Discussion Paper No. 903

EFFECT OF MAJOR DISASTERS
ON GEOGRAPHICAL MOBILITY INTENTIONS:THE CASE OF
THE FUKUSHIMA NUCLEAR ACCIDENT

Eiji Yamamura
Yoshiro Tsutsui
Chisako Yamane
Shoko Yamane

June 2014

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanEffect of major disasters on geographical mobility
intentions: the case of the Fukushima nuclear accidentEiji Yamamuraa, Yoshiro Tsutsuib, Chisako Yamanec, Shoko Yamaned

a
b
c
d

Seinan Gakuin University. 6-2-92, Nishijin Sawara-ku, Fukuoka 814-8511, Japan.
yamaei@seinan-gu.ac.jp
Konan University. 8-9-1 Okamoto Higashinada-ku, Kobe 658-8501, Japan.
tsutsui@center.konan-u.ac.jp
Okayama Shoka University. 2-10-1 Tsushima Kyomachi Kitaku, Okayama 700-8601,
Japan. Yamane@po.osu.ac.jp
Kindai University. 3-4-1 Kowakae Higashi-osaka, Osaka 577-8502, Japan.
syamane@kindai.ac.jp

Abstract
This paper uses panel data from Japan to explore how the Great East Japan
Earthquake influenced the intention to leave one’s place of residence by comparing thesame individuals’ responses before and after the earthquake. Controlling for unobserved
individual fixed effects and various individual characteristics, we found that (1) people
were more willing to leave their place of residence after the disaster when they lived
nearer to Fukushima, (2) the effect of the disaster on intention to leave was reinforced
when respondents had a small child, and (3) after dividing sample by gender, suchtendencies were observed among women but not among men. From the last finding, weconclude that differences between men and women in perceived risk lead to differences
in mobility intentions.

Keywords: Geographical mobility, disaster, perceived risk
JEL classification codes: Q54, J15, J61, D89

This research uses micro data from the Preference Parameters Study of Osaka
University’s 21st Century COE (Center Of Excellence) Program ‘Behavioral
Macrodynamics Based on Surveys and Experiments’ and its Global COE project‘Human Behavior and Socioeconomic Dynamics.’ We acknowledge the program/project’s
contributors: Yoshiro Tsutsui, Fumio Ohtake, and Shinsuke Ikeda.

1. Introduction
Natural disasters have been observed to occur more frequently in the last twenty
years than earlier (Yamamura, 2014a). There are also cases of serious manmade
disasters, such as the Chernobyl and Fukushima nuclear accidents. These accidents
have led people to focus a great deal of attention on environmental issues and the
security of nuclear energy (Berger, 2010). In response to this trend in public opinion,
increasing numbers of studies in the field of economics have examined the influence ofdisasters (e.g., Anbarci et al., 2005; Eisensee and Strӧmberg, 2007, Kellenberg andMobarak, 2008; Skidmore and Toya, 2002). Some researchers have explored how andthe extent to which disasters have influenced subjective perception (e.g., Berger, 2010;
Carroll et al., 2009; Luechinger and Saschkly, 2009). Individuals who have experienced
disasters tend to estimate fatality risks from disaster higher than others do (Visucursi
and Zeckhauser, 2006). Based on cross-national data, perceived risk of a nuclear
accident is positively associated with having experienced technological disasters, but
the perceived risk is not associated with natural disasters (Yamamura, 2012).
Given these past findings, it is plausible that experiencing the disaster caused
certain people to migrate to less disaster-prone areas. That is, the perception of the riskof disasters is thought to be among the key factors determining geographical mobility
after experiencing a disaster. In the case of the Fukushima accident, which occurred in2011 following a massive earthquake and tsunami, radiation leakage drasticallyincreased the level of the radiation dose, not only in Fukushima and its surrounding
prefectures, but also in other areas of east Japan. Therefore, people were evacuatedfrom their places of residence even if the area was not directly affected by the disaster.
Further, parents feared that radiation exposure would result in serious damage to their1children, and they were consequently more likely to move (Saito, 2011: 235–271)1.
Analyzing the effect of the Fukushima accident on an individual’s intention to migrate
can be regarded as a natural experiment to investigate how unexpected large-scaledisasters affect population mobility. There are existing empirical analyses concerning
the Great East Japan Earthquake and the Fukushima accident (e.g., Ando and Kimura,
2012; Goebel et al., 2013; Hanaoka et al., 2014; Ishino et al., 2011; Tanikawa et al.,2014; Uchida et al., 2014)2. In the field of regional science, many researchers haveattempted to elucidate how people decide to move away from their place of residence(e.g., Belot and Ermisch, 2009; David et al., 2010; Deding and Filges, 2010; Eliasson etal., 2003; Kan, 2007; Mocetti and Porello, 2010). However, the existing research does notdeal with the relation between accidents and population mobility. The present study
seeks to fill this gap, using individual-level panel data to compare the willingness toleave the residential prefecture before and after the Fukushima accident, taking into
account distance from Fukushima and the presence of small children.
A key finding of the estimations in this article is that people were more inclined toleave after the disaster if their place of residence was closer to Fukushima. Such atendency was more obvious among female respondents and those with a small child.
The structure of the rest part of this paper is as follows. Section 2 presents an
overview of the Fukushima accident. We then explain the data used in this paper andStudents are less likely to qualify for high school if their mother were exposed to theradioactive fallout of Chernobyl’s accident (Almond et al., 2007).
2 The Great Hanshin Awaji Earthquake, which occurred in 1995, also caused
tremendous damage to Japanese society. Several studies were conducted on thisearthquake (e.g., Sawada and Shimizutani, 2007, 2008, 2011; Yamamura, 2014). Apartfrom the catastrophic disasters such as the Great Hanshin Awaji and the Great East
Japan earthquakes, Japan is regarded as a country where natural disasters veryfrequently occur, and consequently the effects of natural disasters have been
investigated relatively frequently in the Japanese context (Iwata et al., 2014;
Matsubayashi et al., 2013; Yamamura, 2010).
1

2

propose testable hypotheses in section 3. Section 4 describes the method and reports theestimation results. The final section presents the conclusion.

