GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 310

Appraising the Unhappiness due to the Great East
Japan Earthquake: Evidence from Weekly Panel Dataon Subjective Well-being

Fumio Ohtake and Katsunori Yamada

July 2013

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

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

Appraising the Unhappiness due to the Great East
Japan Earthquake: Evidence from Weekly Panel Dataon Subjective Well-being
Fumio Ohtake∗

Katsunori Yamada†

Abstract
After severe disasters, persons living not only in the directly aﬀected areas, butalso in distant areas could be seriously aﬀected thorough images of the disaster on
television and in newspapers. Hence, it can be diﬃcult to deﬁne qualiﬁed beneﬁciaries for policy compensation in terms of psychological suﬀering. Building on
the case of Great East Japan Earthquake, we appraise psychological suﬀering fromdisaster-related news through the experienced utility approach. We take advantageof the serendipitous timing of our original nationwide weekly panel survey that became a timely investigation of subjective well-being in the immediate aftermath ofthe earthquake. Although a pale was cast over the whole society, we found that
there was a robust and large geographical heterogeneity between the disaster areaand non-disaster areas in mental costs. This ﬁnding may capture the focusing eﬀect,suggesting that resources for compensating mental suﬀering should be concentrated
on persons living in the disaster area.

Keywords: Great East Japan Earthquake, subjective well-being, experienced
utility approach, focusing eﬀect, high frequency panel data

JEL classiﬁcations: I31、H56、D61∗†

Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki 567-0047, Japan.
Corresponding author: Institute of Social and Economic Research, Osaka University and MOVE,

Universitat Autonoma de Barcelona. Parmanent Address: 6-1 Mihogaoka, Ibaraki 567-0047, Japan.
Tel/Fax: +81-6-6879-8566. Email: kyamada@iser.osaka-u.ac.jp1

1

Introduction

The Great East Japan Earthquake on March 11, 2011, was one of the largest earthquakes
in record history and resulted in enormous physical destruction (US Geological Survey,
2011; National Police Agency of Japan 2012). Mimura (2011) and Higuchi et al. (2012)
showed that the physical losses from the disaster are estimated to be around 17 trillion
yen in total. Obviously, the losses from the disaster do not just include the economic costsof physical capital and the market structures which were analyzed by Ando and Kimura(2012), but also about the impacts on human beings including disease (Aoki 2012), mental
trauma (Takeda 2011) and changes in risk perception (Goodwin et al. 2012). For thediﬀerential aspects of human loss, there is a widely acknowledged demand among various
ﬁelds of scientists for a single index that provides a comprehensive picture of the humanimpact of disasters. A natural candidate for such an index is monetary evaluation of theloss in the sense of overall well-being. This is because, as Dolan and Metcalfe (2012) and
Frey and Stutzer (2012) suggested, the sense of overall well-being summarizes the statusof human lives and the ultimate goal of governments is to enhance the welfare of citizens.
In this paper, we take advantage of the serendipitous timing of our original nationwide weekly panel survey in the immediate aftermath of the Great East Japan Earthquake, and apply the experienced utility approach proposed by Kahneman et al. (1997)
and Kahneman and Sugden (2005) to test for correlations between individuals’ happiness levels and other covariates such as income levels and earthquake damage information. We then compare the magnitude of the income coeﬃcient with that for earthquake
damage to quantify the monetary value of the non- priced, psychological value of thedisaster. Kahneman et al. (1997) and Kahneman and Sugden (2005) proposed the useof happiness scores to evaluate non-priced goods and bads; in this way, researchers can
avoid biased estimators that inevitably result from other methods of valuing non-priced
events such as the decision utility approach of choice experiments (Train et al. 1987;
McFadden and Train 1996; Navrud et al. 2012). Previous studies that evaluated nonpriced goods through the experienced utility approach include Dolan et al. (2009), Welsch(2009), and Ambrey and Fleming (2011). There are also studies that evaluated non-priced
bads by van Praag and Baarsma (2005), Frey et al. (2009), Oswald and Powdthavee (2008a),
Dolan and Peasgood (2008), and Berger (2010).
Our data collected at the immediate aftermath of the disaster is of great value, because,
as Oswald and Powdthavee (2008b) and Frijters et al. (2011) showed, it is well known that
subjective well-being tends to be aﬀected fairly soon after negative experiences, and later2

