217

Impact of TEPCO Nuclear Accident and Associated Evacuation to
the Demography in Fukushima
Kei GOMI
Fukushima Branch, National Institute for Environmental Studies
10-2 Fukasaku, Miharu Town, Tamura-gun, Fukushima, 963-7700, Japan*E-mail: gomi.kei@nies.go.jp

Abstract
The nuclear accident at Tokyo Electric Power Company’s Fukushima Daiichi Nuclear Power Station
resulted in evacuation orders being issued for the surrounding municipalities. These have been lifted one byone, and people have begun to return to their hometowns. When considering the reconstruction and long-term
development of these areas, population recovery is the most fundamental factor. In this study, I analyze thedemographics of these areas, including how the inhabitants’ return is proceeding, from multiple statisticalsources. The results show that out-migration according to official registration has returned to its previous
trend again since 2013, that progress in evacuees’ return after the lifting of the evacuation order varies fromregion to region, and that the proportion of women of reproduction age is low among residents in the areas
where the evacuation orders have been lifted.
Key words : demographic transition, disaster recovery, evacuation, nuclear accident

1. Introduction
The accident at Tokyo Electric Power Company’s(TEPCO’s) Fukushima Daiichi nuclear power plantfollowing the Great East Japan Earthquake in 2011,
resulted in evacuation orders being issued for thesurrounding areas. Since then, as the accident was
brought under control and radiation levels dropped, theevacuation orders have been lifted one by one. In areas
where the orders have been lifted, residents who had been
evacuated are now returning to their hometowns. For
areas forcibly evacuated due to the disaster, population
recovery is the most basic element of reconstruction.
Therefore, it is important to understand the demographicsof these areas so as to plan for future recovery. It remains
unclear, however, how many people will eventually returnto the areas affected by radioactive contamination, as wehave no experience in recovery from such large-scaleevacuations and the longer the evacuation period, themore settled the residents will become in their evacuationsites. For example, according to a survey conducted by
the Reconstruction Agency and local governments, morethan half of the people in some areas have decided not toreturn (Reconstruction Agency, 2020). In this study, we
will investigate the demographics of the evacuation areasfrom the time of the disaster to the present situation,
analyze trends by factor, clarify the actual circumstances
and identify issues from the perspective of recovery.
Global Environmental Research
24/2020: 217-226
printed in Japan

Morita et al. (2018) analyzed the evacuation
behavior of people in Minamisoma, Japan, when theevacuation orders were issued in the month after thedisaster, and found that (1) males were more likely tohave stayed there against the evacuation orders thanfemales, and (2) residents aged 40 – 64 years old weremore likely to have stayed there than were those aged 75
years or older. Heuer et al. (2020) analyzed population
migration patterns from the affected areas before andafter the disaster on a prefecture-by-prefecture basis bycomparing evacuees (those who remained registered attheir original address) and out-migrants (those who had
registered in other prefectures). Their results showed thatthe composition of the areas to which the out-migrants
had moved had not changed from before the disaster,while the evacuees, unlike the out-migrants, chose to livein more neighboring areas. Higuchi et al. (2012) analyzed
a variety of statistics for the first ten months after thedisaster and found the number of excess out-migrants
from the three affected prefectures to be four times
greater than in 2010, with the largest number in April
2011, nearly 80% of the total number of the excess, waspeople moving out from Fukushima Prefecture, with thelargest number of people moving to Tokyo. Hashimotoand Kawakami (2015) conducted an analysis of population
movement by municipality based on the Basic Resident
Registration System (BRRS) and found that the higher
the average income and the greater the degree of damage,©2020 AIRES

218

K. Gomithe higher the out-migration rate tended to be, and thateven after adjusting for economic factors and the degreeof earthquake and tsunami damage, the out-migration rate
was higher from municipalities in Fukushima Prefecture
than from the other two (Iwate and Miyagi). In relation todeaths, it has been noted that Fukushima Prefecture had ahigher incidence of disaster-related deaths than otherareas affected by the Great East Japan Earthquake due tothe nuclear disaster (Kubo et al., 2017). It has also beenshown that among the elderly in the same affected areasin Fukushima, the mortality rate was higher among theevacuees than among those who did not evacuate(Nomura, 2016).
Based on the fact that several more years havepassed since the previous studies above, and that
evacuation orders have been lifted and people have beenreturning to their hometowns in the meantime, this study
targets the areas affected by the nuclear disaster, with a
special focus on the areas where evacuation orders had
been issued, and clarifies the overall demographic
picture, including progress in evacuees’ return, frommultiple statistical sources. We present the results in
Chapter 3 and discuss their implications from theperspective of recovery in Chapter 4.

