Sociology of Health & Illness Vol. 34 No. 6 2012 ISSN 0141–9889, pp. 858–879
doi: 10.1111/j.1467-9566.2011.01433.x
Welfare state regimes, health and health inequalities
in adolescence: a multilevel study in 32 countries
Matthias Richter1, Katharina Rathman2,3, Saoirse Nic
Gabhainn4, Alessio Zambon5, William Boyce6 and Klaus
Hurrelmann2
1
Institute of Medical Sociology, Martin Luther University Halle-Wittenberg, Halle,
Germany
2
Hertie School of Governance, Berlin, Germany
3
Berlin Graduate School of Social Sciences, Humboldt University Berlin, Germany
4
Health Promotion Research Centre, National University of Ireland, Galway, Ireland
5
Department of Public Health, University of Turin, Italy
6
Department of Community Health & Epidemiology, Queen’s University, Kingston, Canada
Abstract
Comparative research on health and health inequalities has recently started to
establish a welfare regime perspective. The objective of this study was to
determine whether different welfare regimes are associated with health and health
inequalities among adolescents. Data were collected from the ‘Health Behaviour
in School-aged Children’ study in 2006, including 11- to 15-year-old students
from 32 countries (N = 141,091). Prevalence rates and multilevel logistic
regression models were calculated for self-rated health (SRH) and health
complaints. The results show that between 4 per cent and 7 per cent of the
variation in both health outcomes is attributable to differences between countries.
Compared to the Scandinavian regime, the Southern regime had lower odds
ratios for SRH, while for health complaints the Southern and Eastern regime
showed high odds ratios. The association between subjective health and welfare
regime was largely unaffected by adjusting for individual socioeconomic position.
After adjustment for the welfare regime typology, the country-level variations
were reduced to 4.6 per cent for SRH and to 2.9 per cent for health complaints.
Regarding cross-level interaction effects between welfare regimes and
socioeconomic position, no clear regime-specific pattern was found. Consistent
with research on adults this study shows that welfare regimes are important in
explaining variations in adolescent health across countries.
Keywords: welfare regimes, adolescence, subjective health, socioeconomic status, HBSC
Introduction
Socioeconomic differences in health and longevity are well documented. Innumerable studies
have shown that adults with lower education, occupational status and income are more likely
to suffer from adverse health and die earlier than those who are better-off (Mackenbach et al.
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Welfare state regimes, health and health inequalities in adolescence
859
2002, Bartley 2004, Mackenbach 2006). A more complex picture of socioeconomic
inequalities in health emerges among adolescents. Even though the social gradient is less
persuasive and less visible than among adults, several studies have found clear health
differences for individual (West 1997, Goodman 1999, Chen et al. 2002, Starfield et al. 2002),
as well as school- and country-level socioeconomic status in adolescence (Torsheim et al.
2004, 2006). Socioeconomic differences in health and health behaviour among adolescents
within countries now represent an active and important area of research (Richter et al. 2009).
However, although an increasing number of studies have begun to integrate a welfare regimespecific perspective on the social determinants of health (Raphael 2006), little is known about
the relationships between different aspects of the welfare state, health and health inequalities
in adolescence.
Welfare regimes
The welfare state has been subject of academic interest for several years and across a range of
disciplines (Titmuss 1974, Esping-Andersen 1990, 1999). Various classifications have been
suggested to categorise the provision of welfare into different ‘regime types’ (Bambra 2005,
2006, Eikemo and Bambra 2008). Although Esping-Andersen’s ‘three worlds of welfare’
typology is probably the most well-known welfare state classification, it has been criticised by
many scholars (Castles and Mitchell 1993, Ferrera 1996, Arts and Gelissen 2002, Bambra
2006, 2007). Esping-Andersen’s typology of welfare states is based on three principles
(decommodification, social stratification and private-public mix) which according to the
extent of their presence allow the identification of three ‘ideal’ regime types: liberal,
conservative, and social democratic (Eikemo et al. 2008a, 2008b).
Welfare states belonging to the liberal regime (e.g. UK, USA, Ireland and Canada) are
characterised by minimal state provision of welfare, modest benefits and strict entitlement
criteria (Eikemo and Bambra 2008). Further, welfare recipients are usually means-tested and
stigmatised (Esping-Andersen 1990, Bambra 2007). In conservative welfare states (Germany,
France, Austria, Belgium, Italy and, to a lesser extent, the Netherlands) welfare programmes
and benefits are often ‘status differentiating’, related to income levels, and administered
through employers (Bambra 2007). Thus, such welfare politic is geared towards maintaining
existing social patterns (Eikemo and Bambra 2008). The social democratic welfare regime
(Norway, Sweden, Denmark and Finland) represents the smallest cluster and is characterised
not only by its universal, egalitarian and comparatively generous benefits, but also by a
commitment to full employment and income protection. Further, this is a regime which
strongly intervenes to promote equality through a redistributive social security system
(Esping-Andersen 1990).
As a result of an extensive debate about Esping-Andersen’s typology, Ferrera (1996, 2005)
introduced a fourth type: Scandinavian (social democratic), Bismarckian (conservative),
Anglo-Saxon (liberal) and the Southern welfare regime. According to Ferrera (1996) the
Southern European countries, such as Italy, Greece, Portugal and Spain, comprise a
distinctive, southern, welfare state regime. Their welfare programmes are described as
‘rudimentary’ because they are still characterised by their fragmented system of welfare
provision and welfare services (Eikemo and Bambra 2008). Moreover, reliance on the family
and voluntary sector remains a prominent defining feature.
More recently, former ‘Eastern European’ countries, such as Poland and Bulgaria, are
being considered as another distinctive regime type (Esping-Andersen 1999, Kovacs 2002).
These countries have experienced economic upheaval after the breakdown of their
communist regimes and are still undergoing extensive social reforms which are frequently
indicated by a shift towards the Liberal welfare regime. Their welfare benefits are
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860
Matthias Richter et al.
characterised by limited health service provisions and the overall population health is
relatively poor compared to other member states of the European Union (Eikemo and
Bambra 2008). These five regime types represent the most frequent classification in current
approaches to comparative regime-specific research (Bambra 2006, Eikemo et al. 2008a,
2008b, 2008c).
Welfare state regimes and health
An increasing number of studies have shown that population health differs substantially by
welfare regimes (Navarro et al. 2006, Chung and Muntaner 2006, 2007, Eikemo et al. 2008a,
2008b, 2008c, Bambra et al. 2009). Most of these studies focus on infant mortality and low
birth weight. There is no study that investigates adult mortality in a regime comparative
perspective. Navarro et al. (2006) focused on life expectancy, while Eikemo et al. (2008c)
examined differences in morbidity using a welfare state regime approach. In almost all
analyses it has been shown that social democratic countries rank more positively on various
population health indicators than the other regimes, especially those characterised as being
liberal. These findings have been consistent for different welfare regime typologies
irrespective of whether they are based on Esping-Andersen’s classification or on typologies of
political traditions (for example Navarro et al. 2006, Borrell et al. 2007) that are closely
related to Esping-Andersen’s typology, as countries with social democratic traditions usually
also have a social democratic welfare system.
