Shao Health Economics Review (2022) 12:50
https://doi.org/10.1186/s13561-022-00396-6

RESEARCH

Does less working time improve life
satisfaction? Evidence from European Social
Survey
Qinglong Shao*

Abstract

Background: Worktime is one of the main drivers of life satisfaction, and a balanced distribution of working hours
and leisure hours directly impacts feelings of well-being. Based on previous studies, we seek to confirm this relation-
ship in the European context and explore other potential driving forces of life satisfaction. Health condition as the
mediating variable is also examined.

Methods: This article uses an ordered probit model to analyze the impact of working time on life satisfaction using
data extracted from the most recent round (wave 10) of the European Social Survey (ESS). Hypotheses are proposed
to test the impact of working time on life satisfaction, the mediating effect of health in the worktime–satisfaction
nexus, and the effects of social inclusion, social trust, feelings of safety, and digitalization on life satisfaction.

Results: The results reveal a negative and significant correlation between hours of work and life satisfaction, thus
implying that a shorter working week can improve Europeans’ life satisfaction. Health is found to be an important
intermediate variable that plays an essential role in the dynamic through which working times influence life satisfac-
tion. Further, we find that those in the middle class prefer to work shorter hours to achieve a higher feeling of satisfac-
tion and that high earners to a lesser extent, while low earners generally show no preference. Employees of private
firms are more satisfied with shorter working hours, while satisfaction for those working in public institutions is not
affected by changes in hours worked. Finally, we verify the robustness of our estimations by replacing life satisfaction
with happiness.

Conclusions: Working fewer hours contributes to higher life satisfaction in Europe, and health plays an essential
mediating role in this relationship. Social inclusion, social trust, feelings of safety and digitalization all play a factor in
improving life satisfaction. Compared to other job categories, private sector employees can achieve greater life satis-
faction from reducing their total working time.

Highlights

➢ Ordered probit model is used to analyze worktime-satisfaction nexus in Europe.

➢ A shorter working schedule can improve life satisfaction.

➢ Health plays a mediating role in worktime-satisfaction nexus.

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco
mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Open Access

*Correspondence: [email protected]; [email protected]

Institute of Chinese Studies, Freie Universität Berlin, Fabeckstr, 23-25,
14195 Berlin, Germany

Page 2 of 18Shao Health Economics Review (2022) 12:50

Background
There is a small number of studies that theoretically
explain and empirically analyze the determinants of life
satisfaction [1, 2], and income has been identified as an
important driver of satisfaction in numerous other stud-
ies [3, 4]. However, life satisfaction may also remain con-
stant over time despite rising wealth [5, 6]. Therefore,
“we must be highly skeptical of the view that long-term
changes in the rate of growth of welfare can be gauged
even roughly from changes in the rate of growth of out-
put” [7]. On the contrary, economic recessions, for exam-
ple, are likely to reduce psychological well–being, which
entail not only declining income and increasing unem-
ployment but also a sense of emotional loss [8]. Stress
caused by the COVID-19 pandemic has been found to
be strongly correlated with life satisfaction in Poland [9],
and the mental health of jobless people in China should
be of particular concern [10]. Personal characteristics,
such as age, gender, and marital status, are also impor-
tant influencing factors [11, 12]. The introduction of the
European Working Time Directive (EWTD) has greatly
reduced training hours for workers such as surgical resi-
dents, which has enhanced their job satisfaction [13].

In recent years, a growing number of empirical stud-
ies have explored the role of working time in well–being.
Their findings are mixed, particularly on whether a
shorter working week has positive or negative effects
on well-being, and the call for more in-depth research
remains unanswered. Using different methods, particu-
larly the ordered probit and logit models, scholars have
investigated the worktime–satisfaction nexus based on
various national- and regional-level surveys conducted
in, for example, the US, UK, Germany, Australia, France,
Korea, and the EU [14]. We review the literature below to
explore the nexuses between working time and work sat-
isfaction and job satisfaction and overall life satisfaction.
It is worth noting that life satisfaction is not necessarily
correlated with job satisfaction [15]. The empirical lit-
erature on the worktime–satisfaction nexus is presented
chronologically in Table 1.

Working time and working hours satisfaction
Several scholars have investigated how worktime influ-
ences people’s satisfaction with their time spent at work
from the perspective of gender. Booth and Ours [20] find
that working full-time-and especially overtime-dissat-
isfies women, whereas men appear to have the highest

working hours satisfaction if they work full-time, but
not overtime. In a later study, the same authors consider
interdependence within the family and focus on part-
nered men and women to investigate the cross-partner
effects of part-time work on well-being. Their findings
show that both women and men are more satisfied with
their working hours if they work part-time [21]. To tackle
the endogeneity problem, Rudolf [1] uses a fixed-effects
ordered logit model to examine the worktime–satisfac-
tion nexus. His results indicate that, for Korean wives,
a shorter working week may raise their life satisfaction,
which significantly declines if more working hours are
required; likewise, overtime work can reduce the work-
ing hours satisfaction of Korean husbands. Moreover,
women are likely to suffer disproportionately when both
partners’ inter-role strain intensifies [31]. In sum, the
empirical outcomes in various countries indicate that
women have higher working hours satisfaction when
working fewer hours, while men are satisfied with part-
time or full-time jobs according to their own preferences.
Both genders are clearly dissatisfied with overtime work.

Working time and job satisfaction
In general, scholars have verified that a balanced work-
time distribution between work and life increases sat-
isfaction and health [32], and evidence shows that a
mismatch between desired and actual working times
negatively affects German nurses’ job satisfaction [28].
Regarding gender differences, Booth and Ours [20, 21]
reveal a significant positive correlation between part-
time work and job satisfaction in both British and Aus-
tralian females, but not their male counterparts, thus
implying that only women are generally happier when
working fewer hours. Rudolf ’s [1] findings in the Korean
context confirm this relationship: job satisfaction signifi-
cantly declines in wives required to work long hours, and
overtime work can reduce husbands’ job satisfaction. He
also tests cross-partner effects and finds that husbands
working fewer hours can increase Korean wives’ job sat-
isfaction. Using a German longitudinal dataset from 1999
to 2009, Holly and Mohnen [23] find a significant positive
relationship between working hours and job satisfaction
for all employees and separately for men and full-time
workers. This suggests that employees, and particularly
male employees, can achieve a feeling of accomplish-
ment from their overwork. Therefore, it is not strange to
observe a significant negative effect on job satisfaction

➢ Trust, social inclusion, safety and digitalization can promote life satisfaction.

➢ Private firm employees prefer shorter work weeks while others show no preference.

Keywords: Life satisfaction, Working time, Ordered probit model, Health, Job category

Page 3 of 18Shao Health Economics Review (2022) 12:50

Ta
bl

e
1

Su
m

m
ar

y
of

th
e

lit
er

at
ur

e
on

th
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sa

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an
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sh

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po

si
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re
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tio
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in
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an

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an
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:
a

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(a
t 5

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[2

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Page 4 of 18Shao Health Economics Review (2022) 12:50

Ta
bl

e
1

(c
on

tin
ue

d)

A
ut

ho
r(

s)
O

ut
co

m
e

va
ri

ab
le

(s
)

M
et

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ds

D
at

a
st

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ct

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e

M
ai

n
re

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Bo
ot

h
an

d
O

ur
s

(2
00

9)
[2

1]
W

or
ki

ng
h

ou
rs

s
at

is
fa

ct
io

n,
jo

b
sa

tis
fa

ct
io

n,
a

nd
li

fe
s

at
is

fa
ct

io
n

Fi
xe

d-
eff

ec
ts

o
rd

er
ed

lo
gi

t m
od

el
20

01
–2

00
4;

H
IL

D
A

S
ur

ve
y

W
om

en
a

re
h

ap
pi

er
w

ith
p

ar
t-

tim
e

jo
bs

,
an

d
th

ei
r p

ar
tn

er
s

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ki
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fu
ll-

tim
e

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n

en
ha

nc
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ei

r s
at

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B

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so

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–
se

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in
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s

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by

ra
is

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sp
ec

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o

f s
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Kn
ab

e
an

d
Rä

tz
el

(2
01

0)
[3

]
Li

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s

at
is

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n

Po
ol

ed
o

rd
er

ed
p

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; p

ro
bi

t-
ad

ju
st

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19

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–2

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6;

G
SO

EP
Fo

r b
ot

h
m

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a

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, t
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tio
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sh
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b
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in

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ha

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u

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b
y

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cl

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in

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th

e
w

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tim
e

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ria

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e

be
ca

us
e

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im
pa

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f
w

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h

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n

ha
pp

in
es

s
is

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m

al
l.