returns to previous stationary levels. Kimball et al. (2006) reported that after Hurricane
Katrina, the happiness levels of Americans went back to the previous levels in threemonths at the longest.
While the serious psychological consequences after natural disasters are well known,evidence of monetary evaluation of psychological suﬀering after major disasters has been
scarce. For example, in the case of a well-known disaster of Hurricane Katrina, as far
as we know, there have been no studies that provide monetary estimates of psychological suﬀering, and only a few studies such as Kimball et al. (2006), Rateau (2009),
Rhodes (2010), and LaJoie et al. (2010) have addressed well-being after the disaster.
Luechinger and Raschky (2009) and Kountouris and Remoundou (2011) are the scarceexamples of monetary evaluations of psychological suﬀering from natural disasters throughthe experienced utility approach. These works, however, were not based on an acute andunprecedented single event as in the case of our study. In fact, a unique feature of psychological suﬀering after unprecedented natural disasters is that not only directly aﬀected
persons but also people living in distant areas could be seriously aﬀected by the disasterthrough images on television and in newspapers, as was shown by Metcalfe et al. (2011) inthe case the September 11th terrorist attacks, a nonnatural but tremendous disaster. For
policymakers this creates an allocation problem because it is diﬃcult to deﬁne qualiﬁed
recipients of compensation when a pale is cast over the whole society. The paucity of realtime monitoring of psychosocial status after major disasters and the lack of accumulated
statistical evidence explain the implementation of policies on this issue have been lacking.
Our main ﬁndings are as follows. First, using news reports in the media as a proxy
for the seriousness and pervasiveness of earthquake damage, we found that the mental
costs of the disaster were non-negligible when compared with the total physical capitaldamage. Second, there is a large geographical heterogeneity in the mental costs of thedisaster: people outside the disaster area were not aﬀected by the earthquake in termsof happiness, whereas victims in Sendai City (located in the disaster zone) were aﬀected
in terms of happiness. We estimated that the total mental costs in Sendai City in thedamaged area were around 30 billion euros, which is greater than the total physical damages externally reported in the city (around 17 billion euros). Hence, we suggest thatpolicymakers devote serious attention to the mental costs of disasters, in addition to therecovery costs of physical capital, and that the resource should be concentrated on direct
victims, even when the whole society was in gloom after the disaster. This information
will be a help when establishing compensation schemes with greater allocation eﬃciency.

3

Third, cherry blossom viewing (hanami), a traditional Japanese public activity in thespringtime, signiﬁcantly increased happiness in the Japanese people outside of the disaster area. The estimated monetary value of comfort from nature after the disaster appears
signiﬁcant, but again, there is a large geographical heterogeneity: when a Japanese citizen
outside of Sendai City enjoys cherry blossom viewing at full bloom, the increase in happiness is equal to three times the average monthly income of a 35-year-old male worker.
On the other hand, the happiness of people living in Sendai City was not increased bycherry blossom viewing in the gloom. These ﬁndings suggest that Japanese society as
a whole was not mired in unhappiness after the disaster. This ﬁnding might be capturing the focusing eﬀect (Kahneman et al. 2006; Dolan and Kahneman 2008) as illustrated
by Schkade and Kahneman (1998) in the following maxim: “Nothing in life is quite asimportant as you think it is while you are thinking about it.”
The remainder of the paper is organized as follows. In Section 2 we explain the detailsof our original Internet-based survey and data from external sources. The questionnaireand data construction for empirical analyses are also explained in the section. Section 3
outlines the estimation method and presents the results. Section 4 discusses implicationsof the results and concludes the paper.

2

Data

2.1

Original survey

This study analyzed original panel data collected through an Internet-based survey, whichstarted around 10 days after the Great East Japan Earthquake. The weekly surveys commenced on March 22, 2011, and targeted persons residing in six cities: Fukuoka, Osaka,
Nagoya, Niigata, Tokyo, and Sendai. The collected data consist of ﬁve 1-week panel surveys. These cities were chosen because they are major cities from each region of Japan, arespread across the country, and are geographically independent of each other. Respondents
were randomly selected from among the registered users of My Voice Communications,Inc., a subcontracted research company, on the basis of their place of residence and regional information. As there were only a small number of respondents from each city, astratiﬁed random sample was not used to match the distribution of individual characteristic variables such as age and educational level in each city. Overall, 2,816 respondents
completed the ﬁrst survey and 1,766 respondents completed all 5 surveys. Therefore, theaverage retention rate of respondents from one survey to the next was around 90%, which4

is a fairly good rate for this type of survey. Among the 1,766 respondents who completed
all 5 surveys, their places of residence were the following: 281 from Fukuoka, 304 from
Osaka, 291 from Nagoya, 273 from Niigata, 318 from Tokyo’s 23 wards, and 299 from
Sendai. Because of inconsistent responses and/or relocations during the study period,
some respondents were eliminated. In the end, balanced panel data were obtained from1,674 respondents. Table 1 shows the means of the ﬁrst-wave variables for our study
sample.
Each survey wave was continued until the number of respondents reached the targetedamount. The second, third, fourth, and ﬁfth waves were launched on March 28, April 4,
April 11, and April 18, respectively. In most cases, 95% of all data were collected within
the ﬁrst 2 days. Respondents who completed a survey received monetary compensation,and those who completed all 5 surveys received a bonus at the end.
A brief, yet detailed summary of the questionnaire is as follows. This study was
conducted using a “veil of ignorance” approach where the respondents were unaware of
the real intention of the study. This scheme was employed in order to reduce strategic
biases and response resistance from the respondents. For example, in order to disguise thetrue intention of the surveys, questions on the respondents’ interests in news (domestic
and international) and various questions on their personalities were included. Each survey
was entitled Survey on Everyday Life, making it diﬃcult for the respondents to inferthat the aim of the study was to reveal the relationships between the Great East Japan
Earthquake, cherry blossoms, and their happiness. As the ﬁrst survey (the ﬁrst panel
started on March 22) was conducted right after the Great East Japan Earthquake, thequestion concerning the respondents’ happiness was placed fourth on the questionnaire inorder to avoid biasing the respondent’s sense of happiness. Figure 1 shows the evolutionof the mean level of happiness across cities for the entire survey period.
One especially critical aspect from a methodological perspective was obtaining income
information with suﬃcient variation for regression analysis, regardless of the diﬃculties
associated with obtaining such data through 1-week panel surveys. Generally speaking,workers in Japan can be expected to be aware of their monthly, but not weekly, income.
With this in mind, a question on monthly household before-tax income was asked in theﬁrst wave of the survey (March) and in the third wave of the survey (April). In addition,
respondents were asked the following question in every wave of the survey: “Think of youreconomic activities and those of every family member you live with over this past week.
Was there anything that altered your expected income, such as greater than usual overtime5