2. Overview of the Great East Japan Earthquake
On March 11, 2011, Japan not only experienced a major natural disaster but also
suffered a devastating nuclear accident. This was considered one of the most disastrous
events in human history. Regarding the natural disaster, the earthquake occurred off
the coast of Japan, and its magnitude scale was 9.0, making it the fourth largest
earthquake recorded worldwide since 1900. The earthquake then caused a tsunami,
which triggered powerful tsunami waves, pushing water to heights of more than 20
meters in some coastal areas of the northeastern coast of Japan. This was one of thelargest tsunamis ever to hit Japan (Daily Yomiuri, 2011b). Approximately 16,000 people
died, mainly because they could not escape the tsunami. Material stock losses ofbuildings and road infrastructure are estimated at 31.8 and 2.1 million tons,
respectively (Tanikawa et al., 2014). Moreover, the “World Bank and Japanese
government say that there’s somewhere between $122 and $235 billion worth of damage
to clean up” (Hammer, 2011: 28). The earthquake and tsunami caused tremendous
damage in the prefectures of Iwate, Miyagi, and Fukushima, located in the northeast ofJapan along the coast. However, the damage was not restricted to these prefectures3.
The Fukushima Daiichi nuclear power generation plants faced the northeastern coast
and were crippled by the disasters, leading to power outages in other prefectures,
including Tokyo4.

A Japanese prefecture is almost the equivalent of a state in the United States or aprovince in Canada, and there are a total of 47 prefectures in Japan.
4 The water supply was cut off in at least 1.4 million households in 16 prefectures(Daily Yomiuri, 2011a).
3

3

The nuclear plant accident also caused a nuclear leakage. The damage rating for the
nuclear accident was estimated to be level 7, which was equivalent to the Chernobyl
disaster, known thus far as the most serious nuclear accident in history. During theprocess of the nuclear accident, the Japanese government did not provide accurate
information regarding the situation at the nuclear plants. This behavior inevitably
intensified Japanese people’s anxiety about how and the extent to which the nuclear
leakage would damage their health status. People residing in the directly stricken areas,
especially Fukushima Prefecture, escaped and took refuge in other areas (Daily Yomiuri,
2011c). Even in Tokyo, which is located about 250 km from Fukushima, residents fearedthat radioactive materials would reach them and so left Tokyo for areas further from
Fukushima to reduce the risk of radiation exposure (Saito, 2011).

3. Data and Hypotheses

3.1. Data
This article used data from the “Survey of Life Satisfaction and Preferences,” which
was conducted to compile individual-level panel data as a part of the Global Center of
Excellence Program “Human Behavior and Socioeconomic Dynamics” carried out by
Osaka University. Hereafter, the data are referred to as the GCOE data. The panel
survey has been conducted annually throughout Japan since 2004. It is based on
random sampling, and respondents are males and females aged 20–69. The datacollected include comprehensive and basic attributes such as age, sex, household income,family members, willingness to migrate, and place of residence. New samples wereselected and contributed respondents to the survey for the 2004, 2006, and 2009 waves.
The question concerning the key variable in the present study, willingness to migrate,4

was only included in the survey questionnaire in 2009, 2010, and 2012. Therefore, thedata used in this paper covered only these three years. Response rates were 71.2%(2009), 87.8% (2010), and 93.9% (2012). The numbers of observations included in thedata are 6,181 (2009), 5,386 (2010), and 4,588 (2012). In total, this amounts to 16,155
observations. However, we could not obtain information of some variables used in theestimation for some respondents, reducing the sample size used in this study to 11,133.
The definitions and basic statistics of all of the variables used in this paper are
shown in Table 1. The key variables of willingness to move, after accident, closeness to

Fukushima, and presence of child were included to examine the hypotheses presented
in the following subsection. The mean value of willingness to move was 0.12, suggestingthat 12% of respondents were willing to leave their residential prefecture. The meanvalue of presence of child was 0.25, indicating that 25% of respondents have children
under 12 years old who are regarded as primary school students or who have not yet
entered primary school. The mean value of family size, the number of people living
together, was 3.74. The mean values of university and graduate school were 0.24 and
0.02, respectively, meaning that 24% of respondents had graduated from university and
2% had completed a postgraduate degree. The mean values of unmarried and divorced
were 0.14 and 0.05, respectively. This indicates that 14% of respondents wereunmarried and 5% were divorced.

