Discussion Paper No. 122
Spatial analysis of subjective well-being in Japan

Anqi Li, Takaki Sato, and Yasumasa Matsuda

May, 2021

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANSpatial analysis of subjective well-being in Japan
Anqi Li1 , Takaki Sato∗2 , and Yasumasa Matsuda1
1

Graduate School of Economics and Management, Tohoku University, Sendai, Japan
2

Advanced institute for Yotta informatics, Tohoku University, Sendai, Japan

Abstract
This study investigates subjective well-being in Japan using a survey of 22,539 respondents in
46 prefectures in December 2019. We applied a Bayesian hierarchical model to the self-reported
well-being respondents, supposing that well-being is decomposed into regional and individual factors.
As a result, regional heteroscedasticity and individual factors are identified jointly, which clarifies
the interesting features of Japanese subjective well-being. From the identified regional factors in
prefectural levels, we find that coastal areas damaged by the 2011 tsunami and nuclear plant accidents
have the lowest subjective well-being. This finding suggests that residents in the regions have notrecovered and require additional mental and physical public support.

Keywords: Bayesian hierarchical model, Great East Japan Earthquake and Tsunami, Happiness survey,
Regional heteroscedasticity, Spatial error model, Subjective well-being

1

Introduction

For the last century, subjective well-being (or happiness) has been extensively investigated across social sciences.
Various studies in economics have identified factors associated with happiness. Existing happiness studies are
summarised in the following paragraphs.
Several happiness studies have focused on the effects of socio-demographics on individual happiness, such as(i) age (Oswald, 1997), (ii) marital status (Helliwell, 2003; Blanchflower and Oswald, 2004), (iii) health (Graham
et al., 2009). When viewed as a function of age, ageing and well-being for men and women have a U-shaped
relationship, with a minimum in late middle age (Clark and Oswald, 1996).
Figure 2 shows a typical shape. Several countries have shown similar patterns (Oswald, 1997; Gerdtham
and Johannesson, 2001). Helliwell (2003) noted that marriage is positively related to subjective well-being,∗ Corresponding author. E-mail address: takaki.sato.a6@tohoku.ac.jp1whereas being single is evaluated as a more serious negative factor than being divorced or widowed. The potential
influence of physical functioning on mental factors and the positive effects of physical health on well-being arewidely acknowledged (Rasciute and Downward, 2010).
Numerous studies (Clark and Oswald (1994); Gerlach and Stephan (1996); Gerdtham and Johannesson (2001))
describe the relationship between economic factors and happiness. The discussion about income and happinesshas become a controversial topic ever since Richard Easterlin published his work titled Does Economic Growth
Improve the Human Lot? (Easterlin, 1974)). He found that income has a diminishing effect on happiness, namely,
income does not improve happiness when it exceeds a threshold. In related studies on unemployment effects onhappiness, unemployment is believed to be a serious negative factor of happiness (Ohtake, 2004; Winkelmann,2014).
Happiness has a systematic relationship with regional and individual characteristics. Tella et al. (2003)
revealed that macroeconomic factors, such as GDP per capita and unemployment rate, impact Europeans’ wellbeing. Deaton (2008) used the Gallup World Poll to demonstrate a positive relationship between per capita
income and happiness, showing that rich countries have high average scores of happiness.
A geographical analysis of happiness has been attracting attention recently. The first law of geography states
that, ‘everything is related to everything else, but near things are more related than distant things’ (Tobler,1970). Residents in a region are expected to have similar socio-economic, political and cultural environmentsthat contribute to their well-being. Moreover, the social comparison mechanism shows that people are likely tocompare themselves with other people in neighbouring areas, especially those who are close to them. Furthermore,a region’s happiness determinants are likely to be similar to those of neighbouring regions. Therefore, evidence
supports the claim that happiness is spatially dependent.
Okulicz-Kozaryn (2011) argued that people across European regions exhibit substantial regional similarity
in happiness and that happiness and its determinants are spatially correlated. Stanca (2010) applied a two-stepmethod to the World Values Survey (WVS) and found that ignorance of geographical factors may result in bias inunderstanding happiness. Pierewan and Tampubolon (2014) applied a hierarchical model that regards happinessas a spatially dependent latent variable. Using this model, they checked how happiness in an area in Europe
is affected by its surrounding areas. They concluded that happiness is spatially dependent through unobserved
factors, implying that clusters of happiness are often observed.
This study conducts a geographical and individual analysis of happiness in Japan through a survey conductedin December 2019 with 22,539 respondents. We extend Pierewan and Tampubolon (2014)’s hierarchical model todescribe spatial behaviours accurately. Happiness is the sum of regional and individual factors. Regional factors
are given by a spatial regression with prefectural-level independent variables, such as social welfare expenditure(SWEs) and prefectural income, whereas individual factors are given by a regression with several individual
characteristics, such as age, sex and income. Regarding the model as a Bayesian hierarchical model, we employ
the so-called empirical Bayesian approach to examine happiness features in Japan.
The contributions of this paper are summarised in two points. First, we develop a spatial model that can
examine individual and regional components of happiness jointly by extending Pierewan and Tampubolon (2014)’s2

model. We express spatial factors by a regression model with errors following spatial autoregression, whereas
Pierewan and Tampubolon (2014) constructed a spatial autoregression only without regressors. SWE per capita
and ratio of forest area (RFA) in each prefecture will be used as the regressors, significantly improving our study’s
spatial factor evaluation. Second, the identified regional happiness detects severely low scores in the coastal
areas hit by the 2011 Great East Japan Earthquake and Tsunami. We will discuss how the natural disasterand subsequent nuclear plant accidents have been affecting life in the areas in terms of subjective well-being byreferring to several existing studies and our identified geographical distributions of happiness.
The remainder of this paper is organised as follows. Section 2 introduces in details Macromill Co., LTD’s
survey and the obtained individual-level dataset, including prefectural-level dataset obtained from e-Stat database.
Section 3 provides details of the hierarchical model for the individual and prefectural-level datasets. Section 4
examines the identified results, and Section 5 discusses the results in comparison with those of existing studies.
Finally, Section 6 presents a concluding remark.