Th
is

fi
nd

in
g

di
ffe

rs
fr

om
th

at
o

f P
ou

w
el

s
et

a
l.

(2
00

8)

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ku

lic
z-

Ko
za

ry
n

(2
01

1)
[2

2]
H

ap
pi

ne
ss

Po
ol

ed
d

at
a

or
de

re
d

lo
gi

st
ic

m
od

el
19

96
, 2

00
1;

E
ur

ob
ar

om
et

er
s

ur
ve

y
se

rie
s

in
E

ur
op

e
19

96
, 1

99
8,

2
00

0,
a

nd

20
02

; G
SS

Co
m

pa
re

d
to

A
m

er
ic

an
s,

Eu
ro

pe
an

s
ar

e
ha

pp
ie

r w
ith

le
ss

w
or

k.
B

ot
h

po
pu

la
–

tio
ns

ra
tio

na
lly

s
ee

k
to

m
ax

im
iz

e
th

ei
r

ut
ili

ty
: A

m
er

ic
an

s
ca

re
m

or
e

ab
ou

t w
or

k
ou

tc
om

es
, w

hi
le

E
ur

op
ea

ns
c

ar
e

m
or

e
ab

ou
t w

or
k

pr
oc

es
se

s

H
ol

ly
a

nd
M

oh
ne

n
(2

01
2)

[2
3]

Li
fe

s
at

is
fa

ct
io

n,
jo

b
sa

tis
fa

ct
io

n
Fi

xe
d

eff
ec

t r
eg

re
ss

io
n;

O
LS

19
99

–2
00

9;
G

SO
EP

Th
ey

fi
nd

a
p

os
iti

ve
re

la
tio

ns
hi

p
be

tw
ee

n
lif

e
sa

tis
fa

ct
io

n
an

d
lo

ng
w

or
ki

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h

ou
rs

,
w

hi
le

th
e

de
si

re
to

re
du

ce
w

or
ki

ng
h

ou
rs

ha

s
a

ne
ga

tiv
e

im
pa

ct
o

n
sa

tis
fa

ct
io

n

Ru
do

lf
(2

01
4)

[1
]

Su
bj

ec
tiv

e
w

el
l-b

ei
ng

Fi
xe

d-
eff

ec
ts

o
rd

er
ed

lo
gi

t m
od

el
19

98
–2

00
8;

K
or

ea
n

La
bo

r a
nd

In
co

m
e

Pa
ne

l S
tu

dy
(K

LI
PS

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(2

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5)

[2
4]

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Page 5 of 18Shao Health Economics Review (2022) 12:50

Ta
bl

e
1

(c
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tin
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s)
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[2

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lf-
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po
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w

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ei
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20
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; G
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Fo
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be

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di
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t a

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(2

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8)

[2
8]

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in

de
r-

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ax

ac
a

de
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m
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tio

n
19

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–1

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d

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G

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–

m
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(E

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ci

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ly

fo
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w

ith
c

hi
ld

re
n

Page 6 of 18Shao Health Economics Review (2022) 12:50

Ta
bl

e
1

(c
on

tin
ue

d)

A
ut

ho
r(

s)
O

ut
co

m
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ri

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(s
)

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at

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ct

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ai

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su
lts

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n

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2)

[3
1]

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or

k
tim

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tis
fa

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St

ru
ct

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m

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20

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, E

SS
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up
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tim

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an

d
re

st
le

ss
s

le
ep

Page 7 of 18Shao Health Economics Review (2022) 12:50

only when employees want to reduce their working
hours.

Wu [26] explores the relationship between working
hours and job satisfaction based on the heterogeneity of
efforts and rewards for three occupations in China: farm-
ers, industrial workers, and public servants. He finds an
inverted U-shaped relation between working hours and
job satisfaction such that working moderate working
hours (i.e., 6–7  h per day) maximizes job satisfaction,
whereas longer or shorter working hours may reduce
well-being. Since work contents and incomes vary across
the three occupations, the impacts of working hours dif-
fer. Wu [26] finds that this relationship is stronger for
farmers and public servants with high income than for
industrial workers with high income. This may be attrib-
utable to factory employees being constantly engaged in
repetitive physical work, such that their work and leisure
are largely constrained by strict management regulations
and overtime pay comes at the expense of their health.
The situation differs for farmers and public servants.
These observed differences in the interaction between
occupations and the heterogeneity of working hours have
important implications for China’s government, indus-
tries, and workers.

Working time and overall life satisfaction
Researchers have examined the role of working time in
the income–happiness nexus. Pouwels et  al. [19] find
that the wealth effect on happiness would be underesti-
mated if the working time variable were to be excluded,
and that this underestimation is significant for men but
not for women. This suggests that worktime is important
in determining happiness. Following that study, Knabe
and Rätzel [3] re-examine their findings by expanding
the 1999 German Socio-Economic Panel from cross-
sectional data to a panel dataset with eight subsequent
waves. Using the Probit-adjusted OLS, which is a more
widely recognized method in the happiness literature,
their results differ from those of Pouwels et al. [19] in that
they find no supportive evidence that income’s impact
on happiness tends to be downward biased without the
worktime variable. Accordingly, they propose that work-
ing time only plays a peripheral role in determining hap-
piness. In fact, it is common for research outcomes to be
contradictory because researchers frequently use differ-
ent methods and data.

Okulicz-Kozaryn [22] is the first to test empirically
whether working less increases happiness more among
Europeans than it does among Americans. The evidence
confirms that this is the case, which he attributes to the
fact that, in general terms, Americans care more about
the work outcomes whereas Europeans place more value
on work processes. This might be explained by the high

competitiveness that characterizes the free market econ-
omy in the US. Valente and Berry [25] find that Latin
Americans prefer part-time jobs, while US citizens prefer
to work longer hours. This is compatible with the find-
ing of Okulicz-Kozaryn [22] that US employees usually
tend to work longer hours. Okulicz-Kozaryn and Golden
[27] deepen their analysis by proposing that limited flexi-
time does not increase happiness and that a more flexible
work schedule is needed to increase an individual’s life
satisfaction. They also find in a later study in the US that
the greater the instability and unpredictability of work
schedules, the lower an individual’s subjective life satis-
faction is [2]. An inverted U-shaped relation of working
time and life satisfaction is found by Collewet and Loog
[24], which implies that increasing working hours can
enhance well-being, but beyond the peaking point of 37 h
per week, well-being declines. However, the effects of
working time on the life satisfaction of part-time employ-
ees are too weak to confirm. For full-time male workers,
increasing working hours may reduce well-being, but this
is not the case for full-time females.

Based on above discussions, it can be seen that these
studies focus on the role of working time in the income–
satisfaction nexus, and few studies have comprehensively
explored the influence of working time on life satisfac-
tion or sought to verify the mediating role of health and
the effects of other essential driving forces such as social
inclusion, social trust, feelings of safety, and digitaliza-
tion. The worktime–satisfaction nexus in different job
categories thus remains unexplored. To fill this gap, we
use the recently released European Social Survey (ESS)
data [33] to explore the correlation between working
hours and life satisfaction among Europeans. In doing
so, we make four contributions to the literature. First, we
examine the promoting effect of working time on life sat-
isfaction and the mediating effect of health in the work-
time–satisfaction nexus. Second, the promoting effects of
social inclusion, social trust, feelings of safety and digital-
ization on life satisfaction are examined. Third, the effect
of wealth on working time is examined, and we show
that income levels influence workers’ preferences with
regard to working hours in Europe, with mid and high
earners preferring to work less for a higher life satisfac-
tion and low earners showing no preference. Fourth, the
worktime–satisfaction nexus in multiple job categories is
examined, while few studies focused on this point.