Welfare state regimes and health inequalities
Recently a welfare state regime perspective was also introduced to the analyses of crossnational differences in the magnitude of health inequalities for adults (Eikemo et al. 2008a,
2008b, 2008c, Bambra et al. 2009). Eikemo et al. (2008a), for example, showed that the
Southern European welfare regime had the largest and the Bismarckian regime the
smallest disparities in self-rated health and longstanding illness, while the Scandinavian
regime was less favourably placed than the Anglo-Saxon and Eastern regimes. This
exemplary finding is found in many studies: the social democratic welfare states do not
have the smallest health inequalities even though they have the highest level of population
health. In general, conservative countries seem to perform better and show the smallest
inequalities in health (especially for self-rated health). Although there is increasing evidence
for this patterning of health and health inequalities it should be acknowledged that the
so-called ‘Scandinavian paradox’ is highly debated among scholars (Lahelma and
Lundberg 2009). This finding represents an important challenge for public health policy
and practice (Dahl et al. 2006).
So far, comparative research on welfare regimes and health has rarely focused on
adolescents (Zambon et al. 2006). It is reasonable to assume that not only individual
determinants but also macrolevel factors and welfare regime characteristics influence
adolescent health and wellbeing (Zambon et al. 2006, Ravens-Sieberer et al. 2008, Holstein
et al. 2009). These welfare arrangements are likely to work through and along with social and
income inequalities to affect intermediary determinants such as material circumstances,
parental support or health behaviour of both parents and children (CSDH 2008, Beckfield
and Krieger 2009). Thus, macrolevel determinants do not necessarily need to be directly
associated with adverse health but could act indirectly as a stratifying mechanism through
other determinants of health (Torsheim et al. 2004). Further, research has shown that
socioeconomic differences in health in adolescence are less consistent and less pronounced
(West 1997, Spencer 2006, Richter et al. 2009), as compared to health inequalities in
adulthood. In this context it can be expected that the specific characteristics of welfare
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Welfare state regimes, health and health inequalities in adolescence
861
infrastructure and arrangements of a country influence health and health inequalities
cumulatively over the life course (Graham and Power 2004, Blane 2006). Zambon et al.
(2006) were among the first who examined whether different types of welfare regimes mediate
the effect of socioeconomic position on adolescents’ health using data from the ‘Health
Behaviour in School-aged Children (HBSC)’ in 2002. They hypothesised that countries with
stronger redistributive policies (i.e. social democratic and conservative welfare regimes) are
more effective in weakening the association between socioeconomic position and health.
Their findings showed that, even after adjusting for family socioeconomic position (SEP),
levels of general subjective health for adolescents are lower in countries belonging to the
Anglo-Saxon and Eastern European regimes.
The findings from Zambon et al. (2006) reinforce that welfare regimes are also important
determinants of adolescents’ health. However, it is not known whether the challenging
findings observed for adults (i.e. better overall population health in social democratic regimes
but not the smallest health inequalities) also hold for young people. We decided to update
and to extend Zambon et al.’s analyses with more recent data from the HBSC study by
applying multilevel analyses. The objective of our study is therefore to examine whether
different types of welfare regimes are associated with differences in subjective health and with
socioeconomic differences in health among adolescents in 32 European and North American
countries.
Methods
Population
Data were obtained from the Health Behaviour in School-aged Children (HBSC) study
2005 ⁄ 2006, a cross-national survey conducted in collaboration with the World Health
Organization (Currie et al. 2008a). The aim of the HBSC study is to describe young people’s
health and health behaviour and to analyse how these outcomes are associated with social
contexts. Cross-sectional surveys of 11-, 13- and 15-year-old children and adolescents are
carried out every four years in a growing number of countries based on an internationally
agreed protocol (Currie et al. 2009). The latest survey in 2005 ⁄ 06 included a total of 41
countries from Europe and North America. The aims and theoretical framework of the study
have been described elsewhere (Currie et al. 2007, Roberts et al. 2009). The data were
collected by means of standardised questionnaires, administered in school classrooms
according to standard instructions.
Students were selected using a clustered sampling design, where the initial sampling unit
was the school class. The recommended minimum sample size for each country was 1536
students per age group (i.e. 11, 13 and 15 year olds), to assure a 95 per cent confidence
interval of + ⁄ )3 per cent for prevalence estimates of around 50 per cent. The sample size
included a design factor of 1.2 because of the cluster sampling (the design factor of 1.2 was
based on analyses of the 1993 ⁄ 1994 and 1997 ⁄ 1998 HBSC surveys). In some of the
participating countries, HBSC was exempt from the requirement for ethical approval. In
other countries that required approval this was obtained by different institutional review
boards. The present analysis is based on N = 141,091 11 to 15 year olds (66,964 male and
74,127 female students) from 29 European countries, as well as Canada, the United States
and Israel, which were categorised into four regimes according to Ferrera’s welfare typology
(1996) (Scandinavian, Bismarckian, Anglo-Saxon, Southern), with an additional fifth
category for Eastern European countries (Table S1, available in the supplementary files
accompanying the online version of this paper). Data from England, Scotland, and Wales
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Matthias Richter et al.
were merged to represent the United Kingdom as were data from the French and Flemishspeaking parts of Belgium to represent Belgium. Iceland, Malta, Greenland and Turkey were
excluded from the analyses because it was not possible to categorise them into one of the
regime clusters. Furthermore, we excluded Lithuania and Slovakia due to a high number of
missing values (more than 10%) for both SEP indicators.
Health outcomes
The outcome measures were self-rated health and health complaints (Haugland and Wold
2001, Haugland et al. 2001, Hetland et al. 2002, Ravens-Sieberer et al. 2008). In terms of selfrated health (SRH) students were asked to describe their health: ‘would you say your health
is…?’ with response codes of ‘excellent’, ‘good’, ‘fair’ and ‘poor’. The response categories
were dichotomised into ‘very good or good’ versus ‘less than good’ (‘fair’, ‘poor’) health
(Currie et al. 2008a).
Health complaints were measured using the HBSC symptom check list (HSCL). Students
were asked how often in the last 6 months they had experienced the following symptoms:
headache; stomach ache; backache; feeling low; irritable or bad tempered; feeling nervous;
difficulties in getting to sleep and feeling dizzy. The response options for each item ranged
from ‘about every day’ to ‘rarely or never’. These response options were categorised into ‘two
or more symptoms more than once a week’ versus ‘less than two symptoms’ (Currie et al.