2

Data and Methods

This section introduces the dataset together with an analytic strategy to conduct a spatial analysis of happinessin Japan. We mainly aim to detect regional characteristics by applying a spatial econometric model to survey
data on happiness for 22,539 respondents from all over Japan, except Okinawa.

2.1

Data

We confided the happiness survey in this paper to Macromill Co., LTD 1 , a market research company in Japan,
by which happiness for 22,539 respondents from all over Japan, except for Okinawa, was surveyed, including
several demographic information of gender, age, marital status, education level, number of children, personal andfamily incomes, occupation status and health conditions. The happiness survey was conducted in December 2019
for respondents in Japan. They were recruited online for an approximate correspondence of the distribution ofgender, age, residential place of prefecture and income to those of the national population census.
Happiness was recorded in the survey as a response to the question, ‘Currently, how happy do you feel? Score
the degree of your happiness between 10 (very happy) and 1 (very unhappy).’ Figure 1 shows the histogram of
the happiness survey measured in the 1–10 scale. The distribution is left-skewed, with the mean and standard
deviation evaluated as 6.290 and 1.951, respectively.
Table 1 summarises the respondents’ demographic information. In addition, Table 2 lists the size of respondents in each prefecture with the three prefectural-level variables of SWE per capita, gross prefectural domestic
product per capita (GPP) and RFA. These variables were collected from e-Stat database in Japan 2 . Prefecturallevel information will be used to identify regional heteroscedasticity.
Then, let us introduce the details on how the demographic information is summarised as category variables
1 https://www.macromill.com/
2 https://www.e-stat.go.jp/3

22.14 %21.2 %
20

15.38 %
14.75 %

Percent %15

10
7.01 %

6.32 %

6.28 %
5

2.88 %

2.45 %
1.6 %
0
1

2

3

4

5

6

7

8

9

10

Degree of Happiness

Figure 1: Histogram of responses of 22,539 samples to the question: ‘Currently, how happy do you feel?
Score the degree of your happiness between 10 (very happy) and 1 (very unhappy).’that will be incorporated as independent variables. We use age and gender to construct the categories of 22
groups, namely we divide all the respondents into two groups of female and male, each of which is categorised as
11 mutually disjoint subgroups corresponding to (1) age<20, (2)<25, (3)<30, . . . ,(10)<65 and (11)≥65.
As a result, we obtain 22 disjoint groups and define the group of female with age younger than 20 as thebase group. Personal income is categorised into seven mutually disjoint groups of income, i.e. (1)<2 million
yen, (2)<4 million, (3)<6 million, (4)<8 million, (5)<10 million, (6)<12 million and (7)≥12 million yen. The
group less than 2 million yen is set as the base. Family income is categorised into 10 groups of income, i.e.
(1)<2 million yen, (2)<4 million, (3)<6 million, (4)<8 million, (5)<10 million, (6)<12 million, (7)<15 million,(8)<20 million, (9)≥20 million yen and (10) as the group of no response. The group less than 2 million yen is
set as the base. As a result, personal and family incomes are the categorical variables with 7 and 10 subgroups,
respectively. Number of child is summarised as the dummy variable that is 1 for positive number of children
and 0 otherwise. Marital status is recorded as the category variable with five groups of (1) single, (2) marriedmale, (3) married female (full-time), (4) married female (part-time) and (5) married female (housewife). The
single group is set as the base. Occupation status is categorised into 11 disjoint groups, i.e. (1) civil servant, (2)manager, (3) employed(office), (4) employed(engineer), (5) employed(others), (6) self-employed, (7) freelancer,(8) part-timer, (9) student, (10) others and (11) unemployed. The unemployed group is set as the base. Healthcondition is summarised into five dummy variables corresponding with a response to the binary questions ondrinking, smoking, diabetes, hypertension and hyperlipidaemia. Education level categorised into five groups of4

(1) junior school, (2) high school, (3) junior college, (4) university and (5) graduate school. The junior school is
set as the base category.

