GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 331

The Long-term Impact of the 1995 Hanshin-Awaji
Earthquake on Wage Distribution

Fumio Ohtake, Naoko Okuyama, Masaru Sasaki, Kengo Yasui

March 2014

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

The Long-term Impact of the 1995 Hanshin-Awaji
Earthquake on Wage Distribution†
Fumio Ohtake 1, Naoko Okuyama 2, Masaru Sasaki3, Kengo Yasui 4

March 30, 2014

Abstract
This paper explores the effects of the 1995 Hanshin-Awaji Earthquake on the wages ofpeople in the area of the earthquake over the 17 years after its occurrence and identified
which part of the wage distribution has been most affected by this event by comparing
the wage distributions of disaster victims and non-victims. To do this, we used threedecomposition methods, developed by (i) Oaxaca (1973) and Blinder (1973); (ii)
DiNardo, Fortin, and Lemieux (1996) (“DFL”); and (iii) Machado and Mata (2005) and
Melly (2006). Our findings are as follows. First, the Oaxaca and Blinder decomposition
analysis shows that the negative impact of the earthquake still affects the mean wages ofmale workers. Second, the DFL decomposition analysis shows that middle-wage males
would have earned more had the 1995 Hanshin-Awaji Earthquake not occurred. Finally,
the Machado-Mata-Melly decomposition analysis shows that the earthquake had alarge, adverse impact on the wages of middle-wage males, and that their wages havebeen reduced since the earthquake, by 5.0–8.6%. This result is similar to that from the
DFL decomposition analysis. In the case of female workers, a long-term negative
impact of the earthquake was also observed as the wages of high-wage females werereduced by 8.3–13.8%.

JEL Classification: J31, Q54
Keywords: Natural disasters, Wage distribution, Wage decomposition, Earthquake†

Acknowledgments: We thank William DuPont, Lena Edlund, Timothy Halliday, Takahiro Ito, Takao
Kato, Daiji Kawaguchi, Peter Kuhn, Edward Lazear, Colin McKenzie, Hideo Owan, Hugh Patrick, Kei
Sakata, Till Von Wachter, David E. Weinstein, and the seminar participants at the Columbia Business
School, the 27th European Society for Population Economics Conference, the Graduate Center of the City
University of New York, the Japanese Economic Association 2013 meeting, and the 6th Trans-PacificLabor Seminar for helpful conversations and comments.
1
ISER, Osaka University
2
Graduate School of Economics, Kobe University
3
Graduate School of Economics, Osaka University, and IZA
4Faculty of Economics, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan;
yasui@fc.ritsumei.ac.jp

11. Introduction
It has been 3 years since an earthquake of an unprecedented scale with an epicenter off
the coast of the northern part of Japan occurred at 14:46 on March 11, 2011. During thepast 3 years, we have wondered how long it would take Japan and the northeastern area(the Tohoku area in Japanese), in particular, to recover from this devastating Great East
Japan Earthquake. These questions remain. Not only did the earthquake turn the Tohoku
area into rubble, but the resulting tsunami also damaged infrastructure and facilities
along the Pacific coastline of the northern part of Japan—and, in particular, theFukushima No. 1 nuclear power plant.1 It is well known that the tsunami triggered core
meltdowns in three of that power plant’s reactors.
Although 3 years have passed since the Great East Japan Earthquake, thedisaster areas are still in the process of being restored and reconstructed. Although it
appears that post-quake reconstruction is progressing in the Tohoku area, it will take asignificant amount of time for the area to be fully restored to its pre-earthquake level: inparticular, housing projects have not progressed as quickly as victims living in
temporary housing had expected. Many tsunami victims still cannot return to theirhouses located in the designated nuclear decontamination areas near the Fukushima No.
1 nuclear plant. The Tokyo Electric Power Company (TEPCO) and the victims aredeadlocked on the issue of compensation. On the one hand, the disaster destroyed manyjobs in the manufacturing, service, and marine product processing sectors, but, on theother hand, many jobs have been created in the construction sector since the quake. This
1

According to the Japanese National Police Agency, the death toll stood at 15,884 persons and another
2,633 were still missing as of March 10, 2014. The Reconstruction Agency announced that 267,419
evacuees still lived in temporary housing and accommodations as of February 13, 2014. The Agency also
reported that the quake-related death toll, including suicides, stood at approximately 3,000 victims.
According to the Japanese Government’s Cabinet Office, the annualized nominal GDP was JPY 481.1
trillion in the fourth quarter of 2013, still lower than that of the fourth quarter of 2010, just before theearthquake occurred.

2

generates an employment mismatch problem, and the construction sector suffers from a
serious shortage of manpower. From a short-term perspective, the earthquake andsubsequent events have left the disaster victims facing financial and physical hardships.
However, what about the longer-term perspective? It is thus important to investigate
when this hardship is likely to end and how the disaster victims can be assisted to returnto their normal lives.
Because only 3 years have elapsed since the Great East Japan Earthquake, it is
too early to evaluate its devastating impact from a long-term perspective. Instead, we
examine the long-term impact of the Hanshin-Awaji Earthquake of 1995 as a proxyevaluation of the Great East Japan Earthquake. Similar to the Great East Japan
Earthquake, this earthquake inflicted devastating damage on the Hanshin area, between
the major cities of Osaka and Kobe, which was densely populated and largely
industrialized. By assessing the negative impacts of the 1995 Hanshin-Awaji Earthquake
from a long-term perspective, this paper aims to investigate to what extent the negative
impacts of the earthquake had continued unsolved or had been attenuated at the time ofour survey in 2012. This may assist development of a long-term vision relevant to thereconstruction following the Great East Japan Earthquake.
Several studies have analyzed the effects of natural disasters on people’s lives,in economic and mental terms. Notably, Ohtake et al. (2012) estimate the long-term
impact of the Hanshin-Awaji Earthquake on annual income and amount of social capital
relating to interactions with neighbors and friends, using the same online survey datathat we also analyzed here. They explored how the earthquake adversely affected thecurrent level of subjective well-being of the disaster victims through a reduction in
annual income anddeterioration in social capital. More specifically, a disaster victim
3

whose house partially or completely collapsed or was destroyed by fire because of theearthquake would have earned additional annual income as at 2011 had the earthquake
not occurred. The earthquake also resulted in the deterioration of social capital relating
to social interactions. Moreover, these levels of social capital have not yet been fully
restored—in particular, the loss of social capital measured by the degree of anindividual’s interaction with his/her neighbors. The decrease in annual income and thedeterioration in social capital were found to have reduced the subjective well-being of
the disaster victims.
Our paper differs from previous studies in that it focuses particularly on how
large-scale natural disasters, such as the Hanshin-Awaji Earthquake, affect changes in
the wage distribution of disaster victims versus non-victims who were engaged in workfrom the time when the earthquake occurred in 1995 to the time the survey was
conducted in 2012. We paid particular attention to which part of the wage distribution
was most negatively affected by the 1995 Hanshin-Awaji Earthquake.
The objectives of this paper are to explore how the Hanshin-Awaji Earthquake
affected the wage distribution during the 17 years after its occurrence and to identify
who has incurred the largest wage loss: low-wage, middle-wage, or high-wage victims,
in comparison with non-victims. In this paper, we used three decomposition methods:
those proposed by i) Oaxaca (1973) and Blinder (1973); ii) DiNardo, Fortin, andLemieux (1996) (“DFL”); and iii) Machado and Mata (2005) and Melly (2006).
Many people probably share the conventional view that low-wage workers
would be most vulnerable to natural disasters and would thus conclude that the
Hanshin-Awaji Earthquake had the most adverse impact on workers in the lowest
percentiles of the wage distribution during the 17 years after its occurrence. However, it
4

