Munich Personal RePEc Archive
Economic growth and well-being beyond
the Easterlin paradox
Sarracino, Francesco and O’Connor, Kelsey J.
STATEC Research, Laboratory for Comparative Social Research,
Higher School of Economics
13 September 2019
Online at https://mpra.ub.uni-muenchen.de/96013/
MPRA Paper No. 96013, posted 14 Sep 2019 15:56 UTC
E onomi growth and well-being beyond
the Easterlin paradox
Fran es o Sarra ino
�
and Kelsey J. O'Connor
y
September 13, 2019
Abstra t
Re ent studies suggest that e onomi growth and well-being an grow together
in the long run in presen e of generous so ial safety nets, in reasing so ial apital
and de lining in ome inequality. We put these onditions to a test in an attempt
to explain the absen e of a relation between e onomi growth and well-being in
Luxembourg. To this aim we apply an error orre tion model to a panel of 15
Western European ountries, and we use the results to predi t life satisfa tion in
Luxembourg between 1991 and 2015. We �nd that the at trend of life satisfa tion
in Luxembourg is likely the result of four for es a ting in opposite dire tions. This
suggests that the available list of moderating onditions { although not exhaustive
{ is a promising starting point to design new poli ies to durably improve well-being.
Key-words: time-series; subje tive well-being; error orre tion model; life satis-
fa tion; dynami s; in lusive growth.
JEL odes: I31; O11; E6; O21; D60.
�
Institut national de la statistique et des �etudes �e onomiques du Grand-Du h�e du Luxem-
bourg (STATEC), and LCSR National Resear h University Higher S hool of E onomi s, Russian
Federation. Email: Fran es o.Sarra ino�state .etat.lu.
y
Institut national de la statistique et des �etudes �e onomiques du Grand-Du h�e du Luxembourg
(STATEC). Email: Kelsey.OConnor�state .etat.lu.
1
1 Introdu tion
The �nding of no long-run relationship between e onomi growth and subje tive
well-being is ontroversial. This result, ontradi ting a positive ross-se tional
relation, ame to be known as the Easterlin paradox (Easterlin, 1974). Sin e, many
s holars have ontributed with diverging views. There are those who oppose (see
e.g. Easterlin et al., 2010; Bruni and Stan a, 2008; Easterlin and Angeles u, 2009;
Be hetti et al., 2011; Clark et al., 2014; Easterlin, 2017) and those who support
(see e.g. Stevenson and Wolfers, 2008; Deaton, 2008; Sa ks et al., 2012; Veenhoven
and Vergunst, 2013) e onomi growth as a way to improve well-being. Beyond
the views on erning simply whether or not growth will a ompany in reasing
well-being, some re ent eviden e suggests the relation depends on so ial, politi al,
e onomi , ultural, and institutional onditions: if e onomi growth is ompatible
with a ohesive and in lusive so iety, it is reasonable to expe t that well-being
will improve (Oishi and Kesebir, 2015; Miku ka et al., 2017; Easterlin, 2013; Ono
and Lee, 2013). In ontrast, if e onomi growth leads to loneliness and inequality,
well-being may arguably de line.
Although the quest for onditions of \in lusive growth" { a growth that bene�ts
all the members of a so iety { is still in its infan y, past eviden e provides a
preliminary explanation of how and when a positive orrelation between e onomi
growth and well-being an exist over time. This is important be ause it an suggest
ways to promote well-being. Our aim is to distill the eviden e on the onditions
a�e ting the e onomi growth - well-being gradient, to explain the at trend of
life satisfa tion in Luxembourg.
We fo us on Luxembourg as an example of the la k of orrelation between
e onomi growth and well-being in the long run. Panel 1a in Figure 1 shows that
sin e the early 1980s, Luxembourg experien ed substantial e onomi growth, at
least until the e onomi risis of 2008. Yet, the share of very satis�ed people
did not hange substantially over time.
1
The e onomi risis may explain what
happened after 2008, but prior to 2008, it is not lear why e onomi growth did
not improve people's well-being (from a traditional e onomi s perspe tive).
A possible explanation is that life satisfa tion is an unreliable measure. Yet, we
have reasons to believe that life satisfa tion is reliable based on available data and
previous literature. Panel 1b reports the share of very satis�ed people a ording to
Eurobarometer (EB), European Values Survey (EVS) and European Quality of Life
Survey (EQLS). For the years when the data are jointly available, the three surveys
1
Eurobarometer is the only dataset that provides long time-series about life satisfa tion in
Luxembourg. The answers to the question are organized on a four point s ale. The distribution
of this variable over time is remarkably stable with a onsistently fat right tail. Hen e, our
measure of life satisfa tion, the share of very satis�ed people, is onservative be ause the trend
would be even atter than observed if we fo used on the share of satis�ed people.
2
Figure 1: Share of very satis�ed people (Panel A) and real Gross National In ome
(GNI) per apita (Panel B) in Luxembourg in the period 1981-2015. The samples
onsist only of native born individuals.
40
00
06
00
00
80
00
01
00
00
01
20
00
01
40
00
0G
NI
pe
r ca
pita
(co
nsta
nt
20
10
US
$)
25
35
45
55
65
Sh
are
of
ve
ry s
atisfie
d p
eo
ple
(%
)
1980 1990 2000 2010 2020
% very satisfied GNI per capita (constant 2010 US$)
(a) Life satisfa tion (Eurobarometer data) and real Gross National
In ome per apita (World Development Indi ators).
25
35
45
55
65
Sh
are
of
ve
ry s
atisfie
d p
eo
ple
(%
)
1980 1990 2000 2010 2020
EB EVS EQLS
(b) Life satisfa tion in Luxembourg. For the years when three
di�erent data-set are jointly available, they provide omparable
information.
Note: We restri t our analysis on natives only to ensure the omparability of data on life
satisfa tion over time. Eurobarometer data olle ted before 1994 provided data on nationals
only, and after 1994 they in luded information on immigrants from other European ountries.
