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H. Matsuura
equality. RCI assumes that the more equal a municipality’s distribution of income,
the more cohesive the community response to disasters. An inverse of Gini coefficient was used to measure the degree of income equality in both original RCI and
the RCI calculated in this paper. The Gini Coefficient was first calculated using
the municipality-level income distribution data in Housing and Land Survey. This
survey is conducted every five years; thus, I used the latest available dataset from
2013. Then I subtracted the Gini Coefficient from 1 to obtain the measure of income
equality. The second economic indicator is economic diversification. RCI assumes
that economic diversity increases community resilience by reducing the economic
risk of the municipality. The original data come from the Economic Census for Business Frame in 2014. In order to calculate economic diversification, I first calculated
Herfindahl–Hirschman Index (HHI), a measure of concentration to the particular
sector, by using the number of employees across 22 different sectors in each municipality. HHI is equal to 1 if all employees work in one of these 22 sectors. On
the other hand, if employees work equally spread across 22 different sectors, it is
22 ∗
1
22
2 = 0.046. I took an inverse of this value after the z-transformation since a
low value means more economic diversity. The third economic indicator is regional
affordability. Regional affordability is an indicator of economic security, measured
by the proportion of income not spent on rent. The data on average monthly rent
comes from the 2013 Housing and Land Survey. I divided this number by per capita
monthly income calculated from data on taxable income, compiled by Ministry of
Internal Affairs and Communications, and then subtracted it from 1. In the original
RCI, regional affordability was measured by the percentage of households who spend
less than 35% of their income on housing. However, I was unable to obtain these
data at municipality level in Japan. Therefore, I calculated average housing expenditure as a percentage of household income in each municipality. The fourth economic
indicator is business environment indicators. RCI assumes that the more dynamic
the local business environment, the more adaptable and resourceful, and thus more
resilient against disasters. The original RCI used the Indiana Business Center’s 2010
Innovation Index, which measured economically dynamic areas by using multiple
indicators. No such index exists in Japan. However, the Japan Productivity Center has
been publishing labour productivity statistics since 1958. The literature argues that
labour productivity is related to competitive environment, innovative human capital
and flexible workplace (Schnabel 1997; Bauer 2003). For this reason, I simply used
labour productivity as a proxy for the dynamic business environment––obtaining
data on labour productivity in 2016.
Socio-demographic indicators reflect the capacity of the municipality on the basis
of social and demographic characteristics, including (1) educational attainment, (2)
disability, (3) poverty and (4) health care access. The first socio-demographic indicator is the percentage of adults aged 15 and older who have completed a bachelor’s or higher degree, which comes from the large-scale Census and is surveyed
every ten years. I used the latest survey available from 2010. The second indicator
is without disability. RCI assumes that municipalities with a higher percentage of
people with disabilities are more vulnerable to disasters. Unfortunately, the data on
H. Matsuura
equality. RCI assumes that the more equal a municipality’s distribution of income,
the more cohesive the community response to disasters. An inverse of Gini coefficient was used to measure the degree of income equality in both original RCI and
the RCI calculated in this paper. The Gini Coefficient was first calculated using
the municipality-level income distribution data in Housing and Land Survey. This
survey is conducted every five years; thus, I used the latest available dataset from
2013. Then I subtracted the Gini Coefficient from 1 to obtain the measure of income
equality. The second economic indicator is economic diversification. RCI assumes
that economic diversity increases community resilience by reducing the economic
risk of the municipality. The original data come from the Economic Census for Business Frame in 2014. In order to calculate economic diversification, I first calculated
Herfindahl–Hirschman Index (HHI), a measure of concentration to the particular
sector, by using the number of employees across 22 different sectors in each municipality. HHI is equal to 1 if all employees work in one of these 22 sectors. On
the other hand, if employees work equally spread across 22 different sectors, it is
22 ∗
1
22
2 = 0.046. I took an inverse of this value after the z-transformation since a
low value means more economic diversity. The third economic indicator is regional
affordability. Regional affordability is an indicator of economic security, measured
by the proportion of income not spent on rent. The data on average monthly rent
comes from the 2013 Housing and Land Survey. I divided this number by per capita
monthly income calculated from data on taxable income, compiled by Ministry of
Internal Affairs and Communications, and then subtracted it from 1. In the original
RCI, regional affordability was measured by the percentage of households who spend
less than 35% of their income on housing. However, I was unable to obtain these
data at municipality level in Japan. Therefore, I calculated average housing expenditure as a percentage of household income in each municipality. The fourth economic
indicator is business environment indicators. RCI assumes that the more dynamic
the local business environment, the more adaptable and resourceful, and thus more
resilient against disasters. The original RCI used the Indiana Business Center’s 2010
Innovation Index, which measured economically dynamic areas by using multiple
indicators. No such index exists in Japan. However, the Japan Productivity Center has
been publishing labour productivity statistics since 1958. The literature argues that
labour productivity is related to competitive environment, innovative human capital
and flexible workplace (Schnabel 1997; Bauer 2003). For this reason, I simply used
labour productivity as a proxy for the dynamic business environment––obtaining
data on labour productivity in 2016.
Socio-demographic indicators reflect the capacity of the municipality on the basis
of social and demographic characteristics, including (1) educational attainment, (2)
disability, (3) poverty and (4) health care access. The first socio-demographic indicator is the percentage of adults aged 15 and older who have completed a bachelor’s or higher degree, which comes from the large-scale Census and is surveyed
every ten years. I used the latest survey available from 2010. The second indicator
is without disability. RCI assumes that municipalities with a higher percentage of
people with disabilities are more vulnerable to disasters. Unfortunately, the data on