Short communication
Social mobility and health in European countries: Does welfare regime
type matter?
In^es Campos-Matos a, b, *
, Ichiro Kawachi b
a
Department of International Public Health and Biostatistics, Nova University of Lisbon, Lisbon, Portugal
b
Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, USA
a r t i c l e i n f o
Article history:
Received 7 October 2014
Received in revised form
10 August 2015
Accepted 19 August 2015
Available online 22 August 2015
Keywords:
Europe
Welfare regimes
Social mobility
Self-rated health
a b s t r a c t
Health inequalities pose an important public health challenge in European countries, for which increased
social mobility has been suggested as a cause. We sought to describe how the relationship between
health inequalities and social mobility varies among welfare regime types in the European region. Data
from six rounds of the European Social Survey was analyzed using multilevel statistical techniques,
stratified by welfare regime type, including 237,535 individuals from 136 countries. Social mobility
among individuals was defined according to the discrepancy between parental and offspring educational
attainment. For each welfare regime type, the association between social mobility and self-rated health
was examined using odds ratios and risk differences, controlling for parental education. Upwardly mobile
individuals had between 23 and 44% lower odds of reporting bad or very bad self-rated health when
compared to those who remained stable. On an absolute scale, former USSR countries showed the biggest
and only significant differences for upward movement, while Scandinavian countries showed the
smallest. Downward social mobility tended to be associated with worse health, but the results were less
consistent. Upward social mobility is associated with worse health in all European welfare regime types.
However, in Scandinavian countries the association of upward mobility was smaller, suggesting that the
Nordic model is more effective in mitigating the impact of social mobility on health and/or of health on
mobility.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Despite sustained efforts put in effect across European countries,
health inequalities persist as an important public health
challenge (Mackenbach, 2012). A range of policy solutions has been
tried, but so far with relatively little impact. Social mobility has
been identified as an important driver of health inequalities. Social
mobility can occur either between generations (parents and children)
as well as within the life-course of the individual. Truncated
intergenerational social mobility is of particular concern because it
can result in the crystallization of wealth inequality as well as
health inequalities. Ill health is a potent cause of both intraindividual
and inter-generational mobility restriction. For
example, childhood illness has been shown to adversely affect
educational attainment (Case and Paxson, 2008), which will
subsequently affect an individual's success in the labor market. Ill
health in midlife can affect labor force participation (reduced
working hours, job loss), resulting in downward income mobility.
Restricted social mobility can be manifest in multiple dimensions e
educational achievement, occupational status or earnings and income.
Furthermore health selection can be both direct (e.g.
depressive illness directly resulting in truncated educational
achievement) as well as indirect e e.g. depressive illness resulting
in reduced social mobility via intermediary factors such as stigma
and discrimination (West, 1991). Accordingly, social protections
such as universal access to health care or anti-discrimination
legislation represent important policies to promote both intraindividual
and inter-generational social mobility.
An individual's socioeconomic position is a robust determinant
of his/her health, both in terms of their current (or achieved) socioeconomic
position, but also their lifetime trajectory (Marmot
and Macmillan, 2004; Marmot and Wilkinson, 2005). This can
reflect processes of accumulation or a direct impact of social
mobility (Hallqvist et al., 2004). Studies on the effect of social
mobility on health have not always produced clear-cut results, with
* Corresponding author. Department of International Public Health and Biostatistics,
Nova University of Lisbon, Lisbon, Portugal.
E-mail address: ines.matos@ihmt.unl.pt (I. Campos-Matos).
Contents lists available at ScienceDirect
Social Science & Medicine
journal homepage: www.elsevier.com/locate/socscimed
http://dx.doi.org/10.1016/j.socscimed.2015.08.035
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Social Science & Medicine 142 (2015) 241e248
i An update to this article is included at the end
some seeming to indicate that upward social mobility can be just as
deleterious to health as downward social mobility (Hemmingsson
et al., 1999; Liberatos et al., 1988). These mixed results might,
however, be a consequence of the inconsistent ways in which social
mobility has been operationalized in the empirical literature
(Singhammer and Mittelmark, 2010). The use of different indicators
to characterize social groups can also be of consequence, since
different indicators, such as education, occupation or income, as
well as the intergenerational movement between them, can have
different meanings (Galobardes et al., 2006).
Overall, there are both theoretical and empirical grounds to
suggest that the causal relationship between health and social
mobility is bidirectional: individuals have more or less opportunities
for social mobility depending on their health endowment
and their health achievement is affected by transitions between
social strata.
The extent of social mobility varies substantially between
countries (Beller and Hout, 2006). Government actions, such as
expanding access to schooling or investing in the health of children
(e.g. via improved nutrition or vaccination programs) have the
potential capacity to break the inter-generational transmission of
social disadvantage. Considering the strong relationship between
social mobility and health, these governmental actions, systematized
in Fig. 1, can have an important impact on health inequalities.