is important to also explore whether high-wage or middle-wage workers sufferedeconomically from this natural disaster.
In this paper, the case of the 1995 Hanshin-Awaji Earthquake is analyzed with
the intention of being able to better forecast the long-term structural changes that may
occur in areas destroyed by the Great East Japan Earthquake in 2011. However, there
are some limitations to using the Hanshin-Awaji Earthquake case. First, the twoearthquakes differed in terms of the subsequent disasters. Although the Hanshin-Awaji
Earthquake caused fires that destroyed many houses, the Great East Japan Earthquake
created a 15-meter tsunami and the nuclear plant incident. Second, the disaster areas
destroyed by the two earthquakes differed in their industrial characteristics. Whereas the
Hanshin-Awaji Earthquake damaged mainly a manufacturing and services industries
region, the Great East Japan Earthquake devastated a region where the majority ofworkers were employed in the fishery and agricultural industries. Finally, the disasterareas differ in terms of the population distribution by age. There was a relatively large
older population in the Tohoku area when the Great East Japan Earthquake struck,
whereas there was a relatively large younger population in the Hanshin area in 1995.
However, analyzing the Hanshin-Awaji Earthquake also has two advantages.
The first is that this analysis allows us to examine the long-term impact of anearthquake on people’s lives. Moreover, in terms of the scale of the natural disaster, theHanshin-Awaji Earthquake was similar to the Great East Japan Earthquake. This will
enable us to better predict the long-term effects of the Great East Japan Earthquake on
the wage structure and income inequalities in the disaster area. In terms of the second
advantage, our paper contributes to the existing literature because, to our knowledge,
this is the only reported study that empirically analyzes the long-term effects of a5natural disaster on the wage distribution. Although many empirical studies haveanalyzed the economic effects of natural disasters, most have focused on how natural
disasters affect economic growth or consumption behavior at the aggregate level. The
effects of natural disasters on the labor market have rarely used micro data. In thisregard, our analysis of the relationship between the earthquake and the wage
distribution may provide a foundation for future studies assessing the impact of natural
disasters on the accumulation of human and health capital.
We used an Internet survey to collect original data from victims andnon-victims in the disaster area of the Hanshin-Awaji Earthquake and on persons living
in several selected non-disaster areas at the time of the Hanshin-Awaji Earthquake. The
survey was conducted in March 2012. Because natural disasters are usually considered
to be unexpected exogenous shocks, we can conduct a unique natural experiment andthen identify the exact impact of the natural disaster by comparing outcomes between atreatment group and a control group. We defined the disaster victims as the treatmentgroup, whereas the control group consisted of both non-victims from the disaster areaand persons from the selected non-disaster areas.
The main findings can be summarized as follows. First, according to the
Oaxaca and Blinder decomposition analysis, the 1995 Hanshin-Awaji Earthquake
continues to have a negative impact on the mean wages of male workers 17 years after
the earthquake. However, its effect on the mean wages of female workers has
disappeared. Second, the DFL decomposition analysis indicated that middle-wage maleworkers would have earned more had the 1995 Hanshin-Awaji Earthquake not occurred.
That is, middle-wage male workers were the most severely affected by the earthquake.
In contrast, this was not true for female workers. Finally, the6Machado-Mata-Mellydecomposition analysis also showed that the earthquake had a large, negative effect on
the wages of middle-wage males, and that their wages have been lower since theearthquake, by 5.0-8.6%. This result is similar to that of the DFL decomposition
analysis. The negative impact of the 1995 earthquake on the wages of high-wage female
workers, a 8.3–13.8% reduction, was still evident in 2011.
The remainder of the paper is organized as follows. We begin with a literature
review in Section 2 and then provide a brief overview of the 1995 Hanshin-Awaji
Earthquake in Section 3. We discuss the econometric specification in Section 4 and thendescribe our original data set in Section 5. Subsequently, we present the estimatedresults in Section 6 and discuss the interpretation of the results in Section 7. The final
section provides some concluding remarks.

2. Literature Review
This section provides a literature review of research papers relevant to the analysis of
the impacts of natural disasters. 2 We begin with a review of the literature on the
short-term impacts of natural disasters. Raddatz (2007) and Noy (2009) first estimatedthe impacts of natural disasters from a short-term perspective, regressing per capita
GDP on the scale of natural disasters. Both authors found the same result, indicatingthat natural disasters had a negative impact on the economy of the disaster areas in the
short term. Noy (2009) added interaction terms with each country’s economic andpolitical characteristics and a natural disaster term as explanatory variables and thenre-estimated the impact of the natural disasters on the economy. Noy’s findings
indicated that when a country is less economically developed or less mature, the impact
2

Cavallo and Noy (2011) summarized a wide range of previous studies in this field.
7

of a natural disaster was more serious and more persistent.
Several studies have analyzed the long-term impact of natural disasters. Noy
and Nualsri (2011) reported that a natural disaster has a negative economic impact in thelong and short term. In contrast, Skidmore and Toya (2002) reported that a natural
disaster has a positive economic impact in the long term. Skidmore and Toya (2002)
pointed out a “creative destruction” effect to support their finding. They explained that anatural disaster eliminates old inefficient industries and encourages new, more efficientindustries to arise, thereby contributing to economic growth in the long term. According
to the theory, natural disaster-induced creative destruction (Cuaresma, Hlouskova, andObersteiner, 2008) occurs in developed countries, but not in developing countries. Thismay be because it is difficult to introduce and disseminate new technologies indeveloping countries.
Cavallo et al. (2010) showed the difference between the actual economic
growth path and the estimated counterfactual growth path that would have beenaccomplished without a natural disaster and then quantitatively calculated the long-term
impact of a natural disaster on economic growth. They found that the long-term impactof natural disasters on economic growth was negligible. Using the same econometricmethod, DuPont and Noy (2012) estimated the long-term impact of the Hanshin-Awaji
Earthquake on per capita GDP for Hyogo Prefecture, which includes the Hanshin andAwaji areas. They reported that the long-term effect was not negligible, finding that the
per capita GDP of Hyogo Prefecture would have been higher, by JPY500,000, in 2007
had the 1995 Hanshin-Awaji Earthquake not occurred.
As explained above, the economic impacts of natural disasters have been
analyzed in several previous studies. However, most of these studies examined8

economic growth or consumption behaviors in relation to natural disasters, and little
attention has been devoted to the impact of natural disasters on the labor market using
micro data. Thus, our study, which empirically analyzes the long-term effects of anatural disaster on the wage distribution based on micro data, may contribute to a
deeper understanding of the mechanism(s) behind the impacts of natural disasters on the
productivity of individual workers.