2.2

Analytic strategy

For the demographics of xi as the independent variables i.e. age, gender, income etc., as stated above, the usualmodel for happiness yi of 1–10 scale for ith respondent is a regression given byyi = d + x0i β + εi , i = 1, . . . , N,

where d is the intercept and εi is an error sequence of independently and normally distributed random variables
with mean 0 and variance σε2 . Let us extend the regression to a spatial model detecting regional variations which
are not accounted for by the demographics. Denoting happiness for ith respondent residing in jth prefecture, we
extend the regression to a hierarchical one by
yij = dj + x0i β + εi , i = 1, . . . , nj , j = 1, . . . , p = 46,(1)

where dj is a latent variable in jth prefecture that follows a prefectural-level regression
dj = zj0 δ + uj , j = 1, . . . , p = 46,(2)

and zj is the prefectural-level variables of SWE, GPP and RFA. To express a spatial similarity of the regionalvariations in dj , we fit a spatial autoregression (SAR) to uj , given by

uj = ρ

pX

wjk uk + fj , j = 1, . . . , p = 46,

(3)

j=1

fj ∼ N (0, σf2 ),where wij is the first contiguity weight matrix playing a key role in spatial analysis, which is defined by

wjk =




1,

if prefectures j and k are neighbors sharing a boarder



0,

otherwise,

where j, k = 1, . . . , p = 46. The diagonal elements of wjj are designed to be 0. ρ in Equation (3), which needs to
be in (−1, 1) by the stationary condition, is the parameter that controls a strength of spatial correlation of uj .
The spatial correlation is higher when ρ is close to 1.
To account for happiness relative to the demographics and regional variables, our model in Equations (1)(3) can be regarded as a Bayesian hierarchical model. Parameter dj for the regional variation in Equation (1)has priors described in Equation (2), whereas β is supposed to have no priors, for which we assume a Gaussian
distribution with mean 0 and precision matrix 0. Fixing the hyperparameters ρ, δ and σf2 to describe the priors of5

dj , we will evaluate the posteriors dj and β through Bayes’ formula. We will specify the hyperparameters ρ, δ andσf2 by the so-called empirical Bayesian approach, where they are specified to maximise the marginal likelihood
given by marginalising out dj in Equation (1).
We introduce the empirical Bayesian approach in two steps. First, the hyperparameters ρ, δ and σf2 in Equation(2) are specified to maximise the marginal likelihood. Second, the posteriors of dj and β in Equation (1) areevaluate through Bayes’ formula. Our model in Equations (1)-(3) can be expressed conveniently in a matrix form.
Let n and m be the sizes of respondents and prefectures, respectively. Let J be the n by m matrix to express
the categorical variable of prefectures. For the ith row in J, jth column is 1 if i resides in jth prefecture and 0
otherwise. Arranging yij , xi , zj , dj . εi , uj and fj into the suitable vectors or matrices, we obtain the matrix
expression for our model by

Y = Xβ + Jd + ε,(4)d = Zδ + u,u = ρW u + f .

Let us start from the selection of the hyperparameters to maximise the marginal likelihood of Y that margins
out dj in Equation (1 through Equation (2). The marginal distribution substituting dj in Equation (1) with thatin Equation (2) is
N Xβ + JZδ, σε2 In + σf2 JR(ρ)J 0 ,where
R−1 (ρ) = (Im − ρW )0 (Im − ρW ).
Reexpressing the variance matrix, for τ = σε2 /σf2 , asσε2 In + τ J R(ρ)J 0 = σε2 Ω(ρ, τ ), say,we have the marginal log-likelihood function given bylog L(β̃, σε2 , ρ, τ ) = −(Y − X̃ β̃)0 Ω−1 (ρ, τ )(Y − X̃ β̃)n1
log(2πσε2 ) − log |Ω(ρ, τ )| −
,
2
2
2σε2

(5)

where X̃ = (X, JZ), β̃ = (β 0 , δ 0 )0 . Solving the first-order conditions with respect to β and σε2 , we obtaino−1nβ̃(ρ, τ ) = X̃ 0 Ω−1 (ρ, τ )X̃X̃ 0 Ω−1 (ρ, τ )Y ,
o0
n
o
1n
σε2 (ρ, τ ) =Y − X̃ β̃(ρ, τ ) Ω−1 (ρ, τ ) Y − X̃ β̃(ρ, τ ) .
n(6)

Substituting β̃(ρ, τ ) and σε2 (ρ, τ ) into the corresponding ones in Equation (5), we obtain the concentrated marginal

6

log-likelihood function,nn1
log L(ρ, τ ) = − (log(2π) + 1) − log(σε2 (ρ, τ )) − log |Ω(ρ, τ )|.
22
2
We estimate ρ, τ to maximise the concentrated marginal log-likelihood function and then evaluates the estimatorsfor β̃ = (β 0 , δ 0 )0 and σε2 through Equation (6). Then, we evaluate the posteriors of dj and β in Equation (4) fromthe priors specified with the hyperparameters ρ, τ, δ, andσε2 , which were estimated in the first step to maximise
the marginal likelihood. The prior for dj is specified asN Zδ, σf2 R−1 (ρ) ,whereas that of β is the non-informative prior specified by the normal distribution with mean 0 and precision 0
independent of dj . Thus, the prior precision of θ = (d1 , . . . , dm , β 0 )0 is
diag(σf−2 R(ρ), 0q ) = σε−2 diag(τ −1 R(ρ), 0q ) = σε−2 S0 (ρ, τ ), say,where 0q is the q by q 0 matrix with q given by the dimension of β. Applying the Bayes’ formula to Equation(4), we obtain the posterior of θ = (d1 , . . . , dm , β 0 )0 given by the normal distribution with the variance and meanevaluated for K = (J, X),
−1σε2 K 0 K + S0 (ρ, τ )= σε2 S1 , say,

(7)

and


0

S1 K Y + τ−1R(ρ)Zδ ,(8)

respectively.
The estimators of the hyperparameters ρ, δ, σf2 and σε2 are consistent and asymptotically normal under certain
mild conditions to maximise the marginal likelihood in Equation (5). See Sato and Matsuda (2021) for detailsof the conditions and proof. The condition justifies asymptotically our choice of the hyperparameters and hence
the t tests for β, δ through Equations(7) and (8), which shall be employed in the empirical analysis in the next
section.

