007–2009 was a difficult challenge in the area as well, yet provided an opportunity to shift attention away from tourism to high technology, and many of the most high-profile projects have taken place in the central city; that is, the economic recession has influenced asymmetrically to cities and counties in this area. For example, whereas the population growth rate was 8.72% and 9.09% in Orange and Osceola counties (Kissimmee area), respectively, it was only 1.92% in Lake county in 2006–2010. In this sense, the Orlando metropolitan area in 2006–2010 is expected to provide an excellent laboratory for investigating how local governments’ motivation to overcome economic hardship creates a dynamic interaction and strategy toward collaboration.

Table 1.

Descriptive Statistics on Local Governments and Demographic Statistics of the Orlando Metropolitan Area.

	M	SD	Minimum	Maximum
Population	72,800	188,377	15	1,086,480
Median household income	57,100	18,375	31,492	114,917
Education (%)	28.31	12.36	8.50	57.30
Race (White) (%)	77.72	15.53	6.50	100

Table 2.

Mean Values of Demographic Statistics (by County).

	Lake	Orange	Osceola	Seminole
Population	9,454	30,500	46,000	30,000
Median household income	47,100	710,00	43,500	59,100
Education	19.66	38.51	17.55	31.51
Race (White)	80.22	74.51	77.75	79.67
Number of cities	14	13	2	7

The metropolitan area consists of four counties and 36 cities geographically located in Central Florida. The local jurisdictions that responded to the 2006 and 2010 surveys were 27 cities and four counties.⁵ The survey questionnaires from 2006 were replicated in 2010. The survey was mailed to economic development directors in each government, or to city managers if the city had no economic development department, to collect information on informal dyadic network relationships (discussion, advice, shared information, etc.) for local economic development.⁶

Concepts and Variables⁷

This study includes two kinds of homophily effects: political institution homophily and socio-economic homophily. Three variables were included to represent similarity in political institutions, (1) the level of government entities, (2) whether cities belong to the same county, and (3) the form of government. The governmental-level effect is measured as a binary variable coded 1 for county government and 0 for city government. The variable for whether cities belong to the same county is also coded as 1 if cities belong to the same county and 0 otherwise. Last, the form of government is captured by a categorical variable coded 1 for the mayor-council form of government, 2 for the council-manager form of government, and 3 for the council-commissioner form of government. Then, the information (a vector representing each characteristic) coded as either a binary or a categorical variable was used to construct a time-invariant covariate matrix. Usually, 0 in each cell of a covariate matrix represents that actors i and j share the same characteristics. For instance, if both i and j are county governments, which are coded as 1, then the value in sim_ij in the covariate matrix would be 0.

Variables to represent socio-economic similarity, population, education level, proportion of race (White-American), and median household income were included. The education similarity is captured by the portion of residents who hold bachelor or higher degrees in a jurisdiction. The income similarity is measured by the median household income logarithmically transformed. The population similarity is calculated by the size of population logarithmically transformed. Last, the race similarity is captured by the proportion of White-Caucasian Americans in a jurisdiction. The data came from an archived database at the Census Bureau in 2009.

Actor’s attribute variable

This study includes three actor’s attribute variables: government level, form of government, and mayor turnover. First, for the level of government (alter) variable, we assume that city governments are more likely to work with county governments because county governments generally have a larger role in shaping local economic development plans, sometimes financially support cities, and provide information or advice to city governments, especially cities that belong to the county. The alter effect of governmental level is captured by a binary variable coded 1 for county government, and 0 for city government. Second, for the form of government (ego) variable, we suspect a jurisdiction with a city manager who has the professional expertise for city administration or development is more likely to enter into interjurisdictional collaboration for local economic development. The form of government is measured by a binary variable, coded 1 if the city government is operated under a council-manager form of government and 0 if it is under a mayor-council form. The data are compiled based on information reported in International City/County Management Association (ICMA) surveys and the official homepages of each city government. Last, the mayor turnover (ego) captures the idea that the longer mayors in jurisdictions work, the more personal and professional networks around them are established. The ego effect of mayor turnover is measured by the turnover of mayors between 2006 and 2010. If the mayor in the jurisdiction in 2006 has continuously served in the position until 2010, this variable was coded 1, but if the mayor changed at any point between 2006 and 2010, then it was coded as 0. The positive coefficient of the covariate-ego implies that jurisdictions with higher values on each variable tend to increase their out-degrees more rapidly (Ripley and Snijders 2010, p. 43).