5

Table 1. Definition of variables used for estimation and basic statistics
Variable

Definition

Mean

Willingness to
move

Value of 1 was given if respondent was willing tomove to other prefectures, otherwise 0 (%)

0.12Standard
deviation---

After accident

Value of 1 was given if data were collected in2012, otherwise 0 (%)

0.29

---

Distance from
Fukushima
Closeness to
Fukushima
Presence of
child
Age

Distance from Fukushima city (km)

477

312

Distance from Fukushima

0.01

0.001

Value of 1 was given if respondent had a childunder 12 years old, otherwise 0 (%)

0.25

---

Respondent’s age

50.8

12.6

Male

Value of 1 was given if respondent was a man, 0
if the respondent was a woman (%)

0.47

---

Home

Value of 1 was given if respondent’s current
residential prefecture was equivalent to theirresidential prefecture at age 15, otherwise 0 (%)

0.76

---

Family size

Number of people who lived together in thehousehold

3.74

1.78

IncomeHousehold income (millions of yen)

6.55

4.14

University

Value of 1 was given if respondent had
graduated from university but had not gone tograduate school, otherwise 0 (%)

0.24

---

GraduateschoolValue of 1 was given if respondent had
graduated from graduate school, otherwise 0 (%)

0.02

---

Unmarried

Value of 1 is given if respondent was unmarried,
otherwise 0 (%)

0.14

---

Divorced

Value of 1 is given if respondent had been
divorced, otherwise 0 (%)

0.05

---

Widowed

Value of 1 is given if respondent was widowed,
otherwise 0 (%)

0.03

---

Note: Standard deviation of dummy variables is not reported.

3.2. Hypotheses
Based on the government data, Figure 1 illustrates the change in the rate of those
who left their residential prefecture from 2010 to 2011. Therefore, the figure6

demonstrates the difference in population mobility before and after the Fukushima
accident. Consistent with our expectations, we see from Figure 1 that people were more
likely to leave the residential prefecture during this period when the residential
prefecture was located nearer to Fukushima.

Fukushima

Fig 1. Map of Japan showing the change of those who leave theresidential prefecture between 2010 and 2011
Note: Prefectures were darkly shaded if the difference (log of number of
emigrants in 2011 minus that in 2010) was higher than 10% and lightly
shaded if the difference was positive but below 10%.
Source: Website of the Statistics Bureau, Ministry of Internal Affairs
and Communications:
http://www.e-stat.go.jp/SG1/estat/GL71050103.do;jsessionid=G4L3RrH
Y2zckFZYTZQqZhWtfLsGZC9F2ytyn415DXPvW5v6xnsCr!1767260104!737442923?_toGL71050103_&listID=000001105789&forwardFrom=G
L71050101
http://www.e-stat.go.jp/SG1/estat/List.do?lid=000001073599(Accessed on March 7, 2014)7

Based on the data used in this paper, Figure 2 displays the percentage of
respondents who were willing to leave the residential prefecture. Generally consistent
with Figure 1, in 2012, people were more inclined to be willing to leave their place ofresidence when this was located nearer to Fukushima. In 2012, Fukushima residents
were less likely to leave than were residents of some surrounding prefectures, possibly
because significant numbers of people had already left Fukushima by 2012.

Fukushima

Fig 2. Map of Japan showing the rate of willingness to leave the prefecture in 2012
Note: Prefectures were darkly shaded if the percentage willing to leave was
equivalent to or higher than 20%, and lightly shaded if this percentage was
between 15 and 20%.
Source: GCOE data of Osaka University

8Figure 3 displays the association between the percentage of residents who werewilling to leave in each prefecture and the distance of the residential prefecture from
Fukushima. In line with Figures 1 and 2, a cursory examination of Figure 3 reveals thenegative association between willingness to leave and distance from Fukushima.

Willingness to move

0.25
0.2
0.15
0.1
0.05
0
0

500
1000
1500
Distance from Fukushima (km)2000

Fig 3. Relation between distance from Fukushima and willingness to leave
Source: GCOE data of Osaka University

Turning to Table 2, percentages of respondents who were willing to leave theirresidential prefecture were generally higher after the disaster than before the disaster.
Further, respondents who lived in areas less than 300 km from Fukushima were more
willing to leave than were other respondents. It can be inferred from Figures 1–3 andTable 2 that those residing in areas closer to Fukushima considered the risk of radiation
exposure to be higher and were therefore willing to migrate. This is consistent with thefinding of previous research that, in Japan, people have the highest level of anxiety
regarding nuclear waste disposal and nuclear accidents, and this anxiety is even9

greater than their fear of AIDS (Acquired Immune Deficiency Syndrome) and crime(Hinman et al., 1993)5.

Table 2. Differences in willingness to move to other prefectures before andafter the disaster
Before the disaster

After the disaster(2009 and 2010)(2012)

Full sample

0.12

0.13

(10,509)(4,534)

0.11

0.12

(4,942)(2,089)

0.12

0.13

(5,567)(2,445)Distance from Fukushima

0.13

0.16

< 300 km(4,246)(1,806)

Distance from Fukushima

0.11

0.12

≥ 300 km

(6,263)(2,728)Sub-sample
Men
Women

Note: Numbers in parentheses are observation counts. The sample for the“before the disaster” period is the combined 2009 and 2010 samples, and thesample for the “after the disaster” period is the 2012 sample. Hence, sample
size for the “before the disaster” period is about two times larger than thatfor the “after the disaster” period.

