Effects of the Great East Japan Earthquakeon Subjective Well-Being
Takuya Ishino, Akiko Kamesaka, Toshiya Murai, and Masao Ogaki
Working Paper No. 588
December 2014

“Effects of the Great East Japan Earthquakeon Subjective Well-Being”
Takuya Ishino a, Akiko Kamesaka b,c,*, Toshiya Murai d, and Masao Ogaki e
Abstract
Using a large panel data set that samples over 4000 Japanese, we analyze changes inpeople’s subjective well-being (happiness) and altruistic worldview before and after theGreat East Japan Earthquake. As a result we find that 1) more people replied that theirhappiness improved after the earthquake than said it worsened, and also that 2) many
more Japanese people became more altruistic since the earthquake, even in the mostaffected areas. One possible interpretation of these results is that an increase inaltruism due to the earthquake spurred people to give to charity, which in turnincreased their happiness. Our regression analysis yields results that are consistent
with this story.
JEL classification numbers:

I18, I31, I38

Keywords: subjective well-being, happiness, altruism, the Great East Japan
Earthquake, disasters.
a Faculty of Economics, Kanazawa Seiryo Universityb School of Business Administration, Aoyama Gakuin University
c Economic and Social Research Institute, Cabinet Office, Government of Japand Graduate School of Medicine, Kyoto University
e Faculty of Economics, Keio University* Corresponding author. Email: akiko@busi.aoyama.ac.jp

This paper was presented at the 4th OECD World Forum on “Statistics, Knowledge and Policy”
Measuring Well-Being for Development and Policy Making, October 2012, the 2012 Autumn
Meeting of the Japanese Economic Association, the 6th Annual Meeting of the Association of
Behavioral Economics and Finance, and the European Institute of Japanese Studies (EIJS)
Academy Seminar, held at Swedish Embassy in Japan. We appreciate all the comments from
Conal Smith, Noah Smith, Tim Tiefenbach, Yukiko Uchida, Chisako Yamane, and the forum
and session participants. The second and fourth authors’ research is partly supported by
Grants-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science.

1

Introduction
In this paper, we study changes in Japanese people’s subjective well-being(happiness) and feelings of altruism before and after the Great East Japan Earthquakeof March 2011. We use a panel data set compiled by a group of researchers mainly from
Keio University. Although the questionnaire is large, we focus on a question about
people’s altruism. We are interested in altruism because, according to a Japanese
Statistics Bureau report on expenditure by Japanese households, charitable donations
increased by over 850 percent in March 2011 compared to one year earlier (in bothnominal and real terms). This increase is especially striking when we compare it toincreases in spending on other goods; for example, expenditure on mineral water went
up by only 148.8 percent in nominal terms and 161.3 percent in real terms.
Using a large panel survey consisting of responses from over 4000 households allover Japan, we analyzed changes in people’s happiness and altruism before and after
the earthquake. We found that many Japanese people reported more feelings ofaltruism following the earthquake, even in the most affected areas; this is consistent
with the rise in charitable giving. We also found that a large number of people reported
an increase in happiness after the earthquake, in fact, as the number who reported adrop in happiness. An interpretation of this finding is suggested by a recent experimentby Dunn et al. (2008), who find that spending money on others promotes happiness;according to this story, many Japanese people became more altruistic after theearthquake, inducing them to make charitable donations, which in turn made themhappier.
We are interested in seeing how changes in altruism affect changes in happiness.
However, both variables are subjective, so their measurement errors are likely to becorrelated. Therefore, we use a two-step procedure, first identifying the effect ofaltruism on an objective variable, charitable giving, and then measuring the effect ofcharitable giving on happiness. We call this the “Subjective-Objective-Subjective”
method (“S-O-S” for short). In each step of the analysis, we run a two-stage logit
regression, which controls for reverse causality. This S-O-S analysis, which deals
effectively with the aforementioned problem of correlated measurement errors, yields
results that are consistent with our story.
As for the Great East Japan Earthquake, Tiefenbach and Kohlbacher (2014) showthat those living closer to the Fukushima were less happy after the disaster, using
nationwide survey in Japan, the National Survey on Lifestyle Preferences1. Aldrich andKitamura and Hirai (2012) also show the changes in Japanese people’s subjective
well-being and their health condition before and after the Great East Japan1