Methods

SAOMs implemented by Simulation Investigation for Empirical Network Analysis (SIENA) are applied to analyze the dynamics of intergovernmental networks, being based on the “actor-oriented” network model (Snijders 2001, 2002); By explicitly assuming network structure in the implicit utility function that actors may have, this model proposes that individual choices of network partners ultimately shape overall network structure (Lubell et al. 2012). This approach intends to describe individual or microlevel processes that govern network formation and to analyze the evolution of networks based on the incentives and motivations of the actors. With longitudinal data, this model assumes that the network structure at the first period provides a baseline for analyzing changes in the subsequent period. Therefore, it estimates the relative value that individuals place on each structure in the model between the time points, when actors are constantly making decisions on how to initiate, maintain, or disconnect relations (Lubell et al. 2012).⁸ In particular, the SIENA model tests the assumptions about network structures, such as popularity, bridging, reciprocity, and transitivity, as well as the effects of actor’s attributes on networking ties for intergovernmental economic development.

In this study, the longitudinal network analysis bases estimates on the observed structure in 2006. SIENA analyzes changed relationships between local jurisdictions between two time periods (2006 and 2010) based on the observed network patterns in the first period. Therefore, SIENA can capture dynamic partner-selection process of jurisdictions depending on their utilities and the influence of differences of political, institutional, or socio-economic conditions on network patterns. The longitudinal model can reflect the eroding or creating effects of ego, alter, and similarity over time as well as network effects, which the cross-sectional analysis does not capture. Therefore, this longitudinal network analysis is appropriate for examining the network evolution in the economic development policy area, where diverse jurisdictions are actively engaged in creating or withdrawing from the ties to promote their selective benefits.

Estimation Results

Network Visualization

Figure 1 depicts the informal networks for economic development among 27 city and four county governments in the Orlando Metropolitan Area in 2006. Figure 2 shows the networks in 2010.⁹ The circles indicate local jurisdictions, and the size of circles represents population size. The same color group indicates jurisdictions belonging to the same county. The relation represented by the arrow indicates “takes information and advice regarding local economic development from.”

Figure 1.

Informal economic development policy networks in 2006.

Figure 2.

Informal economic development policy networks in 2010.

Network visualization indicates that the informal policy network in the Orlando Metropolitan Area became denser during this time span. The information on changes in network ties in Tables 3 and 4 also confirms this observation. The density increased from .206 in 2006 to .322 in 2010 in Table 3. The average degree also shows that the network ties between the two time periods increased from 6.400 to 9.967. The total number of ties increased considerably, from 192 to 299. Table 3 demonstrates the number of changes in informal policy ties (0 → 1 and 1 → 0) between 2006 and 2010. While 166 new ties were created in the interjurisdictional collaboration network between 2006 and 2010, 59 ties disappeared, meaning that there were many more newly created ties than ties that disappeared. The number of ties that were maintained is 133. The results indicate that, in general, local jurisdictions are likely to maintain their informal collaborative ties and create new ties over time.

Table 3.

Network Density Indicator: Informal Policy Networks.

Network Density Indicator	2006 (t1)	2010 (t2)
Density	.206	.322
Average degree	6.400	9.967
Number of ties (total)	192 (930)	299 (930)

Table 4.

Changing Informal Network Ties between 2006 and 2010.

Periods	No Tie0 → 0	New Tie0 → 1	Broken Tie1 → 0	Maintained Tie1 → 1
t1–t2	572	166	59	133

In particular, whereas collaboration between cities and Lake County was quite active in 2006, it decreased by 2010. In addition, whereas the number of collaborative ties between municipalities in Lake County and those in other counties (Orange, Osceola, and Seminole Counties) decreased, the collaborative ties between municipalities within those other counties became more active, partly because the economic recession exerted an asymmetric influence on cities and counties in this area. In 2006–2010, Orange County, for instance, had actively engaged in building up biotechnology clusters, promoting related businesses and creating partnerships with research institutes, hospitals, universities, and private firms, ranging from information exchange to large-scale development projects. A great deal of resources was spent to revitalize the downtown Orlando area as well. Osceola County, which is known as the hub of entertainment attractions and is adjacent to the City of Orlando, created a synergistic relationship with Orange County’s transition to become a center of high-tech industry. In contrast, Lake County (City of Tavares) was in competition with other communities between Orlando and Tavares. It has generally been considered to suffer from lack of diversity in housing stock, inadequate roadway capacity, presence of a jail and courthouse, and so on. Although Lake county and the City of Tavares also work on their redevelopment projects, these were not as successful as those of Orange and Osceola counties.