work, lottery winnings, unexpected extra income, or fewer than expected work hours?”
They were then asked to specify the amount of ﬂuctuation, if any. It is believed that thisinformation on their weekly income captures the short-term ﬂuctuations in respondents’income. Other points to be noted are as follow. First, because the surveys were conducted
between March and April, questions on cherry blossoms and cherry blossom viewing wereasked. The respondents were ﬁrst asked whether they had taken seen cherry blossom thatyear (behavioral report). Those who had seen cherry blossoms were asked about theirthoughts on the beauty of cherry blossoms (introspective report). For readers’ reference,
we provide a complete list of questions in the Appendix below for the ﬁrst-wave survey
commenced on March 22, 2011.

2.2

Data from external sources

2.2.1

Earthquake damage variable:

To examine the eﬀect that the Great East Japan Earthquake and resulting damage hadon respondents’ level of happiness, the percentage of front page coverage that three major newspapers (Yomiuri, Asahi, and Nikkei) devoted to earthquake-related news wasmeasured. Changes in the percentage of front-page coverage of earthquake-related news
are believed to reﬂect shifts in public interest in earthquake damage at that time. This
study examined if the shifts in public interest actually inﬂuenced the respondents’ levelof happiness. To construct the variable, we measured the percentage of front page coverage that was devoted to earthquake damage-related news in the three newspapers, andtook a weighted average of the shares, taking into account the proportional circulationof each newspaper.1 Such data were obtained every day beginning on March 12, 2011,
the day after the earthquake, until April 21, 2011, the end of the last survey period.
The weighted average share data represent a time series of the volume of information
on earthquake damage in the news media captured twice a day (morning and evening1
We retain record of our measurement of news papers in a pdf format, and it is available upon requests.
The record, however, is not for publication due to property rights of the news paper companies. We infact measured the following six categories of news; (i) negative news on earthquake damages, (ii) positive
news on earthquake damages, (iii) negative news on nuclear power plants, (iv) positive news on nuclear
power plants, (v) negative news on electricity supply, and (vi) positive news on electricity supply while
we used only information on the ﬁrst category in our happiness regressions. This happened because we
noticed that it was possible that positive news on earthquake damage can make readers feel unhappy when
they reﬂected the Earthquake events reading the positive articles. Also, in the situation where nobody
was certain about the consequences of nuclear power plants incidents, a positive news on nuclear power
plants, such as recovery from electrical outage, can make readers feel uneasy, which will decrease, rather
than increase, the happiness of subjects. Given this observation and still on-going mixed discussions on
the issue of nuclear power plants, we decided not to exploit news information related to them.

6papers), and serves as a proxy for a time series of negative feelings and mood in Japanafter the natural disaster. The line with round dots in Figure 2 indicates the raw data.
On March 11, 2011, earthquake-related news was placed on the ad column in addition toregular front-page news area, so the percentage exceeded 100% for that day. The second
spike seen in the series resulted from a large aftershock quake on April 7, 2011, that had aseismic intensity of 6-upper in Sendai. In our Internet survey, information on the date andtime of the response was recorded for each answer, which allowed us to match the amountof disaster-related news coverage from external information sources with each observationin our survey. We also had to keep in mind that information from newspaper came inwith delay, while the subjects participating in the Internet survey tended to have access
to, and be inﬂuenced by, real-time information on the Internet. Thus, when we plug inthe created newspaper coverage information into the happiness equations, we applied thefollowing rule. If a respondent answered the survey between midnight and 3:00 A.M.,
events occurring during that time that might inﬂuence their responses will usually bereported in that day’s morning newspaper; if a respondent answered a survey between
3:00 A.M. and 2:00 P.M., events occurring during that time will be reported in that day’s
evening newspaper; and if a respondent answered a survey after 2:00 P.M. and before midnight, events occurring at that time will likely be reported in the following day’s morningnewspaper. Following these criteria, the appropriate newspaper coverage information waschosen for each respondent on the basis of the time that they completed the survey. If one
assumes that people have no memory of past events and that past news does not aﬀect
current happiness at all, we could simply plug in the newspaper coverage information into
the happiness equations directly. However, for many reasons we believe that past news
information aﬀects current happiness (Clark et al. 2001; Lucas et al. 2004; Clark et al.
2008). We therefore assume an accumulation process for the amount of news information
using the “perpetual inventory method.” The accumulation process is expressed in theequation:
Information(t) = (1 − δ) ∗ Information(t − 1) + news(t),where δ measures how soon information loses its inﬂuence on happiness. We comparedresults using various δ values to see which model best explains the movement of happinessin the happiness regression shown below. Using Akaike’s information criterion, we decided
upon a δ value of 0.3, meaning that a piece of information loses its impact on happinessafter 4 days ((0.7 ∗ 0.7)4 = 0.0576). The line with square dots in Figure 2 represents thetime series calculated using the perpetual inventory method.
7