3. The Hanshin-Awaji Earthquake
In this section, we provide a brief overview of the scale of the Hanshin-Awaji
Earthquake and the extent of damage inflicted by it. At 5:46 a.m. on January 17, 1995,
an earthquake of magnitude 7.3 struck off the coast of the northern part of Awaji Island.
The displacement of the fault line that extends from Awaji Island to Mt. Rokko, locatedbeyond Kobe City, caused strong tremors in the areas located along the line.
The Japanese government officially declared the following 10 cities and
10 municipalities in Hyogo Prefecture to be the most severely damaged region. The 10
cities were Kobe City (damage was particularly severe in Suma, Hyogo, Nagata, Nada,
and Higashi-Nada wards), Amagasaki City, Itami City, Nishinomiya City, Ashiya City,
Takarazuka City, Kawanishi City, Akashi City, Miki City in the Hanshin area, andSumoto City on Awaji Island. The 10 municipalities were Tsuna Town, Awaji Town,
Hokutan Town, Ichinomiya Town, Goshiki Town, Higashiura Town, Midori Town,
Seitan Town, Mihara Town, and Nantan Town, all on Awaji Island. The orange-coloredarea in Figure 1 shows Hyogo Prefecture, including these disaster areas.3
Next, we briefly introduce the extent of the damage inflicted by the earthquake.
3

Outside Hyogo Prefecture, a seismic intensity of 4 on the Japanese scale was recordedin Toyonaka City, Osaka Prefecture.
9

The Fire and Disaster Management Agency (FDMA) finalized a report on the status of
the damage on May 19, 2006. According to its report, 6,434 people were killed andthree persons remained missing, whereas 104,906 houses (accommodating 186,175
households) were destroyed completely, 144,274 houses (accommodating 274,182
households) were destroyed partially, and 269 fires were recorded.4 Additionally, muchof the infrastructure of the region was destroyed. The FDMA reported that 1,579 publicbuildings, 7,245 sections of road, and 330 bridges were damaged. Broadcast footage ofa toppled section of the Hanshin Expressway’s Kobe line shocked many viewers. The
Kobe lines of the railways operated by the West Japan Railway Company and private
railway companies (Hanshin, Hankyu, and Sanyo), as well as the facilities of municipal
subway and bus operators, were also severely damaged, bringing traffic in the region toa standstill. The lifeline infrastructure also sustained significant damage.

4. Econometric Method
To estimate the long-term impacts of the 1995 Hanshin-Awaji Earthquake on wage
differentials, we investigate the causes of the differences in current wage distributions
between disaster victims and non-victims. We used three decomposition methods: those
proposed by (i) Oaxaca (1973) and Blinder (1973); (ii) DiNardo, Fortin, and Lemieux(1996) (“DFL”); and (iii) Machado and Mata (2005) and Melly (2006). Using thesemethods, we decompose the wage differentials into two components: one part explainedby the differences in characteristics of the two groups, and the other part capturing theeffect of the earthquake.

4

See the Hyogo prefectural government Web site:
http://web.pref.hyogo.jp/pa20/pa20_000000015.html
10The wage equation for group V (victims) and group N (non-victims) is
formulated as:

Yg = Xg βg + ug , g = V, N,(1)where Y denotes log hourly wages, X is a vector of individual characteristics affecting
wages (annual income at the time the earthquake occurred, years of schooling, age, andage squared), and β is a parameter vector of returns to these characteristics.
Using the Blinder–Oaxaca decomposition, we investigate whether thedifference in the mean wage between disaster victims and non-victims arose fromdifferences in the observed characteristics of the two groups or from differences in the“prices” for the observed characteristics of the two groups. Differences in prices refer todifferences in the returns to the observed characteristics as a function of whether
individuals were victims. For example, do victims with the same educational attainment
as non-victims earn a different wage because they were affected by the earthquake? We
consider such differences in prices to result from the long-term impacts of theearthquake. The difference in the mean wage between group V (victims) and group N(non-victims) can then be written as:

���
����
���� � ���� � �
YV − ���
YN = (XV − X N )βN + X V (βV − βN ),(2)where the first term on the right side of equation (2) represents the explained component,
and the second term indicates the unexplained component. One issue to be considered isthat estimates of the effect of the earthquake may be biased if differences in the11unobserved heterogeneity between V and N are not considered. To determine theunbiased effect of the earthquake, we considered the difference in the annual income of
V and N at the time the earthquake occurred as one of the explained components
because this difference captures the difference in the possibly unobserved characteristics
between V and N before the earthquake.
In addition to annual income at the time the earthquake occurred, we included
the following variables as explanatory variables: years of schooling, age, andage squared. Our purpose is to decompose the wage differences between V and N into
two parts as equation (2) shows. The first term in equation (2) consists of differences in
the observed characteristics of the two groups at the time the earthquake occurred,
�
����
����
(X
V − X N ) and βN ; the second term consists of differences in the prices for the observed− β�) and ��X��characteristics that are explanatory variables, (β�V . For the purpose ofVN����
obtaining ��X��V − X N , we used the four variables X g in eq. (1)), which we assumed havenot been affected by the earthquake, as explanatory variables.

In contrast, other variables (e.g., length of tenure, years of work experience,and industry type), which could have possibly changed after the earthquake, are omitted
from equation (1) as explanatory variables. This allows the effects of variation in theseomitted variables on wages to be reflected in the coefficient β�g . More specifically, theerror term (ug in equation (1)) may be correlated with the four explanatory variables

because the error term includes the omitted variables.

By so doing, we attempt toestimate the coefficients that include the effects of the omitted variables (whose
variations were outcomes of the earthquake) on wages. That is, we estimate
differences of prices for the four explanatory variables in the current labor market
between victims and non-victims.
12

Next, using the DFL decomposition method, we create the counterfactual wage
distribution for victims, assuming that victims and non-victims would earn the samewage if they shared the same observable characteristics:

FYcV (y) = ∫ FYV |XV (y|X) Ψ(X)dFXV (X),(3)

where Ψ(X) = dFXN (X)/dFXV (X) is a reweighting factor. The idea of the DFL
decomposition method is to replace the distribution of X of group V (FXV (∙)) with the

distribution of X of group N ( FXN (∙) ) using the reweighting factor Ψ (∙) . The
reweighting factor can be estimated as:

Pr(X|g=N)

Ψ(X) = Pr(X|g=V) =Pr(g=N|X)/Pr (g=N)
Pr(g=V|X)/Pr (g=V).