Methods
Hypotheses
The effect of working time on life satisfaction
The impact of working time on overall life satisfaction
has been more extensively studied in the related lit-
erature. In advanced European countries, a work–life

Page 8 of 18Shao Health Economics Review (2022) 12:50

balance with enough leisure hours has been found to
improve overall life satisfaction, and this relationship is
especially significant among men [29, 30]. Other studies
focus exclusively on overtime work. For instance, Holly
and Mohnen [23] find that overtime work shows a highly
significant positive effect on life satisfaction. Weston
et  al. [16] explore the impact of long working time on
well-being for full-time employed fathers with partners
and dependent children in Australia and find a negative
correlation, with well-being declining as working hours
increase. However, long working time is not necessarily
associated with lower well-being for fathers working long
hours because the extra income and feeling of accom-
plishment increase their happiness. Golden and Wiens-
tuers [17] indicate that mandatory overtime work has
mixed impacts on life satisfaction: being required to work
extra hours increases satisfaction in some while reducing
it in others. This effect depends on the interplay between
the positive effects (e.g., worktime pay, sense of achieve-
ment, etc.) and negative effects (e.g., work-family inter-
ference, work stress, etc.). Clark and Senik [18] refer to
the different structures of the French and British labour
markets to explain the respective worktime–happiness
nexuses in these two countries and find that the French
are happier with more working hours, while the British
prefer a shorter work week. Booth and Ours [20] exam-
ine the part-time work effect and find that women with
children are happier if they can work part-time jobs for
less than 15 h per week while raising children. They also
find that men with children aged from 5 to 15 years are
less happy than men with children of other ages. For cou-
ples without children, they find that part-time jobs make
men happier, while the number of working hours has no
impact on women’s life satisfaction. In this study, we use
the actual working hours rather than contracted hours of
work to explore their impact on European’s life satisfac-
tion using the latest 2020 data.

H1: Working time negatively affects life satisfaction.

The mediating effect of health on the worktime–satisfaction
nexus
Although working time preferences differ substantially
among individuals, overemployment (i.e., when actual
hours exceed desired hours) has a significantly negative
effect on workers’ health [28, 32]. Evidence indicates that
longer working hours have an adverse impact on health
[34], and work–life imbalance (i.e., a mismatch between
desired and actual working hours) may also reduce
employees’ self-perceived health conditions [35]. Noda
[29] finds that self-reported health is a determinant fac-
tor that is positively associated with life satisfaction in
OECD countries, and its impact on life satisfaction is not

as significant as work–life balance due to the fact that
Europeans take it good public health for granted. Thus,
this relationship may be stronger than work–life balance
in developing countries. In this study, health is employed
as the intervening variable, and we examine its mediating
role in the worktime–satisfaction nexus for Europeans
using the latest ESS data.

H2: Health is the mediating variable in the work-
time–satisfaction nexus.

Other potential driving forces on life satisfaction
Recent studies focus on the impacts of certain personal-
ity traits on life satisfaction, such as self-reported social
inclusion, social trust, feelings of safety and digitaliza-
tion. People tend to experience high levels of life satisfac-
tion when their physical, social, and psychological needs
are met. Social inclusion, as a sense of being liked and
accepted, is proven to be positively correlated with life
satisfaction [36, 37]. Not surprisingly, home confinement
during COVID-19 pandemic reduced people’s life satis-
faction [38]. Social trust is positively associated with well-
being, and it is a stronger determinant than income in
advanced economies while this is not the case in develop-
ing ones [39]. In China, the happiness of males and urban
residents is more likely to be affected by social trust than
the happiness of female and rural residents [40].

Feelings of safety includes multiple aspects in terms of
social, economic, and personal security. Most economic
and social security depends upon familial solidarity and
savings [41], but the welfare system also helps to provide
this security [42]. Evidence in China confirms the effect
of socio-economic security on life satisfaction [36]. Using
the 2011 Swiss Crime Survey, Staubli et al. [43] confirm
the detrimental effects of theft, attempted burglary and
consumer fraud on happiness, and Kuroki [44] reveals
that experiencing burglary and robbery reduced it in
the Japanese context and that crime victimization hurts
homeowners more than renters. In this study, we use
European respondents’ data and expect to find a positive
association between safety and life satisfaction. Digitali-
zation may reduce social costs and enhance both work
efficiency among government workers and convenience
in people’s daily lives, thus promoting feelings of life sat-
isfaction [45, 46]. Referring to Wang et  al. [47], we use
time spent on the internet to represent digitalization
and investigate its impact on satisfaction. We expect to
find a positive significant correlation, in line with recent
studies.

H3: Social inclusion is positively associated with life
satisfaction.

Page 9 of 18Shao Health Economics Review (2022) 12:50

H4: Social trust is positively associated with life sat-
isfaction.
H5: Safety is positively associated with life satisfac-
tion.
H6: Digitalization is positively associated with life
satisfaction.

Data collection and model specifications
The data are extracted from the ESS, which is an aca-
demically driven multi-country survey that has devel-
oped a series of social indicators, including attitudinal
indicators. Ten ESS surveys have been conducted since
2002, and we use the latest (tenth) round survey in 2020
with 18,060 valid respondents. The life satisfaction ques-
tion reads: All things considered, how satisfied are you
with your life as a whole nowadays? We use this as the
dependent variable in our analysis. The independent vari-
ables are the paid and unpaid working hours per week.
Detailed survey questions and descriptions of the indica-
tors are listed in Table 2. In addition to the life satisfac-
tion and working time variables, we also include personal
characteristic indicators, including health, social inclu-
sion, social trust, feelings of safety, digitalization, income,
marital status, gender, age, religion, and education. The
mediating role of health is tested in this study and we
further explore the worktime–satisfaction nexus in the
three income levels (low-, mid- and high-income) and six
job categories (central or local government, other public
sector (such as education and health), state-owned enter-
prise, private firm, self-employed, and other). We divide
income level into three equal groups with low-, mid- and
high-income.

Robustness checks are applied by replacing life satis-
faction with happiness such that the happiness question
reads: Taking all things together, how happy would you
say you are? Strictly speaking, life satisfaction and hap-
piness have different connotations: the former reflects an
individual’s cognitive judgment about the compatibility
of living circumstances based on their own work and life
experiences [48, 49], while the latter is a hedonic/emo-
tional evaluation of their current state of mind [50]. For
example, Lara et al. [51] regarded life satisfaction as the
cognitive indicator of well-being and examined its asso-
ciation with current happiness. However, Schyns [52]
found a close association between life satisfaction and
happiness and suggests an interchangeable use of these
two indexes. Mainstream literature follows this course
and employs these two indexes to explain the individual’s
subjective well-being [53–56]. Caner [57] estimates and
compares the regression results using life satisfaction
and happiness as outcome variables respectively. This
study replaces life satisfaction with happiness to check its

robustness. The reliability of our analysis is further veri-
fied if the outcomes after variable substitution are similar.
Pairwise correlations for the dependent variables and the
explanatory variables are reported in Table 3. The results
illustrate two facts: first, most variables are significantly
correlated at the 10% level, and second, working time is
negatively correlated with life satisfaction as well as other
explanatory variables except for gender and age. The
observations highlight the importance of careful multi-
variate econometric analysis.

The ordered probit model was proposed by McElvey
and Zavoina [58] for the analysis of categorical, non-
quantitative choices, outcomes, and responses. To tackle
the single crossing property problem inherent in stand-
ard logit/probit models (i.e., that the signs of the mar-
ginal effects can only change once when moving from
the smallest to the largest categories), Boes and Winkel-
mann [59] propose four alternative models: the general-
ized threshold, random coefficients, finite mixture, and
sequential models. The ordered probit model is suitable
for this study [60] considering that the dependent vari-
ables—life satisfaction and happiness—are ordinal data
that range from 0 to 10. More importantly, the ordered
probit model takes into account unobserved heteroge-
neity and ordinarily in life satisfaction scales while using
full information contained in the data [1]. As both the
ordered probit and logit models are commonly employed
to analyze such ordinal data, we choose the former since
it is widely used in the related literature [18, 61, 62]. The
basic equation of the ordered probit model is:

where yi represents the dependent variable and y∗i the
latent variable, denoting 11 levels of life satisfaction. Xi
is a vector of explanatory variables that assesses the attri-
bution of life satisfaction, and βi is the coefficient of Xi ,
a vector of estimated parameters to be projected, which
represents the impact magnitude of the independent on
the dependent variables. Finally, εi is unobserved white-
noise disturbance, where E(εi).

