GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 338

Trust and Happiness: Comparative Study Before and Afterthe Great East Japan Earthquake

Eiji Yamamura, Yoshiro Tsutsui, Chisako Yamane, Shoko Yamane,and Nattavudh Powdthavee

July 2014

GCOE Secretariat
Graduate School of Economics
OSAKA UNIVERSITY
1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Trust and Happiness: Comparative Study Before and After the Great
East Japan Earthquake
Eiji Yamamura  Yoshiro Tsutsui  Chisako Yamane  Shoko Yamane  Nattavudh
Powdthavee1

Abstract The positive relationship between trust and happiness has been demonstrated
by the literature. However, it is not clear how much this relationship depends onenvironmental conditions. The Great East Japan Earthquake of 2011 is considered oneof the most catastrophic events in human history. This disaster caused not only physicaldamage for Japanese people, but also perceived damage. Using individual-level panel
data from Japan covering the period 2009–2012, this paper attempts to probe how the
relationship between trust and happiness was influenced by the Great East Japan
Earthquake by comparing the same individuals before and after the earthquake. A
fixed-effects estimation showed that there is a statistically well-determined positive
relationship between trust and happiness and this relationship was strengthened bydisaster, especially for residents in the damaged area. We argue that social trust is a
substitute for formal institutions and markets, which mitigates the effect ofdisaster-related shock on psychological conditions such as happiness. Therefore, atrustful society is invulnerable to a gigantic disaster.
Keywords: Trust  Happiness  Disaster  Great East Japan Earthquake

E. Yamamura
1Seinan Gakuin University. 6-2-92, Nishijin Sawara-ku, Fukuoka 814-8511, Japane-mail: yamaei@seinan-gu.ac.jp
Y. Tsutsui
Konan University. 8-9-1 Okamoto Higashinada-ku, Kobe 658-8501, Japane-mail: tsutsui@center.konan-u.ac.jp
C. Yamane
Okayama Shoka University. 2-10-1 Tsushima Kyomachi Kitaku, Okayama 700-8601,
Japan
e-mail: Yamane@po.osu.ac.jp
S. Yamane
Kindai University. 3-4-1 Kowakae Higashi-osaka, Osaka 577-8502, Japane-mail: syamane@kindai.ac.jp
N. Powdthavee
CEP, London School of Economics, London, UK; MIAESR, University of Melbourne,
Melbourne, Australia
e-mail: N.Powdthavee@lse.ac.uk

1Introduction

It has become increasingly difficult for researchers to ignore the wealth of benefits thatsocial capital, which can be defined broadly as a combination of social trust,interpersonal networks, and community participation, has on personal well-being(Putnam 1993, 2000). While its benefits for an individual’s health status have been welldescribed by the framework of social supports in the health literature (e.g., Kawachi etal. 1997, 1999, 2007), in theory the relationship between social capital and an
individual’s overall evaluation of quality of life (or subjective well-being [SWB] ingeneral) is not as well developed.
For instance, the relationship between trust and SWB is likely to be context
specific. Even if people do not trust others, markets will function well when the law andorder, which is based on formal rules, and public authority is reliable, and people enjoy
the benefits. However, in a low-trust society, once the law and order is disturbed, people
are less likely to make transactions to avoid being cheated. Furthermore, people resort
to dishonest behavior or illegal activity such as looting or robbery because of a poorly
functioning market, which increases negative externality. Accordingly, trust plays amore important role in reducing negative externality when formal laws are notfunctioning well.
In 2010, after the Great Haiti Earthquake, “a crowd beat a suspected thief todeath and dragged his body through the streets” (Aldrich 2012:24). This shows that asociety lacking trust tends to be ruled by violence and terror, which results in a
man-made secondary tragedy. Without doubt, the detrimental shock of experiencing adisaster is strengthened by distrustful human relations. In 2005, after Hurricane Katrina
in the United States, cooperative behavior was not observed and uprisings and stealing
were rampant and prevalent (Kawachi 2013:15–16). In contrast with the Great Haiti
Earthquake and Hurricane Katrina, disturbances did not arise immediately after theGreat East Japan Earthquake and people endured their difficulties with great patience(Ono 2012; Kawachi 2013). Several economic researchers have investigated the impact
of disasters on modern society (e.g., Skidmore and Toya 2002; Anbarci et al. 2005;
Eisensee and Strӧmberg 2007; Kellenberg and Mobarak 2008; Becchetti and Castriota
2010; Sawada and Shimizutani 2007, 2008, 2011). Some have noted that social capital
1