(4)

To estimate Prob( g = N |X), we pooled the two groups and then estimated theprobability of an individual belonging to group N as a function of X, using a logistic
regression model.
The Machado and Mata (2005) decomposition method allows decomposing thewage differences between victims and non-victims into explained differences andunexplained differences at each quantile of the wage distributions, which is similar to
the Blinder–Oaxaca approach that decomposes the difference in the mean wage.
The decomposition is undertaken based on a counterfactual distribution of YV.
This is defined as the distribution of wages for victims that would have prevailed if thelabor market environment of victims had been the same as that of non-victims. This
�V |XV , β� ) , where Y�V are generated
counterfactual distribution is denoted by F(YN,θ
13values of YV , and β�
are the θth quantile regression coefficients estimated by quantileN,θ
regression using the sample of non-victims.

The steps in the Machado and Mata (2005) algorithm to construct
�V |XV , β� ) are as follows:F(YN,θ1. For each quantile θ = 0.01, 0.02,…, 0.99, we estimate the quantile regression
vector of coefficients β�
for the sample of non-victims.
N,θ�V = β� ′ XV . For each θ,
2. We use the sample of victims to generate fitted values YN,θthis generates NV fitted values, where NV is the sample size of the victims. Next,�V (θ) for each θ and stack these into a
we randomly select s = 100 elements of Y

�V . The c.d.f. of these values is the counterfactual
99 × 100 element vector Y

distribution for victims.

3. We compare the counterfactual distribution with the actual wage distribution of
victims and non-victims.

The wage difference at the θth quantile can be decomposed as follows:�V (θ)� + �Y�V (θ) − YV (θ)�.
YN (θ) − YV (θ) = �YN (θ) − Y(5)

The first bracket on the right side of equation (5) represents the explained component at
the θth quantile. The second bracket indicates the unexplained component, which
presents the true effect of the earthquake, at the θth quantile.
Melly (2006) proposed another estimator that is numerically identical to that14

used by Machado and Mata (2005) if the simulations used in the latter procedure arerepeated to infinity. Melly’s estimator is faster to compute and can be used to bootstrap
results to provide standard errors. Thus, we used the procedure proposed by Melly(2006).

5. Data
We conducted a unique online survey developed exclusively for our project withassistance from Nikkei Research Co. First, we conducted a nationwide screening survey,
followed by the main survey. The questionnaires for the main survey were delivered totargeted individuals based on the population distribution revealed by the screening
survey. Responses were collected between March 15, 2012 and March 23, 2012.
Demographic and regional adjustments were made for the sample layout and allocation,such as prefectures, municipalities, gender, and age.
The survey targeted two types of individual, aged from 20 to 80 years, who
lived in Japan as of March 15, 2012: (i) individuals who lived in any of the 10 cities or
10 municipalities in Hyogo Prefecture in January 1995 that were recognized officiallyas disaster areas by the government, and (ii) individuals who lived in Yokohama City in
Kanagawa Prefecture or in Osaka City, Suita City, Takatsuki City, or Sakai City inOsaka Prefecture. Group (i) includes both disaster victims and non-victims. Group (ii)consists only of non-victims.
We used the extent of housing damage to identify victims and non-victims. We
asked respondents about the extent of housing damage caused by the earthquake.
Participants were asked to choose from the following: (1) own house collapsed
completely or was completely destroyed by fire, (2) own house collapsed partially or
15was partially destroyed by fire, (3) own house was damaged only partially (rather thancollapsed), or (4) own house was not damaged at all.). 5 Those who responded with (1)or (2) were defined as disaster victims, and the rest were considered non-victims.
We considered two approaches to identifying treatment and control groups. The
first involved confining our sample to group (i) and defining victims as the treatmentgroup and non-victims as the control group. An alternative approach involved including
both groups (i) and (ii) and defining victims in group (i) as the treatment group andnon-victims in both groups (i) and (ii) as the control group. We considered it appropriate
to treat non-victims in group (ii) as the control group because the individuals in thisgroup resembled those living in the disaster area in terms of economic, sociological, andcultural characteristics. More specifically, residents of Yokohama city were chosen to be
the control group because of this city’s similarity to the disaster areas with regard to its
industrial profile. Likewise, residents of selected cities in Osaka Prefecture were treatedas the control group because these cities are geographically proximate to the disasterregions, which means that workers in both regions belong to the same labor market.
Thus, we defined all members of groups (i) and (ii) as the “whole sample” and membersof only group (i) as the “subsample.”
In total, we collected 10,387 valid responses: 6,650 from the 10 cities and 10
municipalities (the Hanshin and Awaji areas), 1,646 from Yokohama City, 1,316 from
Osaka City, 203 from Suita City, 216 from Takatsuki City, and 356 from Sakai City. The
blue-colored area in Figure 1 is Kanagawa Prefecture, which includes Yokohama City,and the green-colored area is Osaka Prefecture, which includes Osaka City, Suita City,
Takatsuki City, and Sakai City. The outline of the survey is provided in the Appendix.

5“Own” houses included both owner-occupied and rented houses.

16

We restricted our sample to those who worked at the time of the 1995
Hanshin-Awaji Earthquake and at the time when the survey was conducted because weneeded data on annual income in 1994 to control for unobserved heterogeneity as
mentioned in the previous section. Furthermore, because we asked respondents about
their annual income at the time the quake occurred, in January 1995, the annual income
before the quake reflected respondents’ income in 1994. Similarly, because we
conducted the survey in March 2012, the annual income at the time the survey was
conducted was the respondents’ income in 2011. Annual income was categorized asfollows: zero, less than ¥1 million (equivalent of $12,149.21 6), ¥1-2 million, ¥2-4
million, ¥4-6 million, ¥6-8 million, ¥8-10 million, ¥10-12 million, ¥12-14 million, morethan ¥14 million, and unknown/forgotten. Finally, we restricted our sample to those who
worked more than 35 hours per week at the time the survey was conducted.
Table 1 presents basic statistics for workers by gender. Hourly wages were
calculated by dividing annual income by number of working hours. We used the medianvalues of the categorical choices to recode the annual income in 2011, and working
hours were calculated by multiplying the weekly working hours reported in the survey
by 52 weeks in a year. The left columns of Table 1 summarize the basic statistics formale workers. The mean hourly wage for victims was ¥2,292.69 ($27.85), which islower than the corresponding mean hourly wage for non-victims in the whole sample(¥2560.70 ($31.11)). Similarly, the mean log hourly wage for victims was lower thanthat for non-victims living in the Hanshin and Awaji areas (¥2516.50 ($30.57)). This
indicates that victims earned less than non-victims, on average, even 17 years after the6

Annual incomes and hourly wages in this section were converted at 82.3099 Japanese Yen to
1 US dollar. This exchange rate was the closing price on March 23, 2012, when our online survey wascompleted.

17earthquake.
The right columns of Table 1 present basic statistics for female workers. The
mean wage for victims was lower than was that for non-victims in the whole sample(¥1549.24 ($18.82) vs. ¥1654.49 ($20.10)). Furthermore, the mean wage of victims was
lower than was that of non-victims in the Hanshin and Awaji areas (¥1661.98 ($20.19)).
Similar to the case of male workers, the earnings of victims remained lower than that ofnon-victims, on average, some 17 years later.

