Despite the tremendous destruction wrought by disasters and catastrophes around the world, little quantitative research has connected potential theoretical explanations with the rate of post-disaster recovery. This presentation first lays out five theories explaining variation in the pace of rebuilding following disaster, with special attention paid to the popular but relatively untested factor of social infrastructure. Social networks have been singled out as a critical component of rebuilding, but few studies have had large scale datasets on which to evaluate their efficacy. Using new data from the recovery of Tokyo, Japan, which was struck by super catastrophe in 1923, I test these approaches and uncover evidence that social networks, more than damage, business, socioeconomic, or living conditions, best predicts population recovery.
Daniel P. Aldrich received his Ph.D. and M.A. in political science from Harvard University, an M.A. from the University of California at Berkeley, and his B.A. from the University of North Carolina at Chapel Hill. Daniel has focused on the ways in which state agencies interact with contentious civil society over the siting of controversial facilities such as nuclear power plants, airports, and dams. He has published a number of peer-reviewed articles alongside research for general audiences. His research has been funded by grants from the Abe Foundation, IIE Fulbright Foundation, the National Science Foundation, the Reischauer Institute at Harvard University, the Weatherhead Center for International Affairs and Harvard's Center for European Studies. Daniel is Assistant Professor of Political Science at Purdue University, and currently a Visiting Scholar at the University of Tokyo.