played a crucial role in mitigating damage or recovering from the disaster (Yamamura
2010; Aldrich 2012).
Many studies have found a positive relationship between social capital anddifferent measures of SWB (e.g., Putnam 2000; Bjørnskov 2003, 2006; Helliwell 2003,
2006a, 2006b; Powdthavee 2008; Kuroki 2011). 1 However, the majority of thesestudies were based on data sets obtained from countries during a relatively stable time
period. If SWB is used as the dependent variable and trust is used as the independent
variable, a third factor such as individual personality is possibly related to them. Thisfactor causes a spurious correlation between SWB and trust, which results in estimation
bias. Suicide rates can be regarded as a proxy for SWB, which is useful for providing an
objective measure of social trust. Ten percent of the population in each country thoughtthat others could be trusted, which is associated with a 4.0 per 100,000 drop in theannual suicide rate for males, and a smaller 0.5 per 100,000 drop for females (Helliwell2006a:473–474). If the relationship between individual-level trust and SWB is
examined, the causality is ambiguous. To tackle this problem, Kuroki (2011) examinedthe effect of regional-level trust on SWB. He found that “if a person is exogenously
moved from an ‘average-trust’ prefecture to the prefecture with the highest trust level(8.6-percentage-point increase in trust), his happiness will increase by 0.16 in a 5-point
scale” (Kuroki 2011:455). However, these papers do not deal with the relationshipbetween trust and SWB in an emergent situation.
However, disasters were found to have a sizable impact on subjective
perception such as life satisfaction (e.g., Carroll et al. 2009; Luechinger and Saschkly
2009; Becchetti and Castriota 2010).2 Researchers have attempted to investigate thepsychological impact of the Great East Japan Earthquake on Japanese people (Ishino etal. 2011; Hanaoka et al. 2014; Uchida et al. 2014) and also in German people (Goebel etal. 2013). However, these works do not consider how trust was related to SWB bycomparing trust before and after the disaster. Our novel contribution is to make it

1

Ram (2009) did not find a significant association between trust and happiness.
An unexpected catastrophe such as a terrorist attack was also found to result in higher
levels of mental distress (Metcalfe et al. 2011). The suicide rate is considered as an
objective variable for capturing the degree of life satisfaction in society. Matsubayashi
et al. (2013) examined the relationship between disaster and suicide rates.
22

evident that trust plays a greater role in increasing SWB when people suffer the damage
caused by an unexpected, devastating shock compared with SWB during a relatively
stable time.3
The structure of the remaining part of this paper is as follows: section 2 is an
overview of the Fukushima accident. Section 3 explains data used in this paper andproposes testable hypotheses, and method. Section 4 reports the estimation results. The
final section presents the conclusion.

2

Overview of the Great East Japan Earthquake

A devastating earthquake hit Japan on March 11, 2011, which caused a tsunami andtriggered a nuclear accident at Fukushima. The Great East Japan Earthquake isconsidered one of the most catastrophic events in human history. The earthquake
occurred off the coast of Japan and its magnitude was estimated as 9.0 (Daily Yomiuri
2011a), which was the fourth largest recorded earthquake in history.
The disaster was a composite one because of the devastating tsunami as an
outcome of the earthquake. The powerful tsunami waves pushed water up to heights ofmore than 20 meters in some coastal areas of the northeastern coast of Japan (Daily
Yomiuri 2011b).4 People on the coast were unable to escape from the tsunami andapproximately 16,000 people died (National Police Agency 2014). In addition, residents
in the prefectures of Iwate, Miyagi, and Fukushima suffered devastating damage
because these prefectures have a coastline in their northeast regions.5 In this paper, thedamaged area is defined as the area covering these three prefectures. Before the disaster,
the damaged areas were experiencing depopulation and their populations were3

Ono (2012) pointed out that interpersonal networks are effective and play animportant role in deterring rioting and turmoil in the stricken areas affected by the Great
East Japan Earthquake.
4
Material losses of building and road infrastructure was calculated at 31.8 and 2.1
million tons, respectively (Tanikawa et al. 2014). The “World Bank and Japanese
government say that there’s somewhere between $122 billion and $235 billion worth ofdamage to clean up” (Hammer 2011:28).
5
A Japanese prefecture is almost the equivalent of a state in the United States or aprovince in Canada. There are 47 prefectures in Japan.
3

decreasing, with the exception of Sendai in Miyagi prefecture.6 Before the disaster, in
2008–2009, the population decreased by 8.7%, 6.0%, and 0.7% in Iwate, Fukushima,
and Miyagi prefectures, respectively.7
Even though disaster victims experienced many difficulties in the emergent
situation, they were not confused and did not act impatiently, which helped to preserve
public order. Looting and robbery were not observed in the stricken area (Ono 2012). In
addition, instead of turmoil or rioting, altruistic behavior towards the victims of thedisaster was observed in Japan. Victims of the disaster assisted each other (Kawachi
2013:15–16). Prior to the Great East Japan Earthquake, in 1995, the Great
Hanshin–Awaji Earthquake hit Japan and caused catastrophic damage. However, a large
number of young people volunteered in Kobe, which was directly hit by the earthquake.
The Great Hanshin–Awaji Earthquake greatly motivated people to volunteer(Yamamura 2014). A similar phenomenon was observed after the Great East Japan
Earthquake, for example, 13-year-old boys performed volunteer work in the strickenarea with their fathers and “in the car on our way home, the boys all told that theywanted to volunteer again in Tohoku” (Matsutani 2011).