and some of which are still in place today, were
established in August 2013: Zones in preparation for thelifting of the evacuation order (herein, Zone 1), restricted
residence areas (herein, Zone 2), and difficult-to-returnzones (herein, Zone 3). In Kawauchi Village, all villagers
were evacuated in 2011 after the accident and then began
to return to their homes on January 1, 2012, except forsome areas (later becoming zones 1 and 2), and then in
2016, evacuation orders for Zones 1 and 2, which
together accounted for about 10% of the village’s
population, were lifted. Because of this history and thelack of detailed population information from the town,
we treat the entire town as one area without
distinguishing between the evacuation zones. Table 1 lists
these municipalities and the populations of the evacuation
zones, the timing of the lifting of the evacuation orders,
and the scope of analysis of evacuees’ return in this study.
In principle, the period covered is until the end of June(or July 1, 2019). This is because, in Japan, the new fiscal
year begins in April and many people relocate between
March and April, and this kind of movement is thought tohave settled down by the end of June. However, different
time periods may be used in the individual analyses. In
such cases, the periods are indicated in each item. In
addition, most of the evacuation orders for Zones 1 and 2
had been lifted by that time.
2.2 Information Sources and Analytical Methods
Currently, there are four main sources of information
for determining the population of the evacuated areas.
The BRRS, national census, resident population according
to municipalities’ tallies, and mobile phone location
information. Each of these has its own strengths andweaknesses, but by combining them, we can see the2. Method
2.1 Target Areas and Periods
The target area was the 11 municipalities where
evacuation orders had been issued. The scope and type ofevacuation orders changed several times since
immediately after the disaster. The following three zones,
some of which were in place for a relatively long time
Table 1 Basic profile of target municipalities in this study.

This study examines the 11 municipalities that appear in this table. They have different proportions of the three categories of evacuation zone (Zones 1, 2 and 3).
Population (July 31, 2013)

Evacuation order lifted before July 1, 2019

This stydy’s
target area
for return
analysis

Total

Zone infor Restricted
Total area preparation
residence Difficult-tothe
liftingofreturn zone
evacuated the evacuation
area(Zone 3)(Zone
2)
order (Zone 1)

Other
areas

Minamisoma City

65,175

12,238

12,238510

252,425

Iitate Village

6,250

784

784

5,192

274

0

Kawamata Town

15,086

1,077

1,077

127

0

13,882

Katsurao Village

1,511

1,329

1,329

64

118

0

Tamura City

39,996

351

351

0

0

39,645

Namie Town

19,505

7,902

7,902

8,260

3,343

Futaba Town

6,492

255

255

0

6,237

Okuma Town

10,958

23

23

362

10,5710

Lifted in April 10, 2019 except for Zone 3

None

Tomioka Town

14,413

1,319

1,319

8,821

4,273

0

Lifted in April 1, 2017 except for Zone 3

Lifted area

Naraha Town

7,575

7,525

7,525

0

0

50

Lifted in September 5, 2015

Whole town

Kawauchi Village

2,809

276

276

58

0

2,475

189,770

33,07933,07923,394

24,818108,477

Total

Lifted in July 12, 2016 except for Zone 3

Lifted area

Lifted in March 31, 2017 except for Zone 3

Lifted area

Lifted in March 31, 2017

Whole town

Lifted in July 12, 2016 except for Zone 3

Lifted area

Lifted in April 1, 2014

Lifted area0

Lifted in March 31, 2017 except for Zone 3

Lifted area0

Not lifted yet

None

All residents evacuated in 2011. Return started
on January 1 2012 except for Zone 1 and Zone 2. Whole village
The rest was lifted in June 4 2016.

Demographic impact of TEPCO nuclear accident
whole picture. Table 2 summarizes the advantages anddisadvantages of each statistical source and how they areused in this study. The details of each statistical source
and method of survey and analysis in this study are
discussed below.
The BRRS is municipality-based, middle-frequency
and detailed. It contains basic demographic statistics thatprovide a complete picture of births and deaths by sex
and age, and are usually compiled by local governments
on a monthly basis, while annual reports are madeavailable to the public by the central government ofJapan. However, because the data are based onregistration by residents, people are sometimes registeredin a different place from where they currently live. For
this reason, it is not possible to obtain the actual
population of areas from which residents have evacuatedand begun returning to in this study. In addition, BRRS
does not have information on the daytime population
because it does not include information on where people
work and go to school. Therefore, for example, if abusiness has reopened in the area due to reconstruction,
but the employees do not live in the area but commute towork there from outside the area, it is not possible todetermine the daytime demographics of the area from the
BRRS. In this study, from the BRRS, we obtained thetotal population, net social change, net natural change andcomposition by sex and age on an annual basis. We
compared pre-disaster trends between municipalities
designated as evacuation areas and the rest of Fukushima
Prefecture, focusing on 2011 and covering seven years
before and after the disaster (2004 – 2018). However, due
to municipal mergers within the region, social changes
are calculated from 2007 to 2018, the year after themerger (as it is not possible to determine retrospective
movements between merged municipalities). Regarding
the composition of the population by sex and age, we
compare the residential data for each municipality on