2008a).
Socioeconomic position
Socioeconomic position (SEP) was measured with two different indicators: family affluence
and parental occupation. Family affluence was assessed using the family affluence scale
(FAS) which is a validated measure based on four different aspects of the household’s
material conditions (Boyce et al. 2006, Torsheim et al. 2006, Currie et al. 2008b): Does your
family own a car? (0, 1, 2 or more); how many times did you travel away on holiday with
your family during the past 12 months? (0, 1, 2, 3 or more); do you have your own bedroom
for yourself? (no = 0, yes = 1); and how many computers does your family own? (0, 1, 2, 3
or more). A composite FAS score was calculated by summing the responses to these four
items ranging from 0 to 7. The FAS scores were subsequently recoded in three groups: high
(6–7 points), middle (4–5 points) and low (0–3). Family affluence has several benefits such as
the low percentage of missing responses from young people and documented cross-national
comparability (Currie et al. 2008a, 2008b).
Parental occupation was assessed by two open-ended questions. Students were asked to
indicate separately where their father and mother work and to describe what kind of job they
do. Those students whose parents did not work were asked to indicate what they were doing
instead: ‘is ill or has retired or going to school’, ‘is looking for a new job’, ‘works as a
housewife ⁄ househusband’. Using standard occupational coding guidelines, all countries were
required to code this information into five rank-ordered social classes, in accordance with the
classical RGSC (registrar general social class) coding scheme. Since many mothers were
economically inactive and many responses on parental occupation were missing, information
from the father and mother was combined, using the highest category for each couple as the
parental indicator. In order to obtain three groups of similar size, the original five categories
were recoded into high (I and II), middle (III) and low parental occupation (IV and V).
Students were also classified in the lowest category, if neither parent was working for money
(‘looking for a job’, ‘full time at home’). Adolescents who lived with a single parent were also
grouped in the lowest category, when the single parent was ‘unemployed’, ‘sick, retired, or a
student’ or ‘full time at home’.
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Welfare state regimes, health and health inequalities in adolescence
863
Statistical analysis
In order to assess the extent to which the welfare state regime typology explains the
proportional variation in both health outcomes as well as their association with
socioeconomic inequalities in health, two-level hierarchical logistic regression models were
conducted. The basic principle of hierarchical regression (also known as multilevel modelling)
is that the data are structured in hierarchically nested groups (Merlo 2003, Merlo et al. 2005,
Rabe-Hesketh and Skrondal 2008, Hox 2010). The level 1 units in our sample are individual
students (N = 141,091), the level 2 units are the 32 European and North American
countries. By using self-rated health and health complaints as separate outcomes, we were
able to analyse the extent to which health varies at the individual level compared to the
country level and simultaneously to identify factors (i.e. welfare regime type) that might
explain the variation.
The analyses with random intercepts for both health outcomes were done in five
steps. First, the variation in both health outcomes without using any explanatory
variables was analysed in order to decompose the variance of the intercept into variance
components for each of the two levels (model 1). Such models are called interceptonly ⁄ empty models or just variance component models (Rabe-Hesketh and Skrondal
2008, Hox 2010). In a second step, a model with individual socioeconomic predictors,
namely, parental occupation (model 2a) and family affluence (model 2b) was conducted.
In a third step, we analysed the effect of welfare regime dummy variables (the
Scandinavian regime was used as reference) separately, in order to identify how both
health outcomes vary by welfare state arrangements (model 3). Next, we included
parental occupation (model 4a) and family affluence (model 4b) as well as the regime
dummy variables, respectively.
Finally, in order to assess the association between different welfare regimes and
socioeconomic differences in health, we followed an approach by Rostila (2007) and analysed
two additional models with cross-level-interaction terms between both SEP indicators
(models 5a and 5b) and the four welfare regime dummy variables (high SEP group in the
Scandinavian regime as reference).
The variation by country was expressed as intraclass correlation coefficient (ICC) (RabeHesketh and Skrondal 2008) which indicates the proportion of variance of the outcome that
is attributable to differences between countries. As the level 1 and level 2 variances are not on
the same scale, we have used the latent variable approach (Goldstein 2003). We assume a
latent underlying continuum of both health outcomes; the ICC was computed using a
formula for logistic models (Rabe-Hesketh and Skrondal 2008: 238). In this formula,
residuals are assumed to have a standard logistic variance structure at the level 1
(p2 ⁄ 3 = 3.29), and a normal distributed variance structure (u0j) at level 2 (here: country
where the subscript j refers to the different countries). At the country-level the ICC may be
calculated as u0j ⁄ (u0j + 3.29), indicating the ratio of the random country variance to the
total variance. At the individual-level the ICC equals 3.29 ⁄ (u0j + 3.29). We have presented
these numbers as a percentage of total variance (ICC · 100).
By adding successively individual and country-level predictors to the models, we consider
some part of the compositional differences and disentangle some of the country variance
discovered in the variance component model (model 1). To assess the goodness of fit of
different models, we calculated the deviance values ()2 · log-likelihood) (Albright and
Marinova 2010). In each model we controlled for age (dummy variables coded with three age
groups: 11, 13 and 15; 11 year olds as reference category) and gender (boys as reference). The
conventional 5% level was used to determine statistical significance. All analyses were
conducted using STATA version 11.