3

Data and Hypothesis

3.1

Data

In this paper, we use data from the Survey of Life Satisfaction and Preferences. As apart of the Global Center of Excellence (GCOE) program, Human Behavior andSocioeconomic Dynamics performed by Osaka University, the data were purposefullycompiled to scrutinize the individual subjective perception from a socioeconomics
perspective. Hereafter, this data are called GCOE data.
Since 2004, the panel survey has been conducted annually to cover all parts ofJapan. The collection of data is based on the random-sampling method. Respondents are

6

Sendai is regarded as the most urbanized city in northeastern Japan and has apopulation of over a million people.
7
The data is available from the Web site of the Statistics Bureau of the Ministry ofInternal Affairs and Communications.
http://www.e-stat.go.jp/SG1/estat/NewList.do?tid=000000090001 (Accessed on June 9,2014).
4

male and female adults aged between 20 and 69 years. The data provided information
regarding basic socioeconomic individual characteristics such as age, sex, householdincome, family members, degree of generalized trust, and degree of happiness, andresidential place. New respondents were added to the survey waves in 2004, 2006, and2009. Questions concerning the key variables such as generalized trust and happinesswere only included in the questionnaire during the 2010–2013 surveys. Therefore, data
used in this paper covered only four years (2010–2013). The survey was conductedfrom January to February each year. Therefore, the 2011 data were already collected
when the Great East Japan Earthquake occurred in March 11. Therefore, the data from2010 and 2011 can be defined as predisaster data while the data from 2012 and 2013
can be defined as postdisaster data.
Existing research examining the relationship between trust and happiness were
not based on individual-level panel data (e.g., Bjørnskov 2003, 2006; Helliwell 2003,
2006a, 2006b; Ram 2009; Kuroki 2011). It is crucial to eliminate the individual
time-invariant traits and follow the same individuals to scrutinize how SWB is
determined (Powdthavee 2010:49–73). The great advantage of the panel GCOE data isthat they allows us to follow the same individuals and control for individual fixed
effects when we analyze the relationship between trust and happiness. Hence, this paper
is anticipated to indicate the robustness of these works.
Table 1 shows the definitions of variables used in this paper and their meanvalues during the period 2010–2013. Table 1 also presents the mean difference test
between residents in the damaged area and those in other areas. Key variables were
Happiness, Trust, After disaster, and Damaged.
To identify the happiness level of respondents with a questionnaire, they wereasked, “How would you rate you current level of happiness?” Their responses werescored on an 11-point Likert scale, which is used to measure the degree of happinessfrom 0 (very unhappy) to 10 (very happy). To identify the trust level of respondents,
they were asked whether, “In general, most people are trustworthy?” Their responses
were scored on a 5-point Likert scale from 1 (completely disagree) to 5 (completely
disagree).
The mean value for Happiness in the damaged area was 6.32, which is smaller5

than in other areas (6.48). Furthermore, the difference was statistically significant at the
1% level. Therefore, on average, residents in the damaged area reported a 0.16-point
lower happiness level on the 11-point scale compared with residents in other areas.
However, there was no statistical significance even though the mean value for Trust in
the residents from the damaged area is slightly larger than for other areas. That is, thetrust level of residents in the damaged area was almost equivalent to that of residents inother areas.
The mean value for Income for residents in the damaged area was 5.93 million
yen, which was significantly smaller than that for those in other areas. The variable
Family for residents in the damaged area was 4.40, which was significantly larger thanfor residents in other areas (4.02). In the dataset, residential places were scaled into theclassifications of large-sized city, medium-sized city, small city, and village (or town).8
Dummies for these were constructed, with the exception of large-sized city because alarge-sized city was defined as the reference group in this paper. Mean values ofdummies of Medium city, Small city, and Village for residents in the damaged area were0.32, 0.29, and 0.12, respectively. We can interpret this as indicating that when thesample was restricted to the damaged area, 32%, 29%, and 12% of respondents resided
in the medium-sized city, small city, and village (or town), respectively. However, themean values of dummies of Medium city, Small city, and Village for residents in otherareas were 0.42, 0.22, and 0.08, respectively. We can interpret this as indicating thatwhen the sample was restricted to other areas, 42%, 22%, and 8% of respondents
resided in the medium-sized city, small city, and village (or town), respectively.
Respondents in the damaged area were more likely to reside in a small city or village (ortown) than in other areas. These values show that the damaged areas can becharacterized as having lower income, larger family size, and less urbanization.