Welfare regime types, often used to categorize European countries,
share common policies such as the ones outlined in Fig. 1.
In this cross national comparative study, we sought to examine
the relation between social mobility and population health among
different types of welfare regimes in the European region, in order
to understand how the welfare state might moderate the link between
mobility and health.
2. Methods
2.1. Data sources and variables
Individual data was collected from six rounds of the European
Social Survey (ESS), between 2002 and 2012, from thirty selected
countries. The ESS is a repeated cross-sectional survey that collects
comparable data on individual socioeconomic characteristics and
health status of several European countries (ESS ERIC, 2014). Data is
available online at www.europeansocialsurvey.org.
The outcome variable, self-rated health, was based on the survey
participant's response to the question ‘How is your health in
general?’, dichotomized so that 1 included ‘bad’ or ‘very bad’ (other
possible answers were ‘fair’, ‘good’ or ‘very good’).
Social mobility was measured in relation to mother and father's
achieved level of education according to the International Standard
Classification of Education (ISCED) levels. Although social mobility
is usually measured on the basis of the fathers' social standing, the
increasing participation of women in the workforce and the
importance of the mothers' characteristics on children's health
behaviors (Favaro and Santonastaso, 1995) support the importance
of considering mothers' status in social mobility studies; therefore,
this analysis was done separately. Social mobility was classified in
three possible categories: ‘down’, ‘stable’ and ‘up’, according to
whether the respondent had reached, respectively, a lower, the
same, or higher educational level than his or her parent. Our
measure of mobility controlled for the parent's educational
achievement when the respondent was 14 (the same variable used
to assess mobility). Failing to take into account the ‘social group of
origin’ has been a common pitfall in previous studies of intergenerational
social mobility and health (Singhammer and
Mittelmark, 2010). Controlling for parent's educational achievement
yields mobility coefficients that can be interpreted as independent
from social group of origin.
Other individual-level variables included age (restricted to 25
years and up), gender, marital status, belonging to an ethnic minority
group, self-perceived income, domicile and main occupational
activity. Respondents who were in full-time education were
excluded, since not having completed education did not permit
comparison to parents' achievement. For all these variables a base
category with contrasting indicator variables was specified, except
age, which was centered around it's grand mean.
Fig. 1. Entry points for reducing and eliminating health disparities.
I. Campos-Matos, I. Kawachi / Social Science & Medicine 142 (2015) 241e248242
To reduce the possibility of confounding by economic development,
Gross Domestic Product (GDP) per capita, converted to international
dollars using purchasing power parity, was retrieved
from the World Bank database (World Bank, 2014) and included as
a country-level variable (specified per country, per year).
Additionally, the Gini coefficient, which might also confound the
association between social mobility and health, was retrieved from
the Eurostat database (Eurostat, 2014) and used as a level 2 variable
(country-year specific). However, this was only used as a sensitivity
analysis, since the Gini coefficient was missing for many countries
for several years.
2.2. Welfare regimes
Countries were grouped by welfare regime type and analyzed
separately. Welfare regime classification is a much-debated topic,
not only with disputed typologies, but also regarding which characteristics
should be used to for their classification (Bambra, 2007).
Nonetheless, we started with a widely used typology that divides
European countries into four regime types:
(i) Scandinavian, characterized by universal and generous
benefits and a strong redistributive social security system
(Eikemo et al., 2008a; Fenger, 2007);
(ii) Anglo-Saxon, with a low level of government spending on
social protection, modest benefits, usually means-tested
(Eikemo et al., 2008a; Fenger, 2007);
(iii) Bismarckian, with benefits tied to employment, financed
mainly by employer and employee, and minimal redistribution
(Eikemo et al., 2008a);
(iv) Southern, with a dualist system of welfare provision, which
strongly protects part of the population while underprotecting
another (19).
This classification is primarily based on Esping-Andersen's et al.
(1990) groundbreaking work, which operationalized three principles:
decommodification, social stratification and the publicprivate
mix, to classify the first three typologies (Eikemo et al.,
2008a; Espig Andersen et al., 1990). Ferrera (1996) later added
the Southern type, basing his classification on the coverage of social
protection schemes (Ferrera, 1996). This typology has been replicated
in other attempts to define welfare regime types (Bambra,
2007; Bonoli, 1997) and has been used previously in the health
literature (Eikemo et al., 2008a, 2008b).
The consideration of Central and Eastern European countries to
the European Union adds further complexity to this classification.
