Discussion Paper No.398

Mental Health of Miyagi Prefecture Residents Seven Years
after the Great East Japan Earthquake

Fengming CHEN and Hiroshi YOSHIDA

January, 2019TOHOKU ECONOMICS RESEARCH GROUPGRADUATE SCHOOL OF ECONOMICS AND
MANAGEMENT TOHOKU UNIVERSITY
27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANMental Health of Miyagi Prefecture Residents Seven
Years after the Great East Japan Earthquake
Fengming CHEN*1,† and Hiroshi YOSHIDA*2*1

Smart-Aging Research Center, Tohoku University, 4-1 Seiryocho, Aobaku Sendai 980-8575, Japan†
Corresponding author, E-mail: cfmdbdx@gmail.com*2
Graduate School of Economics and Management, Tohoku University, 27-1 Kawauchi, Aoba-Ku Sendai 980-8576, Japan

Abstract
Seven years have passed since the Great East Japan Earthquake left many injured and dead in the Tohoku area ofJapan in 2011. To understand the long-term effects of the Great East Japan Earthquake on the health status of residents
in the disaster area, we conducted an original survey in 2018. This paper examines the long-term effects of theearthquake on the mental health status of residents of Miyagi prefecture, which was severely devastated by theearthquake and tsunami. Using original survey data, we identify the mechanisms by which different types of damage
have impacted residents’ mental health. Our analyses confirm that injury to or loss of a family member had a significant
long-term effect on respondents’ mental health, as measured by indices of mental health status, cognitive condition, andmotivation for work and daily life. Also, the results of a robustness check using physical health status as the outcome
variable further identify the mechanisms underlying the effects of different types of damage on residents’ health.
Understanding the pathway by which the earthquake has influenced residents’ mental health will help policymakers todesign effective ways to support them. For example, according to our findings, providing ongoing mental healthsupport to those residents facing injury to or loss of family members would effectively relieve the stress associated withthe earthquake and promote the recovery of their mental health and motivation for work and daily life. However, thispaper has some limitations. For example, as the measurements of the types of damage are relatively crude,
measurement errors could have contaminated our statistical results. In addition, we did not use the random sampling
method for data collection, which could have led to biased results.

Keywords: earthquake, mental health, bereavement, the long-term effect1.

Introduction
In 2011, the Great East Japan Earthquake hit the Tohoku area of Japan. The most powerful on record, this earthquakeand subsequent tsunamis brought about a significant loss of life and property. According to the Extreme Disaster
Management Headquarters in Japan, the number of dead and missing persons had risen to 22,199 by March 1, 2018, and1

the estimated cost of the damage was approximately 16.9 trillion yen as of June 24, 2011, including damage tobuildings, lifeline facilities, social infrastructure, and other facilities [1]. Although the succession of natural disasters
and associated events, such as earthquakes, tsunamis, and nuclear power plant accidents, made a recovery more
complex and challenging in the affected area, officials at all levels of government have made their best efforts toaccelerate improvement in the disaster area and have earned some achievements [2]. Therefore, we believe that the
impact of a loss of property on residents’ health status is gradually becoming smaller with the recovery of the economy
in the Tohoku area. Many people purchase private insurance to cover the risk of natural disasters, including earthquake,
because of the multiple types of natural disasters that frequently occur in Japan [3], which would have further reduced
the impact of the loss of property. However, neither economic recovery nor private insurance is helpful to relieve thepain of losing beloved relatives or friends. Therefore, negative emotions such as sorrow and depression can persist andinfluence people’s health. To understand the overall effects of the Great East Japan Earthquake, we should focus not
only on the damage to tangible objects, such as roads, houses, and public capital but also on the damage to disaster area
residents’ health and daily lives. In particular, the impact of the earthquake on people’s mental health is invisible andeasy to neglect, owing to the remarkable recovery and reconstruction. In this paper, we attempt to examine thelong-term impact of different types of damage on the mental health status of residents in Miyagi prefecture.
Our paper makes a twofold contribution to the existing literature. First, our study uses individual-level data to
examine the long-term impact of the disaster. Several previous studies are considering the effect of earthquakes onsuicide rate or mortality using municipality-level data [4, 5]. However, the number of suicides or other deaths
represents only a minority of people seriously influenced by an earthquake. The use of limited indicators such as suicide
rate or mortality is inadequate to evaluate the earthquake’s effect on the majority of residents in the disaster area.
Consequently, it is necessary to use individual-level outcomes to assess the mental health status of respondents.
Second, our study examines the impact of the earthquake with a focus on different types of damage. Few studies are
discussing this issue, as a result of a shortage of data. Our original survey collected abundant information on loss,
including the different types of damage suffered by individuals, which provided us with an opportunity to examine theimpact of different kinds of damage on respondents’ mental health status.
The remainder of this paper proceeds as follows. Section 2 provides a brief overview of the literature. Section 3
introduces the data used in this paper. The hypothesis and empirical results are reported in Section 4, and conclusions
are discussed in Section 5.