2

Sawada (2014) find that tsunami height, stocks of social capital, and level of politicalsupport strongly influenced mortality rates, using original data set from the Pacific
Ocean side of the Tohoku region. Goebel et al (2013) studies the impact of the Great
East Japan Earthquake on German population and find that the meltdown significantly
increased their environmental concerns.
As for other literatures on disasters, in economics, Kimball et al. (2006) studiedchanges in happiness before and after Hurricane Katrina which struck southeast
Lousiana on August 29, 2005. They found that the subjective happiness ofrepresentative U.S. adults dipped significantly in the first week following the hurricane.
The dip was especially strong in the South Central region, closest to the devastation of
Katrina. In the non-economics literature on effects of disasters on happiness, surveyed
by Phifer and Norris (1989), whether the effects are positive or negative, and whether
the size of the effects is substantial or negligible have both been shown to depend on
many factors such as the person’s and her community’s situations before and after thedisaster, age, and the time since the disaster.
A few other researchers have studied the links between social behavior andhappiness. Clark and Senik (2011) try to understand the differences between cognitive,
hedonic and “eudaimonic”2 measures of well-being reflect different aspects of quality oflife, and find that individuals with high “hedonic” well-being (happiness or life
satisfaction) are likely to have high eudaimonic well-being (flourishing, functioning,etc.) as well. (See also Huppert and So (2009), Huppert et al (2009), and Deci, and Ryan
(2006).) Eudaimonic measures of well-being focus on functioning and the realization ofpeople’s potential; this is similar to Amartya Sen’s notion of “capabilities.” Our results
indicates that one of the important element of eudaimonic well-being, altruism,
improves people’s subjective sense of happiness, since we find that people who madecharitable donations experienced a rise in happiness.
The next section describes our data, and provides summary of the basic statistics. In
section 2, we propose our methodology that enables us to see the relation between two
subjective variables accounting for reverse causality. In section 3, we show our results
indicating that many Japanese people became more altruistic after the Great East
Japan Earthquake, made charitable donations, and as a result experienced an increasein happiness, applying our methodology. Section 4 concludes the paper.

Earthquake.
2 “Eudaimonia” is Aristotle’s concept of happiness as a “good life” defined by theacquisition and use of virtue. This is one of the three concepts of happiness listed byFrey (200.8, p.5)
3

1. Data
Our analysis was conducted using a Japanese household panel survey data set
compiled by a group of researchers mainly from Keio University. The survey sampledover 4,000 respondents from all over Japan, and includes residence information. The
data set begins in 2004, but in June 2011 an extra survey was added to measure
responses to the Great East Japan Earthquake. Our data on altruism and happinesscome from this extra survey and from questions asked by the regular panel surveys.
The “extra” questionnaires conducted in June 2011 asked respondents to what
extent they believed themselves to be happy at the time, on a scale of 0 to 100.3 The
June questionnaire also asked the respondents to what extent they thought they had
been happy in February 2011, also on a 0-100 scale.4 Prior to the extra survey, theregular panel survey asked in January 2011 the respondents to score the degree of theirhappiness between 0 (totally unhappy) and 10 (totally happy) considering their whole
life. To measure altruism, the survey asked to what extent people “try to act in theinterest of strangers” 5 at the time of the survey, again on a 0-100 scale. The
questionnaire also asked how altruistic people remembered feeling in February 2011. It

is important to note that throughout this paper, when we use the word “altruism,” werefer specifically to the answer to this survey question, not to altruistic actions such as
charitable giving.
Table 1 shows the distribution of changes in Japanese people’s self-reported
happiness before and after the earthquake. Since there is continuing debate about theappropriateness of inter-personal comparisons when using subjective data, we focusedon the direction of the change in each individual’s happiness before and after theearthquake.
Panel A of table 1 is for changes in happiness between February 2011 (before theearthquake, for retrospective evaluation of happiness) and June 2011 (after theearthquake.) The table shows the numbers of respondents and relative frequency foreach class of changes in happiness. The percentage of respondents who answered that
their happiness had improved, worsened, or remained unchanged were 27.9 percent, 4.5
percent, and 67.6 percent, respectively. One surprising result was that there were many
more people – over six times as many! – who replied that their happiness level improved
In increments of 10.
We calculated correlation coefficients between the retrospective replies regarding
happiness and the replies we collected earlier in 2011 before the earthquake (real timehappiness) to check the reliability of our retrospective data. The correlation was 0.4,
meaning that our retrospective data probably contains sufficient information to makeour analysis reliable.