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Results
Descriptive results: regime-specific prevalence rates
Table 1 shows the prevalence rates for both health outcomes by welfare state regime and
country for boys and girls. Fair ⁄ poor SRH for boys was highest in the Anglo-Saxon welfare
state regime (13.6%) followed by the Eastern European regime (13.3%). For girls the highest
Table 1 Age-adjusted prevalence rates (PR) for fair ⁄ poor health and two or more health complaints more
than once a week by welfare state regime ⁄ country, 11- to 15-year-old students (N = 141091), in percentages
Fair ⁄ poor self-rated health (SRH)
Two or more health complaints
PR boys
PR girls
PR total
Country (N = 32)
welfare regime
10.8
9.1
14.5
8.5
10.8
9.2
13.8
9.1
11.4
10.3
10.0
5.4
10.3
12.7
9.2
5.8
16.4
17.3
13.6
4.2
5.9
3.8
10.7
5.6
5.8
8.3
10.4
9.7
12.4
18.2
13.3
11.5
11.1
22.8
9.3
21.0
13.3
16.1
11.3
19.9
13.5
15.1
13.0
20.9
15.4
15.9
19.8
18.9
9.3
16.3
16.9
12.0
7.2
24.5
25.3
18.9
7.2
11.8
6.0
16.1
9.0
9.7
12.8
16.6
14.0
14.8
25.0
22.4
16.7
18.2
31.4
14.0
44.7
21.0
13.5
10.2
17.2
11.0
13.0
11.1
17.3
12.2
13.7
15.1
14.4
7.4
13.3
14.8
10.6
6.5
20.4
21.3
16.2
5.7
8.9
4.9
13.4
7.3
7.7
10.5
13.5
11.9
13.6
21.6
17.9
14.1
14.6
27.1
11.7
32.8
17.2
Denmark
Finland
Norway
Sweden
Scandinavian
Austria
Belgium
France
Germany
Luxembourg
Netherlands
Switzerland
Bismarckian
Canada
Ireland
Israel
United Kingdom
United States
Anglo-Saxon
Greece
Italy
Macedonia
Portugal
Spain
Southern
Bulgaria
Croatia
Czech Republic
Estonia
Hungary
Latvia
Poland
Romania
Russian Federation
Slovenia
Ukraine
Eastern
PR boys
PR girls
PR total
16.8
19.8
20.4
22.0
19.6
13.3
24.0
29.0
16.7
22.0
15.1
20.9
20.8
25.5
19.7
46.3
22.8
31.8
26.6
31.4
38.1
26.6
14.6
25.1
26.9
30.8
24.8
26.7
26.4
28.1
27.3
29.1
34.8
31.8
16.8
28.1
27.4
27.7
31.6
30.9
35.8
31.3
22.3
32.9
45.8
26.6
41.7
27.8
33.9
33.1
36.3
29.3
53.6
32.1
48.2
37.6
50.2
54.6
37.5
30.7
39.2
41.7
42.6
37.1
41.2
37.5
39.6
42.3
41.1
51.9
42.0
23.6
50.7
40.8
22.2
25.7
25.6
28.9
25.5
17.0
28.5
37.4
21.6
31.8
21.4
27.4
26.9
30.9
24.5
50.0
27.5
40.0
32.1
40.8
46.4
32.1
22.6
32.1
34.3
36.7
30.9
34.0
31.9
33.9
34.8
35.1
43.3
36.9
20.2
39.4
34.1
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Welfare state regimes, health and health inequalities in adolescence
865
rates were also found in the Eastern European (21.0%) and the Anglo-Saxon regime
(18.9%). For both genders, the lowest rates were observed in the Southern welfare regime.
Students in Scandinavian and Bismarckian welfare regimes were ranked between these two
extremes. With regard to health complaints, boys in the Eastern regime and girls in the
Southern regime reported the highest prevalence followed by the Anglo-Saxon (boys) and the
Eastern European regime (girls). Students in the Scandinavian and Bismarckian regimes
showed the lowest prevalence of two or more weekly health complaints. Generally, girls
reported worse ill-health than boys for both health outcomes in all welfare regimes.
Multivariate analyses: welfare regimes and health
In order to assess whether the prevalence of fair ⁄ poor health and two or more health
complaints are significantly different between welfare regimes we calculated logistic multilevel
random intercept models. As our research focus was mainly on general regime-specific
patterns in health and health inequalities, the multilevel analyses were conducted for the
whole sample adjusted for gender (Table 2). In a first step we calculated the intraclass
correlation coefficient (ICC) for the empty model without any covariates (model 1). This
model indicated that 6.7 per cent of the within-subject variation for fair ⁄ poor SRH is
attributable to differences between countries (two or more health complaints: 3.9%).
Models 2a and 2b take into account level 1 variables only (age, gender, parental
occupation and family affluence). The models show that having fair ⁄ poor SRH and two or
more health complaints are both positively correlated with increasing age and being a female
student. Compared to the highest group of family affluence and occupational status the odds
ratios for fair ⁄ poor health increase with decreasing socioeconomic position (low parental
occupation: odds ratio (OR) 1.43, 95% confidence interval (CI): 1.38–1.49; low family
affluence: OR 1.92, 95% CI: 1.84–2.00). For two or more health complaints a similar pattern
was found (low parental occupation: OR 1.26, 95% CI: 1.22–1.29; low family affluence: OR
1.42, 95% CI: 1.37–1.47). After adjustment for individual-level characteristics, the variation
in intercepts between countries indicates that about 7 per cent for fair ⁄ poor health and 4 per
cent for two or more health complaints of the total variation of individual health outcomes is
due to country-level characteristics. These percentages are related to the country level and
might be attributable to contextual factors which are not considered in the model.
We therefore included welfare regime dummy variables in our model (Table 3). In line with
our descriptive results the Southern regime had the lowest odds ratio of fair ⁄ poor health (OR
0.55, 95% CI: 0.33–0.93) compared to the Scandinavian regime, while for the other regimes
no significant association was found. The multilevel model for two or more health complaints
also confirms our findings in Table 1, showing that the Southern (OR 1.53, 95% CI: 1.01–
2.32) and East European welfare regimes (OR 1.50, 95% CI: 1.05–2.16) displayed higher
odds ratios for health complaints compared to the Scandinavian regime. For the AngloSaxon regime a similar but non-significant effect was found. After adjusting for the welfare
regime typology, the unexplained variations between countries were reduced to 4.6 per cent
for SRH and to 2.9 per cent for multiple health complaints compared to the second model. In
other words, the welfare regime type partly explains the variation in both health outcomes
among countries.
In models 4a and 4b we added individual SEP indicators to the model already including
regime dummy variables. The individual-level associations did not substantially change from
the second model. Further, the association between the Southern welfare regime and SRH
was reduced, but remained significant after adjustment for SEP indicators. For two and more
health complaints the effect for the Eastern welfare regime was still significant after adjusting
for parental occupation (model 4a). This indicates that a considerable part of this association
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32
32
<0.001
<0.001
<0.001
<0.001
<0.001
P (OR)
32
172.228,664
)2.01
.039 = 3.9%
.366
Empty model
32
168.783,538
)1.43
.039 = 3.9%
.367
1.09 (1.05–1.12)
1.26 (1.22–1.29)
1.21 (1.18–1.25)
1.42 (1.38–1.47)
1.83 (1.79–1.87)
Parental occupation
OR (95% CI)
1 Adjusted for individual variables: gender-dummies (boys = reference) and age (11 = reference, 13- and 15-year-old students).
