A Longitudinal Network Analysis of Intergovernmental Collaboration for Local Economic Development

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.

A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

The temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.