Moreover, since the coefficients of the ordered probit
model cannot be directly explained while the estima-
tors are very similar to the ordinary least squares (OLS)
model, we also construct the following alternative econo-
metric specification following Ronning and Kukuk [63]:

where Satisfactioni is the life satisfaction level reported
by individual i , Worktimei is the reported working hours
per week reported by individual i , �Individuali is the
vector of the respondent’s individual characteristics, and
µi is an error term. It is worth noting that the results are

(1)y∗i = Xiβi + εi

(2)
Satisfactioni = αWorktimei +�Individuali + µi

Page 10 of 18Shao Health Economics Review (2022) 12:50

Ta
bl

e
2

Su
rv

ey
q

ue
st

io
ns

a
nd

d
es

cr
ip

tio
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o
f t

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ia

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fr
om

th
e

ES
S

da
ta

se
t

W
e

re
co

de
E

du
ca

tio
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as
fo

llo
w

s:
w

e
se

t 5
20

a
nd

b
el

ow
(i

nc
lu

di
ng

5
20

a
nd

0
00

) t
o

1,
b

et
w

ee
n

61
0

an
d

62
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to
2

, b
et

w
ee

n
71

0
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d
72

0
to

3
, a

nd
8

00
a

nd
a

bo
ve

to
4

. M
ar

ita
l s

ta
tu

s i
s

re
co

de
d,

w
he

re
1

d
en

ot
es

m
ar

rie
d,

ot

he
rw

is
e

it
is

0
. T

he
H

ea
lth

v
ar

ia
bl

e
in

cl
ud

es
b

ot
h

ph
ys

ic
al

a
nd

m
en

ta
l h

ea
lth

So
ur

ce
: E

ur
op

ea
n

So
ci

al
S

ur
ve

y
[3

3]

Va
ri

ab
le

s
Su

rv
ey

q
ue

st
io

ns
Re

sp
on

se
s

Li
fe

sa
tis

fa
ct

io
n

B2
7:

A
ll

th
in

gs
c

on
si

de
re

d,
h

ow
s

at
is

fie
d

ar
e

yo
u

w
ith

y
ou

r l
ife

a
s

a
w

ho
le

n
ow

ad
ay

s?
Sc

or
ed

fr
om

0
to

1
0,

w
he

re
0

m
ea

ns
e

xt
re

m
el

y
di

ss
at

is
fie

d
an

d
10

m
ea

ns
e

xt
re

m
el

y
sa

tis
–

fie
d

W
or

kt
im

e
F3

0:
R

eg
ar

dl
es

s
of

y
ou

r b
as

ic
o

r c
on

tr
ac

te
d

ho
ur

s,
ho

w
m

an
y

ho
ur

s
do

/d
id

y
ou

n
or

m
al

ly

w
or

k
a

w
ee

k
(in

y
ou

r m
ai

n
jo

b)
, i

nc
lu

di
ng

a
ny

p
ai

d
or

u
np

ai
d

ov
er

tim
e?

H
ou

rs
w

or
ke

d
pe

r w
ee

k,
b

et
w

ee
n

0
an

d
16

8
h

H
ea

lth
C

7:
H

ow
is

y
ou

r h
ea

lth
in

g
en

er
al

?
‘V

er
y

ba
d’

=
1

; ‘B
ad

’ =
2

; ‘F
ai

r’ =
3

; ‘G
oo

d’
=

4
; ‘V

er
y

go
od

’ =
5

So
ci

al
in

cl
us

io
n

C
4:

C
om

pa
re

d
to

o
th

er
p

eo
pl

e
of

y
ou

r a
ge

, h
ow

o
ft

en
w

ou
ld

y
ou

s
ay

y
ou

ta
ke

p
ar

t i
n

so
ci

al
a

ct
iv

iti
es

?
‘M

uc
h

le
ss

th
an

m
os

t’ =
1

; ‘L
es

s
th

an
m

os
t’ =

2
; ‘A

bo
ut

th
e

sa
m

e’
=

3
; ‘M

or
e

th
an

m
os

t’ =
4

;
‘M

uc
h

m
or

e
th

an
m

os
t’ =

5

D
ig

ita
liz

at
io

n
A

3:
O

n
a

ty
pi

ca
l d

ay
, a

bo
ut

h
ow

m
uc

h
tim

e
do

y
ou

s
pe

nd
u

si
ng

th
e

in
te

rn
et

o
n

a
co

m
–

pu
te

r,
ta

bl
et

, s
m

ar
tp

ho
ne

o
r o

th
er

d
ev

ic
e,

w
he

th
er

fo
r w

or
k

or
p

er
so

na
l u

se
?

Ty
pi

ca
l t

im
e

sp
en

t o
n

th
e

in
te

rn
et

p
er

d
ay

, i
n

m
in

ut
es

Tr
us

t
A

4:
U

si
ng

th
is

c
ar

d,
g

en
er

al
ly

s
pe

ak
in

g,
w

ou
ld

y
ou

s
ay

th
at

m
os

t p
eo

pl
e

ca
n

be
tr

us
te

d,
o

r
th

at
y

ou
c

an
’t

be
to

o
ca

re
fu

l i
n

de
al

in
g

w
ith

p
eo

pl
e?

Sc
or

ed
fr

om
0

to
1

0,
w

he
re

0
m

ea
ns

y
ou

c
an

’t
be

to
o

ca
re

fu
l a

nd
1

0
m

ea
ns

th
at

m
os

t
pe

op
le

c
an

b
e

tr
us

te
d

Sa
fe

ty
C

6:
H

ow
s

af
e

do
y

ou
—

or
w

ou
ld

y
ou

—
fe

el
w

al
ki

ng
a

lo
ne

in
th

is
a

re
a

af
te

r d
ar

k?
‘V

er
y

un
sa

fe
’ =

1
; ‘U

ns
af

e’
=

2
; ‘S

af
e’
=

3
; ‘V

er
y

sa
fe

’ =
4

In
co

m
e

F4
1:

W
hi

ch
le

tt
er

d
es

cr
ib

es
y

ou
r h

ou
se

ho
ld

’s
to

ta
l i

nc
om

e,
a

ft
er

ta
x

an
d

co
m

pu
ls

or
y

de
du

ct
io

ns
, f

ro
m

a
ll

so
ur

ce
s?

Sc
or

ed
fr

om
0

to
1

0,
w

he
re

0
m

ea
ns

e
xt

re
m

el
y

lo
w

a
nd

1
0

m
ea

ns
e

xt
re

m
el

y
hi

gh
, f

or

w
ee

kl
y,

m
on

th
ly

, a
nd

a
nn

ua
l a

m
ou

nt
s

G
en

de
r

F2
: S

ex
‘M

al
e’
=

1
; ‘F

em
al

e’
=

0

Ag
e

F3
: A

ge
Ca

lc
ul

at
ed

b
y

bi
rt

h
ye

ar

M
ar

ita
l s

ta
tu

s
F1

1:
W

ha
t i

s
yo

ur
le

ga
l m

ar
ita

l s
ta

tu
s?

‘Y
es

’ =
1

; ‘N
o’

=
0

Re
lig

io
n

C
11

: D
o

yo
u

co
ns

id
er

y
ou

rs
el

f a
s

be
lo

ng
in

g
to

a
ny

p
ar

tic
ul

ar
re

lig
io

n
or

d
en

om
in

at
io

n?
‘Y

es
’ =

1
; ‘N

o’
=

0

Ed
uc

at
io

n
F1

5:
W

ha
t i

s
th

e
hi

gh
es

t l
ev

el
o

f e
du

ca
tio

n
yo

u
ha

ve
s

uc
ce

ss
fu

lly
c

om
pl

et
ed

?
‘H

ig
h

sc
ho

ol
o

r l
ow

er
’ =

1
; ‘B

ac
he

lo
r’s

d
eg

re
e’
=

2
; ‘M

as
te

r’s
d

eg
re

e’
=

3
; ‘D

oc
to

ra
l d

eg
re

e’
=

4

Jo
b

ca
te

go
ry

F3
2:

W
hi

ch
o

f t
he

ty
pe

s
of

o
rg

an
iz

at
io

n
on

th
is

c
ar

d
do

/d
id

y
ou

w
or

k
fo

r?
‘C

en
tr

al
o

r l
oc

al
g

ov
er

nm
en

t’ =
1

; ‘O
th

er
p

ub
lic

s
ec

to
r (

su
ch

a
s

ed
uc

at
io

n
an

d
he

al
th

)’ =
2

; ‘A

st
at

e-
ow

ne
d

en
te

rp
ris

e’
=

3
; ‘A

p
riv

at
e

fir
m

’ =
4

; ‘S
el

f-
em

pl
oy

ed
’ =

5
; ‘O

th
er

’ =
6

Page 11 of 18Shao Health Economics Review (2022) 12:50

Ta
bl

e
3

Co
rr

el
at

io
n

an
al

ys
is

a d
en

ot
es

th
at

th
e

co
rr

el
at

io
n

is
s

ig
ni

fic
an

t a
t t

he
5

%
s

ig
ni

fic
an

ce
le

ve
l (

2-
ta

ile
d)

. J
ob

c
at

eg
or

y
is

n
ot

in
cl

ud
ed

b
ec

au
se

it
is

n
ot

a
n

or
di

na
l v

ar
ia

bl
e

an
d

w
e

us
e

it
to

d
iv

id
e

di
ffe

re
nt

g
ro

up
s

Va
ri

ab
le

s
1

2
3

4
5

6
7

8
9

10
11

12
13

Li
fe

sa
tis

fa
ct

io
n

1
1.