3
4

4

after the earthquake compared to the number who said their happiness level worsened.
This result, of course, is probably subject to some degree of selection bias, especially inthe most affected areas (Iwate, Miyagi and Fukushima); many of those who wereseverely affected by the earthquake were probably not able to answer our survey due totheir situation. Thus, we think the true level of well-being in the most affected areas
was probably lower than the figure given in the table. The results in table 1 aresummarized in figure 1 by classifying the data into three categories, corresponding to
the sign of the reported happiness change.
Panel B of table 1 is for changes in happiness between January 2011 (real time
data before the earthquake) and June 2011 (after the earthquake).

As in Panel A,

more people report that they are happier after the tragedy.6
We also tried to assess whether or not Japanese people’s worldviews hadchanged since the disaster. We paid special attention to the question about altruism,
since according to a Japanese Statistics Bureau report on expenditure by Japanesehouseholds, the most affected expenditure as March 2011 was charitable donations.
Households’ donations increased by over 850% year/year in March 2011 in both nominal
and real terms, whereas the increase in expenditure on, for example, mineral water was
only 149% in nominal terms and 161% in real terms. The table shows that a large
fraction of Japanese people began to feel more altruistic after the earthquake, even in
the most affected areas. We should note that there was a higher percentage of people
who said they had become less altruistic in Miyagi than in other parts of Japan; wethink it is likely that since Miyagi was the area that was most seriously affected by the
direct effect of the earthquake and tsunami, that there were a number of people there
who were simply not in any condition to help others. The results of table 2 aresummarized in figure 2 by classifying the data into three categories, corresponding to
the sign of the reported happiness change
The results shown in tables 1 and 2 (and figures 1 and 2) are quite striking, since
Kimball et. al (2006)

showed that the average US citizen felt “unhappy” after

Hurricane Katrina, and that this unhappiness continued for two or three weeks forpeople living in the most affected area (the South Central region of the United States).
Our results suggest the opposite – that Japanese people’s well-being improved following
a major natural disaster. Kimball’s analysis on Hurricane Katrina concluded that in theregions of the US outside the affected zone, happiness returned to normal levels in a few

Using internet survey data replied by young adults living in unafflicted areas, Uchida
et al (2014) also found that there were improvement in happiness for those who thoughtof earthquake when they were answering to their questionnaire.

6

5

weeks, but we found that both Japanese people’s well-being and also their altruism
continued to be affected even several months after the earthquake. The Great East
Japan Earthquake may thus have had a much greater impact on the happiness of theaverage Japanese citizen than Hurricane Katrina had on the average citizen in the US.
2.

Methodology
Our interpretation of the data is that many Japanese people became more altruistic

as a result of the Great East Japan Earthquake, made charitable donations, and as aresult experienced an increase in happiness. In order to test this story, we need two
subjective variables: a change in altruistic feelings after the earthquake, and a change
in self-reported happiness. Because the measurement errors in these subjective
variables are likely to be correlated, we avoid using these two variables in the same
regression, and instead use the S-O-S method explained in the introduction.

Thismethod involves two steps. In the first step we analyze the effect of a subjective variable(altruism) on an objective variable (donations), and in the second step we analyze theeffect of this objective variable on a second subjective variable (happiness).

In eachstep, we must also consider the possibility of reverse causality; people who become
happier may give more to charity, and making donations may cause people to report
more feelings of altruism.

In order to control for reverse causality, we use a two-stage

logit analysis in each of the two steps.
In Step 1, we want to see how changes in altruism affect the probability ofmaking a charitable donation. We use a two-stage multinominal logit analysis tomeasure this effect. In the first stage, the dependent variable is a dummy variable
representing the sign of the change in altruism from February 2011 to June 2011, while
the independent variables include two instrumental variables and a number ofthat individual i chooses the j th alternative for altruism

covariates. The probability
is given by
(1)

′

′

,

,

,

,

Where F is the logistic distribution function, x is a vector of covariates, and z is a vectorof instrumental variables. The vector z includes A) a dummy variable for whether or not
any acquaintances (including the respondent’s family) were struck by the earthquake,and B) a dummy variable about whether or not the respondent subscribed to life
insurance before the earthquake. We think that subscription to life insurance did notaffect the amount of charitable donations before the earthquake, and the subscription
6

increased donation only through the changes in altruism after the earthquake.
Japanese people did not usually donate much before the earthquake compared withother developed countries. Increase in the amount of charitable donations after
experiencing any acquaintances being struck by the earthquake should be due to thechanges in respondents’ altruism. The covariates x include A) a dummy variable
representing whether or not the respondent was personally affected by the earthquake,and B) other socio-economic control variables such as household income, wealth,owner-occupier of the house, age, age squared, and region of residence. Note that noneof these independent variables includes quantitative measures of subjective variables;
when data are subjective, we use only dummy variables.
In the second stage, we run a binominal logit regression to explain the dummy
variable for charitable giving after the earthquake, using the fitted values of thealtruism-change dummy predicted in the first stage. The probability

that individual

i chooses the kth alternative for charitable giving is represented by:
(2)

