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The power of people: social capital’s role in recovery from the 1995 Kobe earthquake


Despite the regularity of disasters, social science has only begun to generate replicable knowledge about the factors which facilitate post-crisis recovery. Building on the broad variation in recovery rates within disaster-affected cities, I investigate the ability of Kobe’s nine wards to repopulate after the 1995 Kobe earthquake in Japan. This article uses case studies of neighborhoods in Kobe alongside new time-series, cross-sectional data set to test five variables thought to influence recovery along with the relatively untested factor of social capital. Controlling for damage, population density, economic conditions, inequality and other variables thought important in past research, social capital proves to be the strongest and most robust predictor of population recovery after catastrophe. This has important implications both for public policies focused on reconstruction and for social science more generally.

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  1. The data used in this study are available for replication on the author’s DataVerse Network homepage.

  2. The term NPO includes groups classified by the Japanese government as nonprofit public-interest entities (kōeki hōjin) along with school, religious, medical, and social welfare organizations. Pekkanen points out that in Japan, the borrowed term “NGO” typically refers to organizations involved in international work, while “NPO” involves domestically active groups (2000: 116 fn 12).

  3. Tierney and Goltz (1997: 6) suggested that the lack of an official request from the governor, devastation of communications networks, logistical problems, the size of the event, and ambivalent attitudes toward the role of military in society were responsible for the SDF’s sluggish reaction (see also Yasui 2007: 97).

  4. According to Tsuji (2001: 218, Figures 9–1 and 9–2) the Nagata district had 35% of its population in emergency housing after the earthquake, and it took a full year for them to return to permanent housing. In a similar-sized district of Fukushima, 70% of its population moved into emergency shelters but all had moved out within 100 days.

  5. As autoregressive distributed lag models often create high levels of multicollinearity, I tested the data for multicollinearity using the Variance Inflation Factor (VIF) approach, and found relatively low levels of interaction among the variables (with a VIF of less than 7 for all models) (Rabe-Hesketh and Everitt 2007: 69). A second concern for ADLs is that variables within them remain strictly stationary. A multivariate augmented Dickey-Fuller test indicated that we can reject the null hypothesis that the processes for 1, 2, and 3 period lags are nonstationary (that is, lack equilibrium). Further, the Hausman specification test determines whether a fixed or random effects model is most appropriate when using time-series data, and it indicated in this case that a fixed effects model fit more closely (Chi square value of 0.0001).


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The author wishes to acknowledge Takahiro Yamamoto for his excellent research assistance. The archival and theoretic work for this paper was carried out while the author was on an Abe Fellowship sponsored by the Center for Global Partnership and administered by the Social Science Research Council. Pat Boling, Erik Cleven, Paul Danyi, Jay McCann, Leigh Raymond, Mark Tilton, and Laurel Weldon provided helpful feedback.

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Correspondence to Daniel P. Aldrich.

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Aldrich, D.P. The power of people: social capital’s role in recovery from the 1995 Kobe earthquake. Nat Hazards 56, 595–611 (2011).

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  • Kobe earthquake
  • Population recovery
  • Social capital
  • Disaster
  • Resilience