We dis uss this issue in more detail in se tion Data.
Sour e: Eurobarometer, European Values Study, European Quality of Life Survey data, and
World Development Indi ators, own elaboration.
3
provide a remarkably omparable pi ture. Moreover, a well-established literature
provided eviden e supporting the reliability and validity of life satisfa tion as a
measure of subje tive and obje tive well-being (Blan h ower and Oswald, 2004;
Van Reekum et al., 2007; S himma k et al., 2010; Kahneman and Krueger, 2006;
Layard, 2005).
Another possible explanation is that the trends of life satisfa tion are always at
- at least among the ri hest and most developed ountries in the world. However,
the eviden e does not support this view. Figure 2 shows the trends of the share of
very satis�ed people in Belgium, Fran e, Germany and the Netherlands, i.e. a set
of Western European ountries whi h are lose to Luxembourg. The pi ture shows
that the trends of life satisfa tion are not always at: although average levels may
di�er, the trends in Fran e and Netherlands are monotoni ally positive, whereas
the trend is rather at in Belgium and it follows a `J' urve in Germany. This is
onsistent with previous studies (Sarra ino, 2012).
Figure 2: Trends of the share of very satis�ed people in a sample of Western
European ountries.
10
20
30
40
50
10
20
30
40
50
1980 1990 2000 2010 20201980 1990 2000 2010 2020
Belgium France
Germany Netherlands
Share
of very
satisfied p
eople
(%
)
Graphs by Country
Sour e: Eurobarometer data, own elaboration.
In sum, the at trend of life satisfa tion in Luxembourg does not have a simple
4
explanation. We posit that e onomi growth and life satisfa tion did not grow
together be ause four fa tors a ted in opposite dire tions for well-being, namely
in reasing so ial apital and so ial expenditures { whi h are expe ted to have a
positive impa t on well-being { and in reasing in ome inequality and unemploy-
ment { whi h, on the other hand, ould have a negative impa t.
Unfortunately, we do not have mi ro data providing long time-series for Lux-
embourg: the European Value Study in ludes individual data olle ted in 1999
and 2008, the European So ial Survey was administered in 2002 and 2004, and
the European Quality of Life Survey provides data every four years sin e 2003.
Thus, we adopt a ma ro-e onomi perspe tive. Spe i� ally, we apply an error or-
re tion model to a panel of 15 Western European ountries to explain ountry-year
levels of life satisfa tion using the set of potential explanatory fa tors identi�ed in
previous literature. The results are then used to predi t life satisfa tion and to
assess whether and to what extent the explanatory fa tors explain the trend of life
satisfa tion in Luxembourg.
We build our argument in two steps: in se tion 2 we review the literature
on the Easterlin paradox and on the fa tors moderating the relationship between
e onomi growth and well-being. Subsequently, we apply a time-series approa h
(se tion 3) to a ma ro data-set onsisting of 15 Western European ountries (se -
tion 4). Se tion 5 illustrates the results of the model, whi h we use to predi t and
explain the trend of life satisfa tion in Luxembourg. The last se tion on ludes.
2 The Easterlin paradox and moderating fa tors
The debate on subje tive well-being gained spe ial attention in part be ause it on-
erns an important question: to what extent do modern so ieties bene�t from e o-
nomi growth? For years this question has divided so ial s ientists among: those
who laim that ontemporary so ieties should not expe t signi� ant improvements
in subje tive well-being from e onomi growth (Easterlin, 1974); those who argue
that e onomi growth and in reasing subje tive well-being are asso iated over time
(see e.g. Stevenson and Wolfers, 2008; Deaton, 2008; Sa ks et al., 2012; Veenhoven
and Vergunst, 2013); those who point out that whether a relationship exists de-
pends on the set of ountries onsidered (developed and developing ountries vs.
transition ountries) or the period of time, i.e. e onomi growth and the trends of
well-being orrelate in the short run, but su h orrelation disappears in the long
run (Easterlin and Angeles u, 2009; Be hetti et al., 2011; Easterlin et al., 2010;
Clark et al., 2014; De Neve et al., 2018; Bartolini and Sarra ino, 2014); and those
who laim that even if the trends of subje tive well-being and e onomi growth
are statisti ally related, the magnitude is too small for growth to have a mean-
ingful impa t (Beja, 2014). Re ently, some s holars argued that the question is
5
not whether, but when { under what onditions { e onomi growth orrelates with
in reasing subje tive well-being (Oishi and Kesebir, 2015; Miku ka et al., 2017).
The literature identi�ed three fa tors whi h plausibly a�e t the relation between
e onomi growth and well-being over time: in ome inequality (Oishi and Kesebir,
2015; Miku ka et al., 2017), so ial apital (Uhlaner, 1989; Helliwell, 2003, 2008;
Bartolini et al., 2013; Clark et al., 2014), and so ial poli y (Easterlin, 2013; Ono
and Lee, 2016).
Con erning in ome inequality, the eviden e about the ross-se tional relation-
ship with well-being is mixed (e.g. Alesina et al., 2004; Clark and D'Ambrosio,
2015). These ontradi tions may arise be ause the relationship between inequality
and well-being depends on a ountry's level of development (Jiang et al., 2012;
Iniguez-Montiel, 2014). However, previous studies found that in reasing in ome
inequality is onsistently negatively related to well-being (Bartolini and Sarra ino,
2015; Oishi and Kesebir, 2015; Miku ka et al., 2017). By widening the possibil-
ities to establish so ial omparisons, growing in ome inequality undermines the
positive e�e t of in ome growth for well-being. Raising in ome inequality an also
undermine well-being by redu ing feelings of fairness and trust in others (Oishi
et al., 2011) or by weakening so ial linkages and feelings of ooperation (Graham
and Felton, 2006; Oishi et al., 2011).