3

Empirical Results

Table 3 presents the estimation results by fitting the model in Equations (1) and (2) to the happiness survey
data described in Section 2.1. The results are introduced in two parts. the effects of individual characteristics onhappiness for β in Equation (1) and the regional variations of happiness for dj together with ρ in Equations (2)and (3). See Section 2.1 for the details of the individual characteristics summarised as category variables.

7

3.1

Individual characteristics

Figure 2 below shows the estimated partial effects of gender and age. The figure shows a typical U-shaped curve;

0.23620.1247

0.106

0.0

0

−0.0144−0.0202−0.2398

coefficient−0.2909 −0.3114−0.3507−0.3582−0.4213 −0.4253−0.5−0.5998−0.6379−0.8466−0.9254−1.0−1.1417−1.2634−1.3123 −1.3341−1.3829

0

<2

5
<2

0
<3

5

<3

<4

0

5

<4

<5

0

5

<5

0
<6

5
<6

>6

5

age
GenderFemaleMale

Figure 2: Partial effects of age on happiness identified by the categorical variable of gender and age inEquation (1)the effect of age on happiness observed in several countries. See e.g. Blanchflower and Oswald (2008). The curveis relatively high in early adulthood, then falls, reaching its minimum in middle age and then rises after old age.
Age has its minimum effect at the 50s for male and female. It reaches the minimum at 50-55 and 55-60 years forfemales and males, respectively. Men have smaller coefficients than women in all age groups, indicating that men
are unhappier than women in all the age groups, ceteris paribus.
Figure 3 shows the partial effects of personal and family incomes identified in our analysis. The identified
positive and diminishing effect of income has been observed in several happiness studies in other countries. Seee.g. Easterlin (1974). The positive effect of personal income goes up gradually to the maximum at the group of
12 million Japanese yen, followed by a decrease beyond it. Family income has more significant positive effects onhappiness than personal income. The positive effects maximise at the group of 20 million Japanese yen. Beyond
the tipping point, the effects diminish.
Figure 4 summarises the partial effects of occupation status when the group of unemployment is the base.
The employed groups have negative partial effects, whereas the groups of manager, self-employed, freelancer andstudent have positive partial effects. The student group is statistically significant at the significance level of 1%.
Next, we consider the partial effects of health conditions on happiness using the five dummies of the threemajor adult diseases and habits of smoking and drinking. Smoking has negative effects of −0.2793, which is worse

8

1.3966

1.4

1.2

1.145
1.04511.0

coefficient

0.92050.8

0.73220.64370.6
0.47620.45330.39540.38550.4
0.3121 0.3179

0.2

0.14890.1268
0

0.0
0

5

10

15

20

Income (1 million yen)
IncomeFamilyPersonal

Figure 3: Partial effects of income on happiness identified by the categorical variables of personal income
and family income in Equation (1)0.4941coefficient

0.4

0.2

0.10990.08980.07150.0

0.0009−0.00630.0000−0.0987−0.1179−0.1472 −0.1518

t
d
e)
r
er)
r
rs)
er
nt
ye
an
ce
ge
ffic
he
ine
de
tim
plo
er v
na
lan
(ot
ng
d(o
a
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e
r t−
stu
e
(
ee
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y
f
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o
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e
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y
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s
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d
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Figure 4: Partial effects of occupation status on happiness identified by the categorical variable of occupation in Equation (1)9than those of the three diseases evaluated as −0.1596, −0.1478 and −0.0997, whereas only drinking has positive
significant effect at the 1% level in the health variables.
Table 3 shows contribution to happiness increases as education level raises. The partial effects of high school,
junior college, university and graduate school are monotonically increasing as 0.2388, 0.3112, 0.4623 and 0.5261,
respectively, when junior school is the base.
Finally, we consider the partial effects of marital status and number of children. People with children arehappier than those without, by 0.1829 on average, ceteris paribus. Compared with single status, married statusincreases the partial effect on happiness for women and men. Married men are happier than the single person
by 0.8662 on average, whereas 0.6484, 0.8433 and 0.9046 for full-time working wife, part-time working wife andhousewife than the single group, respectively. The housewife has the greatest partial effect among the marital
statuses.

3.2

Spatial effects

Let us move on to the estimation results for dj and ρ in Equations (2) and (3). At first, the ρ parameter that
controls spatial correlations is estimated at 0.8579, which is positively significant at the 1% significance level.
Thus, dj , happiness in jth prefecture after controlling individual characteristics, is geographically dependent withsmooth behaviours. Table 3 shows that the prefectural-level variables of logged SWE and RFA in Equation (2)are significant, whereas log GPP is non-significant at the 10% level. Therefore, SWE and RFA have clear effects
on regional variations of happiness unlike GPP.

Hokkaido

5.45

Aomori

5.40
Akita
Iwate

Yamagata

NiigataMiyagi

Fukushima

Ishikawa
Toyama5.35

Tochigi
Gumma
IbarakiNaganoSaitama

Fukui

Gifu
Yamanashi

Kanagawa

Tottori
Shimane

Kyoto

Aichi
Osaka

KagawaTokushima

Shizuoka

Mie

Nara
Wakayama

5.30

Ehime

Fukuoka

Kochi

Saga

Chiba

Shiga

Hyogo
Okayama

Hiroshima
Yamaguchi

Tokyo

Oita

Nagasaki
Kumamoto

MiyazakiKagoshima

5.25

5.20
NA

Figure 5: Regional variations of happiness identified by the prefectural dummies in Equation (2), which
was evaluated after controlling for the individual characteristics.