2.2.2Cherry blossoms variable:

A variable on the level of cherry blossom bloom was created using the respondents’ behavioral reports and the 2011 edition of Cherry Blossom Information issued by Weathermap
Co., Ltd.2 First, a well-known site in each city for cherry blossom viewing was chosen
according to the Cherry Blossom Information. Chuo Park, Osaka Castle Park, Nagoya
Castle Park, Nishi Park, Chidorigabuchi, and Toyanogata were selected for Fukuoka, Osaka, Nagoya, Niigata, Tokyo’s 23 Wards, and Sendai, respectively. The Cherry Blossom
Information uses a 7-point scale to denote the cherry blossom status: (1) before bloom;(2) about to bloom; (3) half-bloom; (4) full bloom; (5) about to fall; (6) almost over; and(7) green leaves. Scores of 1 and 7 indicate that cherry blossoms were not suitable forviewing and therefore they were collapsed into one category. Normally, cherry blossoms
can be viewed for two weeks on average. As Sendai City is the most northern city in thesurvey, cherry blossoms there started to bloom in the third week of the survey (around
April 5), and reached full bloom at the end of the survey period (April 20th). On theother hand, cherry blossoms in the southern city of Fukuoka City were completely gone
before April 15. These variations in the timing of blooming across the cities are importantin separating the eﬀects of cherry blossoms on happiness from the eﬀects of other variables
such as society’s mood in general and nationwide economic conditions. Figure 3 depicts
the progression of the cherry blossom “front” for the selected cities. Using the data fromCherry Blossom Information, we assigned a value of 0.2 points for regions having a bloom
status of 1 or 7; 0.4 points for regions with a status of 2; 0.6 points for regions with a status of 3; and 1 point for regions with a status of 4, 5 and 6. Next, a dummy variable wascreated based on the respondents’ behavioral reports of cherry blossoms viewings (Q19in the case of the ﬁrst wave survey). The responses given reﬂect four levels of exposure
to and participation in viewing cherry blossoms: have not seen cherry blossoms, even inpassing; have seen cherry blossoms in passing; have not been to view cherry blossoms;
have been to view cherry blossoms. Persons who had seen cherry blossoms in passingor had been to a cherry blossom viewing were assigned a value of 1, otherwise a valueof 0 is assigned. Finally, a proxy variable for cherry blossom viewing for the regression
was computed as the product of the scores of Cherry Blossom Information report and thedummy variable for cherry blossom viewing participation. Hence, the composite variable
was created so that respondents who had not seen cherry blossoms were assigned a valueof 0 (i.e., 0.4∗0 = 0), those who had seen cherry blossoms during half bloom were assigned
2

Information was available at http://sakura.weathermap.jp/ (Accessed May 1, 2011)8

a value of 0.6 (0.6 ∗ 1 = 0.6), those who had seen cherry blossoms during full bloom wereassigned a value of 1 (1 ∗ 1 = 1), and so forth.

3

Empirical strategy and results

As stated in Section 2, we use three primary sources of data in our analysis: happinessand income levels as captured in our original survey, information from three major newspapers on the damage caused by the earthquake, and reports on the blooming of cherry
blossoms by a weather forecasting company. We included the last variable because theoriginal purpose of the survey was to gauge the non-market value of the natural amenityof cherry blossoms. In a sharp contrast to the destructive forces of nature, it is interesting to see whether cherry blossom viewing-a traditional Japanese public activity inthe springtime-provided psychological comfort to residents in the diﬃcult post-disasterperiod, and furthermore, to place a value on such comfort.
Regarding featured variables for cherry blossoms and amount of earthquake news
coverage, we considered their potential geographically heterogeneous eﬀects on subjective
well-being. The Great East Japan Earthquake occurred oﬀ the eastern coast of Japan’s
main island. We construct dummy variables that take the value 1 for respondents living in
Sendai or in Tokyo (i.e., damaged areas). We capture the potential heterogeneous eﬀects
of earthquake news and cherry blossoms by adding interaction terms for these variables
with the Sendai and Tokyo dummy variables.
We used ordinary least-squares (OLS) estimators to facilitate the monetary estimation of the value of cherry blossoms and earthquake damage news using the estimatedcoeﬃcients. Even though the hierarchical and nonlinear nature of the dependent variablemakes ordered probit or logit models methodologically preferable to OLS, we conﬁrmedthat the results for these models were basically similar to OLS when used with our dataset (Luttmer 2005; Oswald and Wu 2010). As is typical for panel data sets, the Hausman
test rejected the random eﬀects model (P < 0.01) and we exploited the panel structureof our data set by employing a ﬁxed eﬀects model.3 Hence, ﬁxed variables such as genderand region do not appear as independent variables in our main analysis. A ﬁxed eﬀects
model speciﬁcation mitigates the concern of biased estimators due to the omitted variable3

Nonetheless we also show the result from the random eﬀects model for readers’ references in Table 3.
The table shows that widely observed patterns in happiness research such as females being happier thanmales, married people being happier than single subjects, and age aﬀecting happiness in a U-shaped
fashion are reconﬁrmed in our data set. These results are a validation of our original data set.