2

2.

Previous Studies
Based on the duration for which they persist, we can classify the effects of the earthquake on people’s mental healthinto two categories. The first category covers short-term effects, which are broadly identified as the prevalence ofPost-Traumatic Stress Disorder 1 and associated negative emotions. Some previous studies have documented theshort-term impacts of natural disasters on people’s health status covering the analysis of both individual-level data [3, 6,
7] and municipality-level data [4, 5]. For example, [3] uses the Great East Japan Earthquake as a natural experiment toexamine the effect of the earthquake on different outcomes. The results identified significantly positive coefficientestimates for its impact on indices of the respondents’ mental health, measured by items such as “Restless sleep” and“Felt sad,” which indicates the earthquake-induced deterioration in the respondents’ mental health.
The second category covers long-term effects. Because of the lack of individual-level data, few previous studies havebeen able to conduct a detailed analysis of the mechanisms underlying long-term effects. For example, [8] examines thelong-term effects of the Great East Japan Earthquake on residents’ mental health using survey data. However, this paper
points out several limitations of its findings. The health gap observed between the Tohoku area and the Tokyo area
consists of both an initial difference formed before the earthquake and the subsequent difference caused by theearthquake. To model the impact of the earthquake, the author must make the strong assumption that the portion of thehealth gap attributable to causes other than the earthquake has not varied over time.
This paper does not provide enough evidence to explain the mechanisms or potential pathways by which theearthquake could have caused a health deficit in the affected area. Therefore, based on previous studies, the present
study attempts to examine in detail the impact of the earthquake on Miyagi prefecture residents’ mental health bydiscussing the pathways by which different types of damage impact mental health using individual-level data. As themost heavily influenced area, the number of dead and missing persons in Miyagi prefecture was estimated to be 11,789
by March 1, 2018, accounting for about 53.1% of the total amount. It is reasonable to hypothesize that damage suffered
by individuals continues to have a long-term impact on the residents of Miyagi prefecture.

3.

Data

3.1 Background on the Dataset
Data on infrastructure recovery in disaster areas are usually available from the websites of the Japanese Government
or local governments. However, detailed data on individuals in a disaster area is relatively difficult to obtain. After theearthquake, some organizations conducted surveys to understand the direct and short-term effect on the lives, healthstatus, and life satisfaction of the residents in the disaster area; these included the Great East Japan Earthquake Special
1This is a disorder that develops in some people who have experienced a shocking, dangerous event or natural disaster.

3

Survey conducted by the Panel Data Research Center at Keio University [9]. With the arrival of economic recovery, thepublic focus on the disaster area is decreasing. However, the earthquake’s effects on people’s mental health may persist
regardless of the degree of economic recovery.
To understand the long-term effects of the Great East Japan Earthquake on the health status of residents in thedisaster area, we surveyed the lives and health status of residents seven years after the disaster [10] through a premier
online survey company in Japan. The authors’ Institutional Research Ethics Committee approved this original survey.
The respondents were disproportionately recruited from the disaster area and the Tokyo metropolitan area. We took twodays (February 27 and 28, 2018) to conduct this survey and obtained detailed data from 1,045 respondents (520individuals from the disaster area, i.e., Iwate, Miyagi, and Fukushima prefectures, and 525 individuals from the Tokyo
metropolitan area). Because the sampling method of this survey is different from that of other surveys based on random
sampling method, we cannot deny the possibility that our data lacks representativeness, which would contaminate ourstatistical results. The survey consisted of six parts, covering the damage experienced and aid situation, living
environment, health status, the status of children, recovery situation, and individual characteristics. The results,
presented in frequency tables and contingency tables, are beneficial in comprehensively understanding the present
status of residents in the disaster area [10], and the data collected from Miyagi prefecture residents are used for further
analyses in the present study.
3.2 Dependent Variables
In the present study, we use self-rated health status to evaluate the present health status of respondents, including
their physical health condition (Q6.2), cognitive condition (Q6.3), mental health condition (Q6.4), and motivation forwork and daily life (Q6.5). Respondents reported their health status on a five-point scale (1 = very good, 2 = good, 3 =fair, 4 = poor, 5 = very poor). To facilitate interpretation of the results, we adjusted the responses in reverse order (1 =very poor, 2 = poor, 3 = fair, 4 = good, 5 = very good). Table 1 shows descriptive statistics for the data used in this paper,
illustrating that the mean mental health condition score was lower than other health indicators. Table 2 shows the resultsof a test for health outcomes, which shows that the mean mental health condition score is significantly lower than thoseof the physical health and cognitive conditions.