10

Figure 5 illustrates is the map of regional variations in Japan evaluated by the posterior mean of dj . Table 4shows the prefecture ranked in terms of the identified happiness of dj .

RegionCapital region
Chugoku
Hokkaido
Hokuriku
Kinkiken
Kitakanto
kyushuOkinawa
ShikokuTohokuToukaiFigure 6: Name list of regions in Japan
We find non-trivial regional variations of happiness in each prefecture. Let us briefly summarise the regionalfeatures detected by the analysis in Figure 5 together with Table 4. Figure 6 presents the name list of the regionsin Japan necessary to describe the results. Happiness in the south-western area tends to be higher than thatin the northeastern area, except for Hokkaido. Happiness in Kyushu, Kinki and Hokuriku regions is higher,
followed by Chubu and Kanto regions, whereas happiness in Tohoku region is lower. Kyushu region, located in
the southwestern part of Japan, is overwhelmingly the happiest region. Most prefectures in Kyushu are ranked
within the 10th place in the happiness ranking, such as Miyazaki, Kagoshima and Oita.
In addition, the islands of Shikoku and Hokkaido are noteworthy. Shikoku Island has less happy prefectures,
though surrounded by happier neighbours. In contrast, Hokkaido, the second largest isolated island in thenorthernmost part of Japan, displays a spatial similarity of happiness with its neighbouring prefectures of Aomori
and Iwate.

4

Discussion

This section discusses the comparisons of the findings described in the previous section with references to otherhappiness studies all over the world.

11

4.1

Individual components of happiness

The U-shaped effects of age on happiness identified in Figure 2 are not completely consistent between male andfemale. After controlling for personal socio-economic characteristics, happiness for females rises before 30 years
old, which is considered unmarried in current Japanese society. Arguably, this result is because for the majorityof Asian women, the period between the age of 20 and 30 is a period between leaving their parents’ family though
not having their family. This period is the time for females to study, work, invest and enjoy independence andautonomy. From a biological perspective, oestrogen (and fertility) in women hits the highest level from the midto late-20s before a decline (Easton et al., 2010). During this period, women are highly confident physically.
Furthermore, the identified U-shape for females is later than that for male. Thus, women are happier thanmen, which is consistent with the findings in several happiness studies, such as in Graham (2012). One possible
explanation is that men tend to have a higher aspiration and be more stressed than women in society (Frey andStutzer, 2010). As age increases, pressure from all social aspects increases, hence negatively exacerbating theirunhappiness level. Therefore, this condition also indicates that men’s happiness is more sensitive to age than thatof women.
Zimmermann and Easterlin (2006) revealed that marriage is one of the most important factors of happiness,
which is well-demonstrated in this study. In addition, we detect that housewives are the happiest in the category of
married women, with slight differences between them. The question of whether women are happier as housewives
than as working wives is a long-standing debate. Benin and Nienstedt (1985) found no statistically significant
difference in the happiness between a housewife and working wife. However, Treas et al. (2011) found a small but
statistically significant happiness advantage for housewives on cross-national data in 28 countries. They claimed
that housewives are slightly happier than full-time working wives, although they have no advantage over part-timeworkers. Beja (2014) examined the happiness of housewives relative to national economic levels and claimed thatworking wives and housewives in upper-income countries do not significantly differ and that the happiness gap
in low-income countries can reduce through social welfare programmes. These claims are consistent with ourdetected result on housewives in Japan as an upper-income country with intermediate social welfare programmes.
The existing findings in the relationship between parenthood with children and well-being are mixed and differ
across countries on social policy contexts. Haller and Hadler (2006) used WVS data and emphasised that children
have a non-significant effect on happiness after controlling for income. Glass et al. (2016) examined cross-nationalvariations in the association between parenthood and happiness and revealed lower happiness levels among parents
than non-parents in most advanced industrial societies. They found that the US shows the largest disadvantageof parenthood, followed by Ireland, Greece and the UK. Having children in these advanced societies may be afinancial burden. Nevertheless, in other countries, most notably Norway and Hungary, parents are happier thansingles. This finding is consistent with this research detecting that, in Japan, having children has a positive
partial effect on happiness. The variations of the effects of children on happiness across countries may be due tothe public support for parenting, including differences in paid parenting leave, legally mandated vacation and sickdays and workplace flexibility.