219

January 1, 2018 with the composition of the number ofresidents in the area based on mobile spatial statistics (seethe following chapter).
The number of residents in the municipality tally
was compiled by the local governments in whose areas
the evacuation orders had been lifted, and they asked
returning residents to report on the status of their return.
Many of the municipalities publish the population andnumber of households present as well as those stillevacuated monthly on their websites. This information isvery important for identifying the actual situation ofpeople living in the evacuated areas. Each municipality,
however, differs in whether it publishes the information,
how long it is available on the websites, the details onage, sex and location of the population, and whether itclassifies people who have moved into the area fromoutside after the disaster. In addition, because the system
is not as enforceable as BRRS, there may be residents
who do not report their immigration status or return totheir hometowns, or residents who report theirimmigration status but actually live outside the area. In
this study, we collected the number of residents andevacuees published by each municipality from the monthwhen the evacuation order was lifted until June 30 (orJuly 1) 2019 to calculate the rate of return. The rate isdefined as the sum of the number of residents in the area
where the evacuation order was lifted plus the number ofregistered evacuees in the area, divided by the number ofregistered residents in the same area, and is calculated forthe area and time period for which the information isavailable. To confirm progress toward population
recovery and differences in rates, we show changes in therate over time. We also briefly examine the factors behindthe differences.
The census is the most fundamental source ofstatistics in the nation on a municipal basis. In the case of
the evacuation in question, an individual is counted

Table 2 Comparison of information sources on population in the target areas.

 our different statistical information sources were adopted in this study to get an overall picture of the demography in the target area. Each of themF
has strengths and weaknesses as shown in the table, so it is necessary to combine them.
CensusBasic Resident Registration
System (BRRS)

Municipality tally

Mobile phone information

Temporal resolution

Low (every 5 years)

Middle (monthly, yearly)

Middle (monthly)High (hourly)

Spatial resolution

Low (municipality)

Low to Middle(sub-municipality)

Middle (municipality,
evaculation area)

High (500m mesh)

Demogprahic profile
resolution

High (sex, age, working
status, marriage status)

Middle (sex, age)

Low (total only in manycases)

Middle (sex, age)

Availability

High (provided on Web)High (provided on Web)

Middle (varies bymunicipality)

Low (fee for use)

Use in this study- Indicators of basic- Comparison of nighttime
demographic dynamics of- Analysis of gender and age
and daytime population and- Progress of return to theeach municipality (social andstructure in the areas where
gender ratio in theareas where evacuation orders
natural increse, birth andevaculation orders were lifted
municipalities before and after
were lifted - Analysis ofdeath rate) on an annual basis- Comparision of age anddisaster (2010 and 2015) differences in progress- Comparision of age andgender composition against
Analysis of commuting
between areas
gender composition against
BRRS
structure in 2015
mobile phone information

220

K. Gomiamong the population of current residents if the person isstill evacuated, and among the population of theevacuated area if the person has returned. Thus, thepopulation living in the area can be determined in detail
and with considerable accuracy. In addition, since thecensus also surveys work and school commuting areas,
the daytime population and commuter OD (origin anddestination) can be determined. On the other hand, thetemporal resolution is low because it is conducted every
five years. At the time of this study, the latest census had
been conducted in 2015, the first survey conducted after
the disaster, and evacuation orders had not yet been liftedin many areas. In addition, many of the tabulations are at
the municipal level, making it impossible to distinguishbetween evacuated and non-evacuated areas within amunicipality. In this study, we examine the 2010 and2015 census data on the nighttime population, daytime
population and sex ratios in municipalities with
evacuation areas to identify changes in the basic
population structure before and after the disaster. To
determine the commuting structure, the residence of
employees in municipalities with evacuation areas isdetermined from the census. The census is the only
source of information on mobility in the evacuated areas
available free-of-charge.
As a population estimate based on mobile phone
location data, we used mobile spatial statistics in thisstudy. Mobile spatial statistics are population estimatesfrom NTT DoCoMo, Inc. based on the location of theirown mobile phone users. They have a high spatiotemporal resolution and are provided in hourly,
administrative boundary or 500-meter mesh units. Since
they are based on the number of mobile phone users who
were present at a given location at a given time, it ispossible to estimate the daytime population and also tocapture temporary increases in the number of people due
to stays or events. On the other hand, the estimates are
uncertain because they are based on the percentage ofmobile phone subscribers of that company. This isespecially true in areas with small populations. The sameis true for younger and older people, who are less likely
to have mobile phones. In addition, it should be notedthat the number of residents who are staying in the area
without being registered is also estimated (e.g.,
employees of businesses involved with work such as
decontamination, decommissioning of the nuclear plantand interim storage of decontamination waste). With thisinformation, in this study, the total number of people in
December 2017 (average at night between 18:00 and 8:00
a.m.) is compared with the BRRS data (January 1, 2018).
The profile of residents in the area (composition ratios bysex and age) is compared between BRRS and mobile
spatial statistics to identify relatively greater or fewer
people by sex and age using the Jaccard distance. The
Jaccard distance is an application of the Jaccard
coefficient, which was proposed by Jaccard (1912) as amethod of comparing the size of the common parts of
two sets, and it has been applied in various ways sincethen. To determine the similarity of two vectors, we
compute the Jaccard distance Jd using the formula below.
This indicator is for two non-negative vectors of the same
dimension. It ranges from 0 to 1, taking the minimum
value 0 when the two vectors coincide perfectly. The
larger the value, the greater the difference between thetwo vectors. Here, i is the population profile (by sex andage), xi is the population composition ratio based on
mobile spatial statistics, and yi is the population
composition ratio based on the BRRS.