TABLE 1 DESCRIPTIVE STATISTICS
Item
1. Physical Health Condition

Possible responses and encodings
1 (very poor) to 5 (very good)

Mean
3.223

SD
0.927

Min
1.000

Max
5.000

2. Cognitive Condition

1 (very poor) to 5 (very good)

3.277

0.813

1.000

5.000

3. Mental Health Condition

1 (very poor) to 5 (very good)

3.080

0.895

1.000

5.000

4. Motivation for Work and Daily Life

1 (very poor) to 5 (very good)

3.117

0.838

1.000

5.000

Damage to Personal House

Yes = 1, other = 0

0.282

0.451

0.000

1.000

Damage to Work Status

Yes = 1, other = 0

0.154

0.362

0.000

1.000

4

Injury to or Loss of a Family Member

Yes = 1, other = 0

0.106

0.309

0.000

1.000

Age

Years

45.25514.14320.00069.000GenderMale = 1, female =0

0.463

0.500

0.000

1.000

Marital Status

Married = 1, other = 0

0.601

0.491

0.000

1.000

Self-employed

Yes = 1, other = 0

0.069

0.254

0.000

1.000

Homemaker

Yes = 1, other = 0

0.128

0.335

0.000

1.000

Part-time Job

Yes = 1, other = 0

0.149

0.357

0.000

1.000

Moving Related to the Earthquake

Yes = 1, other = 0

0.101

0.302

0.000

1.000

Moving Unrelated to the Earthquake

Yes = 1, other = 0

0.223

0.418

0.000

1.000

Access to Health Resources

Very good/good = 1, other = 0

0.415

0.494

0.000

1.000

Communication with Other Residents

Very good/good = 1, other = 0

0.234

0.425

0.000

1.000

Financial Status

Very good/good = 1, other = 0

0.154

0.362

0.000

1.000

Seismic Intensity

Observed Japan Meteorological
Agency Seismic Intensity Scale on
3.11, 2011 of each municipality.

6.090

0.254

5.000

6.600

Note 1) These results are based on the individual data from [10]3.3 Independent Variables
To investigate the determinants of the respondents’ mental health status, we mainly focused on the types of damage
experienced by them. Three types of damage were recorded using binary variables. Based on the information provided
by responses to Q3.1, a dummy variable representing damage to the respondent’s home was assigned a value of 1 when
the respondent reported that their house had been damaged by the earthquake or tsunami. Otherwise, this binary
variable was assigned a value of 0.
In the same way, we also allocated values to dummy variables representing damage to the respondent’s work status(based on Q3.2) and injury to or loss of family members. Simplifying the analysis, we define the family members only
including grandparents, parents, spouse, siblings, children, and grandchildren. As illustrated in Table 1, we observedthat there was a large amount of variation in frequency among the three types of damage. In total, 28.2% of the
respondents faced the issue of having lost their houses, while 10.6% of the respondents faced injury to or loss of afamily member. To precisely understand the situation of the types of damage, we have shown the results of a test in
Table 2. Although we found a significant difference between damage to home and injury to or loss of a family member,
the impact of the latter will not be quickly eliminated due to economic recovery.

Variable A
Mental Health Condition (Mean)
3.080
Mental Health Condition (Mean)
3.080
Mental Health Condition (Mean)
3.080
Injury to or Loss of a Family Member (Mean)

TABLE 2 RESULTS OF THE T-TEST
Variable B
Physical Health Condition (Mean)
3.223
Cognitive Condition (Mean)
3.277
The Motivation for Work and Daily Life (Mean)
3.117
Damage to Home (Mean)

Diff(B-A)

Observations–0.144**188–0.197***

188–0.037–0.176***

188
188

5

0.106
Injury to or Loss of a Family Member (Mean)
0.106

0.282
Damage to Work Status (Mean)
0.154–0.048188

Note: 1) These results are based on a two-tailed test and calculated by the authors. 2) *, **, *** indicate statistical significance at the 10%,
5% and 1% levels, respectively.