In Figures 3 to 5, the green solid lines represent the ties that disappeared in 2010s, and the red solid lines represent the newly created network ties in 2010. As mentioned above, almost all disappearing ties were relationships between municipalities in Lake County and those in Orange, Osceola, and Seminole counties. Instead, network ties between jurisdictions which belong to the same county around the county government were actively created. On the other hand, the local governments with the largest degree of input were county governments. The result from both graphic representation and the summary on changes in network ties supports the findings from the literature, that governments at the lower level (city governments) are likely to make a connection to those at the higher level (county governments). In particular, city governments are more likely to obtain information from the county where they are located rather than from other county governments. The visualized networks reveal that city governments create ties with the government of the county to which they belong to obtain information or advice for local economic development policies or programs.

Figure 3.

Ties disappeared from 2006 to 2010.

Figure 4.

Ties created from 2006 to 2010.

Figure 5.

Collaborative network evolution between 2006 and 2010.

Statistical Results and Discussion

Network Effects

Table 5 represents the dynamic process of informal collaborative ties and the formation of interjurisdictional collaboration in the Orlando Metropolitan Area. Whereas Model I includes only network structure variables, Model II includes other variables to test mainly the homophily effects as well as network structure variables. According to the results in Table 5, whereas the coefficients on reciprocity and transitivity that represent the preferences of jurisdictions to make closed relationships with others are positive (p < .01), the coefficient on popularity that represents coordinative relations are negative (p < .01). That is, although there is strong evidence for creating mutual and clustered relations with others in collaborative networks for economic development, there is relatively less for making ties to a popular actor in the network, contrary to the hypotheses regarding coordination. In other words, the results reject our original hypothesis that jurisdictions are more likely to prefer creating a tie to a popular actor in the economic development policy network to efficiently acquire information. This finding holds even after adding other variables to Model I (Model II). Although the value of the coefficients changes slightly, the sign remains unchanged. For instance, after controlling for other variables, the negative coefficient value on the popularity of alter becomes larger, from −2.51 to −4.24. That is, each additional incoming tie decreases the odds of selecting the partner. A potential partner with one in-degree would decrease the baseline probability of the observed tie, suggesting that local jurisdictions are less likely to make a link with a central actor who has already been selected by other jurisdictions.

Table 5.

SIENA for Dynamic Networks with Longitudinal Data.

	Model ICoefficient (SE)	Model IICoefficient (SE)
Rate parameter (rho) t 1 and 2	17.5140*** (2.3066)	34.5578*** (6.9241)
Network structure effects
Out degree (density)	−1.0364*** (.1843)	−2.5123*** (.4876)
Reciprocity	1.0258*** (.1636)	.8280*** (.1888)
Transitive triplets	.1241*** (.0145)	.1273*** (.0157)
Popularity of alter	−2.5077*** (.7385)	−4.2394*** (1.0356)
Political institution effects
County Government (alter)		1.0652*** (.3150)
Council-manager form (ego)		−.6083*** (.1603)
Mayor Turnover (btw 2006 and 2010)		.4797*** (.1742)
Homophily effects
1. Political institutional homophily
Same government level		1.5257*** (.3164)
Cities (belong to the same county)		.4863*** (.1662)
Form of government		−.1113 (.1894)
2. Socio-economic homophily
Education (proportion of BA)		.0214 (.2364)
Population (log)		.1367 (2.2134)
Race (proportion of White American)		.0669 (.3001)
Median household income (log)		2.3729 (2.6314)
3. Geographical proximity		2.2220*** (.3662)

Note. SIENA = Simulation Investigation for Empirical Network Analysis.

Coefficients from longitudinal SIENA analysis of directed network.

Numbers in parentheses represent standard errors.

Convergence t-ratio for every variable except out degree (density) is less than .05. t-ratio for out degree (density) is .075.

p ≤ .10. **p ≤ .05. ***p ≤ .01.

Carpenter, Esterling, and Lazer (2004) found that lobbyists who seek to access unique information tend to avoid creating ties with actors with redundant links in terms of valuable information from new sources (Granovetter 1973). Particularly, in collaboration for economic development because the value of information may decrease rather than increase as it is broadly shared, local jurisdictions may need to balance the benefits of collaboration with other actors against the advantage of not sharing unique information. Moreover, as opposed to the private market, it is relatively unclear who the entrepreneur in the area is and who has what kinds of techniques or more resources for the jurisdiction in the public sphere. In uncertain conditions, where resources or information that each jurisdiction secures are not obvious, actors could feel that creating a link to a popular actor in the network is risky and costly. Instead of counting on a central actor, local jurisdictions prefer to maintain existing relations with others and make their relationship more stable and stronger.