9

problem. It is also noteworthy that from the nature of our high-frequency panel data, it
is not plausible that we face another endogeneity problem of reverse causality between
happiness and income level.
Generally in happiness regressions the following points should be noted. Since thedependent variable of the study, the level of happiness, is based on the respondents’ introspective reports, variables that are independent of their introspective reports should be
used as independent variables; objective variables or variables on respondents’ behaviors
should be used as independent variables. Hence, in order to avoid problems of endogeneity, our analysis in this study does not use any proxy variables that are based on
introspective reports. These potentially interesting, but excluded, variables included the
respondents’ subjective evaluations of the cherry blossoms, and self-evaluations of stressand health status, and so forth. When we included these variables in the regression, thesigns of estimated coeﬃcients were as expected. However, the coeﬃcients in the modelusing the introspective reports have an endogeneity problem. Thus, we do not show theresults for this model since the estimated coeﬃcients are subject to biases and are notappropriate for estimating the monetary value of the featured variables for cherry blossom
viewing and earthquake damage news.
We excluded observations where subjects moved to other cities from the six targeted
cities during the survey period. We did not have enough observations from respondents
who changed jobs or marital status during the survey period to examine the eﬀects ofsuch changes on happiness. On the other hand, during the 5-week survey period, around
8% of subjects had a birthday, and thus we could control for the age eﬀect in the ﬁxed
eﬀects model speciﬁcation. The number of observations for respondents who had changes
in age turned out to be too few since in our main regression, the eﬀect of age on happiness
was not statistically signiﬁcant (Table 2 ).4
With these pieces of information we ran “happiness regressions” (Oswald 1997;
Kahneman and Deaton 2010) to test for correlations between individuals’ happiness levels
and other covariates such as income level, earthquake damage, and cherry blossom viewing. We then compare the magnitude of the income coeﬃcient with those for earthquake
damage and cherry blossom viewing to quantify the monetary value of the non-priced,

4

In the random eﬀects model shown in Table 3, the eﬀect of age was signiﬁcant and U-shaped, as is
typically found in happiness research.

10

psychological value of these events. Our happiness equation looks like
SWBj,t = αIj,t + β1 newst + β2 newst ∗ T okyo + β3 newst ∗ Sendai + κ1 SAKU RAt+κ2 SAKU RAt ∗ T okyo + κ3 SAKU RAt ∗ Sendai + xj,t ζ + yj,t η + uj,t ,where SW B stands for happiness of subject j at time t, I is per capita income, news is thepercentage of front page coverage that was devoted to earthquake damage-related news,
SAKU RA captures exposure to cherry blossoms, and Tokyo and Sendai are the region
dummies. xj,t contains age, square of age divided by 100, and experience of job change
in the survey period. In our regression we employed four variables for the time periodof response as follows: (1) a ﬁve-banded variable for the time of day of the response;(2) three dummy variables depicting the day of the week; (3) whether the respondent
completed the survey after the end of the work day; and (4) panel waves. We also createdthree variables on weather conditions at the time of the response: temperature, hourly
amount of rainfall, and hourly length of sunlight.5 These variables are contained in vector
yj,t . Finally, uj,t is the error term.

3.1

Results

The results in Table 2 show strong empirical support for the standard economics theory
that individuals tend to report higher levels of happiness when they have higher levels of
income (p < 0.01). The terms for “earthquake news” and “cherry blossoms” captured thebase category eﬀects for four cities (Fukuoka, Osaka, Nagoya, and Niigata), against which
the eﬀects in Tokyo and Sendai were measured. Somewhat surprisingly, the earthquakenews for the base category did not have a signiﬁcant eﬀect (P = 0.538), although theeﬀect was negative as expected. The Tokyo-speciﬁc eﬀect of the earthquake was negative
but not signiﬁcant (p = 0.222). On the other hand, the Sendai-speciﬁc eﬀect of theearthquake was very strong and robustly signiﬁcant (p < 0.01). We found that the sumof the base category eﬀect and Sendai-speciﬁc eﬀect from the earthquake news was jointly
signiﬁcant (p < 0.05).
Using the coeﬃcient values, we can therefore estimate the monetary value of earthquake news for people in Sendai, and calculate this value as negative 2,922,604 yen (i.e.,(-0.133385-0.4353981) × 10,000/0.0015354)) when the front pages of newspapers werecompletely ﬁlled with negative news. Taking into account the population of Sendai City
5

Information about the weather conditions was taken from http://www.jma.go.jp/jma/menu/report.html.