In addition to the demographic characteristics of respondents, we also controlled in our analysis for some variables
associated with respondents’ socioeconomic environments, including the use of dummy variables representing accessto health resources, communication with other residents, and financial status. Based on responses to Q5.3, a dummy
variable representing access to health resources was assigned a value of 1 if the respondent’s access to health resources
such as hospitals was excellent or good ; otherwise, it was assigned a value of 0. As shown in Table 1, 41.5% of the
respondents reported that their access to health resources was relatively good at present. Similarly, we also defineddummy variables representing communication with other residents (based on Q5.5) and financial status (based onQ5.7). On average, 23.4% of the residents were able to communicate with other residents very easily, while only 15.4%
reported that their present financial status was good. In addition, this survey also collected information on place ofresidence until the earthquake. Using a combination of individual-level data and municipality-level seismic intensity
data enabled us to construct more exact and informative models [11]. As indicated in Table 1, the mean level of seismic
intensity across all municipalities was about 6.1, representative of the fact that Miyagi prefecture was severely
devastated by the earthquake. Table 1 also presents the descriptive statistics and definitions of the other independent
variables.
Fig. 1 Mechanisms underlying the impact of the earthquake on residents’ mental health

Types of Damage Caused by the Earthquake

Injury to or Loss of Family
Members

Damage to Tangible Property

Damage to Work Status

Long-term
EffectShort-term
EffectShort-term
EffectPersistence
Taking a longtime to resilience

Disappearance
Due to Economic
RecoveryMental Health Status of Residents in Disaster Area

Disappearance
Due to Economic
Recovery1) Individual Characteristics
2) Socioeconomic Environment3) Other Factors

Note: This figure is produced by the authors.

64.

Regression Analysis

4.1 Hypothesis
As seen in Table 1, although we found that the proportion of respondents facing damage to their home was relatively
high, the impact of such damage may be expected to diminish with the arrival of economic recovery. However, theimpact of an injury to or loss of family members on respondents’ mental health may persist for a long time as a result of
the shortage of effective interventions. Fig. 1 indicates an outline of the mechanism discussed in this paper. There arethree types of damage, including damage to tangible property (such as houses), damage to work status, and injury to orloss of family members. By the distinct characteristics of these different kinds of damage, their effects on residents’mental health may differ regarding duration. As the first two types of damage are relatively easy to recover from, they
may influence residents for only a short period. In contrast, injury to or loss of a family member may have long-term
effects on mental health, as the recovery of resilience after such an event may take a long time. As the mean score formental health condition was much lower than the mean score for physical health condition seven years after theearthquake, we hypothesized that only injury to or loss of a family member would be found to influence the mental
health status of residents persistently. In addition, other important factors such as individual characteristics andsocioeconomic environment would be expected to determine the mental health status of residents.

4.2

Coefficients Estimated using Ordered Probit Models

In this study, we used several indices to measure the mental health condition of respondents, including indicators ofa mental health condition, cognitive condition, and motivation for work and daily life. Because all of these indices were
measured as ordinal variables, an ordered probit model was used to analyze the results. The independent variables
include damage to the home, damage to work status, loss of a family member, age, gender, marital status, employment
status (self-employed, homemaker, or employed part-time), whether the respondent had moved for reasons related tothe earthquake or for reasons unrelated to the earthquake, access to health resources, communication with otherresidents, financial status, and seismic intensity in their location.
Model 1, presented in Table 3, provides coefficient estimates for the relationships of each of these variables withrespondents’ mental health. With respect to the three different types of damage as predictors, only the coefficient for
injury to or loss of a family member was statistically significant; this coefficient was negative, indicating that injury toor loss of any family member as a result of the earthquake negatively influenced the present mental health condition of
respondents, all else being equal. Because each type of damage originates strictly exogenously, we can infer that thisrelationship is causal. These results are consistent with our hypothesis that the only type of damage exerting a long-term
7effect on the residents would be an injury to or loss of a family member. Next, in Model 2 (Table 3), we used a binary
variable representing cognitive condition as an index of health status. Concerning the coefficient estimates for eachtype of damage, we obtained similar results: damage to one’s home or work status had no significant effects, while
injury to or loss of a family member had a significantly adverse influence, consistent with [12].
Moreover, we also examined the effects on residents’ motivation for work and daily life in Model 3 (Table 3). Again,
we obtained similar results. The results above empirically confirm the fact that injury to or loss of a family member asa result of the earthquake had a persistent causal effect on multiple dimensions of health among respondents in Miyagi
prefecture.
Regarding the estimated coefficients of several other critical independent variables, we also obtained some
impressive results. First, improved access to health resources increased the probability of reporting good health.This
may reflect that improving access to health resources can reduce the cost of using these resources and promote their use.
However, we cannot deny the fact that improving access to health resources can cause a moral hazard to arise, with its
attendant behaviors. Because such behaviors could partially cancel out the positive effect of improved access to healthresources on residents’ health, our estimated coefficients may exhibit a downward bias. Next, concerning the estimatedcoefficients for communication with other residents, strengthening communication between residents can be helpful tofacilitate the accumulation of human and social capital and the soonest possible recovery from injury to or loss of closefamily members. Among the three models, the coefficient relating to this variable was significantly positive only in
Model 3, indicating that strengthening communication can be an effective means of improving the probability ofresidents’ reporting motivation for work. However, residents’ motivation for work might simultaneously influence
their degree of communication with other residents. Because contact with other residents is endogenous, the result
mentioned above may reflect correlation. In addition, we observed that financial status positively correlated with healthstatus.
We hypothesized that an injury to or loss of a family member would be found to have a persistent impact on mental
health status. However, this would not be the case for physical health. Therefore,we employed in a robustness check on
our findings. In Model 4, shown in Table 3, we estimated the coefficients for physical health corresponding to each typeof damage, which were utterly consistent with our argument.