12This present study examines the relationships between diseases, including drinking and smoking habits andhappiness and is in accordance with existing studies (see e.g.Argyle (2013)). The significant negative partial effects
of diabetes, hypertension and hyperlipemia come from their damaging impacts on health. Detecting a significant
negative effect of smoking habit on happiness, which is a major cause of lung cancer, is reasonable (Das, 2003).
Unexpectedly, drinking habit has significant positive partial effects, considering that drinking is harmful to health,i.e. alcohol is the fifth biggest risk factor for premature death and disability globally (Lim et al., 2012). Geiger
and MacKerron (2016) showed a strong and consistent moment-to-moment relationship between happiness anddrinking events. Alcohol drinking is associated with considerably high happiness levels at that moment, i.e. 10.79
points on a 0–100 scale. Therefore, pouring oneself a drink increases one’s happiness by 11%, which is in line with
our results.
One of the most robust findings on happiness in the area of economics is that unemployment is destructive
to well-being (Clark and Oswald, 1994; Tella et al., 2003). The identified partial effects in our study support
this finding, except for the employed group. One possible reason of the negative effect of the employed group isthat the base group of unemployed includes retired individuals and housewives with good family income and thatcertain employed individuals are under increased time pressures of commuting (Hilbrecht et al., 2014; Chatterjee
et al., 2020). Existing studies have shown that self-employment has a multifaceted relationship with well-being.
Alesina et al. (2004) argued on a survey in the US and Europe that self-employed individuals have lower happinesslevels than full-time employees and that self-employment positively impacts high-income individuals only. This
result is consistent with our findings that self- employment has a non-significant positive partial effect. Bardasi
and Francesconi (2004) demonstrated that part-time jobs in the UK are detrimental to subjective well-being.
This finding is in accordance with our result showing the negative but statistically non-significant partial effectpart-time jobs.
The relationship between education level and happiness has not reached a consensus. An increasing number ofstudies suggest that the relationship between higher education and subjective well-being is either non-significant
or negative (Powdthavee, 2010; Powdthavee et al., 2015). However, other researchers examining surveys on several
countries showed that education level is positively related to happiness after controlling for income (Gerdtham
and Johannesson, 2001; Inoguchi and Shin, 2009), which is consistent with our results. Nikolaev and Rusakov(2016) tested the hypothesis that the extent to which education makes individuals happy depends on their current
age. Evidence shows that people with higher education are more likely to be happier on average than their lesseducated counterparts. In addition, Nikolaev (2018) used longitudinal data from the Household, Income andLabour Dynamics in Australia Survey to examine the link between higher education and three different measuresof subjective well-being. He found that individuals with higher education are more likely to report higher levels ofwell-being and more satisfied with most life domains (financial, employment opportunities, neighbourhood, local
community, children at home) compared with less educated persons.

13

4.2

Spatial components of happinessWe jointly evaluated the spatial and individual components of happiness. Spatial components are regional factorsof happiness after controlling for individual characteristics. Table 4 presents these spatial components, indicatingthat the coastal areas of Chiba, Ibaraki, Ibaraki and Fukushima constitute the group with the lowest happinessin Japan. Let us consider certain backgrounds for the results.
The Great East Japan Earthquake on 11 March, 2011, was an exceptionally severe disaster, the worst in thememory of contemporary Japan (Yokoyama et al., 2014). The damage resulting from these related disasters—theearthquake, tsunami and nuclear plant accident— has been shared throughout Japan. The losses from thedisaster include not only economic costs but also strong mental impacts on human beings, including the afflicted
and non-afflicted areas of Japan (Ohtake and Yamada, 2013).
Comparison with the economic losses, many studies have found that people’s subjective well-being after
the disaster has not changed as much as expected. Using panel data following victims for 6 months after theearthquake, Sugano (2016) showed that a significant impact on expenditure and employment but less significant
impact on subjective well-being and health of the elderly survivors. Uchida et al. (2014) tracked the well-being ofyoung people in Japan outside of the afflicted areas before (December 2010) and after (March 2011) the earthquake.
Results suggested that the young people had slightly increased their general well-being after the earthquake
compared with before the earthquake. Furthermore, Ishino et al. (2012) used large panel data consisting of
responses from over 4000 households in all over Japan and found that more Japanese people replied their happiness
improved and that they have become more altruistic after the earthquake. One possible interpretation is thatreflecting on the Great East Japan Earthquake had prompted people to re-evaluate their lives. This mindset
promoted prosocial behaviours, such as making donations, volunteering and donating improved happiness. The
studies reviewed here recognised that happiness has not changed as much as expected in negative aspects after
the earthquake.
By contrast, according to Tanji et al. (2018) and Hikichi et al. (2019), evidence shows that although mosteconomic losses have been recovered after the earthquake through reconstruction, the psychological distress of theaffected people requires careful attention. Tanji et al. (2018) conducted a longitudinal observation on 284 adults
who had lived in prefabricated temporary housing in Miyagi in northeastern Japan. This study investigated
the association between the period of residence in prefabricated temporary housing and psychological distress inthe time of baseline survey (September 2011) and the follow-up survey (January 2016). They found that the
proportion of individuals with more severe psychological distress was higher among participants who had lived in
prefabricated temporary housing for a long period. Among the participants with lower psychological distress atthe baseline, cases of significant deterioration of psychological distress were reported in the group pf people who
lived in prefabricated temporary housing over 4 years.
Hikichi et al. (2019) conducted a follow-up study of older survivors for six additional years with three waves
of surveys. They found that the experience of housing loss was persistently associated with cognitive disability(4.9% and 13.0% in the second and third waves, respectively) and that the proportions of stroke and diabetes

14

increased over time (1.9%–4.4% for stroke, 12.3%–14.3% for diabetes). Thus far, one may suppose that releasing
the psychological pain of the affected people is difficult in the long run and that the depressed impact on survivors
will last for a long time. This condition confirms our results to a certain extent that the coastal regions in Japan
have the lowest subjective well-being, though 10 years have passed since the Great East Japan Earthquake.