00
00

W
or

kt
im

e
2

−
0

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23

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ea

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00

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us

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Page 12 of 18Shao Health Economics Review (2022) 12:50

presented in forest plots to be visually friendly, referring
to Becker and Kennedy [64], Lechner and Okasa [65] and
Kostka et al. [66].

Results
The impact of working time on life satisfaction
and the mediating effect of health
To examine the relationship between weekly hours
worked and self-reported life satisfaction, we present the
estimation results of the ordered probit model in Fig. 1.
All models control for a set of basic individual charac-
teristics. In the basic estimation of Model 1, the weekly
working time is negatively and significantly correlated
with life satisfaction, thus implying that fewer working
hours can raise life satisfaction. Two explanations can
be offered for this finding. First, Europeans have a cul-
tural norm of familyism and are happier working fewer
hours to have more time to discharge family responsi-
bilities and enjoy family relationships [25]. Second, the
income tax rate is always high in European countries in
order to support the welfare system. This suggests that
“larger portions of labor earnings [are] being taken away,
so the marginal return to labor [is] lower, disincentiviz-
ing European workers to labor longer” [67]. Thus, H1 is
supported.

Model 2 and 3 test the mediating effect of health in the
worktime–satisfaction nexus. In Model 2, working hours
positively and significantly affect health, and health posi-
tively and significantly affects life satisfaction in Model 3.
Thus, we identify a significant mediating effect of health
in the worktime-satisfaction nexus referring to Wang
et  al. [47], which is also in line with Wu [26]. Among
Americans, declining health is primarily responsible for
driving down life satisfaction beyond midlife [68]. There-
fore, H2 is confirmed. With regard to the four potential
influencing factors, the results illustrate a positive sig-
nificant impact of trust, social inclusion and feelings of
safety on Europeans’ life satisfaction at the 1% signifi-
cance level and the 10% significance level for digitaliza-
tion, thus implying that a high-trust social environment,
a life with numerous social activities, feelings of safety
and more time spent on the Internet (include both the
leisure and work hours) could improve life satisfaction.
As such, H3-H6 are confirmed.

Regarding individual characteristics, income is found
to be an important driver of life satisfaction at the 1% sig-
nificance level. This effect has been confirmed by many
prior studies [8, 19, 69, 70] and is generally interpreted
as “more income brings greater happiness” [71]. Consist-
ent with prior studies, age is positively and significantly
correlated with life satisfaction in Model 3, which takes
health into consideration, thus implying that elders
are generally happier than those in their youth [19, 25].

This is influenced by the excellent social welfare system
in Europe, as well as wealth accumulated over time. The
negative effect of gender on life satisfaction implies that
females are more likely to be happy than males and that
marriage makes people happy. Religious people are not
necessarily happier than non-religious people. Educa-
tion shows no significant correlations with the dependent
variable, which differs from the findings of Tella et al. [8]
and reflects that the education–satisfaction nexus varies
across countries. Possible explanations for this finding
may be that people with higher levels of education are
more likely to have higher salaries and more social status,
but also have more responsibilities and heavier burdens,
which may result in no net effect on life satisfaction.

Worktime–satisfaction nexus in different income groups
and job categories
Prior research mainly focuses on the impact of income
on life satisfaction, with very few studies analyzing
how working time impacts satisfaction among differ-
ent income groups and job categories. In this section,
we aim to deepen our analysis by identifying the corre-
lations between weekly working hours and self-reported
life satisfaction for the three income groups and six job
categories. The results are shown in Fig.  2. As can be
seen, the worktime–satisfaction nexus is significant at the
1% level in the mid-income group and at the 10% level
in the high-income group, which implies that mid and
high earners tend to working less to achieve life satisfac-
tion. This result is within our expectations because those
in the middle-class must work more to accumulate more
wealth whereas the marginal revenue of work is declining
for those who already possess it. With regard to the vari-
ous types of work, we find that employees of private firms
prefer to work less to achieve a feeling of life satisfaction,
while no significant relations are found in public institu-
tions such as central/local government, education and
health institutions or state-owned enterprises.

Robustness check
This section checks the robustness of the above empiri-
cal results by replacing the life satisfaction variable with
happiness. As shown in Figs. 3 and 4, the results are very
similar to those in Figs.  1 and 2, thus confirming the
robustness of our results.

Discussion
Research on the determinants of life satisfaction have
evolved from being income-driven to being driven by
multiple factors that generally include those analyzed in
this study (i.e., working hours, social trust, social inclu-
sion, feelings of safety and digitalization). In advanced
European countries, a balanced distribution of work and

Page 13 of 18Shao Health Economics Review (2022) 12:50

Fig. 1 Estimation results of the impact of working time on life satisfaction and the mediating effect in 2020 using the ordered probit model. Notes:
Red dots denote the coefficients; blue bars denote the 95% confidence interval; *, **, and *** denote p-values at 10%, 5%, and 1% significance
levels, respectively. The same conventions are followed in all figures

Fig. 2 Estimation results of the impact of working time on life satisfaction at various income levels and job categories. Control variables are omitted

Page 14 of 18Shao Health Economics Review (2022) 12:50

Fig. 3 Estimation results of the impact of working time on life satisfaction and the mediating effect of health in 2020 using the ordered probit
model

Fig. 4 Estimation results of the impact of working time on happiness at various income levels and job categories. Control variables are omitted

Page 15 of 18Shao Health Economics Review (2022) 12:50

leisure hours is more important than income, as satis-
faction comes from multiple economical, spiritual, and
psychological sources, and economic satisfaction fulfils
psychological needs by providing resources such as the
leisure hours need to develop personal interests and care
for the family. This likely explains why women prefer a
shorter workweek, in that partnered women who work
more hours still carry the burden of caring for the fam-
ily, whereas very few men are primarily responsible for
ordinary housework. Therefore, although women gener-
ally work as many total hours as men, they tend to prefer
a shorter working week [72]. Moreover, if society cannot
provide women with sufficient childcare and family-care
hours or adequate pay, then it is not surprising to find
increasing numbers of women working fewer hours to
increase their well-being. Besides, an enhanced feeling of
satisfaction is found among retired elderly people who do
not have to work and can freely arrange their time, and
their self-rated mental health increased as well [73].

Compared to Europeans, Americans’ satisfaction
mainly derives from their work, particularly among man-
datory rather than non-mandatory overtime workers,
although both report higher stress than those who work
no extra hours [17]. Rudolf [1] proposes that “workers
with these very high hours are compensated with (non-
observable) non-monetary rewards, such as higher sta-
tus and decision-making power (wage-employed) or
higher self-determination (self-employed).” Rothbard and
Edwards [74] also point out, from a psychological per-
spective, that “instead of avoiding unpleasant role experi-
ences, people actively try to solve the problems that make
such experiences unpleasant, which requires investing
time in those roles.” This suggests that the problem-solv-
ing effects are triggered by unpleasant experiences [75],
and people prefer to tolerate working long hours in the
short term not because they like those hours but because
they anticipate increased utility in the long term. In this
regard, long working hours, even when mandatory, can
be seen as an investment in the US, which explains why
mandatory overtime workers achieve more satisfaction
than those working less.