8

TABLE 3 ESTIMATED COEFFICIENTS FOR FOUR ORDERED PROBIT MODELS WITH DIFFERENT DEPENDENT VARIABLES
Model 1Model 2Model 3Model 4
Mental Health
Cognitive
Motivation on
Physical Health
Condition
Condition Working and Daily Life
Condition
Damage to Home
–0.003–0.0480.213–0.030(–0.02)(–0.32)(1.12)(–0.13)
Damage to Work Status–0.061–0.290–0.176–0.293(–0.25)(–1.07)(–1.11)(–1.19)Injury to or Loss of a Family Member–0.451*–0.274**–0.492***–0.280(–1.68)(–2.44)(–2.92)(–0.95)Age
0.012**
0.005–0.0040.006(2.13)(1.04)(–0.76)(1.36)
Gender
0.265
0.190
0.287
–0.095(1.55)(0.90)(1.49)(–0.51)
Marital Status
0.242–0.232*0.152–0.101(1.55)(–1.89)(1.04)(–0.86)
Self-employed
0.133
0.392*
0.208
0.231(0.44)(1.72)(0.70)(0.93)
Homemaker
0.268
0.485*
0.383–0.001(0.90)(1.91)(1.54)(–0.01)Part-time Job
0.144
0.149
0.227
–0.007(0.90)(1.00)(0.97)(–0.04)
Moving Related to the Earthquake–0.240–0.401–0.229–0.616***(–1.06)(–1.12)(–0.83)(–3.33)Moving Unrelated to the Earthquake
0.139
0.073
–0.1160.021(0.65)(0.49)(–0.61)(0.07)
Access to Health Resources
0.351*
0.276**
0.476*
0.513***(1.84)(2.19)(1.78)(3.10)
Communication with Other Residents
0.028
0.077
0.423***
0.090(0.19)(0.41)(2.79)(0.73)
Financial Status
0.842***
0.538***
0.406
0.270
(2.60)(3.09)(1.42)(1.05)
Seismic Intensity–0.1450.279–0.515*–0.177(–0.47)(0.88)(–1.87)(–0.81)
Pseudo R2
0.081
0.062
0.081
0.056
N
180
180
180
180
Note 1) Estimates based on analysis carried out by the authors. 2) *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
3) All models include robust cluster standard errors (clustering at the municipality level). 4) As the dependent variables are ordinal variables, ordered probit
models were used to estimate the effects of the earthquake. 5) T-statistic are in parentheses.

5.