Jd（x, y）= 1 -∑ i min（x i ,y i ）
∑ i max（x i ,y i ）...... (1)3. Results
3.1 BRRS Data
Figure 1 shows the total population (ratio to that ofJanuary 1, 2011). The population of most areas had been
decreasing even before the disaster (the exception being
Okuma Town), and after a significant decrease from 2011
to 2012, the pre-disaster trend of population decline
resumed again. However, the post-disaster population ofmost of the 11 municipalities has declined at a greaterrate than in the rest of Fukushima. The social growth rate
(Fig. 2) has been negative in most areas, including therest of Fukushima, for most of the period since before thedisaster. An exception is Okuma town. Before 2011,
relatively young workers were constantly migrating to thetown because of steady growth in industries related to the
nuclear power plants (Okuma Town, 2016). In 2011, inseven out of 11 municipalities, the rate was lower thanthat of the rest of Fukushima. After a significant drop in
2012, the rate of natural increase remained stable in therest of Fukushima. Nine of the 11 municipalities, except
for Okuma and Tomioka, had a lower rate of natural
increase in 2012 than did the rest of Fukushima. Moremunicipalities had lower natural growth rates than rest of
Fukushima in other periods as well. Although the crude
birth rate has been declining over the long term, there arelarge year-to-year fluctuations in each of themunicipalities involved, so a clear trend cannot be readily
discerned from the figure. In the rest of Fukushima,
because of the smoothing effect of aggregation of manymunicipalities, there is a clear trend toward lower birthrates in 2012 and 2013 than in the other years. This may
be because people were discouraged from having children
due to the 2011 disaster. Crude mortality rates had been
increasing overall since before the disaster, with an
overall increase in 2011 and an even higher rate in 2012.
The increase in mortality rates in 2012 compared to 2011was higher in all 11 municipalities in the study area thanin the rest of Fukushima Prefecture.

Demographic impact of TEPCO nuclear accident

221

1.20

1.15

Total population (2011.Jan.1st = 1)

1.10

Envelope of 11 municipalities
Other Fukushima

1.05

TamuraMinamisoma
Kawamata
Naraha1.00

Tomioka
Kawauchi
Okuma

0.95

FutabaNamie
Katsurao

0.90

Iitate

0.85

0.80

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Fig.1 Time-series of registered population in target municipalities and rest of Fukushima Prefecture.

1.0

2.0

0.5

Minamisoma-1.0

Kawamata

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

b) Natural increase rate

1.2

Okuma
Futaba2.0

Crude death rate (%)

1.0
0.8
0.6
0.4

Namie
1.5

Katsurao
Iitate

1.0

0.5
0.2

2018

2017

2016

2015

d) Crude death rate

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

c) Crude birth rate

Fig. 2 Factors in population changes before and after 2011 in BRRS.

2004

0.0

0.0

Tomioka
Kawauchi

2.5

1.4

Crude birth rate (%)

Naraha

2004

2018

2017

2016

a) Social increase rate

2015

2014

2013

2012

2011

-2.0

2010

-8.0

2009

-1.5

2008

-6.0

2008

-4.0

Tamura

-0.5

2007

-2.0

a)All of
Fukushima
Otherb)c)d)Other
Fukushima
Fukushima

0.0

2006

0.0

2005

Natural increase rate (%)

4.0

2007

Social increase rate (%)

 he registered population of the target area in the Basic Resident Registration System shows thatT
the 11 municipalities experienced a faster population decline than rest of Fukushima Prefecture.

 ut-migration from Fukushima was larger in 2012 than in other years. The main contributor to the natural decrease in year 2012 wasOa high death rate, rather than a low birth rate. Death rates in 2012 in the target municipalities were higher than in the rest of
Fukushima, indicating that evacuation from the nuclear accident caused excess deaths among the evacuees.