5

Conclusion

In this study, we consider a method for applying a spatial hierarchical model for multilevel datasets composed ofindividual- and prefectural-level samples. The model can simultaneously detect the partial effects of personal andregional characteristics on personal happiness. Happiness depends not only on individual characteristics but also
on their living conditions, neighbours and natural environment. Happiness across prefectures in Japan is spatiallydependent: prefectures with a certain degree of happiness are also surrounded by a similar degree of happinessat their neighbouring prefectures. After controlling for individual- and prefectural-specific characteristics, spatial
dependence remains strongly.
Further studies are required to detect subjective well-being in Japan in more details. Finding other possible
essential factors on happiness is necessary by exploring unobservable variables. What are the characteristics
that make human objectively feel happier or unhappier? Bright sunshine in Kyushu and beautiful snow scenes in
Hokkaido are possible candidates for objective happiness, whereas high land prices and traffic congestion in Tokyoand harsh climate and frequent earthquakes in Tohoku are possible characteristics of objective unhappiness. Mostof these characteristics are available in the age of big data. Using big data on subjective well-being can help widely
investigate the essential factors of happiness in future studies.

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Appendices
Tables
18

19

12356

10183

7423
>=1

single person

6803

0

Married status

Occupation

Childrenmarried male573

education level

3670

578

386

1003
part-timer

manager

civil servant

freelancer

12826

9713

6782

junior school

high schoolnodrinking

4134

yes

1483

<4m

<2 million yen

Education level

Health status

Family income

5736

10058

<4m

<2 million yen

Personal income

540

478

Male

469

551

<25

FemaleAge<201456

student

4012

8860

university

19652

no

3872

<8m

1747

<8m

947

904

<35

512

others2887

employed(engineer)

2822

housewife

married female

smoking

employed(office)2663

full-time5315

junior college

2887

yes

5201

<6m

3859

<6m

870

843

<30

4185

unemployed2792

employed(others)2605

part-time

1009

21669

no

1058

<12m

260

<12m

1437

1356

<45

1028

self-employeddiabetes

graduate school

870

yes

2289

<10m

679

<10m

1100

1087

<40

Table 1: Summary of demographic information of the whole respondents

292

<20m

1211

1213

<55

3272

yes

19267

no

hypertension

564

<15m

200

>12m

1378

1362

<50

3516

unknown

1169

1242

<65

2022

yes

20517

no

hyperlipidemia

130

>20m

1167

1171

<60

962

1082

>=65

20

Sample size
1476
203
248
1063
258
125
1110
241
354
337
495
973
1048
536
200
228
194
211
1709
91
263
486
305

SWE
54.3
74.1
73.1
46.7
71.7
64.3
62.1
54
57.9
55.7
59
79.3
59.9
57.5
56.4
64.9
60.1
77.3
52.2
79.9
72
68
58.5

GPP
3633
2553
2558
3020
2656
3157
2800
3005
2803
3098
3068
2617
2896
3116
2908
2737
2945
2414
3180
2567
2517
2926
3155

RFA(%)
42.2
70.5
63.8
30.4
70.3
73.9
44.5
67.9
79
63.8
71.8
67.9
66.7
31
66
74.9
46.4
63.4
38.8
83.3
60.4
74.2
64.3

Prefecture
MiyagiMiyazaki
NaganoNagasaki
Nara
Niigata
Oita
Okayama
Osaka
Saga
Saitama
Shiga
ShimaneShizuoka
Tochigi
TokushimaTokyo
TottoriToyamaWakayama
Yamagata
Yamaguchi
Yamanashi

Sample size
450
148
367
198
281
382
196
382
1582
130
1353
237
89
594
338
123
2517
118
215
116
213
214
142

SWE
52.6
72.7
61.4
75.2
62.2
53.8
68.6
57.2
68
69.9
48
60.1
74.4
46.3
53.5
66.4
64
75
49.5
74.7
55.1
62.2
63.4

GPP
2926
2407
2882
2519
2522
2826
2605
2732
3056
2509
2958
3181
2619
3300
3318
2973
5348
2407
3295
2949
2758
3048
2873

RFA(%)
55.9
75.8
75.5
58.4
76.8
63.5
70.7
68
30.1
45.2
31.9
50.5
77.5
63.1
53.2
75.2
34.8
73.3
56.6
76.4
68.7
71.6
77.8

Note: SWE: Social welfare expenditure per capita(thousand yen) ; GPP: Prefectural income per person (thousand yen); RFA(%): Ratio of forest area

Prefecture
Aichi
Akita
Aomori
Chiba
Ehime
Fukui
Fukuoka
Fukushima
Gifu
Gumma
Hiroshima
Hokkaido
Hyogo
IbarakiIshikawa
Iwate
KagawaKagoshima
Kanagawa
Kochi
Kumamoto
Kyoto
Mie

Table 2: Sample size with regional variables in each prefecture

21(0.1623)(0.1076)(0.1083)(0.1025)(0.1023)(0.1043)(0.1028)(0.0987)(0.1041)(0.0448)(0.0945)(0.0575)(0.0674)(0.1265)(0.0257)(0.0377)(0.0886)(0.0724)(0.0936)(0.0974)(0.0746)(0.0962)(0.0532)(0.0748)(0.0365)-0.0144-0.3114∗∗∗-0.4253 ∗∗∗0.2362∗∗-0.6379 ∗∗∗-1.2634∗∗∗-1.3829∗∗∗0.1489∗∗∗0.3855∗∗∗0.1268∗∗0.7322∗∗∗
1.3966∗∗∗0.1007 ∗∗∗-0.1478∗∗∗-0.0063-0.1518∗∗∗
0.10990.00090.2388 ∗∗∗0.5261∗∗∗0.8662 ∗∗∗0.9046∗∗∗0.1829 ∗∗∗S.E.