′

,

giving donation, not giving donation

where F is the logistic distribution function, andis the value of the altruism-change

dummy predicted in the first stage. The covariate vector x is the same as in the firststage. We compute the standard errors by bootstrap with 3000 replications.
For Step 2 of our analysis, we conduct a two-stage multi-nominal logit analysis
explaining Japanese people’s changes in happiness as a function of their charitablegiving. In the first stage, we run a binominal logit regression of the dummy variable forcharitable giving on the same two instrumental variables used in Step 1, and on our
vector of covariates:
(3)

′

′

,

giving donation, not giving donation

where again F is the logistic distribution function. The covariate vector x is the same asin previous regressions. We think that these two instrumental variables can be used forour analysis in Step 2 also, since Dunn et al. (2008) show that making donations
improves happiness.
In the second stage, the dependent variable is the dummy representing the sign ofthe happiness change from February 2011 to June 2011. We run a multinomial logit
regression of the happiness change dummy on the amount of charitable donations
predicted in the first stage:
7

(4)

,

,

where F is the logistic distribution function,,,is the amount of donations predicted in

the first stage, and x is the same vector of covariates. We compute the standard errors
by bootstrap with 3000 replications.7
3. Results
For the sake of brevity, we here discuss only those results that were significant at
the 10% level.
Table 4 shows the multinominal logit regression results when the dummy variable
for making donations is taken as the dependent variable. These are the results for thesecond stage of Step 1 in which we examine how changes in altruism affect donations.
(The S-O part of the S-O-S method).8

For changes in altruism, the base dummy istaken to be a positive change in altruism. The coefficients for predicted values of“altruism changed downward” and “altruism unchanged” have the expected negative
sign.

Both coefficients were significant at the 5% level with the one-sided test.9

For

other variables, an increase in income has a positive effect on the probability of making
donations (significant at the 5% level). Men were less likely than women to makedonations (significant at the 5% level). Respondents with one or more children weremore likely to make donations (significant at the 10% level).

Respondents with morefamily members living together were less likely to make donations (significant at the 5%
level.)
Table 5 shows the mutlinominal logit regression results when the dummy variable
for making changes in happiness from February (in the retrospective question) to Juneis taken as the dependent variable. These are the results for the second stage of Step 2
in which we examine how changes in donation affect changes in happiness. (The O-Spart of the S-O-S method). 10 The predicted value of the donation dummy has the
expected positive coefficient for the upward change in happiness (significant at the 5%
level) and the expected negative coefficient for the no change in happiness (significant

The first stage results are reported in the Appendix.
We use the one-sided test for the dummy variables for the changes in altruism
because our story to explain changes in happiness is that an increase in altruism caused
an increase in donation, which in turn caused an increase in happiness.
10 The first stage results are reported in the Appendix.
898

at the 10% level). 11 Combined with the results in Table 4, we found evidence for ourstory that increased altruism caused people to make donations for earthquake victims,
and the act of making donations improved happiness.
Table 5 also report results for other variables that affected changes in happiness. For
household income, the coefficients for the upward and downward changes in happinesswere significant at the 5% level. For household income squared, the coefficient for the
upward change in happiness was significant at the 10% level and that for the downwardchange in happiness was significant at the 5% level.

From the point estimates of thesecoefficients, people with more income were more likely to report downward changes inhappiness up to income of about 13, 160,000 yen. If household income is more than 21,
180, 000 yen, then people with more income were more likely to report upward changes
in happiness.

However, respondents with more than 13,160,000 yen are about 7% andthose with income more than 21, 180,000 yen were only about 1% of all the respondents.
Hence, for typical households, people with more household income tended to become
unhappier.

For age and age squared, the coefficient for “unchanged” is significant at

the 10% level. The point estimates for “unchanged” imply that older respondents weremore likely to report that happiness was unchanged until the age reached 27 years old.
Men were more likely to show no change and less likely to show upward changes inhappiness (significant at the 10% level). Respondents with more family members living
together were more likely to report upward changes in happiness (significant at the 1%
level), and less likely to report no or downward changes (significant at the 10% level).
Table 6 shows the mutlinominal logit regression results when the dummy variable
for making changes in happiness from January (in the real time data) to June is taken
as the dependent variable.