222

K. GomiTable 3 Population living in the evacuated areas.

 ocal governments in the target area have reported the numbers of residents living in the areas where evacuation orders have been lifted. ProgressLin population recovery varies significantly region by region. Large differences are found in the populations reported by local governments andestimates from mobile phone information.
June 30 or July 1, 2019*

End of 2017 or January 1, 2018

Registerd
population in
the evacuatedarea

Population
living in theevacuated
area

Ratio

9,542
4,240
1,022
1,570
277
16,074
12,905
6,881
2,677
55,188

4,161
1,324
480
421
225
729
1,064
3,761
2,165
14,330

43.6%
31.2%
47.0%
26.8%
81.2%
4.5%
8.2%
54.7%
80.9%
26.0%

Minamisoma
IitateKawamata
Katsurao
Tamura
Namie
TomiokaNarahaKawauhci
Total

Average increase
Nighttime population
Months since
Populationin ratio ofby mobile phoneevacuation
living in the
population in theinformation (averageorder lifted**
evacuated area
area per monthof December 2017)
34
26
26
34
36
26
26
44
63
-

1.28%
1.20%
1.81%
0.79%
2.26%
0.17%
0.32%
1.24%
1.28%
-

2,798
602
279
267
230
291
358
2,203
2,197
9,225

4,841
2,487
1,413
640
3,131
6,689
5,924
1,852
26,976

Difference indemographic
composition(Jaccard
distance)***
0.156
0.376
0.218
0.439
0.326
0.316
0.282
0.288
0.252

* August 31, 2019 in Kawauchi.
** Months by the ratio of population living in the region exceeds 80%.
*** Comparison of sex and age composition between registered population in BRRS and mobile phone information.

0.9

0.9

Tamura0.8

Minamisoma
Kawamata

0.7

0.7

NarahaTomioka

0.6

0.6

Kawauchi
Namie

0.5

0.5

Katsurao
0.4

0.4

Iitate

2019/4/1

2018/12/1

2018/4/1

2018/8/1

2017/12/1

2017/4/1

2017/8/1

2016/12/1

2016/4/1

2016/8/1

2015/12/1

2015/4/1

2015/8/1

2014/12/1

2014/4/1

0
2014/8/1

0
2013/12/1

0.1

2013/4/1

0.1

2013/8/1

0.2

2012/12/1

0.2

2012/4/1

0.3

2012/8/1

0.3

2011/12/1

Share of residents living in the evacuation zone

0.8

0 3

6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87

MonthsFig. 3 Progress in population recovery in areas where evacuation orders were lifted.

 he figure shows the share of population living in the area of the population registered there after the evacuation order was lifted. A “jumping”
Tincrease occurs in March or April, when the new fiscal and school year starts in Japan. The rate of recovery (slope of the lines) varies regionby-region, and is especially low in Namie and Tomioka, which are near to the nuclear power plant and experienced severe contamination.

3.2 Municipality Tally Data
Table 3 shows the status of the percentage of people
living in the area at the end of June or beginning of July
2019. The changes in the percentage of people living in
the area are shown in Fig. 3. The left side of the figure
shows the rate by each year and month, and in Tamura
and Kawauchi, where the evacuation orders were lifted
early, it has recently leveled off at about 80%. A large
increase in March or April is common, probably because
in Japan, the new fiscal year and school year start on

April 1, so the evacuees are expected to return in Marchor April. The right-hand side of the figure shows thepercentage of people living in the region according to thenumber of months after the evacuation order was lifted.
The larger the slope, the greater the increase in the rate ofsettlement per given period, and the faster the return andrecovery of the population. There is considerable
variation in the progress in population recovery by region(see also the “Average increase of …” column inTable 3). The variation might be explained partly by theDemographic impact of TEPCO nuclear accident

223

Table 4 Basic statistics from censuses of 2010 and 2015.

 he census is the most basic form of population statistics in the nation. The region is the area of the municipalities, i.e., in the case that aTmunicipality has evacuation zones and non-evacuation zones, this table will not differentiate between them but shows the total in the municipality.
Even in municipalities with zero nighttime population, a significant daytime population existed in 2015. The sex ratio (number of males divided bynumber of females) is quite large in some regions, i.e., there are many more males than females.
Nighttime population

Minamisoma
IitateKawamata
Katsurao
TamuraNamie
FutabaOkuma
TomiokaNarahaKawauhci
Total

2010
70,8786,209
15,5691,531
40,422
20,905
6,932
11,51516,0017,700
2,820
200,482

2015
57,79741
14,45218
38,503
0
0
0
0
975
2,021
113,807

Daytime population
2015
/2010
0.82
0.01
0.93
0.01
0.95
0.00
0.00
0.00
0.00
0.13
0.72
0.57

2010
69,455
5,628
14,4961,240
36,27019,041
6,462
15,817
15,837
8,046
2,572
194,864

2015
60,143
3,246
14,928979
34,412
1,597
322
3,701
2,961
3,002
1,899
127,1907 speciﬁc municipalities
100%
90%
80%
70%
60%
50%

Other pref.
Other Fukushima Pref.
Fukushima City
Iwaki
Soma

40%

MIharu30%

Hirono20%
10%0%KawauchiNarahaTamuraKawamata
Minamisoma

Fig. 4 Composition of residential locations of employees inthe target municipalities in 2015.