So ial apital is de�ned by the OECD (2001) as \networks together with shared
norms, values and understandings that fa ilitate o-operation within or among
groups". A well-established literature shows that so ial apital orrelates pos-
itively with subje tive well-being at both the individual (Helliwell et al., 2017;
Clark et al., 2014; Be hetti et al., 2009) and aggregate level, over time within
ountries (Bartolini et al., 2013; Bartolini and Sarra ino, 2015; Bro kmann et al.,
2009; Easterlin et al., 2012) and in ountry panels (Bartolini and Sarra ino, 2014).
Helliwell and Aknin (2018) dis uss in detail the relationship between so ial apital
and subje tive well-being.
The experien e of ountries that transitioned from ommunist e onomi sys-
tems illustrate the importan e of so ial safety nets for well-being (Ono and Lee,
2013). Survey data onsistently indi ate that people in European post- ommunist
ountries are among the least satis�ed people in Europe. Moreover, after the tran-
sition, average life satisfa tion de lined. The loss of jobs and the deterioration of
safety nets are among the auses that explain this de line. The ommunist regime
provided people with jobs, basi in ome, health insuran e, edu ation, and other
bene�ts. The transition to market apitalism was a ompanied by widespread
orruption and the ollapse of the so ial insuran e system, whi h invariably led
to greater inequality and lower well-being. In re ent years life satisfa tion re ov-
ered, but it took more than ten years and required an in rease in GDP per apita
6
averaging about 25 per ent above the 1990s value (Easterlin, 2009, p. 142).
2
In China, life satisfa tion exhibited a similar pattern of ollapse and re overy
following the transition, all the while growing at an average annual rate of more
than 9.0%. Bro kmann et al. (2009), Easterlin et al. (2012), Easterlin et al. (2017)
and Bartolini and Sarra ino (2015) dis uss possible explanations for these startling
fa ts. Ea h work partially attributes the de line in life satisfa tion to in reased
so ial omparisons, espe ially fa ilitated by rising in ome inequality. Bartolini
and Sarra ino (2015) do ument the importan e of so ial apital, estimating that
nearly 19.0% of the well-being loss in China is related to a de rease in so ial
apital. Easterlin et al. (2012) and Easterlin et al. (2017) instead emphasize the
role of rising unemployment
3
, whi h was inversely related to life satisfa tion over
the full y le from 1990 to 2010 (while inequality, in ontrast, rose throughout
the period). And, like in the European transition ountries, with unemployment
ame not only in ome losses, but also the elimination of so ial bene�ts. The loss
of these bene�ts arguably signi� antly exa erbated the e�e ts of unemployment.
So ial safety nets are positively related to life satisfa tion in general (Di Tella et al.,
2003; Rothstein, 2010; Pa ek and Rad li�, 2008; Boarini et al., 2013; Easterlin,
2013; Ono and Lee, 2016; O'Connor, 2017), not just in transition e onomies, and
the asso iation is not limited to those dire tly a�e ted (e.g., unemployed) (Carr
and Chung, 2014). In sum, the de line in Chinese well-being an be explained by
(1) in reasing in ome inequality whi h fa ilitated in reasing so ial omparisons,
(2) de lining so ial apital, and (3) in reasing unemployment a ompanied by a
severely redu ed so ial safety net. The re ent re overy appears to be driven by
improvements in trust, employment, and the so ial safety net (Easterlin et al.,
2017).
Previous studies investigating the Easterlin paradox and its moderating fa -
tors fo used mainly on ross- ountry studies or on ountries providing \negative"
examples, i.e. ountries in whi h e onomi growth and in reasing well-being do
not go together. The ase of Japan stands out as a \positive" example: a ountry
where e onomi reforms in the early 1990s shifted the ountry from a pattern of
rampant e onomi growth and stagnant well-being, to one of moderate growth and
in reasing well-being (see �gure 3). The question then be omes: what made this
hange possible?
By the end of the 1980s, Japan was in the middle of two risis: on one side, the
demographi risis; on the other, the de line in the viability of the traditional and
2
It is possible that asymmetri responses to e onomi ollapse and positive in ome growth
ould explain why life satisfa tion did not fully re over at the same time as GDP (e.g., from
loss aversion De Neve et al. (2018)), but that is insuÆ ient to explain the pattern in China as
dis ussed in the next paragraph.
3
Due to government restru turing of state-owned enterprises and large rural to urban migra-
tion asso iated with relaxed internal migration laws.
7
Figure 3: Trends of life satisfa tion and GDP per apita ( onstant 2010 US$) in
Japan between 1981 and 2010.
66
.26
.46
.66
.87
Ave
rag
e life
sa
tisfa
ctio
n
25
00
03
00
00
35
00
04
00
00
45
00
0G
DP
pe
r ca
pita
1980 1990 2000 2010Year survey
GDP per capita
life satisfaction
Note: Life satisfa tion data are from the WVS, whereas GDP �gures, presented
in real dollars with base year set to 2010, are issued from the World Development
Indi ators of the World Bank. The trends in life satisfa tion from WVS are
roughly onsistent with those issued from other sour es.
Sour e: Sarra ino et al. (2019).
8
orporate so ial safety net. Greater urbanization and industrialization, along with
e onomi stagnation and international ompetition, put pressure on the s heme of
so ial safety nets whi h traditionally relied on intergenerational support and on
generous bene�ts for the employees of large orporations. For instan e, the share of
three-generation-family households went from 54 per ent in 1975 to 13 per ent in
2013 (Ministry of Health, Labour and Welfare, 2014), whereas the share of elderly
people living alone nearly doubled. At the same time, e onomi onditions for ed
ompanies to limit the bene�ts granted to their employees, and in parti ular to
newly hired personnel. Moreover, the likelihood of lifetime employment de lined
(Ono, 2010). The share of workers in nonstandard employment more than doubled
from 15 to 38 per ent between 1984 and 2016 (Ministry of Health, Labour and
Welfare, 2014). Consequently, the population in need of so ial prote tion greatly
expanded during the 1990s, as well as in ome inequality (see �gure 4).