However, the result from Model II also suggests a delicate role of the popularity effect, when controlling for other homophily effects. The strong positive alter effect of the county (1.065, p < .01) indicates that local jurisdictions prefer to create links to a county government, which suggests there is a strong chance that county governments play coordinating roles for local economic issues in the Orlando Metropolitan Area. In addition, Figures 1 and 2, which visualize collaborative networks in 2006 and 2010, also suggest that the four county governments coordinate on local development issues in the Metropolitan Area and that the majority of cities make ties to their counties to take useful information or direction for local development. This finding is consistent with the literature which argues that the role of county government has greatly expanded, not only in the wider variety of service provision but also in its function as the administrative arms of state government (Benton and Menzel 1991; LeRoux and Carr 2010; Zeemering 2009). The role of county government becomes larger especially when there is a rapid population growth in the local jurisdictions. Therefore, from the results of Model II, we can infer that, although the coordinating tendency among local jurisdictions, in general, is less apparent in the Metropolitan Area, a few county governments still play coordinating roles for local development.

In Table 5, the results on reciprocal and transitive triad structures that represent creating closer relations with jurisdictions that already had relations support the hypotheses about cooperation among local jurisdictions. The coefficients for reciprocity and transitivity are .828 and .127, respectively, in Model II, indicating that local jurisdictions prefer mutual and closely clustered relations in collaborative networks. For the reciprocity, the likelihood that jurisdiction i (ego) will establish reciprocal ties with jurisdiction j (alter) would increase the baseline probability of the observed tie. For the transitive triad effects, the likelihood that jurisdiction i, which had a tie to jurisdiction h through jurisdiction j forms the closely clustered collaborative network between the three jurisdictions i, j, and h by creating a direct tie to jurisdiction h, slightly increases the baseline probability of the observed tie.

In summary, the results of the network effects suggest that local jurisdictions are more likely to prefer mutual and closely clustered relations, yet have less preference for creating a tie to a popular actor.

Homophily Effects

The homophily effects on political institutions are represented by government level, cities which belong to the same county, and the form of government. The results from the variables for the same government level and the cities which belong to the same county confirm the hypotheses. In particular, the coefficient on whether or not cities belong to the same county is positive and statistically significant (.486, p < .01). The result indicates that cities are more likely to collaborate with other cities within the same county or with the county itself. Being in the same county may mean that jurisdictions under the county share similar political or structural institutions. The similarity can reduce the conflicts that may occur in collaboration between actors and, furthermore, increase the probability of collaborating with each other.

However, contrary to the hypothesis on the same form of government, the tendency for homophily turns out not to be more dominant than random graphs usually predict.¹⁰ The coefficient of the form of government variable is negative and not significant (−.111, p > .10).

To test hypotheses about the socio-economic homophily effects, similarity in education, population, race, and median household income variables were included in this study. The coefficients on three variables turn out not to be statistically significant. On the other hand, geographical proximity controlling for geographic neighboring effect¹¹ turns out to be positively related to creating network ties (2.222, p < .01). In fact, the consideration of neighboring jurisdictions in a physical space is one of the most important components of the local policy-making process (Lee et al. 2012; Minkoff 2012, 2013).

Actor’s Covariate Effects

County governments, the council-manager form of government, and mayor turnover are included as the actor’s covariate effects for political institutions. The coefficient on county government (alter) is statistically significant, indicating that city governments are more likely to create a tie to county governments, possibly seeking to obtain information or advice for local economic development. The result confirms the hypotheses that cities tend to rely on county governments that play a crucial role in shaping local economic development policy. County and city governments are administratively related to each other, and, in particular, they have institutional ties for economic development plans for a county and for cities within the county. In county–city relations, county governments are dominant over city governments in economic development policy because county governments are administratively and politically closer to state or federal governments in receiving financial resources or assistance for economic development (Andrew 2009; Benton and Menzel 1991).

On the other hand, the coefficient on the council-manager form of government (ego) is statistically significant, but negative (−.608, p < .01). This result does not support the hypothesis that local governments with a city manager are more likely to prefer to create collaborative ties with others in economic development policy networks than communities with a non–council-manager form of government are. This is not consistent with the finding from the literature, which argues that the council-manager form of government plays a crucial role in forming collaborative ties in the economic development policy arena because local governments with city managers who pursue practical and professional knowledge are more likely to actively adopt innovative policy tools from others (Zhang 2007). However, our result is the opposite, which can be interpreted as meaning that the leadership of mayors or commissioners is more important in forming collaborative network for local economic growth than are the professional skills of managers. In cities with the mayor-council form, the role of strong mayors is important, and in those with the council-commissioner form, the leadership of the council commissioner is crucial in policy decision making. The leadership or vision of mayors or council commissioners is committed to economic development and job creation for the city (Lewis and Neiman 2009). Therefore, the effect of this variable needs to be further investigated in future research.