Historically, the trajectories of these countries' welfare transformation
can be separated in two, depending on the extent to
which the welfare effort collapsed in the 1990's (Cook and Press,
2010). This typology separates Central and Eastern European
countries (including Poland, Hungary, Czech Republic and the Baltic
states, among others) from the remaining former USSR states (such
as Russia and Central Asian countries). However, using a hierarchical
cluster analysis, Fenger (2007) showed that, based on similarities
on government spending, social situation and political
participation, these countries could be divided in the following
way:
(i) Former USSR, with generally low governmental spending on
social programs, mostly financed through social
contributions;
(ii) Post-Communist European, very similar to the first type, but
with higher levels of economic growth, inflation, social wellbeing
and egalitarianism (Fenger, 2007).
It is important to note that these characteristics do not necessarily
describe the countries in absolute; in fact, most countries
have a mix of different welfare regimes, but nonetheless have
predominant characteristics of one type.
2.3. Statistical analysis
Data was analyzed using multilevel logistic models based on a
logit-link function with a first order quasi-likelihood estimation
procedure. The models were run using MLwiN program version
2.28 (Rasbash et al., 2013). The data was analyzed considering its
hierarchical structure in three levels: individuals (level 1), nested
within years (level 2), nested within countries (level 3). Overall
odds, odds ratios, overall probabilities and rate differences were
calculated for each welfare regime. This was based in part in the
methods used by Hemmingsson et al. (1999), although we applied a
multilevel modeling technique.
The use of multilevel statistical techniques allows for the analysis
of the effect of both individual and contextual variables on the
outcome of interest. In this analysis, the contextual variable was
country- and year-specific, making it mandatory to include both as
levels. Additionally, these models take into account the hierarchical
nesting of individual observations within a year and within a
country, correcting otherwise underestimated standard errors and
allow the modeling of variability at each level of analysis
(Subramanian et al., 2003).
As a sensitivity analysis, the same models were run using
different estimation procedures.
3. Results
Table 1 outlines the distribution of the variables among welfare
regime types. Each type includes between two and seven countries,
ranging in sample size from 23,310 to 62,509 individuals. Countries
with a Scandinavian welfare regime had the lowest proportion of
people reporting bad or very bad health (5.4%), followed by AngloSaxon
(5.8%), Bismarckian (6.9%), Southern (11.9%), PostCommunist
European (14.5%) and Former USSR (19.6%).
Most respondents had achieved a higher educational level than
their parents: between 49.3 and 63.2% had improved in relation to
their mother, and between 45.6 and 61.7% in relation to their father.
Between one third and half of respondents remained in the same
educational level as their parents and a smaller proportion ‘moved
down’ e only 1.6% in southern countries in relation to mother's
achievement, up to 12.1% in Bismarckian countries, in relation to
father's.
Table 2 shows the coefficients for the mobility variable (stable,
upward, downward) from the multilevel models. The full models
are available in Tables 3 and 4 as an online supplement. The general
pattern of association between social mobility and health was
similar across all regime types, i.e. upward mobility was protective,
while downward mobility was detrimental for self-rated health.
Figs. 2 and 3 show the probabilities and risk differences in the
different mobility groups in each welfare regime type. As was noted
in Table 1, the overall probability of bad self-rated health differs
significantly between welfare regime types. The benefits of upward
mobility are present in all regime types, but risk differences are
particularly high for Southern, Post Communist European and
Former USSR regimes and significantly different only in the latter.
Scandinavian countries showed the lowest risk differences between
upwardly mobile and stable individuals when considering
either mothers' or fathers' education.
Downward mobility is generally detrimental. Post Communist
European countries show the biggest absolute differences, with
downward mobility leading to an absolute difference of 2.9 and
I. Campos-Matos, I. Kawachi / Social Science & Medicine 142 (2015) 241e248 243
3.6% in the risk of bad health measured on the basis of maternal and
paternal achievement, respectively. In Scandinavian, Anglo-Saxon,
Bismarckian and former USSR countries, this risk difference
ranges from 0.1 to 1.9%. Overall, Scandinavian countries showed the
smallest absolute difference in health between stable and downward
mobility when measured by the father's achievement, and
former USSR countries when measured by the mother's.
As a sensitivity analysis, the same models were run using
different estimation procedures (second order marginal quasilikelihood,
first and second order predictive quasi-likelihood and
Markov Chain Monte Carlo). All resulted in models with the same
effect of social mobility in the different welfare regimes. A model
was also run with the Gini coefficient as a level 2 variable; this had
very little effect in the odds ratios or risk differences and no effect in
the statistical significance of the results.
4. Discussion
This study sought to describe differences in the relationship
between social mobility and health within different welfare regimes
to better understand the effect that different regime types
might have. Different welfare regimes had a substantially different
proportion of individuals with bad or very bad self-rated health,
lowest in countries in the Scandinavian regime (5.4%), followed by
Anglo-Saxon (5.8%), Bismarckian (6.9%), Southern (11.9%), PostCommunist
European (14.5%) and finally Former USSR (19.6%).