3.2

Hypotheses

Using the GCOE data, Figure 1 shows the mean values of trust and happiness in eachprefecture to illustrate the association between trust and happiness. A cursory
8

The GCOE data provide information about the name of prefecture and the size of thelocal government where the respondents resided. A prefecture consists of local
governments, including many cities, towns, and villages.
6examination of Figure 1 reveals a positive association, which is in line with existing
works (e.g., Bjørnskov 2003; Kuroki 2011). Figure 2 illustrates the change of happinesslevel based on subsample of residents of the damaged area and of other areas. During
the study period, the happiness level of residents in the damaged area was smaller thanthat of other areas. This might reflect that most of the damaged areas appeared toexperience a depopulation problem and their populations were already decreasing
before the disaster, with the exception of Sendai in Miyagi Prefecture. The happinesslevel in the damaged area has clearly declined after the disaster in 2012 compared withthe happiness levels in January or February of 2011 before the disaster. By 2013, thehappiness level in the damaged area increased to a level almost equivalent to the level
before the disaster. These findings imply that the disaster had a detrimental effect onhappiness levels, but the effect is seemingly not persistent. Despite the enormous
material damage caused by the disaster, people affected by the disaster have adapted totheir situation 2 years later. In comparison, the happiness level in other areas has
maintained almost the same level during this period. Therefore, the disaster was
unlikely to influence the happiness level of people who resided in other areas unaffectedby the disaster.
Figure 3 demonstrates the change in Trust based on the subsample of residents
from the damaged area and the sample of residents in other areas. The level of trust
increased for residents in the damaged area from 2010 to 2011, which indicated thatpeople tended to increasingly trust others before the disaster. From 2011 to 2012, thelevel of trust declined, which possibly reflected the effect of the disaster. That is, thenatural disaster decreased the level of trust, which is incongruent with the findings byToya and Skidmore (2012). This may partly be because Toya and Skidmore (2012) usedcross-country data instead of individual-level data. In 2013, the trust level had recovered
to a level almost equivalent to that before the disaster. We argue that these findings
show that the trust level is stable unless a disaster occurs. However, the level of trust ofresidents in other areas was smaller than that of residents in the damaged area before thedisaster. However, this level of trust in other areas had caught up with levels of trust in
the damaged area after the disaster. Overall, the levels of trust in other areas increased
consistently during this period.
7

Following existing works (Bjørnskov 2003, 2006; Helliwell 2003, 2006a, 2006b;
Kuroki 2011), we postulate Hypothesis 1:

Hypothesis 1:

Trust is positively associated with happiness.

The importance of social trust is expected to be acknowledged widely after
suffering damage caused by a natural disaster, which leads people to trust others (Toyaand Skidmore 2012). People are very uneasy after a disaster. In this situation, trust isthought to alleviate their uneasiness. Therefore, we provide Hypothesis 2:

Hypothesis 2:

The positive relationship between trust and happiness isstronger after a disaster than before a disaster.

Damage from the disaster differed between the area where the earthquake andtsunami directly hit and other places. It is appropriate to assume that the post-disastersituation is more emergent and serious in the damaged area than in other areas. Markets
and formal institutions are less likely to function well in the damaged area. In thissituation, trust plays an important role in maintaining order and preventing the
population from rioting. In a more emergent and serious situation, maintaining law andorder is more valuable and important to keep mental conditions stable. Therefore, we
propose Hypothesis 3:

Hypothesis 3:

Trust is more strongly related to happiness in the damaged
area after the disaster than before the disaster.

3.3

MethodThe strategy of our estimation is roughly sketched as below; the purpose is to examine
the effect of disaster and trust in 2011 on the Japanese happiness level. First, we simply
compared residents living in other areas with residents in the prefectures hit by theearthquake in 2011. We focused on the effect of levels of trust.99It should be noted that this paper checks only the correlation between trust and8To test Hypothesis 1, the estimated function takes the following form:
Happiness itp = α1 After disaster t + α2 Trust itp + Y itp B + ki + u itp,