The coefficient on mayor turnover is positive and statistically significant (.480, p < .01). The result suggests that the continuity of the mayor is important in forming interjurisdiction collaboration. This confirms that the longer the mayor holds his/her position, the more collaborative networks with other local governments are made. Generally, the change of leader in an organization influences the given networks established with other organizations. As the mayor plays a crucial role in the decision-making process of economic development policies, the turnover of a mayor may change the relations with existing collaborative partners.

Conclusion

By examining the dynamics of informal networks built for economic development between jurisdictions, this study provides new insights into how fragmented jurisdictions within a metropolitan area self-organize over time to resolve problems and conflicts that result from competition between jurisdictions. Even though previous studies have focused on the influence of collaborative mechanisms on the outcomes of development efforts and, in particular, emphasized the significance of the roles of networks (Feiock, Steinacker, and Park 2009; Lee 2010; Olberding 2002), there are few studies that focused on how collaborative networks emerge and evolve at the micro level, especially in the economic development policy arena. In particular, this study is based on the assumption that coordinative and cooperative strategies are two critical alternatives that can be chosen by local jurisdictions to resolve collective actions with minimal transaction costs for local growth (Feiock and Scholz 2010).

This article investigated how local jurisdictions choose their collaboration partners over time, which in turn, determines the overall configuration of network structures by examining the longitudinal data. Although some network ties disappeared from 2006 to 2010, in part because of the economic recession and in part because of the general partner-selection process, informal collaboration networks in the Orlando metropolitan area generally expanded in terms of the number of ties. Especially, previous relationships with each other turn out to be a strong predictor of the current relationship, confirming the standard transaction costs argument and findings from the longitudinal studies in an ICA framework (Lamothe, Lamothe, and Feiock 2008; Park and Feiock 2012). This also explains how the local jurisdictions prefer cooperative structure, that is, reciprocity and transitivity, for information verification purpose to the coordinative structure. In general, local jurisdictions endeavor to overcome uncertain situations, such as economic recession, by fortifying and expanding existing relationships rather than exploring alternatives.

The microlevel analysis reveals that in the economic development policy arena, local jurisdictions prefer closely clustered relations with others for effective information verification rather than loosely connected relationships for efficient information exchange. Based on this finding, we can infer that local jurisdictions in the Orlando metropolitan area are less likely to prefer to acquire information or resources by just creating ties with central coordinators who have already been selected by other jurisdictions. The results of reciprocity and transitivity confirm that local jurisdictions are more likely to forge reciprocal or closely clustered structures in economic development policy networks. That is, jurisdictions tend to develop closed and dense network structures because collaboration for the development projects that require large budgets and diverse resources incur relatively high risks. Since local jurisdictions must manage uncertain situations, they prefer to achieve mutual benefits by reciprocating ties when they already had incoming ties from others. The reciprocal tie may facilitate in securing safe and continuous relationships between the two jurisdictions in the exchange of resources or information, especially in the economic development policy arena, where opportunistic behavior of jurisdictions frequently occurs based on selective economic benefits and costs (Berardo and Scholz 2010).

In addition, the result of transitivity shows that jurisdictions prefer to forge more closely clustered relationships in the economic development policy arena. Even though the denser and overlapping relationships are relatively inefficient in terms of exchanging information, they can facilitate in preventing potential loss from misinformation in the development collaboration. In other words, it is much more important to cross-check the quality of information and use communication opportunities to build critical mass by reciprocating ties and fortifying closed networks. The actors can achieve this goal by investing in extending existing relationships. The virtue of redundant information becomes more eminent when there is much uncertainty and relatively little previous history about the potential partners.

On the other hand, the results of the homophily enable us to understand the partner-selection process in interjurisdictional collaboration in a metropolitan area that the standard transaction costs explanation cannot account for. The resources alone are not sufficient to determine whether local jurisdictions initiate network relationships or which network partners they should choose. The decision on selecting partners is much more complicated. Even if a jurisdiction considers collaboration with others that can supply the resources needed for local development, it is difficult to finally achieve collaboration if the transaction costs of exchange are high. The local jurisdiction that confronts this problem tends to grope for the most efficient and effective strategies, thereby reducing transaction costs. As one solution, they may search for partners with similar political institutions to minimize transaction costs within the cooperative process. This study confirmed the homophily effects of political institutions. Similarity in the jurisdictions’ characteristics leads to similar policy preferences and goals, predisposes the actors to cooperate, and then reduces transaction costs of exchange (Feiock and Scholz 2010; Gulati and Gargiulo 1999; Lubell 2007). In addition, the result of geographical proximity suggests that the geographic neighboring effect is one of the most critical components of the local policy-making process (Lee et al. 2012; Minkoff 2012, 2013).