Taken together, these findings lead us to propose Hypothesis 1: People are more

willing to leave their place of residence after the disaster if they live closer to the area
where the nuclear accident occurred.
Empirical research has provided evidence that women are more risk averse thanmen in various situations (e.g., Eckel and Grossman, 2008; Jianakoplos and Bernasek,
1998)6. In previous work more directly relevant to this paper, MacGregor et al. (1994)
A similar tendency was also observed in the United States (Hinman et al., 1993).
It is widely observed in empirical work that men are generally more overly confident
than are women (e.g., Barber and Odean, 2001; Beckmann and Menkhoff, 2008; Lenney,
5
6

10

suggested that women are more likely to perceive the transportation of radioactive
waste to be risky for health than men. Consistent with this finding, in Japan after the
Fukushima accident, based on interview data, Morioka (2014) found that comparedwith men, women expressed more concern about radiation and perceived the risk ofradiation to be higher. Also consistent with these findings, Table 2 shows that women in
the present study were more willing to leave the residential prefecture than were men.
These findings lead us to, we postulate Hypothesis 2: Women are more willing to leave

the residential area than are men when the residential area is near Fukushima.
Although it is widely believed among Japanese people that radiation exposure has
more serious detrimental effects on the health of small children, experts’ opinions on
this point vary (Saito, 2011). In Fukushima primary schools, children were restrictedfrom playing outside to reduce the risk of radiation exposure. Because of a lack ofphysical exercise, “an alarming trend toward obesity has been found among children in
the Fukushima Prefecture, which has the highest rate of obese children in every age
group between 5 and 9 years old” (Daily Yomiuri, 2012). Even outside Fukushima
prefecture, parents had a motivation to move away from the place of residence for thesake of the health of their children when they lived closer to Fukushima. Whenhouseholds with school children leave their place of residence, their children inevitably
move to new schools. Such switching of schools is observed to be costly for children(Been et al., 2011). However, if the perceived risk about the radiation exposure issufficiently large in comparison with the cost of switching schools, parents are willing toleave the place of residence. Furthermore, such a tendency is possibly more obvious formothers than for fathers because of gender differences in risk perception (Morioka,

1977; Lundeberg et al., 1992; Niederle and Vesterlund, 2007).
11

2014). Based on these arguments, we propose Hypothesis 3: The differences between

men and women in willingness to leave the place of residence is larger if they havesmall children.
3.3. Econometric framework and estimation strategy
For the purpose of examining Hypotheses 1 and 2, the estimated function takes thefollowing form:

Move itp = α0 + α1 after accidentt * closeness to Fukushima p +α2 after accident t + α3closeness to Fukushima p + X’ itp A + ki +u itp,
where Move itp represents the dependent variable in individual i, year t, and prefecture

p. ki

represents time-invariant individual-level fixed effects. The regression

parameters are denoted by α. A is the vector of the regression parameters for theindividual-level control variables that capture the influence of the respondents’ various
individual characteristics. The error term is denoted by u. After accident takes thevalue of 1 when observations were collected in 2012. Otherwise, the value is 0.

Closeness to Fukushima is an inverse of the distance from Fukushima city to the capitalof the prefecture in which the individual resides. If the coefficient of after accident *closeness to Fukushima takes a positive sign, the closer to Fukushima the respondent’s
residential prefecture, the more likely they were to express intention to leave after the
Fukushima accident. From the Hypothesis 1, the absolute value of the coefficient ofafter accident * closeness to Fukushima is predicted to be larger in the women’s sample
than in the men’s sample. There are, of course, other nuclear power plants in Japan in
addition to the Fukushima plants. The Fukushima accident seems to have increased theperceived risk among people living near a nuclear power generation plant even if theplant was not the Fukushima plant. Such an effect is captured by the time-invariant

12fixed effects, because no nuclear plants have been constructed after the Fukushima
accident.
As for control variables, the effect of income is thought to be nonlinear. Even when
migrating to another area increases the income level, there is a cost associated with
migration. Accordingly, low income earners are less likely to leave their place ofresidence. However, the higher the initial income level, the less likely it becomes to earnmore because the opportunity to gain a higher income decreases. Hence, the predicted
sign of the coefficient of income is positive, while its square is negative. Evidence has
been presented that social ties with neighbors, which can be regarded as a kind of social
capital, generate benefits for residents7. These benefits disappear if residents move,resulting in low residential mobility (Belot and Ermisch, 2009; David et al., 2010; Kan,
2007). This indicates that individual decision making is influenced by the degree ofstrong ties with neighbors. The longer people have resided in an area, the stronger theirsocial ties with neighbors, and the less willing they will be to leave the area. The
coefficient of home, indicating those who have resided in the area from the age of 15, ispredicted to have a negative sign.
To assess Hypothesis 3, the estimated function takes the following form:

move itp = β0 +β1 after accident t * closeness to Fukushima p* presence of child itp+β2 after accident t +β3 closeness to Fukushima p +β4 presence of child itp + Z’ itp C +mi + e itp,
The cross term of after accident, closeness to Fukushima, and presence of child is the
key variable for the examination of the Hypothesis 2. The coefficient of the cross term

after accident * closeness to Fukushima * presence of child is expected to have a positive
It has been observed that the devastating earthquake caused people to participate incommunity activity in Japan (Yamamura, 2014).
7

13

sign. Further, the absolute value of this coefficient is expected to be larger in thewomen’s sample than in the men’s sample. The vectors of the control variables aredenoted by Z, which includes the same variable used in the model suggested earlier.
In the estimated functions above, the dependent variable is a dummy that takes thevalue of either 1 or 0. Hence, the probit model is used. In addition, for a check ofrobustness, estimations using the fixed effects model are also conducted to control theunobserved time-invariant individual fixed characteristics8.

4. Estimation results
Table 3 reports the results where the cross term between after accident and closenessto Fukushima is not included. Tables 4 and 5 present results where the cross terms areincluded. Table 4 presents the results of probit models, whereas Tables 5 and 6 present
the results of fixed effects models. In Tables 3–5, results based on the full sample arereported in columns (1) and (4), results based on the men’s sample are shown in columns(2) and (5), and results based on the women’s sample are in columns (3) and (6). Table 6
exhibits the results where the sample is divided into those residing close to Fukushima
and those residing further away. Not reported in Tables 3–5 are the dummy variables
for scale of residential city, occupation, and types of residence.