ensus data show where the employee in each municipality
Ccommutes from. In several specific municipalities that had
relatively large evacuation zones compared to their whole area,
many of the employees commute from outside even after
evacuation order was lifted. Iwaki City has been a residential place
for many of them in particular.

timing of lifting of the evacuation orders. In short, therate is higher in Tamura (where the order was lifted in2014) and Naraha (2015) than Namie and Tomioka (bothin 2017). However, variation is also observed between
the areas where the order was lifted at the same time(e.g., the rate was much higher in Iitate than Namie, eventhough the orders were lifted at the same time in bothareas). A detailed quantitative analysis of the variation inprogress is beyond the scope of this study, but morediscussion is provided later in this paper.
3.3 Census Data
Table 4 provides the main information fromcensuses: areas with zero nighttime population in 2015,
and areas with significantly fewer employees than in
2010, also have a decent sized daytime population. In

Daytime/Nighttime Sex ratio (nighttime) Sex ratio (daytime)
2015
/2010
0.87
0.58
1.03
0.79
0.95
0.08
0.05
0.23
0.19
0.37
0.74
0.65

2010
0.98
0.91
0.93
0.81
0.90
0.91
0.93
1.37
0.99
1.04
0.91
0.97

2015
1.04
79.17
1.03
54.39
0.89
3.08
0.94
1.12

2010
0.95
1.00
0.95
1.12
0.94
0.95
0.95
0.99
1.06
0.94
1.01
0.96

2015
1.17
0.21
1.02
2.00
0.99
6.56
1.53
1.11

2010
0.93
0.93
0.89
1.04
0.89
0.87
0.85
1.41
1.06
1.09
1.01
0.96

2015
1.24
9.64
1.10
14.30
0.92
14.50
17.94
29.84
16.73
5.60
1.34
1.36

2015, the sex ratio in daytime was greater than 1 in allmunicipalities but Tamura, i.e., more males than females
were there during the day. Especially in municipalities
near the nuclear plant, the ratio was quite large (nearly 30
in Okuma and 18 in Futaba). Figure 4 shows the resident
locations of employees by municipality to facilitate
analysis of commuting OD. In Minamisoma, Kawamata,Tamura and Kawauchi, where the evacuation orders werelifted early, many employees commuted to work in theirown municipality. On the other hand, in the areas where
the evacuation orders had not been lifted or where they
had been issued later, many people commuted from otherareas. The total of seven specific municipalities with aparticularly low or no daytime population are shown inthe same figure. Many of the employees in these areas
were commuting from nearby towns where no evacuation
orders had been issued. This is especially true in Iwaki
City, the southernmost coastal city and the most populated
city in the prefecture.
3.4 Mobile Phone Location Information
There are differences in the numbers of people livingin each area between the municipality tally and thenighttime population based on mobile spatial statistics(Table 3). In some areas, the latter is significantly higher
than the former. This suggests the presence ofreconstruction-related workers, temporary outsiders,
residents who return without reporting to the municipality
and residents who live primarily outside the area but
frequently visit their homes or other places of residence.
The Jaccard distance, an aggregated indicator ofvariations in demographic composition between two
information sources, was the highest in Katsurao (0.439),
followed by Iitate (0.376). It was lowest in Minamisoma(0.156) followed by Kawamata (0.218). These results
indicate that the difference tends to be larger in areas
where the ratio of people living in the evacuation area islower (Table 3). Figure 5 shows the population pyramids
for a total of the eight regions based on BRRS data (for224

K. GomiRegistered population in the municipalities(Basic Resident Registration System, June 1, 2018)

75~

75~

70~74

70~74

65~69

65~69

60~64

60~64

55~59

55~59

50~54

50~54

45~49

45~49

Age

Age

Population staying in the evacuation zone
(mobile phone information, Dec. 2017)

40~44

40~44

35~39

35~39

30~34

30~34
25~29

25~29

Female

20~24

Male

15~19

00~14

00~14
0

500

1000

1500

2000

Female

20~24

15~19

0

2500

2000

4000

6000

8000

10000

Male

12000

Population

Population

Fig. 5 Population pyramids in evacuation zones where the orders were lifted and whole municipalities, from different sources(a total of eight municipalities).
T he figure indicates the population in a total of eight municipalities that have areas where evacuation orders had been lifted by the end of
2017. The age and sex composition in the former evacuation area (left) versus the whole municipality (right) are significantly different. In
sex compositions, more males are found than females in most of the age groups in the left figure, while there are more females in the right.