Prior research has confirmed that self-rated health con-
dition, including both mental [76] and physical health
[77], is one of the main driving forces of life satisfaction
(rather than the opposite, see Shields and Price [78]). For
example, people with acute and chronic physical illnesses
have lower levels of well-being [78], and disability can
also reduce an individual’s life satisfaction [79]. In gen-
eral, there are two possible ways in which physical health
affects life satisfaction: the physical suffering caused by
disease directly affects individual life satisfaction on one
side; and on the other side, physical diseases cause psy-
chological stress and affect satisfaction. Because of the

worry and uncertainty about the disease, most patients
will suffer from anxiety and depression; physical pain and
psychological pressure interact with each other, forming
a vicious cycle that jointly affects individual satisfaction.
This effect is particularly prominent for the elderly, who
are vulnerably affected by illness. Except for the high
risks brought by physical diseases [80], self-rated life
satisfaction is also confirmed to be significantly affected
by mental health in the elderly population [81], and in
certain conditions mental illness has a greater impact
on satisfaction than physical illness [82]. Consider that
approximately 15% of adults aged 60 and over suffer
from a mental disorder, as reported by the World Health
Organization [83], mental health problems in the elderly
should not be ignored to guarantee the general life qual-
ity of the elderly. In light of this, actions should be taken
to provide training for health professionals in providing
care for older people and develop age-friendly services
and settings. This also reminds the governments to put
more emphasis on people’s mental health care in con-
structing the universal healthcare system and formulat-
ing the long-term healthy development plan.

Life satisfaction is also strongly affected by the fre-
quency of engaging in social activities [84], and “the
greater the extent of participation, the greater the degree
of happiness reported” [85]. In fact, work is also a type
of social participation. In a high-trust environment, indi-
viduals are generally convinced that the people around
them, as well as the government, are honest. Such an
environment can promote feelings of satisfaction. On
the contrary, in a low-trust environment, people tend to
worry more. They feel they must always be defensive in
case others try to cheat, exploit or otherwise take advan-
tage of them. This also relates to their feelings of safety,
as happiness tends to be higher in areas with lower crime
rates. In the current digital era, and especially during the
COVID-19 pandemic, the impact of digitalization on life
satisfaction is not as significant as the other three driv-
ing forces. This is because certain individuals may not
achieve feelings of satisfaction by spending more time in
internet, such as employees who work online. Thus, this
factor exhibits a less significant correlation than the other
three driving forces. Moreover, we observe that employ-
ees of private firms tend to prefer working less to achieve
higher life satisfaction while changes in working time
shows no impact for individuals who are self-employed
and employees of public institutions. This is because
approximately 81% of the respondents from private
firms work 40  h or more, thus reducing their working
hours could significantly improve their life satisfaction.
In addition, approximately 65% of the employees of pri-
vate firms are mid and high earners, thus overtime pay
may not as important as leisure hours and they may find

Page 16 of 18Shao Health Economics Review (2022) 12:50

that a shorter workweek brings more satisfaction. This
phenomenon may extend to countries outside of Europe
because private firms often require their employees to
work overtime, even without receiving additional com-
pensation, while working time at public institutions is
always fixed.

The existing literature suggests that people are gener-
ally dissatisfied by long working hours, particularly in
advanced economies, and this study confirms this finding
in the European context. Compared to developing coun-
tries, citizens of advanced countries have comparatively
high earnings and are assisted by comprehensive welfare
systems. Thus, the marginal returns to income are dimin-
ishing and a shorter workweek is likely to be more helpful
in increasing their feelings of satisfaction. It is worth not-
ing that this does not imply that income does not play a
role in promoting life satisfaction; in fact, it shows a very
strong effect for those in the middle-class because they
require additional income to be upwardly mobile. The
income effect in the rich group is not as strong as that
in middle-class because their marginal returns to income
are obviously diminishing considering their already-high
incomes. Low earners in Europe always lose the motiva-
tion to work when they are well-cared for by the welfare
system or lack professional skills.

Conclusions
This study investigates the effect of working hours, as
well as that of other driving forces, on life satisfaction
using an ordered probit model based on the latest ESS
data. The results show that working time is negatively
associated with life satisfaction, which implies that Euro-
peans generally prefer a shorter working week. Health
plays an important role in the worktime–satisfaction
nexus. Social trust, social inclusion, feelings of safety
and digitalization show positive and significant effects
on life satisfaction. In terms of income levels, mid and
high earners prefer to work shorter work weeks while low
earners show no preference. Employees of private firms
prefer shorter work weeks while others show no prefer-
ence. These findings complement the conventional views
on working time and life satisfaction.

Several policies can be proposed based on the findings
of this study. First, regulations that limit hours worked
and protect employees’ health should be enacted or
strengthened. Since health is an important factor in the
relationship between working hours and life satisfac-
tion, good physical and mental health can significantly
improve life satisfaction. However, working either exces-
sively long or too few hours may detrimentally affect life
satisfaction; in the latter case, environmental pressures
might be aggravated by a shorter working week [67].

Thus, it is important to restrict working hours to moder-
ate levels in order to satisfy workers.

Second, economic development should be further pro-
moted to build a digitalized society with low crime rates
as well as high trust and social cohesion, especially under
the current COVID-19 pandemic era. As the results
show, these factors are significant driving forces on life
satisfaction, while economic development is one of the
main promoting forces of these factors, thus growth of
economy is the key to improve the whole satisfaction
level (this can be illustrated in the significant associa-
tion between income and satisfaction). In specific, eco-
nomic growth lowers crime rates partly due to increased
employment [86]; social trust is able to affect long-term
growth [87]; and social cohesion positively affects growth
in multiple countries [88, 89]. Moreover, the pandemic
lockdowns and social distancing measures greatly pro-
moted online consumption and teleworking from home
through virtual spaces, which objectively boosted digi-
talization [90, 91]. As such, digitalization is not a choice
but a necessity.

Third, a strict implementation of new working time
policies for private firms is needed. Typically, it is difficult
to control overtime work in private firms, thus a targeted
law is needed in this regard. Moreover, we should stimu-
late willingness to work and enhance life satisfaction-
especially among low earners-it is necessary to increase
employees’ overtime compensation. Evidence suggests
that Chinese industrial workers are willing to work
longer hours for a higher hourly income [26]. Moreover,
this policy could help to narrow the gap between the rich
and the poor, thereby tackling social inequality.

This study lays the groundwork for at least three future
research directions. First and foremost, more sophisti-
cated techniques, such as the fixed effects model, can
be employed to avoid the potential endogeneity prob-
lem generated from reverse causality [1]. Second, a panel
threshold model can be used to determine the thresh-
olds beyond which longer or shorter working hours
may decrease life satisfaction [67]. Third, factors other
than health potentially play essential roles in the pro-
cess through which working time affects life satisfaction.
For example, the social inclusion indicator in our study
shows significant signs across all models. We there-
fore propose that a shorter working week frees work-
ers to participate in social activities to enhance their
life satisfaction. Finally, few studies have examined the
worktime–satisfaction nexus in different job categories.
Though we briefly examine this issue in this study, the
underlying reasons and concrete explanations call for
further investigation. 

Acknowledgements
The author thanks for the comments from editor and reviewers.

Page 17 of 18Shao Health Economics Review (2022) 12:50

Authors’ contributions
Q.S. is responsible for all the contents. All authors read and approved the final
manuscript.

Funding
Open Access funding enabled and organized by Projekt DEAL. The author
thanks for the support of The Alexander von Humboldt Foundation (CHN
1194898 HFST-P).

Availability of data and materials
Data are available under reasonable request.

Declarations

Ethics approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Competing interests
The author declares no conflict of interest.

Received: 1 April 2022 Accepted: 19 September 2022

References
1. Rudolf R. Work shorter, be happier? Longitudinal evidence from the

Korean five-day working policy. J Happiness Stud. 2014;15:1139–63.
2. Okulicz-Kozaryn A, Golden L. Unhappiness is unpredictability. 2018. Avail-

able from: https:// ssrn. com/ abstr act= 30445 40. Report No.: 3044540.
3. Knabe A, Rätzel S. Income, happiness, and the disutility of labour. Econ

Lett. 2010;107:77–9. Available from:https:// doi. org/ 10. 1016/j. econl et. 2009.
12. 032 Elsevier B.V.

4. Sekulova F, van den Bergh JCJM. Climate change, income and happiness:
An empirical study for Barcelona. Glob Environ Chang. 2013;23:1467–75
http:// linki nghub. elsev ier. com/ retri eve/ pii/ S0959 37801 30013 13.

5. Clark AE, Frijters P, Shields MA, Clark AE, Frijters P, Shields MA. Relative
income, happiness, and utility: An explanation for the Easterlin Paradox
and other puzzles. J Econ Lit. 2008;46:95–144 http:// www. jstor. org/
stable/ 27646 948.

6. See KF, Yen SH. Does happiness matter to health system efficiency? A per-
formance analysis Health Econ Rev. 2018;8:33 Health Economics Review.

7. Abramovitz M. The welfare interpretation of secular trends in national
income and product. Alloc Econ Resour essays Honor Bernard Fr Haley.
Stanford: Stanford University Press; 1959.