This study strove to create a link among network concepts, structures, and theories—transaction costs explanation and homophily theory—to provide a useful tool in understanding collective action among local jurisdictions toward economic development. There have been few studies that matched network concepts with theories and empirical methods to examine the evolution of local collaboration, particularly in the field of public administration and policy.

However, there are limitations in the study. Most of all, although this study attempts to demonstrate the connection of preferred network structures to microlevel motivations of local jurisdictions, microlevel motivations have been neither measured nor tested formally. Thus, a better research design should include micro motivations based on the perception on different types of risks, investigate the relationship between microlevel motivation and preferred network structures, and test the causal relationship between them. Future research may assess more systematically the risk-structure relationship such as linking network analysis to surveys of actors’ perceptions on risk. Second, the way of measuring network relationship needs to be improved in the future research. Although it is attributed to the nature of informal network, knowing that a government interacts with other governments in the form of discussing, advising, or sharing information in the past year does not provide a strong basis for building analysis on the strategic creation of a network tie. In addition, the information on network frequency, measured in 1 to 5 scale, had to be collapsed into a binary variable mainly due to the limitation of SIENA modeling. Third, the scope of this study must be extended to many jurisdictions in Florida or jurisdictions in other metropolitan areas to be generalized. Similar results found in multiple research cases will more systematically reveal how jurisdictions have behaved over time in resolving collective action problems and, as a result, identify the prevailing network structures in the economic development policy arena.

Finally, although the popular actors are less likely to appear in local economic development policy networks, it seems obvious that county governments play crucial roles in coordinating discussion on development issues and spreading the critical information. This conflicting finding can be better tested when we differentiate the effect of the county variable on the probability of the prevalence of the network effects in the observed economic development policy networks, including the interaction terms between the network effects and the county variables. The advanced modeling and estimation methods are expected to enable us to better identify the patterns of interjurisdictional collaboration, considering the interaction between diverse effects.

Supplemental Material

Supplemental_material – Supplemental material for A Longitudinal Network Analysis of Intergovernmental Collaboration for Local Economic Development

Supplemental material, Supplemental_material for A Longitudinal Network Analysis of Intergovernmental Collaboration for Local Economic Development by Youngmi Lee and In Won Lee in Urban Affairs Review

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Youngmi Lee

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Youngmi Lee is an associate professor in the Department of Public Administration at Kyonggi University. Her research focuses on local economic development policy, network management and leadership, collaborative governance, and social network analysis. Her work has appeared in Public Administration Review, Policy Studies Journal, Economic Development Quarterly, and Local Government Studies.

In Won Lee is a full professor in the Department of Public Administration at the University of Seoul. His research areas are urban policy, collaborative governance, interorganizational management, and policy evaluation. His work has appeared in, among others, Urban Affairs Review, Public Administration Review, Policy Studies Journal, and Economic Development Quarterly.

References

Agranoff

Robert

McGuire

Michael

. 2003. Collaborative Public Management: New Strategies for Local Governments. Washington, DC: Georgetown Univ. Press.

Andrew

Simon

. 2009. “Regional Integration through Contracting Networks.” Urban Affairs Review 44 (3): 378–402.

Axelrod

Robert

. 1997. The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton: Princeton Univ. Press.

Benton

J. Edwin

Menzel

Donald C.

1991. “County Service Trends and Practices: The Case of Florida.” State and Local Government Review 23:69–75.

Berardo

Ramiro

Scholz

John T.

2010. “Self-Organizing Policy Networks: Risk, Partner Selection, and Cooperation in Estuaries.” American Journal of Political Science 54(3): 632-49.

Bowman

Ann O’M

. 1988. “Competition for Economic Development among Southeastern Cities.” Urban Affairs Quarterly 23:511–27.

Burt

Ronald Stuart

. 2005. Brokerage and Closure: An Introduction to Social Capital. New York: Oxford Univ. Press.

Carley

Kathleen

. 1991. “A Theory of Group Stability.” American Sociological Review 56:331–54.

Carpenter

Daniel P.

Esterling

Kevin M.

Lazer

David M. J.

2004. “Friends, Brokers, and Transitivity: Who Informs Whom in Washington Politics?” The Journal of Politics 66 (1): 224–46.

10.

Coleman

James S.

1988. “Social Capital in the Creation of Human Capital.” American Journal of Sociology 94:95–120.

11.

Feiock

Richard C.

2004. Metropolitan Governance: Conflict, Competition, and Cooperation. Washington, DC: Georgetown Univ. Press.

12.

Feiock

Richard C.

Lee

In Won

Park

Hyung Jun

. 2012. “Administrators’ and Elected Officials’ Collaboration Networks: Selecting Partners to Reduce Risk in Economic Development. Public Administration Review 72 (Suppl. 1): 58–68.