11

(1 million at the time of the disaster), we estimated the city’s mental costs due to thedisaster to be around 3 trillion yen. Note that we do not argue for the accuracy of thevalue itself; rather, we suggest that the mental cost of the disaster was non-negligible
when compared with the total physical capital damage of 1.7 trillion yen estimated by
Development Bank of Japan (2011) for the inland area of Miyagi Prefecture where Sendai
City is located. More importantly, for the other ﬁve cities, we estimate that the monetary
value of psychological suﬀering from earthquake news was not statistically diﬀerent fromzero. Even though television and newspapers widely disseminated images of the natural
disaster to millions of residents of these other cities in the weeks following the earthquake,
those who lived a safe distance from the disaster zone were unaﬀected in terms of theirhappiness.
We found that cherry blossoms did increase the happiness of subjects in the four basecities following the earthquake (Table 2). The value of cherry blossom viewing as a nonpriced public good was found to be positive and statistically signiﬁcant at the conventional
level (p < 0.05). Using the OLS coeﬃcients for income and cherry blossom viewing, we
can estimate the monetary value of cherry blossom viewing at full bloom in the base cities
as 998,031 yen (i.e., 0.1532377 × 10,000/0.0015354). When a Japanese citizen living inundamaged areas enjoys cherry blossom viewing at full bloom, the increase in happinessis equivalent to three times the average monthly income of a 35-year-old male worker. We
again found heterogeneous geographical eﬀects. The interaction terms of cherry blossoms
with the Tokyo and Sendai dummy variables were both negative, but not signiﬁcant(p = 0.265 for Tokyo and p = 0.376 for Sendai). Interestingly, we could not reject thenull hypothesis that the sum of the base category eﬀect and Tokyo-speciﬁc eﬀect is zero(p = 0.9234). This means that residents of Tokyo did not receive happiness from cherry
blossom viewing during the period of mourning following the disaster. The same can besaid in the case of Sendai (p = 0.9051).

4

Conclusion

Data on subjective well-being collected 2-7 weeks after the Great East Japan Earthquake
reveal that in a city within the disaster zone, the estimated value of psychological suffering due to news reports on the disaster was comparable with the reported physicaldamages in the city, whereas the value of psychological suﬀering in undamaged areas was
not signiﬁcant. We also found a similar diﬀerence in the psychological eﬀects of cherry

12

blossoms between damaged and undamaged areas. The cherry blossoms did not provide
signiﬁcant comfort to citizens in damaged areas but did so in undamaged areas. Hence,
there are two lessons for policymakers to learn from the current study. First, the government should devote serious attention to the mental costs of disasters in addition to
the recovery of physical capital. Second, resources for compensating mental suﬀering
should be concentrated on persons living in the disaster area. This result stands in sharp
contrast to prior reports that bad news in a region aﬀects the happiness of residents of
distant areas, as in the cases of Hurricane Katrina by Kimball et al. (2006) and the 9/11
Attacks by Metcalfe et al. (2011). The Chernobyl Disaster is also an unprecedented nonnatural disaster, and Berger (2010) showed that the incident did not aﬀect the well-beingof people in Germany. Berger (2010) made an important case, but he considered only thespill-over eﬀects of the disaster event and did not provide comparative analyses between
the disaster area and distant and safe places. In this paper, we suggest that even after
an unprecedented natural disaster that casted a gloom over society, there was a robustand large geographical heterogeneity between the disaster area and non-disaster areas in
the mental costs. Our ﬁnding reminds us of the focusing eﬀect (Kahneman et al. 2006;
Dolan and Kahneman 2008) as captured by Schkade and Kahneman (1998) in the maxim“Nothing in life is quite as important as you think it is while you are thinking about it.”Contrary to expectations, our ﬁndings suggest that, overall, Japanese society was not
mired in unhappiness. Following the earthquake, various campaigns by the media andthe government urged the creation of new social bonds and a focus on shared humanity.
Such advocacy may have changed attitudes in Japan, as captured in this survey soonafter the earthquake. Examining the accumulation of social capital in Japan following
the earthquake will be an important research topic for psychologists, sociologists, andeconomists in coming years.
Acknowledgement This work was conducted as part of the “Human Behavior andSocioeconomic Dynamics” project of Osaka University under the Global Centers of Excellence Program funded by Japan’s Ministry of Education, Culture, Sports, Science andTechnology (MEXT). Yamada was partially supported by the “KAKEN (24683006)”
funded by MEXT. We thank Andrew Oswald, Nattavudh Powdthavee, Makoto Saito,
Etsuro Shioji, and Kunio Urakawa for their comments on a draft of this manuscript.
Excellent research assistant work by Sizuru Yamagiwa is greatly appreciated.

13

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5

Appendix

Questionnaire from the ﬁrst-wave:

17

Q1: When you were a child, when did you most often do your homework assigned before
a vacation? Choose the most appropriate option:(1) At the beginning of the vacation; (2) During the ﬁrst half of the vacation; (3) Evenly
every day; (4) During the latter half of the vacation; (5) At the end of the vacation.

Q2: What percentage of chance of rain is high enough for you to take an umbrella withyou when you go out?

Q3: How do you usually feel and behave when people are having fun?
(1) Have fun with them; (2) Try to have fun, but stay aloof; (3) Indiﬀerent/ neutral; (4)Try to keep as much distance as possible; (5) Do not get involved and feel left alone.

Q4: In general, how happy are you now? On a scale from 0 to 10 with 10 being “veryhappy” and 0 being “very unhappy,” how would you rate your happiness?

Q5: Are you sad now? Choose the most appropriate number:(1) Sad; (2) Relatively sad; (3) Neutral; (4) Not really; (5) Not sad.

Q6: Q6: Are you happy now? Choose the most appropriate number:(1) Happy; (2) Relatively happy; (3) Neutral; (4) Not really; (5) Not happy.

Q7: Are you depressed now? Choose the most appropriate number:(1) Depressed; (2) Relatively depressed; (3) Neutral; (4) Not really; (5) Not depressed.