Discussion and Conclusion

Using individual data from our original survey, we not only identified the frequency with which residents suffered
the types of damage caused by the earthquake but also investigated the possibility that different types of damage could
have different impacts on the mental health status of the residents of Miyagi prefecture. The main findings were as
follows.
First, by this original survey data, we found that the proportion of residents suffering damage to tangible property
was almost as three times that of residents suffering an injury to or loss of family members; these figures are difficult toobtain from official government statistics. Second, we confirmed that injury to or loss of a family member had asignificant long-term effect on respondents’ mental health, as measured by indicators of mental health status, cognitive
condition, and motivation for work and daily life, while none of the coefficients relating to damage to the home or9damage to work status were statistically significant. These results provide evidence to support our hypothesis that only
injury to or loss of a family member has a long-term effect on residents’ mental health status. Although themeasurements of types of damage are crude, which would contaminate our statistical results, understanding themechanism by which the earthquake influenced residents’ mental health will help policymakers to design effective
ways to support residents. For example, according to our findings, providing ongoing mental health support forresidents facing injury to or loss of family members would effectively relieve the stress associated with the earthquake
and promote recovery of mental health and motivation for work and daily life. In addition, the results of the robustness
check using physical health status as the relevant outcome further identified the mechanisms underlying the impact ofdifferent types of damage on residents’ health.
We also obtained some impressive results about critical independent variables. Improving access to health resources
can increase the probability of residents’ reporting that their health condition is good. Therefore, it seems that an
efficient way to improve the mental health status of the residents of Miyagi prefecture is to improve the accessibility ofhealth resources. We also found that communication with other residents could increase the probability of residents’reporting high motivation for work and daily life. However, this finding cannot necessarily be explained by a causal
relationship, owing to the problem of simultaneity. Although we expect that strengthening communication with others
could improve residents’ mental health, high motivation for work and daily life may simultaneously promote
communication with other residents. It is impossible to identify the direction of causation specifically on the sole basisof our results. Therefore, future studies should adopt methodologies allowing these simultaneity issues to be resolved.
Furthermore, we should not neglect the limitation of the data used in this study. As data collection was not based on the
random sampling method, its lack of representativeness could bias our estimates, and it is necessary to address thisissue.

References:[1]

The Extreme Disaster Management Headquarters, “On the 2011 earthquake off the Pacific coast of Tohoku” (in Japanese),
http://www.bousai.go.jp/2011daishinsai/pdf/torimatome20180305.pdf [accessed March 9, 2018].

[2]

A. Koresawa, “Government’s response to the Great East Japan Earthquake and Tsunami,” J. Disaster Res., Vol.7, pp.517–527, 2012.

[3]

S. Sugano, “The well-being of elderly survivors after natural disasters: Measuring the impact of the Great East Japan Earthquake,” Jpn Econ. Rev.,
Vol.67, pp.211–229, 2016.

[4]

T. Matsubayashi, Y. Sawada, and M. Ueda, “Natural disasters and suicide: evidence from Japan,” Soc. Sci. Med., Vol.82, pp.126–133, 2013.

[5]

D. P. Aldrich and Y. Sawada, “The physical and social determinants of mortality in the 3.11 tsunami,” Soc. Sci. Med., Vol.124, pp.66–75, 2015.

[6]

N. Okuyama and Y. Inaba, “Influence of natural disasters on social engagement and post-disaster well-being: the case of the Great East Japan
Earthquake,” Jpn World Econ., Vol.44, pp.1–13, 2017.

10

[7]

E. Frankenberg, T. Gillespie, S. Preston, B. Sikoki, and T. Duncan, “Mortality, the family and the Indian Ocean Tsunami,” Econ. J., Vol. 121, pp.F162–
F182, 2012.

[8]

FM. Chen, “Effect of the Great East Japan Earthquake on the mental health of residents in disaster areas” (in Japanese), J. Household Econ, Vol.47,
pp.115–127, 2018.

[9]

The Great East Japan Earthquake Special Survey first wave questionnaire ,https://www.pdrc.keio.ac.jp/uploads/GEES_wave1_JHPS_questionnaire.pdf (Accessed March 9, 2018).

[10] F M. Chen, H. Yoshida, and S. Leem, “A survey on the lives and health status of residents seven years after the Great East Japan Earthquake” (inJapanese), Center for Aged Economy and Society, Tohoku University, TERG Discussion Paper, No.383, pp.1–89, 2018.
[11] JMA, “Earthquake and Tsunami Early Warning-the 2011 off the Pacific Coast of Tohoku Earthquake” (in Japanese),
http://www.jma.go.jp/jma/kishou/books/saigaiji/saigaiji_201101/saigaiji_201101.pdf (Accessed March 9, 2018).
[12] H. Hikichi, T. Tsuboya, J. Aida, Y. Matsuyama, K. Kondo, S. V. Subramanian, and I. Kawachi, “Social capital and cognitive decline in the aftermathof a natural disaster: A natural experiment from the 2011 Great East Japan Earthquake and Tsunami,” Lancet Planet. Health, Vol.1, pp.e105–e113,2017.

11