109.324,54
1.35 (1.30–1.40)
1.92 (1.84–2.00)
109.775,63
<0.001
<0.001
1.17 (1.12–1.22)
1.43 (1.38–1.49)
1.31 (1.26–1.36)
1.69 (1.62–1.75)
)2.73
0.68 = 6.8%
.492
<0.001
<0.001
1.32 (1.26–1.37)
1.72 (1.66–1.79)
1.62 (1.57–1.67)
Family affluence
)2.68
.074 = 7.4%
.513
<0.001
1.66 (1.61–1.71)
P (OR)
Parental occupation
OR (95% CI)
Empty model
OR (95% CI)
OR (95% CI)
OR (95% CI)
Model 2a
Model 1
Model 2a
Model 1
Model 2b
Two or more health complaints
Fair ⁄ poor self-rated health (SRH)
Gender (boys as reference)
Girls
Age (11 years as reference)
13
15
Parental occupation (high as reference)
middle
low
Family affluence (high as reference)
middle
low
Random effects
Intercept
)1.89
ICC
.067 = 6.7%
Between-country
.486
variation (u0j)
Deviance
111.903,852
()2*log likelihood)
Country N
32
Fixed Effects
<0.001
<0.001
<0.001
<0.001
<0.001
P (OR)
32
168.632,79
)1.41
.035 = 3.5%
.346
1.12 (1.09–1.15)
1.42 (1.37–1.47)
1.21 (1.17–1.24)
1.40 (1.36–1.44)
1.81 (1.77–1.85)
Family affluence
OR (95% CI)
Model 2b
<0.001
<0.001
<0.001
<0.001
<0.001
P (OR)
Table 2 Fair ⁄ poor self-rated health (SRH) and two or more health complaints, 11- to 15-year-old students (N = 141091), multilevel logistic regression models
for family affluence and parental occupation, Odds ratios (95% CI)
866
Matthias Richter et al.
(0.57–1.59)
(0.63–1.86)
(0.30–0.89)
(0.81–2.10)
0.859
0.781
0.018
0.271
1.17 (1.12–1.22)
1.43 (1.38–1.49)
0.96
1.08
0.52
1.33
<0.001
<0.001
1.32 (1.26–1.37)
1.72 (1.66–1.79)
<0.001
<0.001
reference)
0.969
0.836
0.026
0.223
<0.001
<0.001
1.66 (1.61–1.79)
<0.001
<0.001
P(OR)
0.96
0.99
0.47
1.09
(0.58–1.58)
(0.58–1.69)
(0.28–0.81)
(0.68–1.73)
1.35 (1.30–1.40)
1.92 (1.84–2.01)
1.31 (1.26–1.36)
1.69 (1.62–1.75)
1.62 (1.57–1.67)
WFS + family
affluence
0.871
0.971
0.006
0.728
<0.001
<0.001
<0.001
<0.001
<0.001
P(OR)
1.03
1.48
1.53
1.50
(0.69–1.51)
(0.98–2.25)
(1.01–2.32
(1.05–2.16)
1.20 (1.17–1.24)
1.41 (1.37–1.45)
1.83 (1.79–1.88)
WFS
0.900
0.064
0.046
0.028
<0.001
<0.001
<0.001
P(OR)
WFS + parental
occupation
P(OR)
OR (95% CI)
WFS
Individual level
Gender (boys as reference)
Girls
1.66 (1.61–1.71)
Age (11 years as reference)
13
1.30 (1.25–1.36)
15
1.70 (1.63–1.76)
Parental occupation
(high as reference)
middle
low
Family affluence
(high as reference)
middle
low
Country level
Welfare Regime (Scandinavian as
Bismarckian
0.99 (0.61–1.62)
Anglo-Saxon 1.06 (0.63–1.79)
Southern
0.55 (0.33–0.93)
Eastern
1.33 (0.84–2.10)
Fixed effects
OR (95% CI)
OR (95% CI)
OR (95% CI)
1.00
1.50
1.47
1.49
(0.68–1.48)
(1.00–2.28)
(0.97–2.23)
(1.03–2.14)
1.09 (1.06–1.12)
1.26 (1.22–1.29)
1.21 (1.77–1.45)
1.42 (1.38–1.47)
1.83 (1.18–1.25)
WFS + parental
occupation
OR (95% CI)
Model 4a
Model 3
Model 4a
Model 3
Model 4b
Two or more health complaints
Fair ⁄ poor self-rated health (SRH)
0.990
0.056
0.071
0.033
<0.001
<0.001
<0.001
<0.001
<0.001
P(OR)
1.01
1.44
1.42
1.36
(0.69–1.48)
(0.95–2.16)
(0.94–2.14)
(0.95–1.93)
1.12 (1.09–1.15)
1.42 (1.37–1.47)
1.21 (1.17–1.24)
1.40 (1.36–1.44)
1.81 (1.77–1.85)
WFS + family
affluence
OR (95% CI)
Model 4b
0.957
0.083
0.092
0.095
<0.001
<0.001
<0.001
<0.001
<0.001
P(OR)
Table 3 Fair ⁄ poor self-rated health (SRH) and two or more health complaints, 11- to 15-year-old students (N = 141091), multilevel logistic regression models
for family affluence and parental occupation and welfare regime indicators, odds ratios (95% CI)
Welfare state regimes, health and health inequalities in adolescence
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Sociology of Health & Illness 2012 Foundation for the Sociology of Health & Illness/Blackwell Publishing Ltd
)2.67
.049 = 4.9%
.412
109.761,78
32
)2.48
.046 = 4.6%
.397
110.115,04
32
WFS + parental
occupation
32
109.311,84
)2.63
.047 = 4.7%
.403
WFS + family
affluence
32
169.031,49
)1.57
.029 = 2.9%
.315
WFS
OR (95% CI)
WFS
OR (95% CI)
OR (95% CI)
OR (95% CI)
32
168.774,01
)1.69
.029 = 2.9%
.316
WFS + parental
occupation
OR (95% CI)
Model 4a
Model 3
Model 4a
Model 3
Model 4b
Two or more health complaints
Fair ⁄ poor self-rated health (SRH)
32
168.625,73
)1.63
.028 = 2.8%
.309
WFS + family
affluence
OR (95% CI)
Model 4b
1 Adjusted for individual variables: gender-dummies (boys = reference) and age (11 = reference, 13- and 15-year-old students).
2 Scandinavian regime (Denmark, Finland, Norway, Sweden), Bismarckian regime (Austria, Belgium, France, Germany, Luxembourg, Netherlands, Switzerland), Anglo-Saxon regime
(Canada, Ireland, Israel, United Kingdom, United States), Southern regime (Greece, Italy, Macedonia, Portugal, Spain), Eastern regime (Bulgaria, Croatia, Czech Republic, Estonia,
Hungary, Latvia, Poland, Romania, Russian Federation, Slovenia, Ukraine).