where Happiness itp represents the dependent variable in individual i, year t, andprefecture p. ki represents time-invariant individual-level fixed effects. Time-invariant
features such as schooling years and gender dummy are completely captured by ki andare not included as independent variables. The regression parameters are denoted by α.
Y is the vector of the individual-level control variables, which capture the influence ofthe various respondents’ individual characteristics. Its vector of the regression
parameters is denoted as B. The error term is denoted by u. After disaster takes 1 whenobservations are collected in 2011 or 2012, otherwise 0. If the disaster decreases
happiness level, the predicted sign of After disaster is negative. Then, Trust is expected
to show a positive sign if Hypothesis 1 is supported.
To examine Hypothesis 2, the function form is described below:
Happiness itp = β1 After disaster t  Trust itp + β2 After disaster t + β3 Trust itp + Y itp B +ki + u itp.
If the coefficient of After disaster t  Trust has a positive sign, trust is more positive andstrongly related to happiness after the disaster than before the disaster. Therefore, from
Hypothesis 2, Disaster t  Trust is expected to show a positive sign.
To examine Hypothesis 3, the function form is described below:
Happiness itp = γ1 After disaster t  Trust itp  Damaged p + γ2 After disaster t  Trust itp+ γ3 After disaster t  Damaged p + γ4 After disaster t + γ5 Trust itp + Y itp B + ki +u itp.
In the model suggested above, if the coefficient of After disaster t  Trust  Damaged
happiness although there seems to be reverse causality and therefore endogenous bias.
The instrumental variables must be used to control for this bias (Kuroki 2011). However,this is beyond the scope of this paper because the appropriate instruments cannot be
obtained.
9

takes a positive sign, the positive relation between trust and happiness for those who
suffered from disaster become stronger. Hence, from Hypothesis 3, the sign of thecoefficient of After Disaster t  Trust  Damaged is predicted to be positive. For thecontrol variables, household income was included to examine the effect of income on
happiness levels. In the fixed-effects model employed in this paper, the variation ofincome for the same individual is captured during the period 2010–2013. Apart from theincome level as an independent variable to control factors related to happiness levels,
this work also incorporates respondent ages, dummies for marital status, number offamily members (Family), and dummies for residential places. The effect of age on
happiness was found to be nonlinear (e.g., Clark and Oswald 1996; Blanchflower and
Oswald 2004; Kuroki 2011). Therefore, with the aim of testing the nonlinear effect ofage, in addition to Age, Age squared is included. Marital status is known to affecthappiness levels (e.g., Oswald and Powdthavee 2008; Powdthavee 2008; Clark et al.
2008; Frijters et al. 2011). Therefore, three dummies (Unmarried, Divorce, and Widow)
are included to capture marital status when currently married people are used as thereference group. As mentioned earlier, the damaged area is mainly a small-scale ruraland depopulated place. However, the damaged area also includes Sendai, which is themost urbanized city in northeastern Japan. This variation in residential places should becontrolled for. This work includes three dummies for residential place (Medium city,
Small city, and Village) when the large-sized city is used as the reference group. In
addition, eight occupation dummies (agriculture, fishery, constructing, or education),and seven residence dummies (private rented house, public rented house, company
owned house, or apartment house) are included.

3

Results

Tables 2, 3, and 4 exhibit the estimation results using a fixed-effects model. Table 2
shows the results where the cross term is not included and Hypothesis 1 is examined.
Table 3 shows the results of the model including where the cross term of After disaster t
 Trust is included to examine Hypothesis 2. Table 4 presents the results of the model
including where the cross term of three variables such as Disaster t  Trust  Damaged
is included to test Hypothesis 3. In each table, the results of four different specifications
10

are presented for the sake of a robustness check of results. The sample size increases
when the number of control variables reduces because some observations do notprovide information about the control variables. To focus on the key variable results, theresults of control variables reported in Table 2 were not reported in Tables 3 and 4 eventhough the results of each column include the set of control variables equivalent to thoseof Table 2. Although not reported, the dummies of occupations and of types of residence
are included in Tables 2–4.
We see from Table 2 that the after-disaster dummy (covering the period
2012–2013) show a positive sign in all results, which is consistent with previous work
exploring the effect of the Great East Japan Earthquake (Ishino et al. 2011; Uchida et al.
2014). However, After disaster was not statistically significant, which shows that there
is no statistical difference in happiness levels before and after the disaster. Consistent
with our prediction, the coefficient of Trust yields a positive sign and was statisticallysignificant at the 1% level in all columns. Hypothesis 1 is strongly supported. Income
yields a positive sign and is statistically significant at the 1% level, which is consistent
with our prediction. In the result shown in column (4) of Table 2, the absolute value ofthe coefficient of Trust is 0.09, which indicates that trust increases by 1 point, leading toan increase in happiness by 0.09 points on the 11-point Likert scale. However, theabsolute value of the coefficient of Income is 0.014, which implies that householdincome increases by 1 million yen, leading to an increase in happiness by 0.014 points
on the 11-point Likert scale. Therefore, the effect of a 1-point increase in Trust on the
5-point Likert scale is almost equivalent to an increase in 6.4 million yen(US$80,000).10 Based on the whole GCOE data sample, the average household income
is around 6.3 million yen. Therefore, the effect of a 1-point increase in Trust on the
5-point Likert scale is considered to be approximately equivalent to the average annual
income in Japan. This is an exceedingly large value, which seems to reflect theestimation bias of income effects. Therefore, its effect is thought to decrease drasticallyafter correcting for the bias (Powdthavee 2010:86–91).11 Instead of household income,10