Just as in Table 5, in which we used data on retrospective

evaluation of happiness for February 2011, just before the earthquake, the predictedvalue of the donation dummy has the expected positive coefficient for the upwardchange in happiness (significant at the 5% level) and the expected negative coefficientfor no change in happiness. For the results of the predicted value of the donation
dummy, the only difference is that the coefficient for not change in happiness is not
significant at the 10% level when real time data are used.
For the other explanatory variables, there are two cases in which the coefficients inboth Tables 5 and 6 are significant at least at the 10% level: the coefficient for the sex
variable for “upward” and the coefficient for the number of family members living apartvariable for “upward.” In both cases, the signs of the coefficients are the same in both
As in the change in altruism varibles in Table 4, we use the one-sided test for
donations in Tables 5 and 6.

11

9

tables.
Thus, except for changes in significance levels for some coefficients, our results arerobust when only real time data for happiness are used instead of data involving
retrospective evaluation in happiness.
4. .Concluding Remarks
We studied changes in Japanese people’s altruistic feelings, charitable giving, andhappiness following the Great East Japan Earthquake, using a large panel data set ofover four thousand respondents. We found that the number of people reporting an
increase in happiness after the earthquake far exceeded the number who said it
worsened. These results, which allow for reverse causality, suggest that many Japanesepeople became more altruistic as a result of the Great East Japan Earthquake, madecharitable donations, and experienced an increase in happiness as a result of theircharitable behavior.
As we have explained before, some of the recent literature on happiness has tried tounderstand whether cognitive, hedonic, and eudaimonic measures of well-being reflect
different aspects of quality of life. Eudaimonic measures of well-being focus onfunctioning and the realization of people’s potential. Our results indicate that altruism,
one of the important elements of eudaimonic well-being, improves people’s sense ofhappiness. We think that people's worldviews are very much related to eudaimonic
well-being, and that these affect people’s happiness or life evaluation. We sometimes
observe social disorder or violence after large disasters, but we did not experience any
riot or serious violence after the Great East Japan Earthquake in Japan. It is possiblethat Japanese people’s culture or worldview promoted resilience or economic recovery
after the disaster. Our analysis on people’s worldview and happiness may thus give us ahint as to how to better cope with natural or/and man-made disasters.

10

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Daishinsai ga kakei ni Ataeta Eikyo (The Impacts of East Japan Earthquake on
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12Figure 1. Japanese people’s happiness before and after 3.11

Figure 2. Japanese people’s altruism before and after 3.11

13

Table 1 Regional distribution of changes in well-being
Panel A: Changes in well-being From February to June
RegionHokkaido
Tohoku less affected area (Aomori, Akita and Yamagata)
Tohoku most affected area
Iwate
Miyagi
Fukushima
Kanto
Chubu
Kinki
Chugoku
Shikoku
KyushuAll Japan

Relative frequency of changes in well-being (%)
Downward
Unchanged
Upward
3.09
69.59
27.32
4.03
69.35
26.61
15.79
12.00
33.33
6.00
3.91
3.42
2.77
3.82
2.48
4.51

Panel B: Changes in well-being From January to June
Region

52.63
44.00
38.10
65.91
66.62
72.80
69.57
65.65
66.59
67.55

31.58
44.00
28.57
28.09
29.47
23.78
27.67
30.53
30.93
27.94

Relative frequency of changes in well-being (%)
Downward
Unchanged
Upward
27.84
23.71
48.45
25.00
24.19
50.81

Hokkaido
Tohoku less affected area (Aomori, Akita and Yamagata)
Tohoku most affected area
Iwate
Miyagi
Fukushima
Kanto
Chubu
Kinki
Chugoku
Shikoku
KyushuAll Japan

31.58
25.00
33.33
26.06
23.14
27.00
25.50
26.52
25.23
25.69

14

15.79
16.67
38.10
27.09
22.88
22.28
21.12
25.76
22.52
24.20

52.63
58.33
28.57
46.84
53.99
50.72
53.39
47.73
52.25
50.11

N
194
124
19
25
21
1,367
767
761
253
131
443
4,105
N
194
124
19
24
21
1,362
765
763
251
132
444
4,099

Table 2 Regional distribution of changes in altruism (From February to June)
Relative frequency of changes in altruism (%)
Downward
Unchanged
Upward
5.82
65.08
29.10
4.88
52.03
43.09