3 WFS (welfare state regime).
Random effects
Intercept
ICC
Between-country
variation (u0j)
Deviance ()2*log
likelihood)
Country N
Fixed effects
Table 3 (Continued)
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Sociology of Health & Illness 2012 Foundation for the Sociology of Health & Illness/Blackwell Publishing Ltd
Between-country
variation (u0j)
Deviance ()2*log
likelihood)
Country N
Welfare regime
Scandinavian
Bismarckian
Anglo-Saxon
Southern
Eastern
Random effects
Intercept
ICC
Fixed effects
1
0.88
1.06
0.50
1.41
(0.53–1.48)
(0.61–1.84)
(0.29–0.88)
(0.87–2.27)
OR (95% CI)
–
0.634
0.828
0.016
0.163
P(OR)
1.20
1.06
0.99
0.98
0.92
P(OR)
0.005
0.496
0.842
0.874
0.265
P(OR)
–
0.670
0.790
0.010
0.326
1.34
1.12
1.11
0.94
0.82
<0.001
0.081
0.114
0.437
<0.001
P(OR)
)2.63
.048 = 4.8%
(1.21–1.48)
(0.99–1.26)
(0.98–1.25)
(0.80–1.10)
(0.72–0.92)
32
(0.54–1.49)
(0.54–1.60)
(0.28–0.84)
(0.79–2.04)
32
1
0.90
0.93
0.49
1.27
OR (95% CI)
109.261,404
<0.001
0.084
0.414
0.557
0.071
P(OR)
109.737,624
(1.28–1.59)
(0.98–1.30)
(0.93–1.21)
(0.89–1.25)
(0.78–1.01)
OR (95% CI)
.407
1.43
1.13
1.06
1.05
0.89
OR (95% CI)
Middle
.411
)2.68
.049 = 4.9%
(1.06–2.36)
(0.90–1.24)
(0.84–1.15)
(0.80–1.21)
(0.80–1.06)
OR (95% CI)
High
High
Low
Family Affluence
Parental occupation
Middle
Model 5b
Model 5a
Fair ⁄ poor self-rated health (SRH)
1.80
1.20
1.15
1.05
0.89
(1.50–2.16)
(0.97–1.47)
(0.94–1.42)
(0.84–1.32)
(0.73–1.08)
OR (95% CI)
Low
<0.001
0.087
0.174
0.664
0.224
P(OR)
Table 4 Fair ⁄ poor self-rated health (SRH) and two or more health complaints, 11- to 15-year-old students (N = 141,091), multilevel logistic regression models
with interaction effects between family affluence, parental occupation and welfare regime dummies, odds ratios (95% CI)
Welfare state regimes, health and health inequalities in adolescence
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1
0.98
1.43
1.43
1.52
(0.66–1.46)
(0.94–2.18)
(0.94–2.19)
(1.05–2.19)
OR (95% CI)
–
0.920
0.098
0.098
0.027
P(OR)
1.07
1.03
1.03
1.01
1.00
P(OR)
P(OR)
(0.64–1.40)
(0.86–1.99)
(0.91–2.10)
(1.02–2.13)
–
0.795
0.206
0.132
0.038
1.05
1.13
1.19
1.04
0.93
(0.97–1.13)
(1.03–1.24)
(1.08–1.31)
(0.94–1.15)
(0.85–1.03)
P(OR)
0.230
0.013
<0.001
0.463
0.155
32
1
0.95
1.31
1.38
1.47
32
<0.001
0.503
0.017
0.369
0.378
OR (95% CI)
168.565,68
(1.12–1.33)
(0.93–1.15)
(1.02–1.26)
(0.94–1.18)
(0.87–1.06)
P(OR)
168.752,13
1.22
1.04
1.34
1.05
0.96
OR (95% CI)
)1.60
029 = 2.9%
.315
0.183
0.618
0.676
0.844
0.984
OR (95% CI)
)1.67
.029 = 2.9%
.316
(0.97–1.17)
(0.92–1.16)
(0.91–1.15)
(0.89–1.15)
(0.90–1.11)
OR (95% CI)
Middle
1.33
1.18
1.21
1.12
0.91
(1.15–1.54)
(1.00–1.39)
(1.02–1.42)
(0.95–1.32)
(0.78–1.07)
OR (95% CI)
Low
<0.001
0.056
0.029
0.184
0.264
P(OR)
1 Adjusted for individual variables: gender-dummies (boys = reference) and age (11 = reference, 13- and 15-year-old students).
2 Scandinavian regime (Denmark, Finland, Norway, Sweden), Bismarckian regime (Austria, Belgium, France, Germany, Luxembourg, Netherlands, Switzerland), Anglo-Saxon regime (Canada, Ireland,
Israel, United Kingdom, United States), Southern regime (Greece, Italy, Macedonia, Portugal, Spain), Eastern regime (Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Poland, Romania,
Russian Federation, Slovenia, Ukraine).
Welfare regime
Scandinavian
Bismarckian
Anglo-Saxon
Southern
Eastern
Random effects
Intercept
ICC
Between-country
variation (u0j)
Deviance ()2*log
likelihood)
Country N
Fixed effects
High
High
Low
Family Affluence
Parental occupation
Middle
Model 5b
Model 5a
Two or more health complaints
Table 4 (Continued)
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Welfare state regimes, health and health inequalities in adolescence
871
is explained by the type of welfare regime independently from individual SEP predictors.
Only for family affluence (model 4b), the significant association between the Eastern welfare
regime and two or more health complaints disappeared compared to model 3.
Welfare regimes and health inequalities
According to our second research question on the association between the welfare regime
typology and socioeconomic differences in SRH and health complaints, Table 4 shows the
results from cross-level interaction analyses between both SEP indicators and regime types
(high SEP group in the Scandinavian regime as reference). The age- and gender-specific
effects (not presented in Table 4) still remained significant for both health outcomes.
In order to examine whether different types of welfare regimes are associated with
socioeconomic differences in health, we calculated cross-level interaction effects between
regime type and both SEP measures. Table 4 shows how both health outcomes of those with
low, medium or high SEP are influenced by residing in the five welfare regime types. The
analyses show very heterogeneous results and no clear patterning of the association between
the welfare regime clusters and socioeconomic differences in health. We could not find higher
odds ratios for both health outcomes of adolescents belonging to low, medium and even the
highest SEP groups in the Anglo-Saxon and Eastern regimes compared to the corresponding
socioeconomic groups in the Scandinavian regime. Nevertheless, a clear social gradient for
fair ⁄ poor health for family affluence and parental occupation was found in the Scandinavian
regime. Further, compared to students with high SEP in the Scandinavian regime, a
significant lower OR for adolescents with high affluence and parental occupation in the
Southern regime was found. Thus, young people with high SEP in the Southern regime are
better off in terms of SRH than in the Scandinavian regime. No significant higher odds ratio
for high family affluence and parental occupation was found for the Eastern regime. Similar
to SRH, a social gradient was also found for health complaints in the Scandinavian regime.
In general, there was a tendency for high SEP students in the Anglo-Saxon, Southern and
Eastern regime to show higher OR for health complaints compared to the reference group.
However, this effect was significant only for the Eastern regime. In all regimes medium and
low SEP adolescents showed no higher ORs as compared to their peers in the Scandinavian
regime.
Discussion
Summary of the results
So far, several studies have analysed the impact of structural characteristics of different
welfare state arrangements for health and health inequalities among adults. To our
knowledge this study is among the first to systematically analyse health and health
inequalities in adolescence between different welfare state regimes using a multilevel
approach.