6. Estimation Results
We begin with results of the Blinder–Oaxaca decomposition of the wage difference formales. The upper panel of Table 2 shows the results using the whole sample (bothgroups (i) and (ii)), and the lower panel shows the results using samples restricted toHanshin-Awaji area (group (i) only). First, wage differences for male workers are
summarized in the left columns. The mean wage of disaster victims was lower than thatof non-victims by 0.129 log points. The composition explained by the differences inobserved characteristics is -0.068 log points, and the unexplained component is -0.060
log points. This indicates that the 1995 Hanshin-Awaji Earthquake still had a negative
impact on the wages of male workers 17 years after the earthquake. When we restricted
our sample to those who resided in the Hanshin and Awaji areas, the results were
unchanged, as shown in the lower panel of Table 2. The mean wage of victims was
lower than that of non-victims by 0.111 log points. The explained component is -0.050
log points, whereas the unexplained component is -0.062 log points. These results are
similar to those obtained from the analyses using the whole sample.
Next, we turn to the case of female workers. The upper panel of Table 2 shows18

the results using the whole sample. The mean wage of disaster victims was lower thanthat of non-victims by 0.052 log points, but the difference was not statisticallysignificant. This indicates that the earthquake had no long-term impact on the 2011wages of female workers. The results using the subsamples of those who resided in the
Hanshin and Awaji areas are shown in the lower panel of Table 2. The mean wage ofvictims was lower than was that of non-victims, by 0.047 log points. This result issimilar to the results obtained from the analyses based on the whole sample. Although
the statistically insignificant results may have resulted from the small sample size of
female workers, the difference in magnitude and the unexplained component are muchsmaller than are those of male workers.
Figure 2 shows the results of the DFL decomposition of the wage distribution
for males. The upper panel includes two figures showing the wage distributions of thewhole male sample. The first figure on the upper left side has two lines: the dashed line
represents the actual distribution for victims, whereas the solid line indicates thecounterfactual distribution for victims calculated by the DFL method. The
counterfactual wage distribution thus captures what the wage distributions would havebeen, assuming that victims and non-victims obtained the same wage if they shared thesame observable characteristics. The solid line is to the right of the dashed line in the
middle of each distribution. This means that the victims in the middle quantile of thewage distribution would have earned more had the Hanshin-Awaji Earthquake notoccurred. The figure at the upper right has a dashed line representing the actual
distribution for non-victims and a solid line indicating the counterfactual distribution forvictims, which is identical to the solid line in the left panel. The solid line is very close
to the dashed line, suggesting no significant difference in characteristics between
19victims and non-victims. The two figures in the lower panel present the results of the
DFL decomposition of the wage distribution for the male subsample residing in the
Hanshin-Awaji area. We obtained the same result as with whole sample, in the sensethat disaster victims in the middle quantile of the wage distribution suffered the most
severe damage from a long-term perspective.
Figure 3 shows the results of the DFL decomposition of the wage distribution
for females using the same structure as Figure 2. On the right side, the solid and dashedlines are very close, again suggesting no significant difference in characteristics
between victims and non-victims, as was seen in male workers. However, in contrast to
the case for male workers, the figures on the left side show no significant difference
between the solid and dashed lines. This indicates that the negative impact of theearthquake on the wage distribution of female workers in both the whole sample and thesubsample from the Hanshin-Awaji area had already been eliminated when the survey
was conducted.
Table 3 presents the results of the

Machado-Mata-Melly decomposition of

the wage difference in males using the whole sample and the sample restricted to the
Hanshin-Awaji area. First, the left columns show the results using the whole sample. At
all quantiles, the differences were statistically significant, indicating that the wages of
non-victims were higher than were those of victims in all quantiles. The differenceswere larger at lower quantiles. At the 10th percentile, the wages of disaster victims werelower than were those of non-victims, by 0.166 log points. At the 90th percentile, thewages of disaster victims were lower than were those of non-victims, by 0.067 log
points. However, at the 10th, 20th, and 90th percentiles, the unexplained components
were not statistically significant. These results indicate that the negative impact of the20

1995 Hanshin-Awaji Earthquake on wages had already been eliminated for low- andhigh-wage workers. However, the unexplained component was significant for the
middle quantile. The unexplained component at the median is 0.078 log points, which islarger than the unexplained component at the mean (0.060 log points), estimated by theBlinder–Oaxaca decomposition. This indicates that the earthquake had a considerable
negative impact on the wages of middle-wage workers.
We then restricted the sample to those who resided in the Hanshin and Awaji
areas. The right columns of Table 3 present the results. Similar to the left columns, thewage differences were statistically significant in all quantiles, but the unexplained
components were significant only in the middle quantiles. The unexplained component
at the median (0.081 log points) was larger than that at the mean (0.062 log points).
Table 4 presents the results of the

Machado-Mata-Melly decomposition of

the wage difference for females. The left columns show the results in the whole sample.
In contrast to the case for males, the differences were statistically significant at the 60th
percentile and above. Furthermore, the unexplained components were significant at the80th and 90th percentiles. This indicates that the negative impact of the 1995 earthquakeon wages persisted for high-wage females. The right columns of Table 4 present theresults using the subsample of Hanshin-Awaji residents. Similar to the left columns of
the whole sample, the differences were statistically significant at the 60th percentile andabove, whereas the unexplained components were significant only at the 80th and 90th
percentiles. The unexplained component at the 90th percentile was 0.138 log points.