RegionHokkaido
Tohoku less affected area (Aomori, Akita and Yamagata)
Tohoku most affected area
Iwate
Miyagi
Fukushima
Kanto
Chubu
Kinki
Chugoku
Shikoku
KyushuAll Japan

5.26
16.67
4.76
6.71
4.23
3.85
2.00
3.88
6.62
5.27

15

63.16
33.33
47.62
58.48
60.71
65.38
60.40
50.39
60.50
60.19

31.58
50.00
47.62
34.81
35.05
30.77
37.60
45.74
32.88
34.54

N
189
123
19
24
21
1,356
756
754
250
129
438
4,059

Table 3 Descriptive Statistics
N
Well-being in January
Well-being in February
Well-being in June
Altruism in february
Altruism in June
Made donations regarding the earthquake
Respondent was struck by the earthquake
Any acquaintances (include R's family) were struck by the earthquake
Subscribe life insurance
Living in the region affected by rolling brackouts
Household annual income (in 10thousands JPY)
Household wealth (in 10thousands JPY)
Household debt (in 10thousands JPY)
Owner-occupier
Subscribe earthquake insurance
Age
Sex (1 if male)
Marital status (1 if married)
Any child(ren) in the household
Number of family members living together
Number of family members living apart
JHPS (1 if respondents of JHPS)
Living in Hokkaido (Base dummy is living in Kyushu)
Living in Aomori, Akita or Yamagata pref.
Living in Iwate, Miyagi or Fukushima pref.
Living in Kanto
Living in Chubu
Living in Kinki
Living in Chugoku
Living in Shikoku
Living in Kyushu
Living in designated city
Living in municipal
Living in village or town

16

6161
4144
4114
4108
4070
4210
4210
4138
4078
3704
3883
3953
4120
4170
3964
4210
4210
4210
4210
4210
4109
4210
4210
4210
4210
4210
4210
4210
4210
4210
4210
4210
4210
4210

Mean
64.28
66.71
71.80
44.92
51.48
0.74
0.02
0.36
0.82
0.25
678.751201.44504.41
0.83
0.40
53.19
0.47
0.79
0.63
3.19
0.21
0.49
0.05
0.03
0.02
0.33
0.19
0.19
0.06
0.03
0.11
0.30
0.61
0.09

S.D.
20.46
22.11
21.94
21.64
22.28
0.44
0.12
0.48
0.39
0.43
476.452197.841176.10
0.38
0.49
14.29
0.50
0.41
0.48
1.39
0.58
0.50
0.21
0.17
0.13
0.47
0.39
0.39
0.24
0.18
0.31
0.46
0.49
0.28

Table 4: Estimation result of Making donations (Structural Form)
Structural Form
Marginal Effect(S.E.)-1.3214 (0.7663) **-1.4241 (0.7927) **-0.0702 (0.1702)
0.0727 (0.0544)
0.0165 (0.0077) **-0.0020 (0.0023)
0.0166 (0.0109)
0.0067 (0.0155)
0.0490 (0.0458)
0.0274 (0.0338)
0.0020 (0.0096)-0.0281 (0.0973)-0.0716 (0.0354) **-0.0271 (0.0582)
0.1125 (0.0623) *-0.0379 (0.0171) **-0.0428 (0.0305)-1446.12
2725

Altruism changed downward (Predicted value. Base dummy is Upward)
Altruism were unchanged (Predicted value. Base dummy is Upward)
Respondent was struck by the earthquake
Living in the region affected by rolling brackouts
Household annual income (in million JPY)
2

(Household annual income) /10
Household wealth (in 10million JPY)
Household debt (in 10million JPY)
Owner-occupier
Subscribe earthquake insurance
Age2(Age) /1000
Sex (1 if male)
Marital status (1 if married)
Any child(ren) in the household
Number of family members living together
Number of family members living apart
Log likelihood
N

Notes: ***, ** and * indicate that the estimated marginal effects are significant at 1%, 5% and 10% levels, respectively.
For the dummy variables for changing altruism, the signigficance level is based on the one seided test. For the othervariables, the significance level is on the two sided test. Dummy variables for city-sizes, regions and data sets are also
controlled but are omitted from the results.Standard errors are in the parentheses.