Figure 4: Evolution of the Gini index of in ome in Japan.
25
30
35
40
Gin
i in
dex
1980 1990 2000 2010
SWIID Post WIID
Note: Lowess smoothed urves. The two lines in the hart refer to measures of
Gini issued from two di�erent sour es of data: the Standardized World In ome
Inequality Database, and the World In ome Inequality Database. Together, the
two series of data provide onsistent eviden e that in ome inequality in Japan
in reased sin e 1980.
Sour e: Solt (2016) and UNU-WIDER (2018).
To fa e these hallenges the government introdu ed a state-sponsored so ial
support system to share so ial risk equitably by the so iety (Horioka and Kanda,
9
2010). A number of poli ies targeting elderly people, as well as work-family poli-
ies were introdu ed in the mid-1990s with the aim of improving the living and
health onditions of elderly people, alleviating the osts of having hildren, and
fa ilitating women a ess to the job-market. Figure 5 shows the trend of welfare
state generosity in Japan (S ruggs et al., 2017).
Figure 5: Average life satisfa tion and Generosity of Welfare State (Japan 1981-
2010).
6.5
6.6
6.7
6.8
6.9
Life
sa
tisfa
ctio
n
11
.02
1.0
41
.06
Ind
ex (
19
90
= 1
00
)
1980 1990 2000 2010
Gen. of Welfare State Policy Life satisfaction
Note: Lowess smoothed urves.
Sour e: Sarra ino et al. (2019).
In the years following the poli y reforms that introdu ed a state-sponsored
so ial safety net in Japan, people's satisfa tion with life in reased, and in parti ular
the satisfa tion of people in the targeted groups. By 2010, aging was asso iated
less negatively to life satisfa tion than in 1990, i.e. before the introdu tion of
the reforms; average health improved; trust in others and so ial parti ipation of
elderly people nearly doubled; single people reported higher life satisfa tion than
previously. All this happened while the e onomy grew, although at a lower pa e
ompared to the previous period.
10
2.1 Our ontribution
Available studies indi ate that: i. so ial apital, so ial safety nets, and in ome
inequality a�e t the relationship between e onomi growth and well-being over
time; ii. poli y-makers an adopt poli ies to promote well-being in the long run.
Our aim is to assess whether the fa tors dis ussed above an help explain the
at trend of life satisfa tion in Luxembourg. This test is important to evaluate
the reliability of available knowledge about the onditions to promote well-being
in the long run, and to identify possible areas of intervention for poli y-makers.
Additionally, in present work we extend the list of moderating onditions to in lude
unemployment. It is well established that unemployment is one of the major auses
of ill-being. Thus it is possible that the hanges in unemployment ontribute to
explaining the trend of life satisfa tion.
3 Method
We use an error orre tion model (ECM) to analyze the fa tors that ontribute
to life satisfa tion in the long-run. The main reason is that ECMs allows us
to estimate onsistent long-run relations between the explanatory variables and
dependent variable. Additional reasons are more te hni al. First, explanatory
variables in levels (e.g., GDP p ) often exhibit unit root properties, whi h ould
lead to the estimation of spurious relations (Engle and Granger, 1987). First-
di�eren ing the variables an be used to address su h spurious relations, but �rst-
di�eren ing limits the interpretation of the results to short-run hanges. ECMs
separately estimate the short- and long-run relations to avoid spurious relations
(under ertain onditions dis ussed below). Also, the estimated long-run relations,
referred to as long-run e�e ts in the time-series literature, are onsistent in the
presen e of short-run reverse ausality (Chudik and Pesaran, 2015; Pesaran, 2015).
Before presenting the ECM, we begin with our assumed data generating pro-
ess, represented by Equation 1. LS
it
represents life satisfa tion for ountry i
at time t, the ve tor X
i;t
in ludes the explanatory variables, and �xed ountry
hara teristi s are represented by �
i
.
LS
it
= �
i
� LS
i;t�1
+ �
0
i0
�X
i;t
+ �
0
i1
�X
i;t�1
+ �
i
+ "
it
(1)
The error orre tion form is the reparameterization of Equation 1 given by Equa-
tion 2.
�LS
it
= �
i
� (LS
i;t�1
� �
0
i
�X
i;t
) +
0
i
��X
i;t�1
+ �
i
+ "
it
(2)
where �
i
= (�
i
� 1); �
0
i
= (�
0
i0
+ �
0
i1
)=(1� �
i
) and
0
= ��
0
1i
.
11
The short-run relations are aptured by and the long-run e�e ts by �. With
a large hange in X
i;t
, the response in the LS
i;t
might overshoot the long-run
equilibrium relationship. When this happens, the error orre tion term, �, serves
to bring the relationship ba k to the long-run one.
For an error orre tion parameterization to be appropriate, (1) the error or-
re tion term should be statisti ally signi� ant, negative, but greater than negative
two, and (2) there must be a long-run ointegrating relationship between the level
variables. Condition 2 is ne essary for the term (LS
i;t�1
� �
0
i
�X
i;t
) to be station-
ary, whi h is ne essary for the error term to be stationary when � is statisti ally
signi� ant. Condition 1 is tested dire tly in the regression analysis. To he k
ondition 2 we test the regression residuals for stationarity using panel unit root
tests (Fisher type augmented Di key Fuller tests). The results indi ate that the
residuals are indeed stationary and ondition 2 is met.
We adjust Equation 2 to develop our �nal spe i� ation (presented below). No-
ti e the oeÆ ients are indexed by i in Equation 2, indi ating that they are allowed
to vary a ross ountries. We allow the short-run relations to be heterogeneous in
order to apture exibly any reverse ausality. However, in our �nal spe i� ation
we assume the long-run e�e ts (�) are ommon a ross ountries. Without this
assumption, we ould not simultaneously assess ea h of the fa tors dis ussed in
Se tion 2 that may explain the trend of life satisfa tion in Luxembourg. This
spe i� ation is referred to as a pooled mean group model (PMG).