Q8: Think of the most important personal news or one event that happened around you
recently. It can be either good or bad. How would you rate the news or the event on ascale of 0 to 10 with 10 being “the best” and 0 being “the worst”?

18

Q9: What did the news or the event you chose for Q8 concern? Choose the most appropriate option from the following:(1) Human relationships; (2) Work; (3) Family life; (4) Health/sickness/injury; (5) Education and future; (6) Financial issues; (7) Other.

Q10: Think of the most important news you have read in the newspaper or seen on TV
recently. It can be either good or bad. How would you rate the news on a scale 0 to 10
with 10 being “the best” and 0 being “the worst”?

Q11: What did the news you chose for Q10 concern? Choose the most appropriate
category from the following:(1) Domestic politics; (2) Domestic economics; (3) Domestic crime; (4) Domestic natural disasters/accidents; (5) Domestic sports; (6) Domestic entertainment; (7) Domestic
education and science; (8) Domestic social issues; (9) Other domestic topic; (10) International politics; (11) International economics; (12) International crime; (13) International
natural disasters/ accidents; (14) International sports; (15) International entertainment;(16) International education and science; (17) International social issues; (18) Other international topic.

Q12: Q12: Did you sleep well last night?
(1) Not at all; (2) Not very much; (3) Neither yes nor no; (4) Somewhat well; (5) Verywell.

Q13: Q13: How healthy are you?
(1) Not healthy; (2) Not very healthy; (3) Neither healthy nor unhealthy; (4) Relatively
healthy; (5) Healthy.

Q14: Do you currently have hay fever?
(1) Yes; (2) Somewhat yes; (3) Neither yes nor no; (4) Not really; (5) No.

19

Q15: Are you worried or stressed about something now?
(1) Yes; (2) Somewhat yes; (3) Neither yes nor no; (4) Not really; (5) No.

Q16: Have you worked or done household chores today (or if a student, have you hadclasses)? Or will you later today?
(1) Yes, I have; (2) Not yet, but I am going to; (3) I am doing so right now; (4) Only
household chores; (5) Not yet and not going to either.

Q17: Think of one thing you did recently for a change or for recreation. Which one of
the following was that?
(1) Sports; (2) Drinking and eating; (3) Travel; (4) Shopping; (5) Making something/doing
carpentry/creating something; (6) Movie, TV, or music; (7) Other.

Q18: Answer this question if you chose answer (3) for Q17. Were the cherry blossoms
blooming where you went?
(1) Cherry blossoms were in full bloom; (2) Cherry blossoms were about to be in fullbloom; (3) Neither yes nor no; (4) Cherry blossoms were not about to be in full bloom;or (5) Cherry blossoms were not blooming.

Q19: Have you recently seen news on cherry blossoms or have actually viewed cherry
blossoms?
(1) I have not seen cherry blossoms either in the newspaper or on TV news, nor have I
seen cherry blossoms in person; (2) I have seen cherry blossoms in the newspaper and/oron TV, but have not seen them in person; (3) I have not been to view cherry blossoms,
but I have seen them in passing; (4) I have been to view cherry blossoms.

Q20: Answer this question if you chose answer (2), (3), or (4) for Q19. How would you
rate the cherry blossoms you saw on a scale of 0 to 10 with 10 being “very beautiful” and0 being “not beautiful at all”?

20Q21-Q25: Questions on individual attributes: gender (Q21), age (Q22), marital status(Q23), number of family members in household (Q24), and occupation (Q25).

Q26: How much will your before-tax household income be this month? Add up theincomes of every family member you live with and round up to the nearest thousand yen.
Give an approximate estimated amount from previous months.

Q27: Think of your economic activities and those of every family member you live withover this past week. Was there anything that altered your expected income, such as
greater than usual overtime work, lottery winnings, unexpected extra income, or fewer
than expected work hours? If there was any ﬂuctuation, write down the diﬀerence in the
expected income caused by such events from the amount you answered for Q26. Indicate
an unexpected income increase with a plus sign (+) and an unexpected income decrease
with a minus sign (-).

Q28: Where is your current place of residence?
(1) Fukuoka City; (2) Osaka City; (3) Nagoya City; (4) Niigata City; (5) Tokyo’s 23
Wards; (6) Sendai City; (7) Other (please specify).

21

Figure 1: Change in Happiness by City from March 22 to April 21, 2011

The average levels of happiness for each panel wave by city are displayed. Whisker
bars show the 5% conﬁdence intervals.

22

Figure 2: Time Series of Twice-daily Information on the Amount ofEarthquake-related News Coverage

The black circle line shows raw weighted news coverage per period (half a day) forthe major newspapers (Yomiuri, Asahi, and Nikkei). The blue square line shows
cumulative news coverage using the perpetual inventory method (δ = 0.3)

23

Figure 3: Progress of Cherry Blossoms during the Survey Period

The report Cherry Blossom Information by Weathermap Co., Ltd., uses a 7-point
scale: (1) before bloom; (2) about to bloom; (3) half-bloom; (4) full bloom; (5)about to fall; (6) almost over; and (7) green leaves. Scores of 1 and 7 indicate thatthe cherry blossoms were not suitable for viewing and therefore they were collapsed
into a single category.