The descriptive findings showed that the Eastern and Anglo-Saxon regimes had the
highest prevalence for fair ⁄ poor SRH, while the lowest rates were found in the Southern
regime. For two or more health complaints a different picture emerged: adolescents in the
Southern and Eastern regime reported the highest prevalence compared to the Scandinavian
regime showing the lowest rate. The multilevel analyses indicated that country-level
characteristics accounted for up to 7 per cent of the variation in both health outcomes.
Further, the multilevel analyses largely confirmed the descriptive findings: adolescents in the
Southern regime showed lower odds ratios of fair ⁄ poor SRH, while for two or more health
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Matthias Richter et al.
complaints higher odds ratios were found for students in the Eastern and Southern regime.
After adjusting for the welfare regime typology the country-level variation for both health
outcomes was reduced significantly. This indicates that grouping countries into welfare
types contributes to explaining the variation of both health outcomes among countries.
In contrast to our expectation that welfare regimes with less redistributive welfare policy
characteristics, such as in the Anglo-Saxon and Eastern regimes, would display higher odds
ratios for adverse health in all socioeconomic groups compared to the Scandinavian regime,
we found no clear regime-specific pattern for young people. Even though socioeconomic
inequalities in health among adolescents are evident within all countries – no matter to which
regime type they belong – the applied regime typology does not mirror the variation in health
inequalities across countries.
Interpretation and comparison with previous research
Our results showed only a partially clear pattern for regime-specific differences in health and
health inequalities for adolescents as the findings varied according to indicators of health
and measures of family SEP. We further found that the welfare regime classification is
important for explaining the variation in health outcomes across countries. However, for
socioeconomic differences in adolescent health the clustering in welfare regimes revealed
little explanatory power for cross-national variations. Unfortunately, as of yet there are very
few studies on welfare regimes, health and health inequalities among young people. Thus,
the following comparison and discussion largely has to take studies of adult populations
into consideration. Caution should therefore be applied to our findings until they are
replicated.
In general, our study supports the findings from Zambon et al. (2006) which also found
lower rates of fair ⁄ poor SRH in the Southern regime. However, findings for adults showed
that compared to the Southern welfare regime, adults in the Scandinavian and Anglo-Saxon
welfare regimes have better self-perceived health (Eikemo et al. 2008c). Given the uncertainty
of the research literature in this field, it is only possible to speculate on possible explanations
for these different findings.
According to Esping-Andersen (1990) the three principles of decommodification, social
stratification and the private-public mix are defining characteristics of welfare regimes. Thus,
the effect of welfare regime on health is likely to be transferred via these determinants, which
is in line with other authors (Navarro et al. 2006, Commission on Social Determinants of
Health (CSDH) 2008, Hurrelmann et al. 2011). One explanation for the different findings
between adolescents and adults could be a shift in the relative importance of determinants of
health over the life course. Eikemo et al. (2008a) argue that relative deprivation, class-related
health behaviours and social exclusion may be contributory mechanisms. All these
mechanisms emerge later in life when individuals have their own socioeconomic position and
are more directly exposed to these factors than during adolescence. Thus, empirical evidence
on the role of these causal mechanisms over different stages of the life course should be
explored in future studies (Hurrelmann et al. 2011).
As another branch of explanations rather distal welfare state characteristics such as levels
of decommodification and the extent of welfare services might also contribute to these
different findings (Eikemo et al. 2008c). The different picture in adolescence may be due to
adolescents’ embedded position in families and their dependent status in most western
countries, which benefits from the principle of familialism, for example in Southern regimes.
In addition, as many welfare regimes have increasingly integrated ‘liberal’ aspects to their
policies a cohort effect cannot be ruled out.
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For two or more health complaints we found that adolescents in regimes with less
egalitarian and less redistributive welfare provision, the Anglo-Saxon, Southern and Eastern
European welfare regimes, reported higher odds ratios compared to other regimes (i.e.
Scandinavian and Bismarckian). Interestingly, these findings are in keeping with studies
among adults, which have also found a higher likelihood of health complaints in the
Southern European and Anglo-Saxon regimes (Olsen and Dahl 2007, Eikemo et al. 2008a,
2008b, 2008c). Using the same data set as in the present analyses, Holstein et al. (2009) also
found higher rates of health complaints for adolescents in Southern and Eastern European
countries. Similar results are reported by Zambon et al. (2006). Thus, welfare regimes with
less substantial welfare services and less redistributive welfare provision in combination with
a variety of social policies, characteristic for these regimes, seem to have a negative effect not
only on health in adulthood but also on young people’s health (Navarro et al. 2006, Zambon
et al. 2006, Chung and Muntaner 2007).
Our findings of higher rates of health complaints in the Eastern regime may be related to
the basic provision of social welfare in these countries. In contrast to other welfare regimes,
the social welfare systems of the ex-communist countries have not yet been redeveloped on
the basis of general principles or characteristics (Bambra 2006, Eikemo and Bambra 2008),
and they have experienced a general increase in social inequalities with a dramatic worsening
of health indicators (Offe 1997). Additionally, their welfare programmes differ widely and
have changed quickly due to extensive economic upheavals and social reforms throughout
the 1990s (Kovacs 2002, Fenger 2007, Eikemo and Bambra 2008). Therefore, the
interpretation of results from this regime is difficult and further research is certainly required.
With regard to the Anglo-Saxon welfare regime, its pursuit of more neo-liberal policies and
a corresponding rise in reliance upon the market as a provider of welfare benefits has low
redistributive tendencies and is characterised by less public expenditure on welfare (Coburn
2004). In this context, its social welfare policy offers basic levels of provision, modest social
transfers and the least generous social safety net (Eikemo et al. 2008b). Further, the AngloSaxon regime cluster consists of the most unequal countries in terms of income inequality
(Bambra 2006, 2007, Eikemo and Bambra 2008). This combination of circumstances and
processes may result in worse health outcomes.
It is surprising that the Southern European welfare regime was associated with the lowest
prevalence rates in fair ⁄ poor health, but showed the highest rates in complaints. This was in
line with our multilevel findings. Zambon et al. (2006) reported similar findings for
adolescents using data from the HBSC study in 2002. The authors argue that the higher rates
in health complaints in the Southern countries may be due to cultural differences, for
example, greater expression of emotion compared to populations from countries with a more
stoic attitude. However, it should be acknowledged that SRH might be comprehended
differently in the South from elsewhere and that this might bias our findings.
In general, we found no clear pattern for the association between welfare regime clusters
and socioeconomic differences in subjective health. These findings are in line with Dahl et al.