In our �nal spe i� ation, we address two further issues: ross-se tional depen-
den e and lag order for serial orrelation. Cross-se tional dependen e o urs when
there is omitted orrelation a ross ountries. A ommonly orrelated e�e t, su h
as the impa t of European Union poli ies, an be a sour e of ross-se tional depen-
den e. To address ross-se tional dependen e we add to equation 2 ross-se tional
means of both the dependent and independent variables (as suggested by Chudik
and Pesaran (2015)). This approa h is similar to adding year dummies, but has
some advantages: adding year dummies greatly in reases the number of ontrols,
and ross-se tional means allow the ommonly orrelated e�e t to a�e t ea h oun-
try through multiple hannels and to di�erent degrees a ording to their di�erent
variable values. Cross-se tional means are also in luded among short-run variables
in the PMG model, meaning their oeÆ ients vary a ross ountries. Con erning
lags, we hose one lag in levels (as spe i�ed in equation 1) be ause the full model
did not onverge when using additional lags. However, we ran regressions using
one explanatory variable at a time in luding up to six lags (in levels). Generally
the long-run e�e ts maintain signi� an e and dire tion and the magnitudes are
larger when in luding additional lags, suggesting our estimates represent lower
bounds. The ex eption is for so ial expenditures, whi h is insigni� ant (presented
with the Results).
12
Our �nal spe i� ation is presented as Equation 3.
�LS
it
= �
i
� (LS
i;t�1
� �
0
�X
i;t
) +
0
i
��X
i;t�1
+ '
i
LS
t
+ Æ
i
X
t
+ �
i
+ "
it
(3)
4 Data
Individual life satisfa tion data are from repeated ross-se tional Eurobarometer
(EB) surveys (European Commission, 2018). Life satisfa tion is measured on a
s ale from 1 to 4 using the responses to the question, \On the whole, are you very
satis�ed, fairly satis�ed, not very satis�ed or not at all satis�ed with the life you
lead?". This question was �rst asked in 1973 in seven ountries and ontinues to-
day for more than the present 28 EU ountries. In ea h year, multiple surveys are
ondu ted that ask about life satisfa tion. Annual observations of life satisfa tion
were onstru ted in ea h ountry as the weighted proportion of native-born individ-
uals reporting the top response ategory, \very satis�ed". Foreign-born individuals
were ne essarily ex luded, be ause prior to 1994, the Eurobarometer target pop-
ulation only in luded native-born individuals, and in 1994, the target population
expanded but still ex luded individuals born in non-EU ountries (S hmitt et al.,
2009, p. 56).
The explanatory variables in lude the natural log of real Gross National In ome
per apita (GNI), unemployment rate, the Gini oeÆ ient of in ome, so ial expen-
ditures, and trust in others. GNI and unemployment data (national estimate) are
from the World Development Indi ators (WDI) (World Bank, 2018). We use the
Gini oeÆ ient of inequality in equivalent household disposable (post-tax, post-
transfer) in ome from the Standardized World In ome Inequality Database (Solt,
2016).
4
Data for so ial expenditures per apita are available every �ve years from 1980-
2015 and 2016 from the OECD So ial Expenditures Database (OECD, 2018).
The variable in ludes all publi so ial expenditures on a tive labour market pro-
grammes, family, health, housing, in apa ity related, old age, other so ial poli y
areas, survivors, and unemployment. We adjusted the variable to more losely
represent the generosity of the welfare state poli y. Con eptually subje tive well-
being relates to the generosity of poli ies not to expenditures; that is be ause so ial
4
The SWIID provides the longest, most omplete, and omparable set of data on in ome
inequality. It is based on data from the World In ome Inequality Database (WIID), but it
hinges on additional assumptions to ease ross-se tional omparability and to impute missing
data. For these reasons some s holars have expressed riti ism towards the SWIID (Jenkins,
2015). However, we �nd that in our ase, �gures from SWIID positively and signi� antly orrelate
with data from WIID and the World Inequality Database(WID) in the years and ountries when
the three data sour es are jointly available.
13
expenditures in rease me hani ally when people retire or when unemployment in-
reases. Indeed O'Connor (2017) �nds, so ial expenditures relate to subje tive
well-being, but the relation be omes statisti ally insigni� ant when ex luding a
ontrol for the old age dependen y ratio. In the present analysis, we ould also
in lude the old age dependen y ratio, but given the small number of degrees of
freedom, we hose instead to partial out the old age dependen y ratio and also the
unemployment rate. Spe i� ally, we used the residuals from a regression of so ial
expenditures on the old age dependen y ratio and unemployment rate. We then
use linear interpolation to fa ilitate annual analysis.
Trust in others is based on responses to the question, \Generally speaking,
would you say that most people an be trusted, or that you ould not be too areful
in dealing with people?" Individual responses are obtained from Eurobarometer
surveys in the years 1986, 2004, 2009, 2010, and 2014. These responses are then
aggregated at the ountry level for ea h year as the portion of people feeling most
people an be trusted. However, omparison over time is limited by di�eren es
in the response s ales. The largest hange o urred beginning in 2009, when the
s ale went from two dis rete hoi es to a s ale ranging from 1 to 10. In order to
produ e annual estimates and a ount for the hange in s ale, we impute trust in
the following steps:
1. The weighted per entage of people stating most people an be trusted is
al ulated by ountry year. For the years 2009, 2010, and 2014 responses
7-10 are re orded as most people an be trusted;
2. These s ores are de-meaned by subtra ting the average level of trust within
a ountry over the years 1986 and 2004 (the years based on the previous
response s ale);
3. The de-meaned trust values are then linearly interpolated and extrapolated
over the sample period with an ex eption { trust is not extrapolated to the
years before 2004 if it is unobserved in 1986;
4. Additional data from the World Values Survey (WVS, 2014) and European
Values Study (EVS, 2011) is used to provide additional information on trust.