13.

Feiock

Richard C.

Lee

In Won

Park

Hyung Jun

Lee

Keon

. 2010. “Policy Networks Among Local Elected Officials: Information, Commitment, and Risk Aversion.” Urban Affairs Review 45 (6): 1–22.

14.

Feiock

Richard C.

Scholz

John

eds. 2010. Self-Organizing Federalism: Providing Pubic Goods and Managing Resources in a Federalist System. Cambridge: Cambridge Univ. Press.

15.

Feiock

Richard C.

Steinacker

Annette

Park

Hyung-Jun

. 2009. Institutional Collective Action and Economic Development Joint Ventures. Public Administration Review 69 (2): 256–70.

16.

Goodreau

Steven M.

Kitts

James A.

Morris

Martina

. 2009. “Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks.” Demography 46(1): 103-25.

17.

Gordon

Victoria

. 2007. “Partners or Competitors? Perceptions of Regional Economic Development Cooperation in Illinois.” Economic Development Quarterly 21:60–78.

18.

Grady

Dennis O.

1987. “State Economic Development Incentives: Why Do States Compete? State and Local Government Review 19 (3): 86–94.

19.

Granovetter

Mark

. 1973. “The Strength of Weak Ties. American Journal of Sociology 78 (6): 1360–80.

20.

Gulati

Ranjay

Gargiulo

Martin

. 1999. “Where do Interorganizational Networks Come from?” American Journal of Sociology 104 (5): 1439–93.

21.

Heckathorn

Douglas

Maser

Steven

. 1987. “Bargaining and the Sources of Transaction Costs: The Case of Government Regulation.” Journal of Law, Economics, & Organization 3:69–98.

22.

Lamothe

Scott

Lamothe

Meeyoung

Feiock

Richard C.

2008. “Examining Local Government Service Delivery Arrangements over Time.” Urban Affairs Review 44 (1): 27–56.

23.

Laumann

Edward O.

1966. Prestige and Association in an Urban Community. Indianapolis: Bobbs-Merrill.

24.

Lee

Youngmi

Lee

In Won

Feiock

Richard C.

2012. “Interorganizational Collaboration Networks in Economic Development Policy: An Exponential Random Graph Model Analysis.” Policy Studies Journal 40(3): 547-73.

25.

Lee

Youngmi

. 2010. “Impact Fees Decision Mechanism: Growth Management Decision in Local Political Market.” International Review of Public Administration 15 (2): 59–72.

26.

LeRoux

Kelly

Brandenburger

Paul W.

Padney

Sanjay K.

2010. “Interlocal Service Cooperation in U.S. Cities: A Social Network Explanation.” Public Administration Review 70 (2): 268–78.

27.

LeRoux

Kelly

Carr

Jered B.

2010. “Prospects for Centralizing Services in an Urban County: Evidence from Eight Self-Organized Networks of Local Public Services.” Journal of Urban Affairs 32 (4): 449–70.

28.

Lewis

Paul G.

Neiman

Max

. 2009. Custodians of Place. Washington, DC: Georgetown Univ. Press.

29.

Long

Norton E.

1958. “The Local Community as an Ecology of Games.” American Journal of Sociology 64:251–61.

30.

Lubell

Mark

. 2007. “Familiarity Breeds Trust: Collective Action in a Policy Domain.” Journal of Politics 69:237–50.

31.

Lubell

Mark

. 2013. “Governing Institutional Complexity: The Ecology of Games Framework.” Policy Studies Journal 41 (3): 537–59.

32.

Lubell

Mark

Schneider

Mark

Scholz

John

Mete

Mihriye

. 2002. “Watershed Partnerships and the Emergence of Collective Action Institutions.” American Journal of Political Science 46:148–63.

33.

Lubell

Mark

Scholz

John

Berardo

Ramiro

Robins

Garry

. 2012. “Testing Policy Theory with Statistical Models of Networks.” Policy Studies Journal 40 (3): 351–74.

34.

Marsden

Peter V.

1988. “Homogeneity in Confiding Relations.” Social Networks 10:57–76.

35.

Maser

Steven M.

1998. “Constitutions as Relational Contracts: Explaining Procedural Safeguards in Municipal Charters.” Journal of Public Administration, Research and Theory 8 (4): 527–64.

36.

McCabe

Barbara

. 2004. “Special District: An Alternative to Consolidation.” In Reshaping the Local Landscape: Perspectives on City-County Consolidation and Its Alternatives, edited by Carr

Jered

Feiock

Richard C.

, 131–52. Armonk: M. E. Sharpe.

37.