Table 1
Composition of each welfare type.
Scandinavian Anglo-Saxon Bismarckian Southern Post-Communist European Former USSR
Countries Denmark
Finland
Norway
Sweden
Iceland
United Kingdom
Ireland
Austria
Belgium
Switzerland
Germany
France
Luxembourg
Netherlands
Spain
Greece
Italy
Portugal
Czech Republic
Hungary
Poland
Slovenia
Slovakia
Croatia
Bulgaria
Estonia
Latvia
Lithuania Russia
Ukraine
n (level 2) 23 12 33 18 34 16
n (level 1) 37,975 23,310 62,509 31,789 51,698 30,072
Level 1 variables
% bad or very bad health 5.4 5.8 6.9 11.9 14.5 19.6
Mobility, mother (%)
Down 6.3 9.3 4.7 1.6 4.0 9.9
Stable 30.4 35.2 32.9 49.0 38.0 29.2
Up 63.2 55.5 62.4 49.3 58.0 60.9
Mobility, father (%)
Down 10.1 10.2 12.1 3.4 6.8 9.4
Stable 34.6 36.7 42.3 50.9 46.1 28.9
Up 55.4 53.1 45.6 45.6 47.0 61.7
Women (%) 50.0 55.0 52.9 56.6 55.1 61.7
Mean age (years) 51.7 51.7 51.6 52.4 51.8 52.7
Urban (%) 64.3 66.0 56.9 65.0 61.3 70.2
Main activity (%)
Paid work 63.5 49.7 53.9 48.0 48.9 50.6
No activity 31.3 35.3 32.9 35.5 42.0 42.0
Other 5.2 15.0 13.3 16.5 9.1 7.3
Feeling about income (%)
Living comfortably 49.5 35.4 38.1 15.3 11.3 4.8
Coping 41.4 45.3 46.3 45.0 45.3 37.7
Difficult 7.0 14.3 12.3 27.7 29.0 37.0
Very difficult 2.0 5.0 3.3 12.1 14.5 20.5
Minority (%) 2.4 5.0 4.6 3.0 5.7 12.7
Marital status (%)
Married 57.5 55.0 59.8 63.7 61.9 52.9
Separated/divorced 12.2 11.2 11.9 6.5 9.5 14.7
Widow 7.0 10.9 9.3 12.2 14.2 19.8
Single 23.3 22.9 19.1 17.6 14.3 12.7
Level 2 variables
GDP per capita (international dollars) 39,459 36,705 37,075 26,396 19,924 16,599
Note: ISCED International Standard Classification of Education.
Table 2
Mobility odds ratios and 95% confidence intervals from the multilevel models in each welfare regime type.
Scandinavian Anglo-Saxon Bismarckian Southern Post-Communist European Former USSR
Mother n (level 1) 30,458 19,752 53,644 29,030 46,556 24,959
Stable 1 1 1 1 1 1
Down 1.39 (1.06,1.83) 1.40 (1.11,1.78) 1.09 (0.89,1.31) 0.72 (0.43,1.21) 1.35 (1.13,1.60) 1.01 (0.84,1.22)
Up 0.77 (0.68,0.86) 0.70 (0.60,0.81) 0.77 (0.71,0.84) 0.56 (0.49,0.62) 0.69 (0.65,0.75) 0.67 (0.61,0.74)
Father n (level 1) 29,837 19,184 52,326 28,417 45,500 23,036
Stable 1 1 1 1 1 1
Down 1.18 (0.95,1.45) 1.51 (1.19,1.91) 1.20 (1.06,1.37) 1.24 (0.92,1.68) 1.45 (1.28,1.65) 1.17 (0.98,1.39)
Up 0.76 (0.67,0.86) 0.68 (0.59,0.79) 0.76 (0.69,0.82) 0.57 (0.51,0.64) 0.73 (0.68,0.78) 0.68 (0.62,0.75)
Note: bold indicates OR significant at p < 0.05.
I. Campos-Matos, I. Kawachi / Social Science & Medicine 142 (2015) 241e248244
This difference was reproduced in the multilevel models, which
controlled for several socioeconomic individual and country
characteristics.
Both on a relative and on an absolute scale, upward mobility was
associated with better health, regardless of welfare regime type.
However, on the relative scale, these were significant for all regime
types, whereas on the absolute scale there was only a true difference
in countries from the Former USSR. Downward mobility was
generally associated with worse health, but to differing extents and
following a less clear pattern.
It has been argued before that the use of only absolute or relative
measures can be misleading, and our findings reiterate this argument
(King et al., 2012; Kelly et al., 2007). In fact, relative measures
of health inequalities are insensitive to equiproportionate changes,
while absolute measures are insensitive to uniform changes, which
reflects different equity value judgments implied in the empirical
analysis (Allanson and Petrie, 2013). Thus, different results between
relative and absolute scales might be a consequence of different
levels of overall ill-health: former USSR countries had the highest
prevalence of bad or very bad self-rated health, making absolute
differences more likely to emerge.