17

Table 5 Estimation result of changes in well-being (From February to June)
UpwardMarginal Effect(S.E.)
Donation (Predicted value)
0.5793 (0.2641) **Respondent was struck by the earthquake
0.0075 (0.1075)
Living in the region affected by rolling brackouts-0.0197 (0.0308)
Household annual income (in million JPY)
-0.0161 (0.0067) **2
0.0036 (0.0019) *(Household annual income) /10
Household wealth (in 10million JPY)
0.0005 (0.0051)
Household debt (in 10million JPY)
0.0085 (0.0096)
Owner-occupier-0.0431 (0.0391)
Subscribe earthquake insurance-0.0066 (0.0214)
Age0.0052 (0.0061)
2-0.0097 (0.0059)(Age) /100
Sex (1 if male)-0.0463 (0.0269) *Marital status (1 if married)
0.0227 (0.0319)
Any child(ren) in the household-0.0348 (0.0299)
Number of family members living together
0.0313 (0.0114) ***Number of family members living apart0.0361 (0.0180) **Log likelihood
N

Unchanged
Marginal Effect(S.E.)-0.4078 (0.2681) *-0.0333 (0.1051)
0.0032 (0.0306)
0.0062 (0.0069)
0.0002 (0.0021)-0.0003 (0.0055)-0.0105 (0.0095)
0.0307 (0.0398)
0.0012 (0.0214)-0.0061 (0.0060)
0.0112 (0.0059) *0.0475 (0.0268) *-0.0212 (0.0308)
0.0208 (0.0304)-0.0209 (0.0114) *-0.0371 (0.0181) **-1999.96
2622

Downward
Marginal Effect(S.E.)-0.1714 (0.1341)
0.0258 (0.0357)
0.0165 (0.0130)
0.0100 (0.0045) **-0.0038 (0.0020) **-0.0003 (0.0034)
0.0020 (0.0039)
0.0125 (0.0183)
0.0054 (0.0096)
0.0009 (0.0028)-0.0015 (0.0027)-0.0012 (0.0129)-0.0015 (0.0142)
0.0140 (0.0148)-0.0104 (0.0059) *0.0010 (0.0089)

Notes: ***, ** and * indicate that the estimated marginal effects are significant at 1%, 5% and 10% levels, respectively. For the donation variable, the signigficancelevel is based on the one seided test because of Dunn et al (2008). For the other variables, the significance level is on the two sided test. Dummy variables for citysizes, regions and data sets are also controlled but are omitted from the results.Standard errors are in the parentheses.

18

Table 6 Estimation result of changes in well-being (From January to June)
UpwardUnchanged
Downward
Marginal Effect(S.E.)
Marginal Effect(S.E.)
Marginal Effect (S.E.)
Donation (Predicted value)
0.4381(0.2342) **-0.2149(0.1956)-0.2233 (0.2073)
Respondent was struck by the earthquake-0.2029(0.1125) *0.0898(0.0798)
0.1131 (0.0978)
Living in the region affected by rolling brackouts-0.0267(0.0313)0.0259(0.0241)0.0008 (0.0269)
Household annual income (in million JPY)
-0.0080(0.0064)0.0030(0.0058)
0.0050 (0.0055)
20.0016-0.0003(0.0019)-0.0013(0.0018)(0.0015)(Household annual income) /10
Household wealth (in 10million JPY)
-0.0155(0.0061) **0.0082(0.0043) *0.0073 (0.0047)
Household debt (in 10million JPY)
0.0212(0.0090) **-0.0172(0.0080) **-0.0040 (0.0078)
Owner-occupier-0.0396(0.0366)
0.0003(0.0311)0.0393 (0.0330)
Subscribe earthquake insurance-0.0367(0.0207) *0.0319(0.0172) *0.0047 (0.0181)
Age-0.0075(0.0057)
0.0084(0.0049) *-0.0009 (0.0048)
2
0.0070-0.0073(0.0048)0.0003 (0.0047)(0.0055)(Age) /100
Sex (1 if male)-0.0779(0.0251) ***0.0046(0.0212)0.0733 (0.0223) ***Marital status (1 if married)-0.0232(0.0296)0.0135(0.0249)
0.0097 (0.0264)
Any child(ren) in the household-0.0324(0.0275)
0.0290(0.0226)
0.0034 (0.0238)
Number of family members living together
0.0130(0.0113)-0.0077(0.0092)-0.0053 (0.0100)
Number of family members living apart
0.0477(0.0200) **-0.0208(0.0170)-0.0269 (0.0177)-3021.10
Log likelihood
2956
N
Notes: ***, ** and * indicate that the estimated marginal effects are significant at 1%, 5% and 10% levels, respectively. For the donation variable, thesignigficance level is based on the one seided test because of Dunn et al (2008). For the other variables, the significance level is on the two sided test. We use one
sided test for the marginal effect of donation. Dummy variables for city-sizes, regions and data sets are also controlled but are omitted from the results.Standard
errors are in the parentheses.