24

Table 1: Summary Statistics for the First Wave of ”Survey on Everyday Life” (N=1,674)

MeanLife happiness
7.062
Monthly household income (10,000 JPY) 65.441
Number of household
2.854
Monthly income per capital (10,000 JPY) 24.354
Female0.510
Age41.729Marital status (=1 if single)
0.386
Fukuoka
0.161
Osaka
0.173
Nagoya0.163
Niigata
0.180
Tokyo
0.157
Sendai
0.166
Sadness (1-5 Scale)
2.989
Pleasure (1-5 Scale)
3.014
Depression (1-5 Scale)
2.795
Healthy (1-5 Scale)
3.357
Sleep well (1-5 Scale)
3.020
Hay fever (1-5 Scale)
2.578
Stress (1-5 Scale)
3.898

25

SD
2.198
151.092
1.331
48.937
0.500
9.923
0.487
0.368
0.378
0.370
0.385
0.363
0.372
1.221
0.970
1.199
0.986
1.307
1.667
1.044

26
∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1

Coef.
Std. Err.
95% CI
Montly income per capita (in 10,000 JPY)
0.001535 0.000479 0.000597
0.002474
Cherry blossom variable (from 0 to 1)
0.153238 0.074981 0.006251
0.300224
Tokyo * Cherry blossom variable-0.165874 0.148745 -0.457462
0.125714
Sendai * Cherry blossom variable-0.175115 0.197795 -0.562856
0.212626
Earthquake news variable (1 = all of the front page) -0.133385 0.216453 -0.557702
0.290932
Tokyo * Earthquake news variable-0.435398 0.153402 -0.736114-0.134682
Sendai * Earthquake news variable-0.201391 0.165028 -0.524898
0.122116
Constant
4.810457 8.124161 -11.115500
20.736410
Observations
8370
Numebr of subjects
1674
R-squared
0.057
Tests for Tokyo and Sendai: the main eﬀect and region speciﬁc eﬀect
Cherry blossom + Cherry blossom * Tokyo = 0?
F(1,6670) =0.01, Prob > F = 0.9234
Cherry blossom + Cherry blossom * Sendai = 0?
F(1,6670) =0.01, Prob > F = 0.9051
Earthquake + Earthquake * Tokyo = 0?
F(1,6670) =1.87, Prob > F = 0.1716
Earthquake + Earthquake * Sendai = 0?
F(1,6670) =5.31, Prob > F = 0.0212

Table 2: Regression Result (Fixed Eﬀects Model)

Table 3: Regression Result (Random Eﬀects Model)
Coef.
Std. Err.
Montly income per capita (in 10,000 JPY)
0.002***
0.000
Cherry blossom variable (from 0 to 1)
0.197***
0.075
Tokyo * Cherry blossom variable-0.1660.148
Sendai * Cherry blossom variable-0.1600.197
Earthquake news variable (1 = all of the front page)-0.144
0.216
Tokyo * Earthquake news variable-0.416***0.154
Sendai * Earthquake news variable-0.2150.165
Age-0.115***0.034
Age squared/ 100
0.130***
0.039
Have you worked or done household chores today?
Yes I have
0.104**
0.049
Not yet, but I am going to-0.0140.070
I am doing so right now
0.153***
0.062
Only household chores (omitted category)
1st wave0.430***
0.079
2nd wave
0.299***
0.047
3rd wave
0.060
0.054
4th wave-0.0060.112
5th wave (omitted category)Monday (omitted category)
Tuesday-0.0800.127
Wednesday-0.0400.107
Thursday
0.121
0.105
Time when answered the survey
0:00AM to 3:59AM (omitted category)
4:00AM to 8:59AM-0.0530.076
9:00AM to 14:59PM
0.161*
0.089
15:00PM to 20:59PM-0.0200.071
21:00PM to 23:59PM
0.048
0.037
Hourly amount of rainfall
0.014
0.015
Hourly sunlight
0.000
0.001
Temperature-0.007
0.007
Fukuoka
0.590***
0.171
Osaka
0.740***
0.168
Nagoya0.550***
0.170
Niigata
0.364***
0.170
Tokyo
0.560***
0.169
Sendai (omitted category)Single-0.963***
0.093
Female0.948***
0.093
Job change
0.182
0.165
Constant
8.430***
0.740
Observations
8370
R-squared
0.053∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.

27

Z value
4.010
2.640-1.120-0.810-0.670-2.710-1.300-3.4003.300

95% CI
0.001
0.003
0.051
0.343
-0.456 0.124-0.546 0.226-0.568 0.280-0.718 -0.115-0.539 0.108-0.182 -0.049
0.053
0.207

2.110
-0.1902.460

0.007-0.151
0.031

0.201
0.124
0.275

5.450
6.330
1.120
-0.0500.275
0.207-0.045-0.225

0.584
0.392
0.166
0.213

-0.630-0.3801.150-0.330-0.250-0.086

0.169
0.169
0.328

-0.7001.810-0.2801.290
0.890-0.150-1.020
3.450
4.410
3.240
2.140
3.310

-0.201-0.014-0.159-0.025-0.017-0.002-0.022
0.255
0.411
0.217
0.030
0.228

0.095
0.335
0.119
0.122
0.044
0.002
0.007
0.926
1.068
0.883
0.698
0.891

-10.350
10.2301.110
11.390-1.1450.767-0.1416.980-0.781
1.130
0.506
9.881