This study is sensible to a number of limitations. The outcome
measure, self-rated health, is very culturally-sensitive, complicating
cross-national comparisons (Jylh€a et al., 1998). Nevertheless,
it is an important predictor of mortality in every society where it
has been examined (Idler and Benyamini, 1997), making it a much
used and valued health measure. Additionally, social mobility
doesn't have a unanimously accepted operationalization. Differences
in educational achievement are not necessarily a reflection of
different societal prestige or access to different social resources.
Indeed, occupational mobility is often preferred (Beller and Hout,
2006), but the occupational measures available in the ESS were
crude and difficult to compare between respondents and their
parents. Also importantly, although the analyses controlled for
parental education, this operationalization of social mobility might
be measuring processes of accumulation. The welfare regime
classification is also debatable. Although most of the regime types
that were used in this study have been extensively used before, and
despite both level 2 and level 3 variability being very low and nonsignificant
(hinting to a high homogeneity between countries and
country-years), they might not reflect the characteristics of welfare
regimes that have an impact on the relationship between social
mobility and health. Finally, it is not possible to assess causality
between social mobility and health, considering that our analyses
are based on cross-sectional data. Indeed, it is possible that the
health of participants in our sample was already affected by
mobility in the previous time period. The association between
health and socioeconomic status is likely dynamic and bidirectional
across the life course.
Overall, it is interesting to note that although all welfare regime
types show relative differences in bad self-rated health for upwardly
mobile individuals, on an absolute scale the Scandinavian
regime shows the smallest differences and the former USSR group
the largest. Although welfare regime type seems to account for an
important part of the variation in self-perceived health among
European countries (Eikemo et al., 2008b), the extent to which it
impacts health inequalities has been questioned. Mackenbach et al.
(2008) reported a surprisingly high degree of health inequalities in
northern European countries, showing that, despite egalitarian
policies, lifestyle-related risk factors remain an important cause of
mortality inequalities (Mackenbach et al., 2008). Eikemo et al.
(2008a) also showed a clear gradient of health inequalities
Table 3
Multilevel models: mobility from mother's education.
Scandinavian Anglo-Saxon Bismarckian Southern Post communist Former USSR
n (level 1) 30,458 19,752 53,644 29,030 46,556 24,959
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Fixed Parameters
Intercept 0.008 (0.005,0.012) 0.009 (0.006,0.027) 0.011 (0.007,0.018) 0.019 (0.009,0.037) 0.016 (0.011,0.022) 0.036 (0.026,0.050)
Individual Level
Mobility (reference: stable)
Down 1.39 (1.06,1.83) 1.40 (1.11,1.78) 1.09 (0.89,1.31)* 0.72 (0.43,1.21)* 1.35 (1.13,1.60) 1.01 (0.84,1.22)*
Up 0.77 (0.68,0.86) 0.70 (0.60,0.81) 0.77 (0.71,0.84) 0.56 (0.49,0.62) 0.69 (0.65,0.75) 0.67 (0.61,0.74)
Gender (ref: male) 1.03 (0.92,1.15)* 0.89 (0.77,1.02)* 1.07 (0.98,1.15)* 1.59 (1.45,1.75) 1.06 (0.99,1.13)* 1.21 (1.12,1.32)