It is appropriate to use the conditional logit model to control for fixed effects when thedependent variable is a dummy. Hence, we also conducted estimations using conditional
logit. The sign and statistical significance of the key variables were almost the same as
the estimation results using the fixed effect model, although the value of the coefficientswas very large in the ancillary model. Therefore, reported results are considered to berobust to alternative models.
8

14

Table 3. Determinants of willingness to move
(1)
Full

After accident

0.17***(5.61)(2)
Men
Probit0.15***(3.09)

(3)
Women

(4)
Full

(6)
Women

0.04**(2.20)(5)
Men
Fixed Effects
0.01(0.80)

0.19***(5.08)

Closeness to
Fukushima

0.07*(1.65)

0.16***(3.00)−0.08(−1.28)Presence of child−0.15***(−3.85)−0.14*(−1.89)−0.16**(−2.53)-0.01(-0.58)0.02(1.15)−0.05(−1.42)Age−0.02***(−9.60)−0.01***(-6.13)−0.01***(−6.44)-0.01(-0.72)0.001(0.17)−0.02(−1.22)

Male

−0.07
(−1.26)

Home−0.54***(−8.88)−0.42***(−4.45)−0.63***(−9.17)

Family0.01(0.93)−0.003(−0.16)0.01(1.34)

0.001(0.60)

0.002(0.73)

0.001(0.03)

Income0.01(0.19)

0.01(0.29)

0.01(0.41)

0.1***(2.73)

0.01*(1.82)

0.01*(1.96)

Income2−0.02*10-4(−0.55)−0.02*10-4(−0.43)−0.02*10-4(−0.36)−0.02*10-4**(−2.13)−0.02*10-4(−1.15)−0.04*10-4*(−1.77)

University

0.08(1.30)

0.11(1.63)

0.05(0.64)

Graduate school

0.09(1.00)

0.21*(1.79)−0.18(−0.66)

Unmarried

0.007(0.11)−0.01(−0.15)0.02(0.33)−0.001(−0.05)−0.01(−0.25)−0.01(−0.23)

Divorced

0.08(1.10)

0.06(0.49)

0.09(1.00)−0.03**(−1.97)−0.04(−1.45)−0.03(−1.42)

Widowed0.02(0.28)

0.40*(1.83)−0.13(−0.96)−0.02(−0.51)−0.01(−0.25)−0.02(−0.41)Log
pseudolikehood−4,065−1,843−2,202

Observations

11,133

5,343

5,790

11,133

5,343

5,790

0.08**(2.32)

Note: Numbers in parentheses are z-statistics calculated using robust standard errors adjusted forclusters in prefectures. *, ** and *** indicate significance at the 10, 5 and 1% levels, respectively.
Numbers above the numbers in parentheses indicate marginal effects. Dummy variables for scale ofresidential city, occupation, and type of residence are included, but these results are not reported. In
addition to the results exhibited, estimations using conditional logit models have also been conducted,
resulting in effect signs and statistical significance that were almost the same as those reported inTable 3, although the value of the coefficients was very large.

In Table 3, time-invariant variables such as closeness to Fukushima, home, male,15

and education level are completely captured by the fixed effects. Therefore, their results
are reported in the probit model but not in the fixed model estimations9. The results ofthe probit model show the coefficient of after disaster to have a positive sign, and thiscoefficient was statistically significant in all estimations. Turning to the results of the
fixed effects mode, results based on the full sample and the women’s sample continue toindicate statistical significance and a positive sign for after disaster. However, as isshown in column (5), after disaster was not statistically significant for men, although its
sign is still positive. This can be interpreted as suggesting that there was no differencein willingness to leave before and after the Fukushima accident for men, while women
were more willing to leave after the accident occurred. This finding is in line withprevious work suggesting that women are more likely than men to perceive radiation
exposure as risky (MacGregor et al., 1994; Morioka, 2014). The significant negative
coefficient for presence of child is observed in columns (1)–(3). This finding is consistent
with the argument that changing residence and therefore school is costly for children(Been et al., 2011). However, as shown in columns (4)–(6), the effect of presence of childis not statistically significant after controlling for the fixed effects. Home exhibits thepredicted negative sign, and the effect is statistically significant at the .01 level,
consistent with the claim that social ties reduce the motivation to leave (Belot andErmisch, 2009; David et al., 2010; Kan, 2007). Income was not statistically significant in
the probit model estimations. After controlling for the fixed effects, income and income2
have positive and negative signs, respectively, and both are statistically significant in

Respondents who had left the residential prefecture during the 2009–2010 period
were not followed up by the survey and are therefore excluded from the sample
although they were followed up in 2012. Therefore, to conduct the fixed effects
estimation, we assumed that residential prefecture did not change during the period,and hence is a time-invariant variable in the data.
916

the full sample and among women, a finding that is in line with the predictions.