0.8579∗∗∗0.1922∗

Coefficients

Note: ∗ p < 0.1; ∗∗ p < 0.05; ∗∗∗ p < 0.01

Prefectual level :Spatial effect ρ
Log SWE
Individual level :20<Female<25
Female<40Female<55
Female>65
Male<30
Male<45
Male<60
Personal Income (P) :200<P<400
P<1000
Family income (F):200<F<400
F<1000F<2000
Health Conditions:
Drinking
Hypertension
Occupation Status:
Civil servant
Employed(engineer)
Freelancer
Others
Education level :High school
Graduate school
Marital Condition:
Married male
Married female: housewife
Children

Variables

Married female: full-time

Junior college

Manager
Employed(others)
Part-timer

Smoking
Hyperlipidemia

F<600
F<1200F>2000

P<600
P<1200Female<30Female<45Female<60
Male<20
Male<35
Male<50
Male<65

Constant
Log GPP

Variables

0.6484∗∗∗0.3112∗∗∗0.0715-0.0987-0.1179-0.2793∗∗∗-0.0997∗∗

0.4533∗∗∗0.9205∗∗∗1.1450∗∗∗0.3121∗∗∗0.4762∗∗∗0.1247-0.3582 ∗∗∗-0.3507∗∗∗0.1060-0.8466∗∗∗-1.3123∗∗∗-0.9254∗∗∗3.527 ∗∗∗
0.066

Coefficients

Table 3: Estimated Results(0.0613)(0.0781)(0.1116)(0.0703)(0.0738)(0.0375)(0.0445)(0.0581)(0.0800)(0.1838)(0.0562)(0.1338)(0.1030)(0.1004)(0.1035)(0.1060)(0.1032)(0.0996)(0.1040)(0.7578)(0.0805)

S.E.

Married female: part-time

University

Employed(office)Self-employedStudent

Diabetes

F<800
F<1500
F=unknown

P<800
P>1200Female<35Female<50
Female<65
Male<25
Male<40
Male<55
Male>65

Log RFA

Variables

0.8433 ∗∗∗(0.0798)(0.0769)(0.0691)(0.0787)(0.0823)-0.1472∗∗∗
0.08980.4941∗∗∗

0.4623 ∗∗∗(0.0653)(0.0616)(0.0975)(0.0563)-0.1596 ∗∗∗0.6437∗∗∗1.0451∗∗∗0.2712 ∗∗∗(0.0711)(0.1626)(0.1040)(0.1001)(0.1025)(0.1036)(0.1016)(0.1025)(0.1072)-0.2909 ∗∗∗-0.4213 ∗∗∗-0.0202-0.2398 ∗∗∗-1.1417 ∗∗∗-1.3341∗∗∗-0.5998∗∗∗
0.3179∗∗∗0.3954∗∗∗(0.0477)

S.E.

0.1264 ∗∗∗Coefficients

22

Happiness

5.46435.45395.45295.45175.43875.42585.42585.41375.4131
5.41
5.40795.40595.40595.39245.39145.39035.38495.38415.37945.378
5.37515.37315.3724Prefecture

MiyazakiKagoshima
Wakayama
Oita
Kumamoto
Kyoto
Nagasaki
Yamanashi
Fukui
ShimaneNara
Hokkaido
TottoriNagano
Gifu
Kochi
Mie
Saga
Hyogo
Fukuoka
Akita
Yamaguchi
Aomori0.04060.04040.03240.03840.03940.03090.04310.03030.03220.03920.03220.03630.03610.02990.03140.04940.03130.041
0.03040.03580.035
0.03940.0364

S.E.
KyushuKyushuKinkiken
KyushuKyushuKinkiken
KyushuHokuriku
Hokuriku
Chugoku
Kinkiken
Hokkaido
Chugoku
Hokuriku
Toukai
Shikoku
ToukaiKyushuKinkiken
KyushuTohokuChugokuTohokuRegion
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Rank
Hiroshima
Iwate
Ishikawa
Okayama
Shiga
Tokushima
Ehime
Shizuoka
Tokyo
Gumma
Aichi
Fukushima
Yamagata
Niigata
Osaka
ToyamaTochigi
Kanagawa
Miyagi
KagawaIbaraki
Saitama
Chiba

Prefecture
5.36885.36565.36055.36025.35325.34955.34835.34675.34365.336
5.324
5.32395.32225.32185.32115.31475.30925.30115.28585.2702
5.24695.22455.2128Happiness
0.038
0.03550.03220.03450.03170.05010.04980.02990.02870.03030.02870.03140.03230.03030.03020.03080.03090.02950.03340.049
0.03050.02940.0296

S.E.

Table 4: Rankings of Estimated Happiness across Japan

ChugokuTohokuHokuriku
Chugoku
Kinkiken
Shikoku
Shikoku
ToukaiCapital region
Kitakanto
ToukaiTohokuTohokuHokuriku
Kinkiken
Hokuriku
Kitakanto
Capital region
TohokuShikoku
Kitakanto
Capital region
Capital region

Region
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46

Rank