Age 0.99 (0.99,1.00)* 1.00 (0.99,1.01)* 1.01 (1.01,1.01) 1.04 (1.04,1.05) 1.04 (1.04,1.04) 1.05 (1.04,1.05)
Domicile (ref: urban) 0.94 (0.84,1.05)* 0.85 (0.74,0.98) 0.88 (0.82,0.95) 1.14 (1.05,1.24) 1.11 (1.04,1.18) 0.97 (0.89,1.05)*
Main activity (ref: paid work)
No activity 6.29 (5.39,7.35) 6.17 (5.00,7.61) 4.35 (3.90,4.84) 2.54 (2.22,2.89) 3.35 (3.06,3.67) 2.63 (2.35,2.93)
Other 2.83 (2.21,3.62) 2.55 (1.96,3.31) 1.82 (1.59,2.09) 1.91 (1.64,2.21) 1.97 (1.74,2.24) 1.56 (1.31,1.86)
Income (ref: living comfortably)
Coping 1.40 (1.24,1.59) 1.49 (1.26,1.78) 1.52 (1.38,1.67) 1.38 (1.15,1.64) 1.55 (1.33,1.81) 1.10 (0.84,1.45)*
Difficult 2.97 (2.49,3.54) 2.61 (2.12,3.21) 3.29 (2.94,3.68) 2.28 (1.90,2.73) 3.00 (2.57,3.51) 1.76 (1.35,2.29)
Very difficult 4.57 (3.56,5.86) 4.09 (3.18,5.27) 5.21 (4.47,6.06) 3.97 (3.28,4.82) 5.37 (4.55,6.32) 2.86 (2.19,3.75)
Minority (ref: no) 1.44 (1.05,1.97) 0.86 (0.61,1.21)* 1.27 (1.08,1.49) 1.03 (0.79,1.34)* 0.97 (0.86,1.09)* 1.02 (0.91,1.15)*
Marital status (ref: married)
Separated/divorced 1.37 (1.18,1.59) 2.02 (1.67,2.43) 1.28 (1.14,1.42) 1.23 (1.03,1.47) 1.03 (0.93,1.14)* 1.28 (1.14,1.43)
Widow 1.07 (0.89,1.27)* 1.13 (0.92,1.37)* 1.08 (0.97,1.21)* 1.06 (0.95,1.17)* 1.06 (0.98,1.14)* 1.18 (1.08,1.30)
Single 1.02 (0.87,1.19)* 1.19 (0.99,1.43)* 1.17 (1.05,1.31) 1.29 (1.12,1.51) 1.09 (0.97,1.21)* 1.31 (1.13,1.52)
Mother's education (ref: ISCED V/VI)
ISCED I 2.39 (1.76,3.26) 1.63 (1.18,2.25) 2.14 (1.66,2.77) 1.23 (0.79,1.92)* 2.48 (1.93,3.21) 2.02 (1.68,2.43)
ISCED II 2.00 (1.49,2.71) 1.25 (0.91,1.72)* 1.72 (1.34,2.21) 0.67 (0.39,1.13)* 1.89 (1.48,2.41) 1.76 (1.45,2.12)
ISCED III 1.67 (1.25,2.23) 1.20 (0.82,1.77)* 1.34 (1.04,1.71) 1.03 (0.62,1.71)* 1.26 (0.99,1.59)* 1.25 (1.04,1.49)
ISCED IV 1.64 (1.04,2.59) 0.37 (0.17,0.79) 1.58 (1.09,2.27) 0.53 (0.15,1.88)* 1.54 (1.05,2.26) 1.12 (0.91,1.38)*
Level 2
GDP 0.99 (0.99,1.00)* 0.99 (0.99,1.00)* 1.00 (0.99,1.00)* 0.99 (0.99,0.99) 0.99 (0.99,0.99) 0.99 (0.99,0.99)
Random Parameters
Level 3 variance (svo) 0.08 (0.06)* 0.19 (0.19)* 0.22 (0.12)* 0.23 (0.17)* 0.07 (0.04)* 0.03 (0.02)*
Level 2 variance (suo) 0.01 (0.01)* 0 (0) 0 (0) 0.01 (0.01)* 0.03 (0.01) 0.01 (0.01)*
Notes: * not significant at p < 0.05. ISCED International Standard Classification of Education. OR Odds ratio. CI Confidence interval.
I. Campos-Matos, I. Kawachi / Social Science & Medicine 142 (2015) 241e248 245
between European welfare states, from Southern (with the highest
inequalities) to Bismarckian (with the lowest) (Eikemo et al.,
2008a). This ‘paradox’ was examined by Mackenbach (2012), who
postulated that social mobility might be one of the drivers of health
inequalities in Western European welfare states. Some studies have
indeed shown that increased social mobility is associated with
stronger health inequalities (Simons et al., 2013; Asgeirsdottir and
Ragnarsdottir, 2013; Elstad, 2001). However, Brekke, Grunfeld and
Kverndokk (2014), showed that higher health inequalities in more
egalitarian countries might be solely a consequence of a more equal
health distribution, since the concentration index is more sensitive
to health-contingent income transfers than to income-contingent
health transfers (Brekke et al., 2012).
Our findings suggest that some welfare states are in fact more
Table 4
Multilevel models: mobility from father's education.