Table 4. Effect of cross terms on willingness to move (probit model)

After accident * Closenessto Fukushima

(1)
Full
0.25***(6.54)(2)Men
0.21***(3.23)(3)
Women
0.39***(7.45)

After accident * Closenessto Fukushima *Presence of child

(4)
Full

(5)
Men

(6)
Women

0.48***(7.10)−1.14**(−2.16)

1.29***(12.7)

After accident

0.16***(5.48)

0.14***(2.99)

0.18***(4.97)

0.17***(5.58)

0.15***(3.11)

0.18***(5.02)

Closeness to Fukushima−0.02(−0.45)0.09(1.63)−0.24***(-3.21)0.04(1.06)

0.17***(3.29)−0.20***(−2.85)Presence of child−0.15***(−3.86)−0.13*(−1.90)−0.16**(−2.55)−0.15***(−3.88)−0.13*(−1.86)−0.16**(−2.60)Log pseudolikehood−4,065−1,842−2,202−4,065−1,842−2,201

Observations

11,133

5,343

5,786

11,133

5,343

5,786

Note: Numbers in parentheses are z-statistics calculated using robust standard errors
adjusted for clusters in prefectures. *, ** and *** indicate significance at the 10, 5 and 1%
levels, respectively. Numbers above the numbers in parentheses indicate marginal effects.
Control variables used in estimations of Table 3 are incorporated but not reported. In addition
to results exhibited, estimations using conditional logit have also been conducted, resulting in
effect signs and statistical significance that were almost the same as those reported in Table 4,
although the value of the coefficients was very large.

Now turning to Table 4 and focusing on the results of key variables (although otherindependent variables were controlled), after accident * closeness to Fukushima has apositive sign and is statistically significant at the .01 level. This finding is consistent
with Hypothesis 1. Further, its marginal effect is larger in the women’s sample than in
the men’s sample, providing corroboration for Hypothesis 2. As is shown in columns(4)–(6), the coefficient for after accident * closeness to Fukushima * presence of child has
a positive sign in the full sample and in the women’s sample, but it has a negative signin the men’s sample. These results are statistically significant at the .01 level. This can

17be interpreted to suggest that the cost for switching schools is perceived to be
sufficiently large for fathers, but not for mothers. This finding is in line with Hypothesis

3.

Table 5. Effect of cross terms on willingness to move (fixed effects model)

After accident * closenessto Fukushima

(1)
Full
0.02***(4.02)(2)Men
0.03**(2.45)(3)
Women
0.02***(2.64)

After accident * closenessto Fukushima *Presence of child
After accident

(4)
Full

(5)
Men

(6)
Women

0.21***(11.5)−0.02(−0.81)

0.46***(21.4)

0.04**(2.18)

0.01(0.78)

0.08**(2.32)

0.04**(2.18)

0.01(0.80)

0.08**(2.31)

Presence of child−0.01(−0.59)0.02(1.14)−0.05(−1.42)−0.01(−0.60)0.02(1.15)−0.05(−1.44)

Observations

11,133

5,343

5,790

11,133

5,343

5,790

Closeness to Fukushima

Note: Numbers in parentheses are z-statistics calculated using robust standard errors
adjusted for clusters in prefectures. *, ** and *** indicate significance at the 10, 5 and 1%
levels, respectively. Numbers above the numbers in parentheses indicate marginal effects.
Control variables used in estimations of Table 3 are incorporated but not reported. In addition
to results exhibited, estimations using conditional logit have also been conducted, resulting in
effect signs and statistical significance that were almost the same as those reported in the
Table 5, although the value of the coefficients was very large.

Table 5 shows that after accident * closeness to Fukushima continues to show asignificant positive sign, even after controlling for the fixed effects, although there is adifference in this coefficient between men and women. Results indicate that the sign ofafter accident * closeness to Fukushima * presence of child is positive and statisticallysignificant in the full sample and in the women’s sample. However, in the men’s sample,
this coefficient is negative and not statistically significant. It seems plausible that theeffect of distance from Fukushima is not linear. For checking this possibility, weinvestigated whether the effect of the accident was mainly observed in areas close to18

Fukushima. To this end, the sample was divided and estimations were conducted. As isshown in Table 6, after accident has a positive sign in all columns. The coefficient forafter accident is statistically significant in the sample of those who resided within 400
km of Fukushima, although this finding does not hold for the men’s sample. Hence, theaccident did not influence the willingness to leave when distance between theresidential area and Fukushima was greater than 400 km. The discussion to this point,on the combined results of Tables 3–5, strongly support Hypotheses 1–3.

Table 6. Effect of cross terms on willingness to move (Fixed Effects model): Subsample estimation(1)
Full
Distance
from
Fukushima
< 400

(2)
Full
Distance
from
Fukushima
≥ 400

(3)
Men
Distance
from
Fukushima
< 400

(4)
Men
Distance
from
Fukushima
≥ 400

(5)
Women
Distance
from
Fukushima
< 400

(6)
Women
Distance
From
Fukushima≥ 400

After accident

0.05***(3.13)

0.03(1.01)

0.02(1.12)

0.01(0.27)

0.13**(2.81)

0.05(1.08)

Presence of child

0.003(0.13)−0.02(−0.87)0.07*(1.83)−0.01(−0.35)−0.06(−1.08)-0.03(-0.95)Observations

5,175

5,958

2,773

2,605

3,185

2,570

Note: Numbers in parentheses are z-statistics calculated using robust standard errors adjusted forclusters in prefectures. *, ** and *** indicate significance at the 10, 5 and 1% levels, respectively.
Numbers above the numbers in parentheses indicate marginal effects. Control variables used inestimations of Table 3 are incorporated but not reported. In addition to results exhibited,
estimations using conditional logit have also been conducted, resulting in effect signs and statisticalsignificance that were almost the same as those reported in the Table 5, although the value of thecoefficients was very large.