Meier

Kenneth J.

O’Toole

Lawrence J.

2002. “Public Management and Organizational Performance: The Impact of Managerial Quality.” Journal of Policy Analysis and Management 21 (4): 629–43.

38.

Minkoff

Scott L.

2012. “The Proximate Polity: Spatial Context and Political Risk in Local Developmental Goods Provision.” Urban Affairs Review 48 (4): 354–88.

39.

Minkoff

Scott L.

2013. “From Competition to Cooperation: A Dyadic Approach to Interlocal Developmental Agreements.” American Politics Research 41 (2): 261–97.

40.

Olberding

Julie C.

2002. “Does Regionalism Beget Regionalism? The Relationship between Norms and Regional Partnerships for Economic Development.” Public Administration Review 62 (4): 480–91.

41.

O’Leary

Rosemary

Vij

Nidhi

. 2012. “Collaborative Public Management: Where Have We Been and Where Are We Going?” The American Review of Public Administration 42 (5): 507–22.

42.

Ostrom

Elinor

. 1998. “A Behavioral Approach to the Rational Choice Theory of Collective Action.” American Political Science Review 92 (1): 1–22.

43.

O’Toole

Lawrence J.

2000. “Research on Policy Implementation: Assessment and Prospects.” Journal of Public Administration Research and Theory 10 (2): 263–88.

44.

Park

Jongsun

Feiock

Richard C.

2012. “Stability and Change in County Economic Development Organizations.” Economic Development Quarterly 26 (1): 3–12.

45.

Podolny

Joel M.

1994. “Market Uncertainty and the Social Character of Economic Exchange.” Administrative Science Quarterly 39:458–83.

46.

Post

Stephanie S.

2002. “Cities and Their Suburbs: Going along to Get” PhD dissertation, Rice Univ., Houston.

47.

Provan

Keith G.

Milward

H. Brinton

. 1995. “A Preliminary Theory of Interorganizational Network Effectiveness: A Comparative Study of Four Community Mental Health Systems.” Administrative Science Quarterly 40:1–33.

48.

Putnam

Robert D.

1995. “Bowling Alone. Journal of Democracy 6 (1): 65–78.

49.

Ripley

Ruth M.

Snijders

Tom A. B.

2010. “Manual for SIENA Version 4.0.” Oxford: Department of Statistics, Nuffield College, Univ. of Oxford.

50.

Rubin

Irene S.

Rubin

Herbert J.

1987. “Economic Development Incentives: The Poor Pay More. Urban Affairs Quarterly 22:32–62.

51.

Skyrms

Brian

. 2004. “The Stag Hunt and Evolution of Social Structure.” Cambridge: Cambridge Univ. Press.

52.

Snijders

Tom A. B.

2001. “The Statistical Evaluation of Social Network Dynamics.” Sociological Methodology 31:361–95.

53.

Snijders

Tom A. B.

2002. “Markov Chain Monte Carlo Estimation of Exponential Random Graph Models.” Journal of Social Structure 3 (2): 1–40.

54.

Tsebelis

George

. 1990. Nested Games: Rational Choice in Comparative Politics. Berkeley: Univ. of California Press.

55.

Waugh

William L.

1994. “Regionalizing Emergency Management: Counties as State and Local Government. Public Administration Review 54 (3): 253–58.

56.

Williamson

Oliver E.

1985. The Economic Institutions of Capitalism. New York: Free Press.

57.

Zeemering

Eric S.

2009. “California County Administrators as Sellers and Brokers of Interlocal Cooperation.” State and Local Government Review 41 (3): 166–81.

58.

Zhang

Yahong

. 2007. “Local Official’s Incentives and Policy-Making: Through the Lens of the Politics-Administration Relationship.” Doctoral dissertation, Florida State Univ., Tallahassee.

Supplementary Material

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A Longitudinal Network Analysis of Intergovernmental Collaboration for Local Economic Development

Abstract

Keywords

Introduction

Interjurisdictional Collaboration and ICA

Partner Selection as Interjurisdictional Collaboration

Hypotheses

Coordination

Cooperation

The Effect of Homophily: Political and Administrative Institutions

The Effect of Homophily: Socio-Economic Conditions

Research Design

Sample and Data

Concepts and Variables 7

Actor’s attribute variable

Methods

Estimation Results

Network Visualization

Statistical Results and Discussion

Network Effects

Homophily Effects

Actor’s Covariate Effects

Conclusion

Supplemental Material

Supplemental_material – Supplemental material for A Longitudinal Network Analysis of Intergovernmental Collaboration for Local Economic Development

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Supplemental Material

Notes

Author Biographies

References

Supplementary Material

Concepts and Variables⁷