Evaluation in US dollars is calculated based on the average foreign exchange rate in2011. This method was also applied in the other parts to evaluate the effect of trust.
11
Analysis using US data found that life events such as being widowed or marital
separation would make it necessary to provide an individual with US$100,000 extra per11

let us consider the effect of trust by using a marital status dummy (Unmarried). The
coefficient’s absolute value of Unmarried is 0.35 and its sign is negative, which meansthat unmarried people’s happiness levels were lower by 0.35 points than for marriedpeople. Therefore, a 4-point increase in Trust compensates for the gap in happinessbetween married and unmarred people.12
As for other control variables, the sign of coefficient of Age and Age squared isnegative and positive, respectively. Furthermore, they are statistically significant incolumns (3) and (4). This indicated that the relationship between age and happiness is
U-shaped, as often found in the happiness literature (Clark and Oswald 1996;
Blanchflower and Oswald 2004; Kuroki 2011). With respect to marital status, thecoefficient of Unmarried, Divorced, and Widow has the negative sign in all estimations.
They are almost statistically significant, which is in line with previous work (e.g.,
Oswald and Powdthavee 2008; Powdthavee 2008; Clark et al. 2008; Frijters et al.
2011).13 Turning to scales of residential area, Medium city, Small city, and Village
shows positive signs, with the exception of Small city. However, they are notstatistically significant in any column, which suggests that there is no difference inhappiness level between scales of residential place. Eight occupation dummies andseven residence dummies are included in the specification presented in column (4).
None show statistical significance; therefore, happiness levels do not differ between
occupations and types of residence although this is not reported in Table 2.
We now focus on the cross term between the after disaster dummy and degreeof trust in Table 3. The sign of After disaster  Trust is negative while being statisticallyinsignificant, which is not consistent with Hypothesis 2. Conversely, Trust shows apositive sign and is statistically significant at the 1% level. Therefore, the relationshipbetween trust and happiness does not change after the disaster in Japan although trust is
positively related to happiness.
annum (Blanchflower and Oswald 2004:1373).
12
In monetary terms, approximately US$300,000 compensates for the gap in happinessbetween married and unmarried people, which is equivalent to the UK (Powdthavee
2010:88).
13
Existing works also provided evidence that the effect of divorce (or death of spouses)
on happiness is not persistent several years after the event (e.g., Oswald andPowdthavee 2008; Clark et al. 2008; Frijters et al. 2011).
12

Table 4 shows that the cross term among three variables such as After disaster Trust  Damaged produces a positive sign and is statistically significant in allcolumns. The positive relationship between trust and happiness became stronger forresidents in the damaged area than in other areas after the disaster. This strongly
supported Hypothesis 3. Apart from this, it is interesting to observe that the coefficientof After disaster  Damaged shows the negative sign while being statistically significant
in all columns. The happiness level for residents in the damaged area declined directly
after the disaster, which is in line with Figure 2. Furthermore, considering the results ofAfter disaster in Table 2 and of After disaster  Damaged leads us to claim that thedisaster reduced the happiness levels in the damaged area, but did not change itthroughout Japan. Trust continues to show a significant positive sign in columns (1)–(4).
The discussion of the results shown in Tables 2–4 strongly supports Hypotheses 1 and 3.
In discussing the economic significance derived from the results shown in
Table 4, we now turn to the absolute values of the coefficients of cross terms presented
in column (4). The absolute value of the coefficient of After disaster  Trust  Damaged
is 0.36, while that of After disaster  Damaged is 1.30. These can be interpreted asfollows. Compared with other areas after the disaster, the happiness level of residents inthe damaged area is 1.30 points smaller when the effect of trust is not considered. The
value of the coefficient of Income is 0.014 even though it is not reported in Table 4.
That is, the negative effect of the disaster is estimated to be compensated by 93 million
yen (US$1.2 million), which is considered as a gigantic amount. However, as mentioned
earlier, it should be noted that the estimated amount of compensation was overestimatedbecause of the endogeneity bias (Powdthavee 2010). An increase in trust by 1 point on
the 5-point Likert scale leads to an increase in happiness by 0.36 points. This means thata 1-point increase of trust for residents of damaged areas reduces the gap of happinesslevels from residents of other areas by 0.36 points. Therefore, the happiness level ofresidents in the damaged area is 0.22 points smaller than those in other areas if the trustlevel is 3. However, the happiness level of residents in the damaged area is 0.14 points
larger than those in other areas if the trust level is 4. That is, the happiness level ofresidents in the damaged areas is possibly higher than others if their trust level is 4 or 5.
Therefore, the effect of trust is sizable on SWB and becomes crucial as a remedy for an
13unpredictable, gigantic shock such as a disaster.