7. Discussion
In this section, we consider three issues as we discuss and interpret the results. The first21

is the difference in the impact of the earthquake on wage levels by gender. As presented
in the previous section, male workers with mid-range wages suffered most from theearthquake, whereas only high-wage female workers were negatively affected. One
possible explanation for the difference between genders in the wage levels affected by
the earthquake is that the wage distribution of females generally lies to the left of that ofmales. Table 5 shows the gender gap in hourly wages: indeed, the hourly wages offemales in the top quantile are similar to the wages of males in the middle quantile. This
indicates that male middle-wage workers and female top-wage workers may, in fact,
work in a similar labor environment; these people, who all earned about the same, were
all similarly negatively affected by the earthquake.
The second issue is why the earthquake resulted in a reduction in the current
wages of people whose houses were damaged by the earthquake 17 years ago. It may
seem more likely that losing workplaces due to the earthquake would be related to a
reduction in wages. To evaluate the adequacy of our definition of “victims” as thosewith damaged houses, we conducted the same analysis to examine whether theearthquake affected the wages of victims whose workplaces were damaged by theearthquake. However, we found no long-term negative impact on the wages of thosewith damaged workplaces after the earthquake. One possible reason that only those
whose houses were damaged by the earthquake were negatively affected in terms ofwages is that these individuals may have changed their consumption behavior; that is,they may have spent more money to reconstruct their houses and were not able to afford
to invest in health and human capital. Such reductions in investment in skills and healthmay have resulted in a long-term negative effect on their wages. This interpretation is
supported by Sawada and Shimizutani (2007) and Sawada and Shimizutani (2008), who
22reported that victims of the 1995 Hanshin-Awaji Earthquake decreased theirconsumption levels. Another possibility is that when victims moved to other regions
after the earthquake, they had to change to new careers that did not match their abilities
and skills, which did not enable them to earn as much as they would have if theearthquake had not occurred.
Thus far, we have explained the reasons for the reduction in the wages ofdisaster victims from a labor-supply viewpoint. One may also ask whether the negative
impact on wages may instead be related to a reduction in labor demand after theearthquake. However, it should be noted that both the victims and the non-victims in thewhole sample belonged to the same labor market within and near earthquake-damaged
areas, with the exceptions of Yokohama City, the Hanshin-Awaji area, and Osaka. Thus,a negative change in labor demand should have affected both victims and non-victims
equally. Our findings suggest that the wages of victims were more negatively affected
than were those of non-victims given that both victims and non-victims face the samelabor-demand conditions.
Finally, we present the characteristics of the middle-wage male victims and thehigh-wage female victims and discuss why they suffered from long-lasting wage losses.
Indeed, the negative effects on middle-wage male victims and high-wage female victims
may be explained by their individual characteristics. Table 6 reports the descriptive
statistics regarding educational status, the type of employment (employee of privatecompany or organization, government employee, management position, self-employed,
family employee in a self-employed business, and unknown/forgotten), and industry.
This is summarized by wage level: low-wage (25th percentile and below), middle-wage(between 25th and 75th percentiles), and high-wage (above the 75th percentile) workers.
23

One might think the reason that middle-wage males have suffered the mosteconomically is because most of them are self-employed and have businesses that are
run from their houses; thus, they may have lost their workplace. However, the left
columns of Table 6 indicate that 77.8% of the middle-wage males were employees ofprivate companies or organizations. This proportion is indeed higher than is those of
low-wage males and high-wage males. In contrast, the self-employed accounted for
only 6.1% of the total employment type of middle-wage males, and this proportion islower than those of low- and high-wage males. Thus, most middle-wage males were
employees of private companies/organizations, not self-employed. This shows that thereason for the negative effect on middle-wage males was not due to self-employment
and losing both their houses and workplace after the earthquake.
The right columns of Table 6 show the educational status, type of employment,
and industrial affiliation of female victims. The proportion of high-wage females who
were self-employed was higher than the proportion of low- and middle-wage females
who were self-employed, but only 2.8% of the high-wage group was self-employed.
This suggests that self-employment and loss of the house/workplace were also not likely
explanations of the negative effect on the wages of high-wage females.

8. Concluding Remarks
Three years have passed since the Great East Japan Earthquake struck the eastern part ofJapan. It is important to examine the extent to which the earthquake has affected
people’s lives and its likely long-term impact on the economy. This paper examined asimilar large-scale natural disaster in Japan, the 1995 Hanshin-Awaji Earthquake, andestimated its long-term impact on wage distribution. This exercise may facilitate24

anticipation of the long-term impact of the Great East Japan Earthquake.
We collected original data through an Internet survey, which included a rangeof variables indicating the extent of housing damage, individual characteristics, andannual income, both at the time of the earthquake and when the survey was conducted,
for both victims and non-victims of the earthquake.
We summarize our findings as follows. We used three methods to examine how
the 1995 Hanshin-Awaji Earthquake has affected the wage distribution: (i) the
Blinder–Oaxaca

decomposition,

(ii)

the

DFL decomposition,

and

(iii)

the

Machado-Mata-Melly decomposition. First, the Blinder–Oaxaca decomposition analysis
showed that the 1995 Hanshin-Awaji Earthquake still had a negative impact on themean wages of male workers 17 years after the earthquake, by 6.0-6.2%, but that its
impact had disappeared for female workers. Second, our finding from the DFL
decomposition analysis is that the wages of middle-wage workers would have been
higher had the 1995 Hanshin-Awaji Earthquake not occurred. This analysis indicatedthat middle-wage workers were affected most adversely by the earthquake. This result
did not hold true for female workers. Finally, similar to the results of the DFL
decomposition analysis, theMachado-Mata-Melly decomposition analysis showedthat the earthquake had a major negative impact on the wages of middle-wage maleworkers, and that they would have earned 5.0–8.6% more had the earthquake notoccurred. Additionally, the negative impact of the 1995 earthquake on wages remainedfor high-wage females, who would have earned 8.3–13.8% more had the earthquake notoccurred.
It is surprising that, although 17 years have passed since the earthquake, thewages of middle-wage male victims and high-wage female victims are still negatively
25affected by the earthquake. These estimated results of the Hanshin-Awaji earthquake
can be interpreted in a policy context. It would be expected that low-wage workers
would be the victims who suffered most severely from a natural disaster and that theywould need more support to recover to pre-disaster levels. However, the results of thisstudy provide an in-depth understanding of the mechanisms underlying the effects ofnatural disasters on the wage distribution: middle-wage male workers and high-wage
female workers were the most negatively affected by Hanshin-Awaji earthquake. We
need to formulate long-term recovery plans for middle-wage males and high-wage
females to more effectively assist them in recovering from natural disasters, and, inparticular, from the Great East Japan Earthquake.

26

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Journal of Human Resources, 8:436–455.

Cavallo, Eduardo, and Noy, Ilan (2011). “Natural Disasters and the Economy: A Survey,”
International Review of Environmental and Resource Economics, 5(1):63–102.

Cavallo, Eduardo, Powell, Andrew, and Becerra, Oscar (2010). “Estimating the Direct
EconomicDamagesoftheEarthquake

in

Haiti,”

Economic

Journal,120(August):F298–312.

Cavallo, Eduardo, Galiani, Sebastián, Noy, Ilan, and Pantano, Juan (2010).
“Catastrophic Natural Disasters and Economic Growth,” mimeo, Inter-American
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Cuaresma, Jesus C., Hlouskova, Jaroslava, and Obersteiner, Michael (2008). “Natural
Disasters as Creative Destruction? Evidence from Developing Countries,” Economic
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DiNardo, John, Fortin, Nicole M., and Lemieux, Thomas (1996). “Labor Market
Institutions and the Distribution of Wages, 1973–1992: A Semiparametric Approach,”Econometrica, 64:1001–1044.

DuPont, William, and Noy, Ilan (2012). “What Happened to Kobe? A Reassessment of27

the Impact of the 1995 Earthquake in Japan,” SEF Working paper: 09/2012, March 2012,
School of Economics and Finance, Victoria University of Wellington.

Machado, José F. and Mata, José (2005). “Counterfactual Decomposition of Changes inWage Distributions Using Quantile Regression,” Journal of Applied Econometrics,
20:445–465.