(2006) who reported no apparent patterning of health inequalities by welfare regime for
adults. It needs to be acknowledged, though, that there are other studies that found a
stronger association between the regime typology and socioeconomic differences in health
(Eikemo et al. 2008a, 2008b). Nevertheless, it is difficult to compare these findings as these
studies are based on adult samples and multilevel modelling was rarely applied. It is
interesting that in the Southern regime high SEP students had lower odds ratios for SRH
compared to adolescents with high family affluence and parental occupation in the
Scandinavian regime. In this respect, Southern European countries with their ideal-typical
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874
Matthias Richter et al.
stronger reliance on informal social networks and familial support may have an advantage in
comparison to the Scandinavian and Bismarckian countries.
Study strengths and limitations
Before drawing conclusions, there are several methodological issues that may have influenced
our results and should be considered. The HBSC study presents an outstanding opportunity
to analyse cross-national patterns of health and health inequalities among young people. The
strengths of this study include the use of a large cross-national dataset, standardised data
collection and the availability of multiple measures of socioeconomic position.
One possible limitation is the application of self-reported measures of health which may
vary by country, cultural interpretation and socioeconomic position. For example, it might
be possible that cultures not only differ in the frequency of health complaints but also in the
specific complaints expressed. Additionally, they might differ in the way they understand the
exact meaning of the concept. However, various studies have shown that the pattern of
subjective health complaints is consistent across countries (Haugland et al. 2001) and that the
HBSC symptom checklist allows a comparison across cultures and an unbiased assessment of
subjective health complaints for 11 to 15 year olds (Ravens-Sieberer et al. 2008). Sleeping
difficulties was the only item which worked rather differently across the different countries.
As we have used a dichotomous measure of the symptom sum score we do not believe that
the findings are strongly influenced by reporting bias. In addition, several studies among
adults have found that subjective health measures correlate well with other indicators of
mortality and morbidity (Jylhä 2009); they are also considered to be good indicators for
cross-national comparisons (Cohen et al. 1995, Robine and Jagger 2003, Bambra et al. 2009).
Furthermore, it should also be acknowledged that there are variations in self-rated health
between age groups and different welfare regimes will have policies that act differently at
various stages in the life course (Bambra et al. 2009).
It should also be acknowledged that the family affluence scale measures only one
dimension of socioeconomic position. Although FAS is not an ideal measurement of SEP, it
has demonstrated its usefulness in many prior studies of socioeconomic variations (Currie
et al. 2008b), not least because it is easy for respondents to answer the questions. From a
methodological point of view it is important to recognise that different SEP indicators may
give different and therefore complementary information. We therefore decided to also use
parental occupation as an additional measure of socioeconomic position. However, questions
on parental occupation using adolescents’ self-report are also problematic. Even though
several studies indicate that the classifiable answers of adolescents can be considered as good
proxy reports of parental occupation (Lien et al. 2001, West et al. 2001, Vereecken and
Vandegehuchte 2003), it remains difficult that many adolescents are not able to indicate their
parents’ occupation at all. If missing or unclassifiable responses are unequally distributed
among social groups, it might influence the results. Therefore, the results on differences by
parental occupation should be interpreted cautiously. It would be interesting if other studies,
using different indicators of socioeconomic position, found similar results.
Another possible limitation is our choice of welfare regime typology. So far there is no
categorisation which has been generally accepted as the ‘standard’ regime typology. It is
likely that different regime typologies will show different findings. For example, two studies
of educational inequalities in health (Borrell et al. 2007, Eikemo et al. 2008a) utilised different
regime typologies and reported differing regime-specific results. In addition, the selection and
the availability of countries to be clustered in welfare regimes also influence the extent of
differences in health and health inequalities between regimes. Compared to existing studies
we included a relatively large number of countries in our sample. This may also account for
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Welfare state regimes, health and health inequalities in adolescence
875
the inconsistent findings on the association between the regime typology and socioeconomic
differences in our study. Even though our clustering of countries in welfare state regimes is
rather heterogeneous it is notable that our results underline the advantage of the Southern
regime in terms of SRH as well as the disadvantage of the Eastern, Anglo-Saxon and
Southern regime in terms of health complaints.
Conclusion
Similar to previous research among adults, we found that welfare state regimes are associated
with the overall level of subjective health in adolescence. Compared to the Scandinavian
welfare regime the Southern regime had better self-rated health. Regarding health complaints
the Eastern, Anglo-Saxon as well as the Southern regimes were observed to show higher
prevalences and higher odds ratios than the Scandinavian and Bismarckian regime. For
socioeconomic inequalities in health we could not find a clear pattern between the welfare
regime types. Thus, the regime type contributed to the explanation of cross-national
variations in both health outcomes, but the picture is far less clear for socioeconomic
differences in health. So far, little is known on the causal interactions between the welfare
state regulation and individual health. Future research should increasingly focus on the
explanation of the impact of welfare regimes on health and health inequalities. Generally, all
welfare states are designed to address issues of social security and wellbeing of citizens, but
they do so in different ways and to different extents (Esping-Andersen 1990). Improving
health should continue to be an important public health strategy with emphasis on the youth
population in all welfare regimes. Future social welfare policy should remain to tackle
inequalities by introducing or maintaining stronger redistributive policies, which contribute
to establish better health conditions for future adult populations, especially for people with
low socioeconomic position.
Address for correspondence: Matthias Richter, Institute of Medical Sociology (IMS),
Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, 06112
Halle, Germany
e-mail: m.richter@medizin.uni-halle.de
Acknowledgements
The ‘Health Behaviour in School-aged Children (HBSC)’ study is an international survey conducted in
collaboration with the WHO Regional Office for Europe. The current International Coordinator is
Candace Currie, CAHRU, University of Edinburgh, and the Data Bank Manager is Oddrun Samdal,
University of Bergen. We are grateful to Timo-Kolja Pförtner (SOCLIFE Research and Training
Group, University of Cologne), Terje Eikemo (Department of Public Health, Erasmus MC, University
Medical Centre Rotterdam) and Tobias Wolbring (Institute of Sociology, Ludwig-MaximiliansUniversity Munich) for providing us with useful comments on earlier drafts on this article.
Supporting Information
Additional Supporting Information may be found in the online version of this article
Table S1. Country statistics.
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876
Matthias Richter et al.
Table S2. Distribution of family affluence and parental occupation by welfare state regime ⁄ country
(%), 11- to 15-year-old students (N=141091).
Please note: Blackwell Publishing are not responsible for the content or functionality of any
supporting materials supplied by the authors. Any queries (other than missing material) should be
directed to the corresponding author for the article.
Contribution
MR and KR contributed equally to the present work by analysing and interpreting the data
and writing the article. They also agreed to share first authorship. MR supervised the study
activities and led the writing. KR performed the statistical analysis and equally participated
in writing. KH, AZ, WB and SNG assisted with writing and data interpretation and
participated in reviewing and revising the article. All authors helped to conceptualise ideas,
interpret findings and review drafts of the article.
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