The two surveys provide di hotomous answers to a question asking respon-
dents whether people an be trusted. As in step 1, the answers are de-meaned
(within ountry) and extrapolated;
5. To remove the e�e t of the hange in s ale that o urred from 2004 to 2009,
EB trust from step 3 is regressed on EVS/WVS trust from step 4, a dummy
variable demarking the period post-2004, a quadrati trend, and intera -
tions between EVS/WVS trust and both the dummy and trend. Trust is
14
predi ted after ex luding the impa t from the intera tion between post-2004
and EVS/WVS trust; and
6. Last, the ountry means from step 2 were added ba k to obtain our �nal
predi tion of trust.
Our sample of ountries in ludes the �rst �fteen European Union member
states (EU15) be ause only these ountries have suitably long enough series to
be in luded. The period of analysis in ludes the years 1991 { 2016. Our sample
for regression analysis begins in 1991 to ensure there were at least ten ountries
observed in ea h year. Prior to 1991 data for fewer ountries were available when
in luding lags. It is important to use as many ountries as possible be ause the
analysis in ludes ross-se tional means in ea h period. We would prefer to begin
the sample with more than ten ountries, but data for all 15 ountries are not
available until 1995, and with lags, that would signi� antly redu e the time di-
mension. Table 1 presents the sample hara teristi s and average variable values
for ea h ountry.
Table 1: Des riptive statisti s
Country First Last Life satisfa tion Gross National In ome Gini index Unemployment Trust Adj. So ial Expenditures
year year (% very happy) ln(US$ per apita) (0 - 100) rate (%) (% an be trusted) ln(US$ per apita)
Austria 2001 2016 23.95 10.74 27.58 5.10 47.78 0.42
Belgium 1991 2016 27.35 10.62 25.75 7.99 35.10 0.16
Denmark 1991 2016 65.82 10.92 23.85 6.16 78.45 0.51
Finland 2001 2016 33.11 10.73 25.37 8.63 70.02 0.27
Fran e 1991 2015 15.90 10.57 28.69 9.83 29.14 0.15
Germany 1991 2015 17.85 10.57 27.38 7.86 45.06 0.13
Gree e 1991 2016 8.29 10.07 33.58 12.98 37.04 -0.67
Ireland 1991 2016 36.02 10.51 31.13 9.48 37.67 -0.22
Italy 1991 2015 11.41 10.45 32.99 9.83 30.93 -0.07
Luxembourg 1991 2015 43.39 11.25 26.67 3.80 35.68 0.89
Netherlands 1991 2016 47.47 10.73 26.14 4.96 64.32 0.17
Portugal 1992 2015 4.01 9.93 34.03 8.03 31.60 -0.73
Spain 1992 2016 18.17 10.24 32.93 17.38 35.53 -0.44
Sweden 2001 2016 46.83 10.86 25.20 6.77 71.07 0.40
United Kingdom 1991 2016 35.30 10.49 33.83 6.74 44.17 -0.24
Sample average 28.76 10.57 29.22 8.48 45.06 0.02
5 Results
Simple des riptive statisti s suggest that in ome inequality, unemployment, so ial
trust and so ial expenditures in reased in Luxembourg sin e the early 1980s. Panel
6a in Figure 6 shows that in ome inequality in reased by about 5 points, from 23.9
to 28.7, between 1985 and 2015. Similarly, Panel 6b indi ates that unemployment
as a per ent of total labor for e was 0.7% in 1980 and 6.7% in 2015, a nearly 9 fold
in rease in 35 years. A ording to previous literature, we should expe t that su h
in reases hindered life satisfa tion, probably over oming the positive ontribution
of e onomi growth expe ted from traditional e onomi theory.
15
Figure 6: In reasing in ome inequality (Panel A) and unemployment (Panel B) in
Luxembourg.
24
25
26
27
28
29
Gin
i in
de
x
25
35
45
55
65
Sh
are
of
ve
ry s
atisfie
d p
eo
ple
(%
)
1980 1990 2000 2010 2020
Share of very satisfied people Gini index of eqv. hh. disp. income
(a) Gini index of equivalent household
disposable in ome.
02
46
8U
ne
mp
loym
en
t (%
of
tot.
la
bo
r fo
rce
)
25
35
45
55
65
Sh
are
of
ve
ry s
atisfie
d p
eo
ple
(%
)
1980 1990 2000 2010 2020
Share of very satisfied people Unemployment (% of total labor force)
(b) Unemployment as a per entage of
total labor for e.
Sour e: authors' own elaboration.
The in reases in so ial trust and so ial expenditures, on the other hand, are
expe ted to have positively ontributed to life satisfa tion. Sin e 1980 the share of
people who feel that others an be trusted nearly doubled (see Panel 7a in Figure
7), whereas so ial expenditures rose from 8,190 US$ per apita (base year 2013) in
1980 to 23,880 in 2015, i.e. a nearly 3 fold in rease (Panel 7b). It is possible that
the e�e ts on life satisfa tion of in reasing in ome inequality and unemployment,
on one side, and in reasing so ial trust and expenditures, on the other, o�-set
ea h other. To test this hypothesis formally, we turn to the results of the error
orre tion model.
Table 2 presents the results of the error orre tion model. The �rst �ve rows
present the long-run relations orresponding to the � in equation 2; ECT orre-
sponds to the �; the middle rows present the short-run- hange relations orre-
sponding to the s, and the �nal rows the ross-se tional means of life satisfa tion
and the independent variables in levels. The �rst �ve olumns use one explana-
tory variable at a time, olumn 6 reports the results from the model in luding ea h
explanatory variable, and 7, standardized oeÆ ients of the long-run e�e ts from
the full model.
