Executive Summary
Disaster researchers have long wondered which factors matter most in the process of recovery.
Popular theories focus on the amount of aid or capital in an area, the damage done by the event,
quality of governance, and levels of social cohesion. Given the impossibility of experimental
investigations, social scientists have instead looked to actual recovery patterns to better
understand the ways that local conditions condition outcomes such as repopulation and
rebuilding. This dataset, “Replication data for: Time-series, cross-sectional dataset on police
jurisdictions in Tokyo, Japan from 1922 until 1933,” uses data collected by highly motivated
police throughout the capital city before and then a decade following the massive 1923
earthquake. The timing of this data allows researchers to understand the ways that local
conditions altered outcome such as the number of people coming into the city after the
earthquake.
In the 1920s, the Tokyo police was concerned about the cultural expression and pro-West
ideologies during the period known as Taisho Democracy. Keeping close tabs on the inhabitants
near their police boxes, or koban, these officers recorded monthly observations on a number of
behaviors from population migration to crime to factory workers in the area. This dataset uses
the records collected from the police officers alongside other historical records to create a way of
understanding how neighbors in Tokyo changed after the Great Tokyo earthquake. The dataset
has information on population, crime rates, geographical location, political gatherings, factory
workers, suicides, pawn broker lending levels, damage levels from the earthquake,
unemployment, population, and the number of inns, rickshaws, plays, commercial and private
cars, among other areas.
This dataset yielded an interesting finding, namely that, holding all other factors constant, levels
of collective action strongly influenced population recovery rates. As I said in the abstract to one
of the articles that was based on this data, “Despite the tremendous destruction wrought by
catastrophes, social science holds few quantitative assessments of explanations for the rate of
recovery. This article illuminates four factors—damage, population density, human capital, and
economic capital—that are thought to explain the variation in the pace of population recovery
following disaster; it also explores the popular but relatively untested factor of social capital.
Using time-series, cross-sectional models and propensity score matching, it tests these
approaches using new data from the rebuilding of 39 neighborhoods in Tokyo after its 1923
earthquake. Social capital, more than earthquake damage, population density, human capital, or
economic capital, best predicts population recovery in post-earthquake Tokyo. These findings
suggest new approaches for research on social capital and disasters as well as public policy
avenues for handling catastrophes” (Aldrich 2012). The same data was also used in a chapter of
my book on the role of social cohesion and networks in disaster recovery (Aldrich 2012b).
Sources:
Aldrich, Daniel P. (2012). Social, not physical, infrastructure: the critical role of civil society
after the 1923 Tokyo earthquake Disasters Volume 36, Issue 3, pages 398–419, July 2012
Aldrich, Daniel P. (2012b). Building Resilience: Social Capital in Post-Disaster Recovery.
Chicago: University of Chicago Press.