Melly, Blaise (2006). “Estimation of Counterfactual Distributions using Quantile
Regression,” University of St. Gallen, Discussion Paper.

Noy, Ilan (2009). “The Macroeconomic Consequences of Disasters,” Journal of
Development Economics, 97(3):221–31.

Noy, Ilan, and Nualsri, Aekkanush (2011). “Fiscal Storms: Public Spending andRevenues in the Aftermath of Natural Disasters,” Environment and Development
Economics, 16(1):113–28.

Oaxaca, Ronald (1973). “Male–Female Wage Differentials in Urban Labor Markets,”
International Economic Review, 14:693–709.

Ohtake, Fumio, Okuyama, Naoko, Sasaki, Masaru, and Kengo, Yasui (2012)“Measuring the Long-lasting Effect of Natural Disasters: The Case of the 1995
Hanshin-Awaji Earthquake.” mimeo28

Raddatz, Claudio. (2007). “Are External Shocks Responsible for the Instability of
Output in Low-Income Countries?” Journal of Development Economics, 84(1):155–87.

Sawada, Yasuyuki, and Shimizutani, Satoshi. (2007). “Consumption Insurance against
Natural Disasters: Evidence from the Great Hanshin-Awaji (Kobe) Earthquake,”
Applied Economics Letters, 14(4): 303–306.

Sawada, Yasuyuki, and Shimizutani, Satoshi. (2008). “How Do People Cope with
Natural Disasters? Evidence from the Great Hanshin-Awaji (Kobe) Earthquake in 1995,”
Journal of Money, Credit and Banking, 40.(2-3): 463–488.

Skidmore, Mark, and Toya, Hideki (2002). “Do Natural Disasters Promote Long-run
Growth?” Economic Inquiry, 40(4):664–87.

29

Table 1. Basic statistics for males
Victims / Non-victims
Victims

Variables
wage
incomeincome in 1995
years of schoolingageworking hours

Males
Obs.
mean
s.d.
423 2292.69 1452.04
423 613.00 341.25
423 507.62 268.44
423
14.84
2.17
423
49.28
7.71
423
55.09
16.04

min
max
133.55 8413.46
75.00 1750.00
75.00 1750.00
9.00
21.00
27.00
79.00
35.00 108.00

Obs.
mean
139 1549.24
139 369.78
139 307.91
139
13.76
139
45.88
139
47.99

Femaless.d.
984.27203.11
162.16
1.93
6.50
12.92

min
max
225.36 9615.38
75.00 1750.00
75.00 1100.00
10.50
21.00
31.00
63.00
35.00 108.00

Non-victims(Whole sample)

wage
incomeincome in 1995
years of schoolingageworking hours

2029 2560.70 1442.67
2029 665.16 346.10
2029 560.50 257.72
2029
14.80
2.10
2029
49.60
7.00
2029
52.62
14.72

144.23 8856.28
75.00 1750.00
75.00 1750.00
9.00
21.00
21.00
79.00
35.00 108.00

544 1654.49
544 399.95
544 327.07
544
14.05
544
46.23
544
47.09

984.77251.03185.79
1.96
6.50
11.62

240.39 8413.46
75.00 1750.00
75.00 1750.00
9.00
21.00
23.00
72.00
35.00 108.00

Non-victims(Hanshin-Awaji area)

wage
incomeincome in 1995
years of schoolingageworking hours

1101 2516.50 1404.84
1101 651.66 337.19
1101 550.18 249.87
1101
14.75
2.11
1101
49.88
6.99
1101
52.31
14.77

144.23 8856.28
75.00 1750.00
75.00 1750.00
9.00
21.00
70.00
21.00
35.00 108.00

311 1661.98 1020.50
311 402.17 257.50
311 317.68 203.13
311
14.05
2.00
46.82
6.77
311
311
47.05
10.73

244.46 8413.46
75.00 1750.00
75.00 1750.00
9.00
21.00
23.00
72.00
35.00 100.00

Note: Table 1 presents basic statistics for workers by gender. Hourly wages were calculated by dividing the annual income by the number of working
hours; the unit is Japanese Yen. Income is measured in 10,000 Japanese Yen. The left columns of Table 1 summarize the basic statistics for maleworkers, and the right columns summarize them for female workers.

30

Table 2. Blinder–Oaxaca decomposition of wage differences
Wage differences forVictims vs. Non-victims

Males

Females

Whole sample

Difference
Explained
Unexplained-0.129 ***-0.068 ***-0.060 **(0.034)(0.017)(0.030)-0.052-0.033-0.019(0.056)(0.028)(0.054)

Hanshin–Awaji

Difference
Explained
Unexplained-0.111 ***-0.050 ***-0.062 *(0.036)(0.019)(0.032)-0.047-0.004-0.043(0.062)(0.032)(0.058)

Note: The upper panel shows the results using the whole sample, and the lower panel shows the results
for the Hanshin-Awaji area. Wage differences for male (female) workers are summarized in the left (right)
columns. “Difference” indicates the mean wage differential between disaster victims and non-victims
decomposed into “explained” and “unexplained” components. Standard errors are in parentheses. *, **,and *** indicate significance at 10, 5, and 1%, respectively.

31

Table 3.
Males

Machado-Mata-Melly decomposition of wage differences for males
Whole Sample

Hanshin and Awaji areas

Difference

Explained

Unexplained

Difference

Explained

Unexplained

Quantile .1

0.166***

0.112

0.054

0.154***

0.087

0.067

Quantile .2

0.152***

0.090

0.062

0.150***

0.073

0.077*

Quantile .3

0.165***

0.088***

0.077***

0.155***

0.069*

0.086**

Quantile .4

0.165***

0.085***

0.079***

0.150***

0.066

0.084**

Quantile .5

0.151***

0.073***

0.078***

0.136***

0.055

0.081**

Quantile .6

0.142***

0.067***

0.075***

0.126***

0.049

0.078**

Quantile .7

0.129***

0.067**

0.061**

0.116***

0.047

0.070*

Quantile .8

0.111***

0.061**

0.050*

0.096***

0.040

0.056

Quantile .9

0.067***

0.048

0.019

0.049***

0.030

0.019

Note: This table presents the results of the

Machado-Mata-Melly decomposition of the wage

differences for males. The left (right) columns show the results for the whole sample (subsample).
“Difference” indicates the wage differential in each quantile between disaster victims and non-victims
decomposed into “explained” and “unexplained” components. *, **, and *** indicate significance at 10,
5, and 1%, respectively.