Scandinavian Anglo-Saxon Bismarckian Southern Post communist Former USSR
Level 1 n 29,837 19,184 52,326 28,417 45,500 23,036
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Fixed Parameters
Intercept 0.009 (0.007,0.014) 0.009 (0.005,0.019) 0.013 (0.009,0.018) 0.014 (0.008,0.024) 0.017 (0.013,0.023) 0.033 (0.023,0.047)
Individual Level
Mobility (reference: stable)
Down 1.18 (0.95,1.45)* 1.51 (1.19,1.91) 1.20 (1.06,1.37) 1.24 (0.92,1.68)* 1.45 (1.28,1.65) 1.17 (0.98,1.39)*
Up 0.76 (0.67,0.86) 0.68 (0.59,0.79) 0.76 (0.69,0.82) 0.57 (0.51,0.64) 0.73 (0.68,0.78) 0.68 (0.62,0.75)
Gender (ref: male) 1.03 (0.92,1.15)* 0.89 (0.77,1.03)* 1.05 (0.97,1.14)* 1.56 (1.42,1.72) 1.07 (1.00,1.14) 1.26 (1.15,1.37)
Age 0.99 (0.99,1.00)* 1.00 (0.99,1.01)* 1.01 (1.01,1.01) 1.04 (1.04,1.05) 1.04 (1.04,1.04) 1.05 (1.04,1.05)
Domicile (ref: urban) 0.95 (0.85,1.06)* 0.88 (0.76,1.02)* 0.88 (0.82,0.95) 1.14 (1.04,1.24) 1.11 (1.04,1.18) 0.95 (0.87,1.03)*
Main activity (ref: paid work)
No activity 6.11 (5.23,7.14) 5.87 (4.76,7.24) 4.44 (3.97,4.95) 2.54 (2.22,2.91) 3.29 (2.99,3.60) 2.70 (2.41,3.04)
Other 2.67 (2.07,3.45) 2.47 (1.89,3.22) 1.19 (1.64,2.17) 1.94 (1.67,2.26) 1.91 (1.68,2.17) 1.58 (1.32,1.90)
Income (ref: living comfortably)
Coping 1.41 (1.24,1.61) 1.45 (1.21,1.72) 1.50 (1.36,1.65) 1.35 (1.13,1.61) 1.55 (1.32,1.81) 1.08 (0.82,1.43)*
Difficult 2.86 (2.39,3.42) 2.54 (2.06,3.13) 3.25 (2.90,3.65) 2.24 (1.87,2.69) 2.94 (2.51,3.45) 1.71 (1.29,2.26)
Very difficult 4.66 (3.62,6.01) 3.74 (2.89,4.85) 5.05 (4.33,5.90) 3.94 (3.24,4.78) 5.31 (4.49,6.27) 2.83 (2.13,3.76)
Minority (ref: no) 1.46 (1.06,2.01) 0.97 (0.69,1.35)* 1.33 (1.13,1.57) 1.03 (0.79,1.35)* 0.97 (0.86,1.09)* 1.01 (0.89,1.15)*
Marital status (ref: married)
Separated/divorced 1.42 (1.34,1.96) 2.06 (1.69,2.49) 1.26 (1.13,1.41) 1.23 (1.03,1.48) 1.04 (0.93,1.16)* 1.25 (1.11,1.42)
Widow 1.08 (0.84,1.34)* 1.17 (0.96,1.43)* 1.06 (0.95,1.19)* 1.06 (0.95,1.18)* 1.07 (0.99,1.16)* 1.18 (1.07,1.30)
Single 1.03 (0.88,1.22)* 1.19 (0.99,1.44)* 1.15 (1.03,1.29) 1.31 (1.13,1.53) 1.08 (0.96,1.21)* 1.15 (0.98,1.35)*
Education (ref: ISCED V/VI)
ISCED I 2.18 (1.70,2.79) 1.59 (1.19,2.12) 1.92 (1.61,2.28) 1.70 (1.24,2.33) 2.27 (1.87,2.74) 2.12 (1.77,2.54)
ISCED II 1.67 (1.29,2.16) 1.09 (0.82,1.45)* 1.59 (1.34,1.88) 1.33 (0.94,1.89)* 1.72 (1.44,2.06) 1.79 (1.49,2.15)
ISCED III 1.42 (1.13,1.78) 1.16 (0.82,1.64)* 1.36 (1.17,1.57) 0.99 (0.68,1.47)* 1.21 (1.02,1.43) 1.39 (1.17,1.67)
ISCED IV 1.67 (1.21,2.29) 0.93 (0.54,1.62)* 1.26 (0.98,1.61)* 1.11 (0.52,2.38)* 0.83 (0.58,1.18)* 1.17 (0.94,1.45)*
Level 2
GDP 0.99 (0.99,1.00)* 1.00 (0.99,1.00)* 1.00 (0.99,1.00)* 0.99 (0.99,0.99) 0.99 (0.99,0.99) 0.99 (0.99,0.99)
Random Parameters
Level 3 variance (svo) 0.09 (0.07)* 0.19 (0.19)* 0.21 (0.12)* 0.24 (0.17)* 0.08 (0.05)* 0.03 (0.02)*
Level 2 variance (suo) 0.01 (0.01)* 0 (0) 0 (0) 0.003 (0.004)* 0.03 (0.01) 0.01 (0.01)*
Notes:* not significant at p < 0.05. ISCED International Standard Classification of Education. OR Odds ratio. CI Confidence interval.