19Appendix

This appendix explains the first stage results in each step.

Table A.1 shows the first stage results in Step 1 for changes in altruism. The table indicates that the altruism of people who
subscribed to life insurance before the earthquake tended to increase (5% significance level).

Men were less likely than women to show

downward changes in altruism.
Table A. 1: Estimation result of changes in altruism (From February to June)
UpwardUnchanged
Downward
Marginal Effect(S.E.)
Marginal Effect(S.E.)
Marginal Effect(S.E.)
Subscribe life insurance
0.0552 (0.0264) **-0.0542 (0.0269) **-0.0010 (0.0117)
Any acquaintances were struck by the earthquake
0.0187 (0.0193)-0.0118 (0.0198)-0.0069 (0.0091)
Respondent was struck by the earthquake-0.0065 (0.0869)-0.0461 (0.0893)
0.0526 (0.0285) *Living in the region affected by rolling brackouts-0.0354 (0.0275)
0.0297 (0.0282)
0.0057 (0.0119)
Household annual income (in million JPY)
-0.0004 (0.0044)-0.0007 (0.0045)
0.0011 (0.0022)
2-0.0005 (0.0012)
0.0008 (0.0013)-0.0003 (0.0007)(Household annual income) /10
Household wealth (in 10million JPY)
-0.0023 (0.0043)
0.0043 (0.0044)-0.0020 (0.0024)
Household debt (in 10million JPY)
-0.0101 (0.0091)
0.0058 (0.0090)
0.0043 (0.0031)
Owner-occupier
0.0044 (0.0275)
0.0039 (0.0281)-0.0083 (0.0128)
Subscribe earthquake insurance-0.0039 (0.0199)
0.0173 (0.0205)-0.0134 (0.0095)
Age-0.0028 (0.0053)
0.0050 (0.0054)-0.0022 (0.0024)
2
0.0031-0.0065(0.0053)
0.0034 (0.0023)(Age) /100
(0.0051)
Sex (1 if male)
0.0100 (0.0184)
0.0154 (0.0189)-0.0254 (0.0089) ***Marital status (1 if married)
0.0397 (0.0263)-0.0460 (0.0269) *0.0063 (0.0122)
Any child(ren) in the household-0.0561 (0.0250) **0.0693 (0.0256) ***-0.0132 (0.0114)
Number of family members living together
0.0103 (0.0090)-0.0130 (0.0092)
0.0027 (0.0041)
Number of family members living apart0.0099 (0.0172)-0.0197 (0.0176)
0.0098 (0.0074)-2218.47
Log likelihood
2725
N
Notes: ***, ** and * indicate that the estimated marginal effects are significant at 1%, 5% and 10% levels, respectively. Dummy variables for city-sizes, regions anddata sets are also controlled but are omitted from the results.Standard errors are in the parentheses.

20

Table A.2 shows the first stage results in Step 2 for making donations. The two instrumental variables are significant at the 1% level
and have the expected positive sign.

Table A. 2: Estimation result of Making donations (Reduced Form)
Reduced Form
Marginal Effect(S.E.)
Any acquaintances (include R's family) were struck by the earthquake
0.0803 (0.0202) ***Subscribe life insurance
0.0497 (0.0167) ***Respondent was struck by the earthquake-0.1482 (0.0667) **Living in the region affected by rolling brackouts
0.0295 (0.0229)
Household annual income (in million JPY)
0.0153 (0.0036) ***2-0.0025 (0.0009) ***(Household annual income) /10
Household wealth (in 10million JPY)
0.0114 (0.0050) **Household debt (in 10million JPY)
-0.0100 (0.0065)
Owner-occupier
0.0661 (0.0216) ***Subscribe earthquake insurance
0.0028 (0.0172)
Age-0.0004 (0.0043)
20.0005 (0.0421)(Age) /1000
Sex (1 if male)-0.0632 (0.0155) ***Marital status (1 if married)
0.0338 (0.0215)
Any child(ren) in the household
0.0288 (0.0212)
Number of family members living together-0.0229 (0.0074) ***Number of family members living apart-0.0236 (0.0146)-1616.58
Log likelihood
3019
N
Notes: ***, ** and * indicate that the estimated marginal effects are significant at 1%, 5% and 10% levels, respectively. Dummy variables
for city-sizes, regions and data sets are also controlled but are omitted from the results.Standard errors are in the parentheses.
.
21