4

Conclusion

The outcome of unexpected terrors has drawn the attention of researchers. Its
psychological impact on life satisfaction has been increasingly explored in empirical
works on natural disasters (Carroll et al. 2009; Luechinger and Saschkly 2009) andterrorism (Metcalfe et al. 2011). Social trust is known to be positively associated withlife satisfaction (Bjørnskov 2003). The effectiveness of social trust possibly depends on
the situation. For instance, formal institutions and markets do not function wellimmediately after an unexpected, devastating event. In this case, trust towards others
appears to become more important for avoiding chaos and turmoil. The role of social
trust in the chaotic situation after the disaster is worth analyzing because distrust
deteriorates the situation and in turn reduces social welfare. However, existing works
did not probe how and the extent to which the relationship between trust and happinesschanges after the disastrous event.
To deal with the relationship between trust and happiness after the disaster, thispaper used the individual-level panel data from Japan, which covered the period before
and after the Great East Japan Earthquake. By employing fixed-effects estimations, wefound that there is a positive relationship between trust and happiness and thisrelationship is strengthened for residents in the damaged area. This finding implies thatsocial trust plays a greater role in increasing happiness during a chaotic situation than intime of peace. We therefore derived the argument that social trust is a substitute forformal institutions and markets to mitigate the shock of disaster on psychologicalconditions such as happiness. Therefore, a trustful society is invulnerable to a gigantic
disaster.
Of course, there is an endogenous bias when trust is included as an independent
variable and so causality is ambiguous. Before the disaster, trust and happiness levels
differed between residents in the damaged area and other areas. It is more appropriate to
conduct an examination in the case that these values of residents living in the damaged
area are almost as the same as those of residents living in other areas before the disaster.
For closer examination, it is important to use instrumental variables to control for
14

endogenous bias, although it is difficult to identify the instrumental variables.
Furthermore, conditions before the event should be similar between the damaged areas
and other areas. Therefore, it is also worth conducting experiments in similar situations
before a disastrous event (Becchetti et al. 2012). These are remaining issues to beaddressed by future research.

Acknowledgement
This research uses microdata from the Preference Parameters Study of Osaka
University’s 21st Century GCOE program, Behavioral Macrodynamics based on
Surveys and Experiments, and its GCOE project, Human Behavior and Socioeconomic
Dynamics. We acknowledge the contribution from the program and project contributors:
Yoshiro Tsutsui, Fumio Ohtake, and Shinsuke Ikeda.
The first author gratefully acknowledge financial support in the form of
research grants from the Japan Center for Economic Research as well as the JapaneseSociety for the Promotion of Science (Foundation C 20368971, PI: Eiji Yamamura).
Further, the first author wishes to acknowledge the financial support of The Kikawada
Foundation.

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19

Figure 1. Association between trust and happiness.

Figure ２. Change of happiness levelFigure 3. Change of trust levelTable 1. Definition of variables used for estimation and its comparison between the damaged areas and other areas.
Definitions

Damaged

OthersThe degree of happiness.
Question: How would you rate you current level of happiness?
0(Very unhappy)- 10(Very happy)
The degree of trust.
Question: In general, most people are trustworthy.
1(completely disagree)- 5(completely agree)
It is 1 if the data was collected after the disaster (2011-2013), otherwise 0.
It is 1 if the data was collected in damaged areas (Iwate, Miyagi and
Fukushima prefectures), otherwise 0.

6.32

6.48

t-statistics
2.69***

3.18

3.14

1.29

---------------------

Age
Age square
Man
IncomeUnmarried
Divorce
Widow
FamilyMedium city

Ages
Square of ages
It is 1 if respondent is man, otherwise 0.
House hold income (million yen)
It is 1 if respondent is unmarried, other wise 0.
It is 1 if respondent is divorced, other wise 0.
It is 1 if respondent is widow, other wise 0.
Number of family members.
It is 1 if the residential place is city withthousand=<population<200 thousand), otherwise 0.