Fig. 2. Probability of ‘Bad’ or ‘Very Bad’ self-rated health per to mobility group, defined from mother's educational achievement, per welfare type (error bars are 95% confidence
intervals) and risk difference.
I. Campos-Matos, I. Kawachi / Social Science & Medicine 142 (2015) 241e248246
effective in separating social mobility from health, namely Scandinavian
countries exhibit smaller differences while former USSR
societies the largest. This is not surprising considering that, for
example, comprehensive social policies seem to be associated with
fewer inequalities in ‘sickness’ in European countries, as well as
lower rates of non-employment (van der Wel et al., 2011). It is
understandable then, that Scandinavian countries will manage to
disassociate social mobility from health more effectively, leading to
the small risk differences we found for upward mobility in these
countries.
Importantly too, Central and Eastern European countries underwent
considerable transitions in the last decades, with important
consequences to their social structures (Saar et al., 2012). Our
results for the post-Communist European and former USSR welfare
regimes, which tended to show the largest absolute differences in
health, might reflect, at least partially, these important structural
changes and not just relative social mobility.
Our findings also reinforce the need to assess health inequalities
using both relative and absolute measures, since the use of only one
might be very misleading.
Interestingly too, when comparing the association of downward
mobility with health as assessed in reference to paternal versus
maternal achievement, the former was larger in every welfare
regime except the Scandinavian region. Considering that Scandinavian
countries have the best indicators of gender equality
(European Institute for Gender Equality, 2013), a possible explanation
for this is that in other, less gender-egalitarian countries, the
father's status is more decisive in determining the family's socioeconomic
status, and therefore a downward mobility from his social
position has a greater impact.
To the best of our knowledge, this is the first research into the
moderating effect of welfare regimes on the relationship between
social mobility and health, and to measure social mobility separately
based on maternal and paternal achievement. Further exploration of
our findings would benefit from measuring occupational social
mobility in addition to educational mobility. It would also benefit
from a separate analysis for each gender, since the effect might be
different for women and men and might help explain the differences
in the association of downward mobility when measured on the
basis of maternal and paternal achievement.
Previous studies have questioned the contribution of the
welfare regime in mitigating the extent of health inequalities and
identified increased social mobility as a possible cause for this
(Mackenbach, 2012). However, the present results show that
important systematic differences exist between regime types with
regard to upward mobility and health, with a notably attenuated
association on the absolute scale in Scandinavian countries and
stronger association in the former USSR regimes. This suggests that
social mobility is not a cause of high health inequalities found in
Scandinavian countries in previous analyses.
Acknowledgments
I. Campos Matos is supported by a Fulbright grant for Public
Health research, awarded by the U.S. Department of State and the
Portuguese Government, and by a studentship from the Portuguese
Foundation of Science and Technology.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.socscimed.2015.08.035.
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Update
Social Science & Medicine
Volume 144, Issue , November 2015, Page 38
https://doi.org/10.1016/j.socscimed.2015.09.016DOI:
Corrigendum
Corrigendum to “Social mobility and health in European countries:
Does welfare regime type matter?” [Soc. Sci. Med. 142 (2015)
241e248]
In^es Campos-Matos a, b, *
, Ichiro Kawachi b
a
Department of International Public Health and Biostatistics, Nova University of Lisbon, Lisbon, Portugal
b
Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, USA
The authors regret that an error is present in the abstract of the above published paper. The corrected abstract is given below:
Health inequalities pose an important public health challenge in European countries, for which increased social mobility has been
suggested as a cause. We sought to describe how the relationship between health inequalities and social mobility varies among welfare
regime types in the European region. Data from six rounds of the European Social Survey was analyzed using multilevel statistical techniques,
stratified by welfare regime type, including 237,535 individuals from 136 countries. Social mobility among individuals was defined
according to the discrepancy between parental and offspring educational attainment. For each welfare regime type, the association between
social mobility and self-rated health was examined using odds ratios and risk differences, controlling for parental education. Upwardly
mobile individuals had between 23 and 44% lower odds of reporting bad or very bad self-rated health when compared to those who
remained stable. On an absolute scale, former USSR countries showed the biggest and only significant differences for upward movement,
while Scandinavian countries showed the smallest. Downward social mobility tended to be associated with worse health, but the results
were less consistent. Upward social mobility is associated with better health in all European welfare regime types. However, in Scandinavian
countries the association of upward mobility was smaller, suggesting that the Nordic model is more effective in mitigating the impact of
social mobility on health and/or of health on mobility.
The authors would like to apologise for any inconvenience caused.
DOI of original article: http://dx.doi.org/10.1016/j.socscimed.2015.08.035.
* Corresponding author. Department of International Public Health and Biostatistics,
Nova University of Lisbon, Lisbon, Portugal.
E-mail address: ines.matos@ihmt.unl.pt (I. Campos-Matos).
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