mobile phone information against registered population

Ratio of age and gender composition of population in the zone estimated with

2.5

2.0

Katsurao

1.5

KawauchiKawamata
NarahaMinamisoma
Iitate1.0

Tomioka
Namie
Average
8地域平均

0.5

70~74M
70~74F65~69M
65~69F60~64M
60~64F55~59M
55~59F

50~54M
50~54F45~49M45~49F40~44M
40~44F35~39M35~39F30~34M
30~34F25~29M
25~29F

20~24M
20~24F15~19M
15~19F00~14M00~14F

0.0

Fig. 6 Differences in population composition between evacuation zones where the orders havebeen lifted and whole municipalities.
 ifferences in population composition (age and sex) vary region-by-region, but in general, males ofDworking age have a higher share in areas where evacuation orders have been lifted, while younger
people and females of reproductive age have lower shares.

the entire municipality) and mobile spatial statistics (forareas where the evacuation orders had been lifted).
According to a comparison of mobile spatial statistics
with BRRS data, males are more likely to be there thanfemales, and the working-age population is more likely tobe there than the other age groups. The ratio of thecomposition of the two information sources is shown in
Fig. 6. A higher value indicates a higher proportion of theresident population in each age and sex group in themobile phone information compared to the registeredpopulation. There is considerable variation among
regions. Among the working-age population, there are
more males and females between the ages of 45 and 59.
Among the younger age groups, there are more males
than females in the same respective age group.

Demographic impact of TEPCO nuclear accident

4. Discussion
From the results presented above, in general, we cansay that the demographic phenomena are not uniform
among the different municipalities. In this chapter we
discuss implications of the above results to the recovery
and sustainable development of this region.
4.1 Changes in Registered Population
It has been pointed out that the Earthquake, tsunami
and accident have had an impact on the population
decline in Fukushima Prefecture as a whole. The 11
municipalities that had been designated as evacuation
zones have been affected more heavily than others. As forsocial changes, the tendency to move out of the areas
evacuated has been higher in many areas than in the restof Fukushima Prefecture. However, this trend was clear
only until 2012. The number of registered residents
declined significantly immediately after the disaster but
has not changed much since 2013. This suggests thatmany people decided to move out in the first two years
after the disaster, but from the third year onwards, thetrend has been the same as it was before the disaster. In
terms of natural increase or decrease, the 2012 mortality
rate increase was higher in municipalities with evacuatedareas than in the rest of Fukushima. This is consistent
with the facts reported by Nomura (2016).
4.2 Population Composition
Fewer young people living in the area are in theirteens or younger; there are more men in their late 40s to
50s, and fewer women in their 20s to 30s. This suggeststhat households with children to care for and women ofchildbearing age have tended not to return. If thisstructure does not change in the future as the number ofresidents increases, the long-term demographic structureof the area will be one of extreme aging, followed by a
sharp decline in population. This may have a significant
impact on communities’ sustainability in the future.
4.3 Progress in Evacuees’ Return
In Tamura and Kawauchi, the percentage of people
living in the region has remained almost unchanged ataround 80%. This value is nearly identical to the
combined total of “want to return” and “have not decided”
in an opinion survey by the Reconstruction Agency(2015). Whether this applies to other regions as well is
not clear at this time, but it may serve as a reference or abenchmark for the long-term population outlook. Fromthe comparison of progress in evacuees’ return and timingof the lifting, as we mentioned above, it has beensuggested that the timing of lifting of the evacuation
orders may be a factor affecting differences in the
progress in evacuees’ return (i.e., increase in the annual
rate of residence in the area). Among the areas where theevacuation orders were lifted at the same time, the townsof Namie and Tomioka have town centers close to the
nuclear power plant, and in Namie, where progress has
been slower than in Tomioka, the proportion of Zone 3

225

areas is higher than in Tomioka in terms of both area andpopulation (actually it is the highest of the all 11
municipalities). The same discussion may apply to
Tamura, where contamination was relatively low andevacuation was ordered for only a small part of the city.
These conditions suggest that factors that slow progress
may include proximity to the nuclear power plant andlarge proportions of residents in areas that have not yet
had their orders lifted . We think this is an important
finding for predicting the progress in evacuees’ return to
the towns of Okuma and Futaba, which were not includedin the analysis of evacuees’ return, as well as to otherspecific centers for reconstruction and rehabilitation in
Zone 3.
4.4 Commuting Structure
Where evacuation orders have been lifted, in general,many people commute from the outside, instead of livingin those areas. Results of a survey by the Reconstruction
Agency (2020) may explain the reason for that. Many
respondents reported that a lack of medical and shopping
facilities and support for housing were the main barriers
hindering their return. From our results and the survey,
even though economic activity has recovered thanks topolicy initiatives, without such facilities and support, thenighttime population may not recover. Based oncomparison of the areas where the evacuation orders havebeen lifted so far, though, the percentage of commutersfrom outside the areas is expected to decrease as the
population recovers. However, especially in the areas
where the evacuation orders have been lifted or will belater, many commuters will continue to commute fromthe outside until the population recovers. To mitigate this,
it is important that those who wish to return should be
able to do so as soon as possible. In addition, among
those who work in the region, some were not residents
before the accident, but arrived because new industrial
development led by the government necessitated different
expertise among employees compared to before thedisaster. It will be necessary to remove obstacles forpeople moving into the area and joining the community.