4. Conclusion
Catastrophic events like natural disasters influence subjective perceptions such as
life satisfaction (Carroll et al., 2009; Ishino et al., 2011; Luechinger and Saschkly, 2009;
Uchida et al., 2014) and perception about risk (Goebel et al., 2013; Hanaoka et al., 2014).
This, in turn, affects individual behavior. Therefore, disasters have an effect on the19economic condition not only through physical channels but also through psychologicalchannels. The Great East Japan earthquake caused the Fukushima nuclear accident,
resulting in a large number of evacuees and consequently a significant amount ofgeographical population mobility in Japan. By using individual level panel data
covering time periods before and after the Fukushima accident, in this article we haveattempted to explore how and the extent to which major disasters change individuals’
willingness to leave their place of residence by taking into account distance from
Fukushima and having of a small child.
Key findings obtained after controlling for the individual-level fixed effects are that
the Fukushima accident led people to leave their place of residence if they lived near
Fukushima. Such a tendency was particularly strong when respondents had a smallchild. Further, this tendency was observed for women but not for men when the sample
was divided by gender. The effect of the Fukushima accident can be explained by theJapanese trait of exhibiting a high level of anxiety toward nuclear accidents (Hinman etal., 1993). Further, the differences in the influence of the accident on willingness toleave between men and women can be seen as consistent with previous work suggesting
differences in risk perception between men and women (Eckel and Grossman, 2008;
Jianakoplos and Bernasek, 1998; MacGregor et al., 1994). More specifically, the findingsof this study are consistent with the claim that, after the Fukushima accident, women
expressed more concern over radiation than did men (Morioka, 2014).
The focus of this study was limited to the intention to leave. Therefore, the article
has not examined the influence of catastrophic events such as the Fukushima accident
on actual population mobility. Furthermore, although the massive inflow of evacuees
has become a critical issue in Japan (Aoki, 2012), this paper has not explored the inflow

20

of evacuees from Fukushima to other prefectures. Additionally, social capital such associal trust and community participation are known to be important in recovery fromnatural disasters (Yamamura 2010, 2014). The role of social capital after the Great East
Japan earthquake is worthy of further examination. These are remaining issues to beaddressed in future research.

21

References

Almond, D., Edlund, L., Palme, M., 2007. Chernobyl's subclinical legacy: Prenatal
exposure to radioactive fallout and school outcomes in Sweden. Quarterly
Journal of Economics. 124(4), 1729–1772.
Anbarci, N., Escaleras, M., Register, C., 2005. Earthquake fatalities: The interaction ofnature and political economy, Journal of Public Economics. 89, 1907–1933.

Ando, M., Kimura, F., 2012. How did the Japanese exports respond to two crises in
the international production networks? The global financial crisis and theGreat East Japan Earthquake. Asian Economic Journal. 26(3), 261–287.
Aoki, M., 2012. Tohoku fears nuke crisis evacuees gone for good. The Japan Times
Special Report 3.11, One Year on: a Chronicle of Japan’s Road to Recovery. The
Japan Times, Tokyo.
Been, V, Ellen, I.G., Schwartz, A.E., Stiefel, L., Weinstein, M., 2011. Does losing
your home mean losing your school? Effects of foreclosures on the school
mobility of children. Regional Science and Urban Economics. 41(4), 407–414.
Belot, M., Ermisch, J., 2009. Friendship ties and geographical mobility: Evidence
from Great Britain. Journal of the Royal Statistical Society: Series A. 172(2),427–442.
Barber, B.M., Odean, T., 2001. Boys will be boys: Gender, overconfidence, andcommon stock investment. Quarterly Journal of Economics. 116(1), 261–292.
Beckmann, D., Menkhoff, L., 2008. Will women be women? Analyzing the genderdifference among financial experts. Kyklos. 61(3), 364–384.

22

Berger, E.M., 2010. The Chernobyl disaster, concern about the environment and life
satisfaction, Kyklos. 63, 1–8.
Carroll, N., Frijters, P., Shields, M., 2009. Quantifying the costs of drought: New
evidence from life satisfaction data. Journal of Population Economics. 22,
445–461.
Daily Yomiuri, 2011a. 2000 missing in 2 towns: Magnitude of Friday’s quake revised
upward to 9.0. Daily Yomiuri, March 14, 2011.
Daily Yomiuri, 2011b. Tsunami topped 15 meters on Sanriku coast. Daily Yomiuri,
March 19, 2011.
Daily Yomiuri, 2011c. Fewer want to return home: Delayed recovery dampens
evacuees’ hopes for the future. Daily Yomiuri, June 12, 2011.
Daily Yomiuri, 2012. Obesity rising for Fukushima kids / 5- to 9-year-olds show highest
rates of obese children across the nation. Daily Yomiuri, December 27, 2012.

David, Q., Janiak, A., Wasmer, E., 2010. Local social capital and geographicalmobility. Journal of Urban Economics. 68(2), 191–204.
Deding, M., Filges, T., 2010. Geographical mobility of Danish dual-earner couples:
The relationship between change of job and change of residence. Journal of
Regional Science. 50(2), 615–634.
Eckel, C.C., Grossman, P.J., 2008. Men, women and risk aversion: Experimental
evidence, in: Plott, C.R., Smith, V.L. (Eds.), Handbook of Experimental
Economics Results. Elsevier, New York, pp 1061–1073.
Eliasson, K., Lindgren, U., Westerlund, O., 2003. Geographical labour mobility:
Migration or commuting? Regional Studies. 37(8), 827–837.
Eisensee, T., Strӧmberg, D., 2007. News droughts, news floods, and U.S. disaster relief,

23

Quarterly Journal of Economics. 122(2), 693–728.