The long-run e�e ts generally orrespond with our expe tations, with the ex-
eption of the Gini oeÆ ient, whi h is statisti ally signi� ant and positive. Per-
manent in reases in GNI p , in ome inequality, so ial trust, and adjusted so ial
expenditures are positively related to life satisfa tion in the long run, and unem-
ployment, negatively.
5
The long-run e�e ts are generally onsistent between the
5
We use the word permanent to distinguish the hanges in levels that trigger the long-run
16
Figure 7: In reasing so ial trust (Panel A) and so ial expenditures (Panel B) in
Luxembourg.
20
30
40
50
% p
eo
ple
tru
stin
g o
the
rs
25
35
45
55
65
Sh
are
of
ve
ry s
atisfie
d p
eo
ple
(%
)
1980 1990 2000 2010 2020
Share of very satisfied people Pred. % People Can Be Trusted
(a) Share of people de laring that others
an be trusted.
51
01
52
02
5S
ocia
l e
xp
en
ditu
res p
.c.
(20
13
US
$/1
00
0)
25
35
45
55
65
Sh
are
of
ve
ry s
atisfie
d p
eo
ple
(%
)
1980 1990 2000 2010 2020
Share of very satisfied people Social Expend. pc Interp./1000
(b) So ial expenditures per apita.
Sour e: authors' own elaboration.
redu ed models ( ols. 1-5) and the full model ( ol. 6). The magnitudes and signif-
i an e of GNI p and so ial expenditures are redu ed. Indeed so ial expenditures
are no longer statisti ally signi� ant. This �nding is surprising in light of the
positive relations found in ross-se tional eviden e; however, insigni� an e ould
be due to multi ollinearity and low statisti al power (re all adjusted so ial expen-
ditures are positive and signi� ant in olumn 5). The magnitudes of the other
variables (Gini, unemployment rate, and trust) in rease in size. A ross variables,
trust has the largest standardized oeÆ ient. The oeÆ ient of trust in others is
more than two times bigger than the one of GNI and nearly three times larger
than the one of inequality or unemployment (in absolute terms). This indi ates
that trust in others is the strongest orrelate of the hanges of life satisfa tion in
the long run among the onsidered variables. The magnitude, however, is small: a
one standard deviation di�eren e in trust is related to 0.6 per entage point greater
life satisfa tion. The standard deviation of life satisfa tion over the full sample is
17.5 per entage points.
It is surprising that the Gini oeÆ ient is positively related to life satisfa tion,
however, as mentioned in the literature se tion, positive relations have been ob-
tained in ross-se tional studies. The Hirs hman tunnel e�e t ould explain the
relation { in reasing in ome of a few, leading to greater inequality, may signal
that the in omes of everyone are in reasing, thereby raising subje tive well-being.
Future resear h should fo us further on inequality and revaluate it in a time-series
ontext.
e�e ts from annual deviations asso iated with short-run di�eren es in life satisfa tion.
17
Among the ross-se tional means, the most important impa t is for GNI. While
a permanent in rease in GNI in a parti ular ountry and year is asso iated with
a long-run in rease in life satisfa tion, when GNI in reases in all ountries in the
same year, the impa t is negative. A three per ent growth rate in ea h ountry
is asso iated with a de rease in life satisfa tion of 0.5 per entage points per year
(based on olumn 6 in luding the long run e�e t and ross-se tional mean but not
the insigni� ant short-run relations: -0.05 = 0.03 * 10.6 + 0.03 * -27.4). This
suggests that GNI positively a�e ts the life satisfa tion in a parti ular ountry if
it grows at a signi� antly greater rate than in other ountries: to break even, the
GNI hange in a ountry needs to be 27.4/10.6 = 2.58 times the average hange
a ross ountries. For the other four fa tors, the impa ts of the signi� ant ross-
se tional means are also in the opposite dire tion but they are smaller than the
long-run e�e ts. Con erning mean life satisfa tion, the impa t of ross-se tional
life satisfa tion is positive indi ating positive spill-over e�e ts. If life satisfa tion
in reases in one ountry, then life satisfa tion will in rease in the other ountries
by approximately one �fteenth of that in rease (the oeÆ ient rounds to 1.0 in
olumn 6).
Short-run variation in various fa tors has theoreti ally distin t impa ts on life
satisfa tion ompared to permanent in reases, espe ially for in ome as illustrated
in Bartolini and Sarra ino (2014). For instan e, we would expe t short run hanges
in unemployment to be signi� antly (and negatively) orrelated to the hanges
of life satisfa tion. However, the present study annot omment on the short-
run relations. The short-run variation is not independent and the relations are
generally statisti ally insigni� ant.
Perhaps the most intuitive way to illustrate our results is to use the model's
predi tion of life satisfa tion, whi h in ludes the impa ts of ea h variable and
their interdependen ies. Figure 8 presents the predi tion and observed share of
very satis�ed people in Luxembourg, based on the estimates presented in table 1,
olumn 6. From this �gure, it is lear the model has high predi tive power. The
model does not get the level of life satisfa tion right, but the short-run hanges
and long-run trend mat h well. Indeed, predi ted and observed life satisfa tion
are strongly orrelated at 84% (signi� ant at 1%). The di�eren e in level is due
to a strong error orre tion term being applied to a relatively high level of life
satisfa tion (Luxembourg averages 43.4 ompared to 28.8 in all ountries), whi h
brings the predi ted level of life satisfa tion in Luxembourg toward the average.
Although we in luded ountry �xed e�e ts in the model, they are treated as short-
run deviations that are ounterbalan ed by the error orre tion term. The results
indi ate that the at trend of life satisfa tion in Luxembourg is due, at least
in part, to o�setting in u es of in reases in: GNI per apita, in ome inequaltiy,
unemployment, so ial trust, and so ial expenditures.
18
Table 2: Results from the ECM model applied to the panel of 15 Western European
ountries (1991 - 2016).