52.3---0.53
5.93
0.13
0.02
0.02
4.40
0.32

52.5
---0.45
6.32
0.12
0.04
0.03
4.02
0.42

0.39
------2.57**---------5.37***----

Small city

It is 1 if the residential place is city with population (<100 thousand),otherwise 0.

0.29

0.22

----

Village

It is 1 if the residential place is village or town, other wise 0.

0.13

0.08

----

Happiness
Trust
After disaster
Damagedpopulation(100

Note: The damaged areas are defined as Iwate, Miyagi and Fukushima prefectures. Numbers in parentheses are absolute value oft-statistics for test of mean difference between the damaged and other areas. . *, ** and *** indicate significance at the 10%, 5%
and 1% levels, respectively.

Table 2. Dependent variable is the happiness level (Fixed effects estimation).
(1)

(2)

(3)

(4)

After disaster

0.03(1.00)

0.04(1.19)

0.04(1.17)

0.02(0.52)

Trust

0.09***(5.21)

0.10***(5.43)

0.09***(5.16)

0.09***(5.04)

Age-0.05(-1.51)-0.05(-1.48)-0.06*(-1.75)-0.06*(-1.74)Age square

0.0004(1.63)

0.0004(1.56)

0.0005*(1.90)

0.0006**(2.09)

0.14***(2.80)

0.14***(2.81)

0.14***(2.82)

Unmarried-0.40**(-2.54)-0.35**(-2.11)

Divorce-0.27*(-1.70)-0.15(-0.92)

Widow-0.42***(-2.59)-0.39**(-2.14)

Family-0.001(-0.22)0.001(0.10)

Medium city

0.11
(0.60)

0.09(0.46)

Small city

-0.16(-0.42)0.002(0.01)

Village

0.24(0.82)

0.21(0.71)

IncomeOccupation
dummies
Residence
dummiesNot
includedNot
includedNot
included

IncludedNot
includedNot
includedNot
included

Included

Observations

16,697

15,17815059

14,379Note: Numbers in parentheses are t-statistics. *, ** and *** indicate
significance at the 10%, 5% and 1% levels, respectively. Occupation
dummies capture various sectors where respondent work such as
agriculture and fishery, mining, construction, financing, manufacturing,
real estate, transportation, education, energy, service and others.
Residence dummies capture types of residence such as detached house(in one’s possession), private rented house, public rented house, company
owned house, apartment house, rented room, dormitory and other one.

Table 3. Dependent variable is the happiness level and independent variables
include cross term between After disaster and Trust (Fixed effects
estimation).
(1)

(2)

(3)

(4)

After disaster*TrustAfter disaster-0.01(-0.42)-0.01(-0.78)-0.02(-0.83)-0.01(-0.72)0.06(0.80)

0.10(1.21)

0.10(1.24)

0.08(0.88)

Trust

0.09***(4.60)

0.10***(4.99)

0.10***(4.79)

0.10***(4.65)

Occupation
dummies
Residence
dummiesNot
includedNot
includedNot
included

IncludedNot
includedNot
includedNot
included

Included

Observations

16,697

15,17815059

14,379Note: Numbers in parentheses are t-statistics. ** and *** indicate significanceat the 5% and 1% levels, respectively. In all estimations, the set ofvariables used in Table 2 is included as independent variables, but they
are not reported here.

Table 4. Dependent variable is the happiness level and independent variables
include cross terms between After disaster, Trust and Damaged (Fixed
effects estimation).
(1)

(2)

(3)

(4)

After disaster*Trust*Damaged
After disaster*TrustAfter disaster*Damaged
Trust*Damaged
After disaster0.24**(2.11)

0.29**(2.31)

0.32**(2.57)

0.36***(2.73)-0.02(-0.87)-0.03(-1.21)-0.03(-1.30)-0.03(-1.21)-0.84**(-2.31)-1.05**(-2.54)-1.15***(-2.77)-1.30***(-2.98)-0.01(-0.02)0.02(0.20)

0.01(0.18)

0.01(0.12)

0.10(1.26)

0.14*(1.66)

0.15*(1.74)

0.13(1.39)

Trust

0.09***(4.48)

0.10***(4.83)

0.10***(4.65)

0.10***(4.53)

Occupation
dummies
Residence
dummiesNot
includedNot
includedNot
included

IncludedNot
includedNot
includedNot
included

Included

Observations

16,697

15,17815059

14,379Note: Effect of Damaged is completely captured by the fixed effects because
respondents did not move between prefectures in the sample. Numbers inparentheses are t-statistics. ** and *** indicate significance at the 5%
and 1% levels, respectively. In all estimations, the set of variables used inTable 2 is included as independent variables, but they are not reported
here.