8. Di Tella R, MacCulloch RJ, Oswald AJ. The macroeconomics of happiness.
Rev Econ Stat. 2003;85:809–27 http:// www. mitpr essjo urnals. org/ doi/ 10.
1162/ 00346 53037 72815 745.

9. Trzebiński J, Cabański M, Czarnecka JZ. Reaction to the COVID-19
Pandemic: The influence of meaning in life, life satisfaction, and assump-
tions on world orderliness and positivity. J Loss Trauma. 2020;25:544–57.
https:// doi. org/ 10. 1080/ 15325 024. 2020. 17650 98 Routledge.

10. Zhang SX, Wang Y, Rauch A, Wei F. Unprecedented disruption of lives and
work: Health, distress and life satisfaction of working adults in China one
month into the COVID-19 outbreak. Psychiatry Res. 2020;288:112958.
https:// doi. org/ 10. 1016/j. psych res. 2020. 112958 Elsevier Ireland Ltd.

11. Myers DG. The funds, friends, and faith of happy people. Am Psychol.
2000;55:56–67.

12. Chari AV, Heath R, Maertens A, Fatima F. The causal effect of maternal
age at marriage on child wellbeing: Evidence from India. J Dev Econ.
2017;127:42–55. https:// doi. org/ 10. 1016/j. jdeve co. 2017. 02. 002 Elsevier.

13. Schimmack S, Hinz U, Wagner A, Schmidt T, Strothmann H, Büchler MW,
et al. Maximizing time from the constraining European Working Time
Directive (EWTD): The Heidelberg New Working Time Model. Health Econ
Rev. 2014;4:1–10.

14. Burger AS. Extreme work hours in Western Europe and North America:
Diverging trends since the 1970s. Socio-Economic Rev. 2020;18:1065–87.

15. Rode JC. Job satisfaction and life satisfaction revisited: A longitudinal test
of an integrated model. Hum Relations. 2004;57:1205–30.

16. Weston R, Gray M, Qu L, Stanton D. Long work hours and the wellbeing
of fathers, Vol. 35. Melbourne: Australian Institute of Family Studies; 2004.
p. 1–33. Available from: https:// econw pa. ub. uni- muenc hen. de/ econ- wp/
lab/ papers/ 0405/ 04050 07. pdf.

17. Golden L, Wiens-tuers B. To your happiness? Extra hours of labor supply
and worker well-being. J Socio Econ. 2006;35:382–97.

18. Clark AE, Senik C. The (unexpected) structure of “rents” on the French and
British labour markets. J Socio Econ. 2006;35:180–96.

19. Pouwels B, Siegers J, Vlasblom JD. Income, working hours, and happiness.
Econ Lett. 2008;99:72–4.

20. Booth AL, Van OJC. Job satisfaction and family happiness: The part-time
work puzzle. Econ J. 2008;118:77–99.

21. Booth AL, Van OJC. Hours of work and gender identity: Does part-time
work make the family happier ? Economica. 2009;76:176–96.

22. Okulicz-Kozaryn A. Europeans work to live and Americans live to work
(Who is happy to work more: Americans or Europeans?). J Happiness
Stud. 2011;12:225–43.

23. Holly S, Mohnen A. Impact of working hours on work–life balance. SOEP
Pap. 2012;465:1–31. Available from: http:// papers. ssrn. com/ sol3/ papers.
cfm? abstr act_ id= 21354 53% 5Cnft p:// alhaba: merha [email protected] hermes. hsu- hh.
de/ liter atur_ pdfs/ Holly ,Mohnen_ 2012. pdf.

24. Collewet M, Loog B. The effect of weekly working hours on life satisfac-
tion. Maastricht; 2015. p. 1–33. Available from: https:// confe rence. iza. org/
confe rence_ files/ SUMS_ 2015/ colle wet_ m21737. pdf.

25. Valente RR, Berry BJL. Working hours and life satisfaction: A cross-cultural
comparison of Latin America and the United States. J Happiness Stud.
2016;17:1173–204 Springer Netherlands.

26. Wu W. Impact of hours worked on occupational well-being: An empirical
analysis based on three typical occupations (in chinese). China Ind Econ.
2016;3:130–45.

27. Okulicz-Kozaryn A, Golden L. Happiness is flextime. Appl Res Qual Life.
2017;13:355–69.

28. Alameddine M, Otterbach S, Rafii B, Sousa-Poza A. Work hour constraints
in the German nursing workforce: A quarter of a century in review. Health
Policy (New York). 2018;122:1101–8. https:// doi. org/ 10. 1016/j. healt hpol.
2018. 07. 023 Elsevier Ireland Ltd.

29. Noda H. Work–life balance and life satisfaction in OECD countries: A
cross-sectional analysis. J Happiness Stud. 2020;21:1325–48. https:// doi.
org/ 10. 1007/ s10902- 019- 00131-9 Springer Netherlands.

30. Henriques CO, Marcenaro-Gutierrez OD, Lopez-Agudo LA. Getting a
balance in the life satisfaction determinants of full-time and part-time
European workers. Econ Anal Policy. 2020;67:87–113. https:// doi. org/ 10.
1016/j. eap. 2020. 07. 002 Elsevier B.V.

31. Tan X, Ruppanner L, Hewitt B, Maume D. Restless sleep and emotional well-
being among European full-time dual-earner couples: gendered impacts
of children and workplace demands. Contemp Soc Sci. 2022;17:396–411.
Available from: https:// doi. org/ 10. 1080/ 21582 041. 2022. 20333 05.

32. Zwickl K, Disslbacher F, Stagl S. Work-sharing for a sustainable economy.
Ecol Econ. 2016;121:246–53. https:// doi. org/ 10. 1016/j. ecole con. 2015. 06.
009 Elsevier B.V.

33. ESS. Data file edition 1.2. Sikt – Norwegian Agency for Shared Services in
Education and Research, Norway – Data Archive and distributor of ESS
data for ESS ERIC. 2022. Available from: https:// www. europ eanso cials
urvey. org/. Cited 27 Jul 2022.

34. Caruso CC. Possible broad impacts of long work hours. Ind Health.
2006;44:531–6.

35. Bell D, Otterbach S, Sousa-Poza A. Work hours constraints and
health. FZID Discussion Paper, No. 36-2011. Stuttgart: Universität
Hohenheim,Forschungszentrum Innovation und Dienstleistung
(FZID).https:// nbn- resol ving. de/ urn: nbn: de: bsz: 100- opus- 6555.

36. Abbott P, Wallace C, Lin K, Haerpfer C. The quality of society and life satis-
faction in China. Soc Indic Res. 2016;127:653–70 Springer Netherlands.

37. Fors Connolly F, Johansson SI. Agreeableness, extraversion and life satis-
faction: Investigating the mediating roles of social inclusion and status.
Scand J Psychol. 2021;62:752–62.

38. Ammar A, Chtourou H, Boukhris O, Trabelsi K, Masmoudi L, Brach M, et al.
Covid-19 home confinement negatively impacts social participation and
life satisfaction: A worldwide multicenter study. Int J Environ Res Public
Health. 2020;17:1–17.

Page 18 of 18Shao Health Economics Review (2022) 12:50

39. Awaworyi Churchill S, Mishra V. Trust, social networks and subjective
wellbeing in China. Soc Indic Res. 2017;132:313–39 Springer Netherlands.

40. Lu H, Tong P, Zhu R. Longitudinal evidence on social trust and hap-
piness in China: Causal effects and mechanisms. J Happiness Stud.
2020;21:1841–58. https:// doi. org/ 10. 1007/ s10902- 019- 00159-x Springer
Netherlands.

41. Lin K. Social quality and happiness—An analysis of the survey data from
three Chinese cities. Appl Res Qual Life. 2016;11:23–40.

42. Lin K. The prototype of social quality theory and its applicability to Asian
societies. Int J Soc Qual. 2011;1:57–69.

43. Staubli S, Killias M, Frey BS. Happiness and victimization: An empirical
study for Switzerland. Eur J Criminol. 2014;11:57–72.

44. Kuroki M. Crime victimization and subjective well-being: Evidence from
happiness data. J Happiness Stud. 2013;14:783–94.

45. Elmassah S, Hassanein EA. Digitalization and subjective wellbeing in
Europe. Digit Policy, Regul Gov. 2022;24:52–73.

46. Bolli T, Pusterla F. Decomposing the effects of digitalization on workers’
job satisfaction. Int Rev Econ. 2022;69:263–300. https:// doi. org/ 10. 1007/
s12232- 022- 00392-6 Springer Berlin Heidelberg.