Goebel, J., Krekel, C., Tiefenbach, T., Ziebarth, N.R., 2013. Natural disaster, policy
action, and mental well-being: The case of Fukushima," IZA Discussion Paper
No. 7691.
Hammer, J., 2011. Inside the danger zone. Newsweek, April 11, 2011, 28–31.
Hanaoka, C., Shigeoka, H., Watanabe, Y., 2014. Do risk preferences change?
Evidence from panel data before and after the great east Japan earthquake.
Mimeo
Hinman, G.W., Rosa, E.A., Klainhesselink, R.R., Lowinger, T.C., 1993. Perception of
nuclear and other risks in Japan and the United States. Risk Analysis. 13(4),449–455.
Ishino, T., Ogaki, M., Kameyasu, A., Murai, S., 2011. Effect of the great East Japandisaster on happiness. keio/Kyoto Global COE Discussion Paper Series,
DP2011-38.
Iwata, K., Ito, Y., Managi, S., 2014. Public and private mitigation for natural
disasters in Japan. International Journal of Disaster Risk Reduction. 7,
39–50.
Jianakoplos, N.A., Bernasek, A., 1998. Are women more risk averse? Economic
Inquiry. 36, 620–630.
Kan, K., 2007. Residential mobility and social capital. Journal of Urban Economics.
61, 436–457.
Kellenberg, D.K., Mobarak, A.M., 2008. Does rising income increase or decrease damage
risk from natural disasters? Journal of Urban Economics. 63, 788–802.
Lenney, E., 1977. Women’s self-confidence in achievement settings, Psychological24

Bulletin. 84(1), 1–13.

Luechinger, S., Saschkly, P.A., 2009. Valuing flood disasters using the life
satisfaction approach. Journal of Public Economics. 93, 620–633.
Lundeberg, M.A., Fox, P.W., Punccohar, J., 1992. Highly confident but wrong:
Gender differences and similarities in confidence judgments. Journal of
Educational Psychology. 86(1), 114–121.
MacGregor, D., Slovic, P., Mason, R.G., Detweiler, J., Binney, S.E., Dodd, B., 1994.
Perceived risks of radioactive waste transport through Oregon: Results of astatewide survey. Risk Analysis. 14, 5–14.
Matsubayashi, T., Sawada, Y., Ueda, M., 2013. Natural disasters and suicide:
Evidence from Japan. Social Science & Medicine. 82(C), 126–133.
Mocetti, S., Porello, C., 2010. How does immigration affect native internal mobility?
New evidence from Italy. Regional Science and Urban Economics. 40(6),427–439.
Morioka, R., 2014. Gender difference in the health risk perception of radiation from
Fukushima in Japan: The role of hegemonic masculinity. Social Science &Medicine. 107, 105–112.
Niederle, M., Vesterlund, L., 2007. Do women shy away from competition? Do men
compete too much? Quarterly Journal of Economics. 122(3), 1067–1101.
Saito, M., 2011. Economics of the Crisis of the Nuclear Plants: What I Considered asa Social Scientist (in Japanese). (Gempatsu Kiki no Keizai-gaku: Shakai
kagaku-sha to shite Knagaeta-koto). Nihon Hyoron sha, Tokyo.
Sawada, Y., Shimizutani, S., 2007. Consumption insurance against natural
disasters: Evidence from the Great Hanshin-Awaji (Kobe) earthquake. Applied25

Economics Letters. 14(4), 303–306.
Sawada, Y., Shimizutani, S., 2008. How do people cope with natural disasters?
Evidence from the Great Hanshin-Awaji (Kobe) Earthquake in 1995. Journalof Money, Credit and Banking. 40(2–3), 463–488,
Sawada, Y., Shimizutani, S., 2011. Changes in durable stocks, portfolio allocation,and consumption expenditure in the aftermath of the Kobe Earthquake.
Review of Economics of the Household. 9(4), 429–443.
Skidmore, M., Toya, H., 2002. Do natural disasters promote long-run growth? Economic
Inquiry. 40(4), 664–687.
Tanikawa, H., Managi, S., Lwin, C.M., 2014. Estimates of lost material stock ofbuildings and roads due to the Great East Japan Earthquake and tsunami.
Journal of Industrial Ecology, forthcoming.
Uchida, Y., Takahashi, Y., Kawahara, K., 2014. Changes in hedonic and eudaimonic
well-being after a severe nationwide disaster: The case of the Great East Japan
Earthquake. Journal of Happiness Studies. 15(1), 207–221.
Viscusi, W K., Zeckhauser, R.J., 2006. National survey evidence on disasters and relief:risk beliefs, self-interest and compassion. Journal of Risk and Uncertainty. 33(1/2),
13–36.

Yamamura, E., 2010. Effects of interactions among social capital, income andlearning from experiences of natural disasters: A case study from Japan.
Regional Studies. 44(8), 1019–1032.
Yamamura, E., 2012. Experience of technological and natural disasters and theirimpact on the perceived risk of nuclear accidents after the Fukushima nuclear
disaster in Japan 2011: A cross-country analysis. Journal of Socio-economics.

26

41, 360–363.
Yamamura, E., 2014a. Impact of natural disaster on public sector corruption.
Public Choice, forthcoming.
Yamamura, E., 2014b. Natural disasters and social capital formation: The impact of
the

Great

Hanshin-Awaji

earthquake.”

forthcoming.

27

PapersinRegional

Science,