32

Table 4.
Females

Machado-Mata-Melly decomposition of wage differences for females
Whole Sample

Hanshin and Awaji areas

Difference Explained

Unexplained

Difference

Explained

Unexplained

Quantile .1

0.039

0.029

0.010

0.050

-0.0170.067

Quantile .2-0.0420.023-0.065-0.083-0.004-0.079Quantile .3-0.0380.019-0.057-0.057-0.002-0.055

Quantile .4

0.010

0.028

-0.018-0.0130.010-0.023

Quantile .5

0.022

0.018

0.004

0.017

0.009

0.008

Quantile .6

0.063**

0.024

0.039

0.070*

0.017

0.053

Quantile .7

0.093***

0.032

0.060

0.108***

0.029

0.080

Quantile .8

0.110***

0.027

0.083*

0.114***

0.026

0.088*

Quantile .90.160***

0.033

0.126***

0.173***

0.035

0.138***

Note: This table presents the results of the Machado-Mata-Melly decomposition of the wage differences
for females. The left (right) columns show the results of the whole sample (subsample). “Difference”
indicates the wage differential in each quantile between disaster victims and non-victims decomposed into
“explained” and “unexplained” components. *, **, and *** indicate significance at 10, 5, and 1%,
respectively.

33

Table 5. Gender differences in wage distribution (unit: Japanese Yen)

Percentile
10
20
30
40
50
60
70
80
90
Obs

Whole
Males
824.181246.441550.87
1923.082243.592518.32
2991.45
3461.544326.922824

Hanshin-Awaji Area
Males
Females805.86412.091201.92
721.151442.31
978.12
1885.371254.182206.811442.312447.55
1479.292926.42
1797.433461.542236.144230.772692.311731
544

Females412.09721.151068.38
1282.05
1442.311518.221814.222289.38
2719.78824

34

Table 6. Individual backgrounds of victims
Low wage(25 percentile andbelow)

Males
Middle wage
(between 25 and75 percentiles)

High wage(above 75
percentile)

Low wage(25 percentile
and below)

Females
Middle wage
(between 25 and75 percentiles)

High wage(above 75
percentile)

Educational status
Lower than high school graduates
Equal to and higher than university graduates

3.7%
43.0%

2.8%
47.2%

2.9%
69.2%

6.3%
28.1%

2.8%
29.6%

0.0%
30.6%

Type of employment
Employee of private company or organization
Government employee
Management position
Self-employed
Family employee in self-employed business
Unknown/forgotten

63.6%
4.7%
1.9%
17.8%
11.2%
0.9%

77.8%
10.8%
2.8%
6.1%
0.9%
1.4%

70.2%
19.2%
2.9%
6.7%
0.0%
1.0%

93.8%
0.0%
0.0%
0.0%
6.3%
0.0%

88.7%
1.4%
0.0%
1.4%
2.8%
5.6%

83.3%
13.9%
0.0%
2.8%
0.0%
0.0%

Industry
Agriculture and related industries
MiningConstruction
Manufacturing
Wholesale trade/Retail trade
Finance and insurance
Real estateTransportation/Telecommunications
Utilities
Services
Education
Medical, health care, and welfare
Government employee
OthersUnknown/forgotten

0.0%
0.0%
8.4%
12.1%
16.8%
4.7%
0.9%
9.3%
1.9%
30.8%
3.7%
0.9%
1.9%
8.4%
0.0%

0.5%
0.0%
6.6%
21.7%
12.7%
3.8%
2.4%
8.0%
0.9%
16.5%
6.1%
5.2%
7.1%
8.0%
0.5%

0.0%
0.0%
1.0%
25.0%
6.7%
7.7%
1.0%
3.8%
3.8%
10.6%
6.7%
6.7%
15.4%
10.6%
1.0%

0.0%
0.0%
12.5%
12.5%
12.5%
6.3%
0.0%
3.1%
0.0%
18.8%
3.1%
6.3%
3.1%
21.9%
0.0%

0.0%
0.0%
1.4%
23.9%
8.5%
7.0%
8.5%
4.2%
0.0%
25.4%
2.8%
9.9%
0.0%
5.6%
2.8%

0.0%
0.0%
0.0%
16.7%
2.8%
5.6%
5.6%
0.0%
0.0%
22.2%
5.6%
13.9%
11.1%
16.7%
0.0%

35

Figure 1. Areas used for the analyses

Note: This figure presents a map of Japan. The orange-colored area is Hyogo Prefecture, where theHanshin-Awaji earthquake struck. This prefecture includes 10 cities and 10 municipalities that theJapanese government officially declared to be the most severely damaged region. The green-colored area
is Osaka Prefecture, and the blue-colored is Kanagawa Prefecture.

36

Figure 2. DFL decomposition of the wage distribution for males

Note: The four figures indicate the wage distributions for male workers. The left figures indicate theactual and counterfactual distributions for victims, and the right figures indicate the actual distribution for
non-victims and the counterfactual distribution for victims. The difference between the upper panels andlower panels concerns the sample composition: the upper panels use the whole sample, and the lower
panels use the subsample residing in the Hanshin and Awaji areas. In each figure, the dashed line
represents the actual distribution for victims or non-victims, whereas the solid line indicates thecounterfactual distribution for victims calculated using the DFL method.

37

Figure 3. DFL decomposition of the wage distribution for females

Note: The four figures indicate the wage distributions for female workers. The left figures indicate theactual and counterfactual distributions for victims, and the right figures indicate the actual distribution for
non-victims and the counterfactual distribution for victims. The difference between the upper panels andlower panels concerns the sample composition: the upper panels use the whole sample, and the lower
panels use the subsample residing in the Hanshin and Awaji areas. In each figure, the dashed line
represents the actual distribution for victims or non-victims, whereas the solid line indicates thecounterfactual distribution for victims calculated using the DFL method.

38

Appendix. Outline of the survey
Survey Method
Screening
Regional range

Population• Internet survey (Nikkei Research. Inc.)• A screening survey was conducted across the country before the main survey. Survey questionnaires weredelivered to the targets and responses were collected according to the distribution of the screening survey.
• Nationwide• Residents in the following municipalities at the time of the earthquake in 1995; Kobe, Itami, Amagasaki,
Nishinomiya, Ashiya, Takaraduka, Kawanishi, Akashi, Miki, Sumoto, Tuna-cho, Awaji-cho, Hokutan-cho,
Ichinomiya-cho, Goshiki-cho, Higahiura-cho, Midori-cho, Nishitan-cho, Mihara-cho, Nantan-cho,
Yokohama, Osaka, Suita, Takatsuki, and Sakai.
• Males and females aged from 20 to 80 years old who lived in Japan as of March 15, 2012.
• Targets were abstracted from the registered monitors for internet surveys.

Sample design• Demographic and regional information for sample layout and allocation such as prefecture, municipalities,
sex, and age are not considered.
• No. scheduled for collection: 8500s (Hanshin: 5100s, Yokohama: 1700s, Osaka: 1100s, Suita: 300s,
Takatsuki: 150s, Sakai: 150s)

Valid responses

Time period• No. of those who completed the survey: 10,715s (Yokohama: 1,698s, Osaka: 1,358s, Suita: 208s,
Takatsuki: 219s, Sakai: 366s, Hanshin: 6,866s)• No. of eligible survey responses: 10,387s (Yokohama: 1,646s, Osaka: 1,316s, Suita: 203s, Takatsuki: 216s,
Sakai: 356s, Hanshin: 6,650s)• March 15, 2012 to March 23, 201239