47. Wang S, Cao A, Wang G, Xiao Y. The Impact of energy poverty on the
digital divide: The mediating effect of depression and Internet percep-
tion. Technol Soc. 2022;68:101884. https:// doi. org/ 10. 1016/j. techs oc. 2022.
101884 Elsevier Ltd.

48. Diener E. Subjective well-being. Psychol Bull. 1984;95:542–75.
49. Diener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. J

Pers Assess. 1985;49:71–5.
50. Ivlevs A. Happy moves? Assessing the link between life satisfaction and

emigration intentions. Kyklos. 2015;68:335–56.
51. Lara R, Vázquez ML, Ogallar A, Godoy-Izquierdo D. Psychosocial resources

for hedonic balance, life satisfaction and happiness in the elderly: A path
analysis. Int J Environ Res Public Health. 2020;17:1–18.

52. Schyns P. Crossnational differences in happiness: Economic and cultural
factors explored. Soc Indic Res. 1998;43:3–26.

53. Welsch H. Environment and happiness: Valuation of air pollution using life
satisfaction data. Ecol Econ. 2006;58:801–13.

54. Bjørnskov C. How comparable are the Gallup World Poll life satisfaction
data? J Happiness Stud. 2010;11:41–60.

55. Christoph B. The relation between life satisfaction and the material
situation: A re-evaluation using alternative measures. Soc Indic Res.
2010;98:475–99.

56. Angeles L. Children and life satisfaction. J Happiness Stud. 2010;11:523–38.
57. Caner A. Happiness and life satisfaction in Turkey in recent years. Soc

Indic Res. 2016;127:361–99. https:// doi. org/ 10. 1007/ s11205- 015- 0948-z
Springer Netherlands.

58. McKelvey RD, Zavoina W. A statistical model for the analysis of ordinal
level dependent variables. J Math Sociol. 1975;4:103–20.

59. Boes S, Winkelmann R. Ordered response models. All Stat Arch.
2006;90:167–81.

60. de Luca G, Perotti V. Estimation of ordered response models with sample
selection. Stata J. 2011;11:213–39.

61. Bosselmann AS. Mediating factors of land use change among coffee
farmers in a biological corridor. Ecol Econ. 2012;80:79–88. https:// doi. org/
10. 1016/j. ecole con. 2012. 05. 007 Elsevier B.V.

62. Alemi F, Circella G, Mokhtarian P, Handy S. What drives the use of ridehail-
ing in California? Ordered probit models of the usage frequency of Uber
and Lyft. Transp Res Part C Emerg Technol. 2019;102:233–48. https:// doi.
org/ 10. 1016/j. trc. 2018. 12. 016 Elsevier.

63. Ronning G, Kukuk M. Efficient estimation of ordered probit models. J Am
Stat Assoc. 1996;91:1120–9.

64. Becker WE, Kennedy PE. A graphical exposition of the ordered probit.
Econom Theory. 1992;8:127–31.

65. Lechner M, Okasa G. Random forest estimation of the ordered choice model.
St.Gallen; 2022. p. 1–65. Available from: https:// arxiv. org/ pdf/ 1907. 02436. pdf.

66. Kostka G, Steinacker L, Meckel M. Between security and convenience:
Facial recognition technology in the eyes of citizens in China, Ger-
many, the United Kingdom, and the United States. Public Underst Sci.
2021;30:671–90.

67. Shao Q, Shen S. When reduced working time harms the environ-
ment: A panel threshold analysis for EU-15, 1970–2010. J Clean Prod.
2017;147:319–29. Available from:www.sciencedirect.com/science/article/
pii/S0959652617301300. Elsevier Ltd

68. Easterlin RA. Life cycle happiness and its sources Intersections of psychol-
ogy, economics, and demography. J Econ Psychol. 2006;27:463–82.

69. Diener E, Biswas-Diener R. Will money increase subjective well-being? Soc
Indic Res. 2002;57:119–69.

70. Pedersen PJ, Schmidt TD. Happiness in Europe. Cross-country differ-
ences in the determinants of satisfaction with main activity. J Socio Econ.
2011;40:480–9. https:// doi. org/ 10. 1016/j. socec. 2010. 10. 004 Elsevier Inc.

71. Easterlin RA. Income and happiness: Towards a unified theory. Econ J.
2001;111:465–84.

72. Burda MC, Hamermesh DS, Weil P. Total work, gender and social norms.
IZA DP No. 2705. Bonn; 2007. p. 1–45. Available from: http:// hdl. handle.
net/ 10419/ 25230.

73. Gorry A, Gorry D, Slavov SN. Does retirement improve health and life
satisfaction? Heal Econ (United Kingdom). 2018;27:2067–86.

74. Rothbard NP, Edwards JR. Investment in work and family roles: A test of
identity and utilitarian motives. Pers Psychol. 2003;56:699–730.

75. Edwards JR, Baglioni AJ, Cooper CL. Stress, type-A, coping, and psycho-
logical and physical symptoms: A multi-sample test of alternative models.
Hum Relations. 1990;43:919–56.

76. Lombardo P, Jones W, Wang L, Shen X, Goldner EM. The fundamental
association between mental health and life satisfaction: Results from
successive waves of a Canadian national survey. BMC Public Health BMC
Public Health. 2018;18:1–9.

77. Gwozdz W, Sousa-Poza A. Ageing, health and life satisfaction of the oldest
old: An analysis for Germany. Soc Indic Res. 2010;97:397–417.

78. Shields MA, Price SW. Exploring the economic and social determinants of
psychological well-being and perceived social support in England. J R Stat Soc
Ser A. 2016;168:513–37. Available from: (https:// www. jstor. org/ stable/ 35598 37).

79. Oswald AJ, Powdthavee N. Does happiness adapt? A longitudinal study
of disability with implications for economists and judges. J Public Econ.
2008;92:1061–77.

80. Lin YT, Chen MC, Ho CC, Lee TS. Relationships among leisure physical
activity, sedentary lifestyle, physical fitness, and happiness in adults 65
years or older in Taiwan. Int J Environ Res Public Health. 2020;17:1–12.

81. Mahmoodi Z, Yazdkhasti M, Rostami M, Ghavidel N. Factors affecting
mental health and happiness in the elderly: A structural equation model
by gender differences. Brain Behav. 2022;12:1–8.

82. Luchesi BM, de Oliveira NA, de Morais D, de Paula Pessoa RM, Pavarini SCI,
Chagas MHN. Factors associated with happiness in the elderly persons
living in the community. Arch Gerontol Geriatr. 2018;74:83–7.

83. WHO. Mental health of older adults. World Heal. Organ. 2017 [cited 2022
Sep 17]. p. 1. Available from: https:// www. who. int/ news- room/ fact-
sheets/ detail/ mental- health- of- older- adults.

84. Becchetti L, Giachin E, Pelloni A. The relationship between social leisure and life
satisfaction: Causality and policy implications. Soc Indic Res. 2012;108:453–90.

85. Phillips DL. Social participation and happiness. Am J Sociol.
1967;72:479–88.

86. Goulas E, Zervoyianni A. Economic growth and crime: Does uncertainty
matter? Appl Econ Lett. 2013;20:420–7.

87. Bjørnskov C. Social trust and economic growth. In: Uslaner EM. Oxford
Handbook of Social and Political Trust. Aarhus; 2017. p. 535–55. Available
from: https:// papers. ssrn. com/ sol3/ papers. cfm? abstr act_ id= 29062 80.

88. Pervaiz Z, Chaudhary AR. Social cohesion and economic growth: An
empirical investigation. Aust Econ Rev. 2015;48:369–81.

89. Majeed MT. Economic growth and social cohesion: Evidence from
the organization of Islamic Conference Countries. Soc Indic Res.
2017;132:1131–44 Springer Netherlands.

90. Nanda A, Xu Y, Zhang F. How would the COVID-19 pandemic reshape
retail real estate and high streets through acceleration of E-commerce
and digitalization? J Urban Manag. 2021;10:110–24. Available from:
https:// doi. org/ 10. 1016/j. jum. 2021. 04. 001. Elsevier Ltd.

91. Gabryelczyk R. Has COVID-19 accelerated digital transformation? Initial
lessons learned for public administrations. Inf Syst Manag. 2020;37:303–9.
Available from: https:// doi. org/ 10. 1080/ 10580 530. 2020. 18206 33. Taylor
and Francis

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