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The Use of Exponential Random Graph Models to Investigate the Micro-Level Processes of Inter- Organizational Network Formation

The response systems that emerge after large-scale disasters like the Haitian Earthquake on January 12th, 2010, or the recent series of events unfolding in Japan, are defined by the complex interactions of organizations. These response systems are often analyzed by investigating the structure of the network and the consequences of its structural properties. While there is a growing body of work on the consequences of response networks, there is limited research on the antecedents of these networks. This paper provides statistical inferences on the selection processes that exist among organizations entering the response system and highlights the micro-level processes that give rise to the global structure. Part of the reason for the lack of research on the antecedents of disaster response networks has been due to limitations in the statistical modeling of networks that required the researcher to investigate potential generative processes in isolation. Recent advances in exponential random graph models (also referred to as p* models) provide a flexible statistical framework to examine multiple interdependent processes simultaneously. This paper proposes and tests theoretically driven micro-level processes believed to be responsible for generating the global structure of the observed response networks that emerged after recent earthquakes in Indonesia, Haiti, and Japan. These findings offer fresh insight into the mechanisms driving tie formation between organizations in emergencies and provide guidance on future research into the antecedents of networks Michael Siciliano Center for Disaster Management Graduate School of Public and International Affairs University of Pittsburgh mds55@pitt.edu