5. Concluding Remarks
In this study, migration and return to the areas
affected by the nuclear power plant accident were
investigated using multiple sources of data to clarify the
demographic situation. Each source had its own inherent
limitations, and care needed to be taken in interpreting
the results. Analysis using mobile spatial statistics isparticularly subject to uncertainty: since the estimates are
scaled up according to the percentage of subscribers to
NTT DoCoMo’s mobile phone service, deviations fromthe true values may be large, especially in small areas. In
addition, in many areas, there is no differentiation amongpeople living in the evacuation zone, evacuees andnewcomers. Therefore, this study has not been able to226

K. Gomidiscern how many of the residents in the area are
returnees.
The information we have been able to obtain so far
in this study is limited, and many issues remain to beaddressed in analyzing the population of the evacuatedareas. First, it will be necessary to continue to survey thesituation among newer returnees, including those to areas
where the evacuation orders have been lifted very
recently. Next, quantitative analysis of factors affecting
regional differences in the progress in evacuees’ return
could help project future progress of their return andidentify what is needed to enable residents who wish to
return to their homes to do so more quickly. Once theseissues are addressed, many of the insights needed for
estimating the future of the population over the long term
will be available, allowing for a long-term socioeconomic outlook that explicitly takes uncertainty into
account.
References
Hashimoto, N. and Kawakami, Y. (2015) An empirical analysis of
migration in the area affected by the Great East Japan
Earthquake. Japan NPO Research Association Discussion Paper.
(in Japanese) Retrieved from https://www.janpora.org/dparchive/
pdf/2015001J.pdf (Accessed 26 November 2020)
Hauer, M.E., Holloway, S.R. and Oda T. (2020) Evacuees andmigrants exhibit different migration systems after the Great East
Japan Earthquake and Tsunami. Demography, 57, 1437–1457.
Higuchi, Y., Inui, T., Hosoi, T., Takabe, I. and Kawakami, A. (2012)
The impact of the Great East Japan Earthquake on the labormarket: Need to resolve the employment mismatch in thedisaster-stricken areas. Japan Labor Review, 9(4), 4–21.
Kubo, M., Tsuchida, S. and Shizuma, K. (2017) A study of deaths
related to the Great East Japan Earthquake in Fukushima
Prefecture. Journal of Atomic Energy Society of Japan, 59(129),
49–53. (in Japanese)
Morita, T., Nomura, S., Furutani, T., Leppold, C., Tsubokura, M.,
Ozaki, A., Ochi, S., Kami, M., Kato, S. and Oikawa, T. (2018)
Demographic transition and factors associated with remaining inplace after the 2011 Fukushima nuclear disaster and related
evacuation orders. Plos One, 13(3), e0194134. Retrieved fromhttps://doi.org/10.1371/journal.pone.0194134
Nomura, S., Blangiardo, M., Tsubokura, M., Nishikawa, Y.,
Gilmour, S., Kami, M. and Hodgson, S. (2016) Post-nuclear
disaster evacuation and survival amongst elderly people in
Fukushima: A comparative analysis between evacuees and nonevacuees. Preventive Medicine, 82, 77–82. Retrieved from http://dx.doi.org/10.1016/j.ypmed.2015.11.014
Okuma Town (2016) Creation of Towns, People and Jobs:
Population Vision. (in Japanese) Retrieved from http://cms.town.
okuma.fukushima.jp/uploaded/attachment/4513.pdf (Accessed
26 November 2020)
Reconstruction Agency (2015) Heisei 27 Resident Intention Survey
in Evacuation Area due to the Nuclear Disaster. (in Japanese)
Retrieved from https://www.reconstruction.go.jp/topics/maincat1/sub-cat1-4/ikoucyousa/ (Accessed 30 August 2020)
Reconstruction Agency (2020) Reiwa 1 Resident Intention Survey
in Evacuation Area due to the Nuclear Disaster in Fukushima
Prefecture. (in Japanese) Retrieved from https://www.reconstruction.
go.jp/topics/main-cat1/sub-cat1-4/ikoucyousa/ (Accessed 30
August 2020)

Kei GOMI
Dr. Kei Gomi is a senior researcher in the
Regional Environmental Renovation Section of
the National Institute for Environmental Studies,
Fukushima Branch. His research activities havefocused on integrated modeling for climate action
and sustainable society scenario development inmunicipalities across Japan and in other Asian
countries. Since 2014, he has been involved in research toward recovery
and renovation from the Great East Japan Earthquake and Tsunami of 2011.
His recent research interests are integration of the SDGs into planning
processes and comprehensive recovery from nuclear disasters. He received
his Ph.D in global environmental studies from Kyoto University in 2010.

(Received 16 October 2020, Accepted 21 December 2020)