� Life satisfa tion � oeÆ ients
(1) (2) (3) (4) (5) (6) (7)
GNI 38.274
���
10.648
�
0.259
�
(3.990) (6.004)
Gini 0.526
��
0.734
���
0.190
���
(0.249) (0.231)
Unemployment rate -0.437
���
-0.572
���
-0.181
���
(0.249) (0.134)
Trust in others 0.175
��
0.494
���
0.570
���
(0.082) (0.046)
So ial expenditures 19.689
���
0.695 0.023
(3.449) (3.101)
ECT -0.745
���
-0.741
���
-0.693
���
-0.610
���
-0.770
���
-1.036
���
(0.049) (0.062) (0.061) (0.075) (0.043) (0.090)
� GNI -6.238 8.324
(9.132) (12.123)
� Gini -0.424 0.441
(0.538) (1.183)
� Unemployment rate 0.085 0.202
(0.212) (0.309)
� Trust in others 0.162
���
-0.170
(0.060) (0.171)
� So ial expenditures p ) 5.943 -15.673
(16.668) (28.448)
Mean Life Satisfa tion 0.955
���
0.869
���
0.860
���
0.868
���
0.926
���
0.984
���
(0.139) (0.140) (0.137) (0.149) (0.123) (0.169)
Mean GNI -30.443
���
-27.442
�
(5.410) (16.434)
Mean Gini -0.475 -2.541
���
(0.923) (0.877)
Mean Unemployment rate 0.352
���
0.319
(0.131) (0.291)
Mean Trust in others -0.118 -0.399
�
(0.124) (0.232)
Mean So ial expenditures -15.231
���
15.418
(4.621) (11.047)
Constant 14.287 -2.074 -5.714
��
-5.191 -5.112 222.303
(63.727) (25.398) (2.846) (6.063) (3.342) (173.954)
N 353 353 353 353 353 353
Note: So ial expenditures are per apita, adjusted and transformed in logarithm. GNI is per apita
and transformed in logarithm. For more details, please, refer to se tion 4.
Standard errors in parenthesis.
�
p < 0:10,
��
p < 0:05,
���
p < 0:001
19
Figure 8: Predi ted life satisfa tion Vs. observed data.
−20
020
40
60
80
Perc
ent R
espondin
g V
ery
Satisfied
1990 1995 2000 2005 2010 2015
Pred. % Very Satisfied % Very Satisfied
Sour e: authors' own elaboration.
20
6 Con lusions
Previous literature suggests that the relationship between well-being and e onomi
growth depends on a set of onditions: if e onomi growth is a ompanied by ex-
tensive so ial safety nets, high so ial apital, and low in ome inequality, than it
is likely to be asso iated with in reasing well-being. In this arti le we test this
view. In parti ular, we he k whether the at trend of life satisfa tion in Luxem-
bourg, despite a growing e onomy, an be explained by the onditions identi�ed
in previous literature.
Between 1980 and 2008 { the year of the e onomi risis { the Gross National
In ome per apita in Luxembourg grew by 6.35% yearly, while the share of very
satis�ed people remained onstant at about 35%. If the eviden e from previous
studies is orre t, we should expe t that the onditions mentioned above have a
zero net e�e t on life satisfa tion. Unfortunately, the la k of long time-series of
mi ro data prevents a mi ro-e onomi analysis in Luxembourg. We thus adopt a
ma ro-e onomi perspe tive, and we apply an error orre tion model to a panel
of 15 Western European ountries to predi t life satisfa tion in Luxembourg on
the basis of a known set of explanatory fa tors. These are: in ome inequality,
unemployment, so ial expenditures, and trust in others { a ommonly used proxy
of so ial apital.
We �nd the fa tors explain the at trend of life satisfa tion in Luxembourg rea-
sonably well and broadly onsistently with expe tations. In reases in unemploy-
ment o�set the positive in uen es of in reasing so ial trust and e onomi growth.
On the ontrary, in reases in in ome inequality apparently positively a�e ted life
satisfa tion, and so ial expenditures, did not have the statisti ally robust impa t
on well-being that we expe ted. A ross fa tors, the single most impa tful is trust
in others. Standardized oeÆ ients indi ate that the long-run e�e t of trust is
nearly twi e the e�e t of e onomi growth. We also found that the ross-se tional
average of GNI per apita attra ts a signi� ant and negative oeÆ ient. Usu-
ally ross-se tional averages are in luded in the regression to ontrol bias due to
ross- ountry orrelations, su h as the impa t of European Union poli ies, but in
this ase, average GNI plays an unexpe ted role. E onomi growth in a parti ular
ountry has a weak, positive, impa t on well-being, but when ea h ountry grows
at a similar pa e the total e�e t on well-being is negative; this is reminis ent of a
\so ial omparisons" e�e t, but this time a ross ountries. As far as we know, this
is the �rst time that su h eviden e has been do umented in a panel of ountries.
Our �ndings should be viewed with aution. They are based on the best avail-
able data, but several assumptions were ne essary to develop the long time series.
In parti ular, so ial trust is adjusted to improve omparability over time and so ial
expenditures is adjusted to obtain a better proxy for so ial safety nets. Moreover,
the sample size limits the degrees of freedom and our ability to in lude additional
21
ontrol variables and time lags. Our results re e t a preliminary assessment that
ould hange with new data or methods. Indeed, the availability of a new wave of
data from the European Value Study will soon allow us to perform a mi ro analy-
sis overing the period 1999-2019, and therefore to gain a more re�ned knowledge
about what happened to life satisfa tion in Luxembourg.
Nonetheless, we believe our results are en ouraging. They support the view
that the quality of growth matters for well-being. The quest to determine the
onditions that hara terize this \quality" is still in its infan y, but we have a
promising starting point. Further assessment is ne essary, but it is plausible that
jointly onsidering e onomi growth, so ial safety nets, so ial apital, unemploy-
ment, and in ome inequality is the best route to promote a lasting well-being.
22
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