ArticlePDF Available

The relationship between health and partnership history in adulthood: insights through retrospective information from Europeans aged 50 and over


Abstract and Figures

The association between health and partnership status is a growing concern within the social sciences. Some partnership situations exhibit positive effects on health, while partnership breakdowns display negative impacts. However, case studies show that these associations may change with age, due to potential sources of heterogeneity within a population. The current analysis explored this association over the adult life course (ages 30–64) of Europeans aged 50 years and older based on retrospective information on health and partnership from SHARELIFE (N = 23,535 after data screening). The data allowed us to control for socio-demographic covariates as well as for individual infirmity, measured by childhood health. We also considered contextual survival selection effects by comparing 13 European countries for which pre-adult mortality levels largely differed among the cohorts involved (1907–1958). Discrete-time hazard analyses examined the risk of suffering from a major episode of poor health (self-reported) in adulthood as a function of partnership history, using two approaches: a pooled model and country-specific models. The results revealed no differences between those who lived with a partner (first union) and single individuals in terms of the retrospective hazards of poor health. We hypothesize that this result stems from the cumulative effect of survival selection on individuals in advanced ages according to partnership status. The results also partially point to the plausibility of a contextual survival selection, which should be confirmed by further research based on additional health indicators.
Content may be subject to copyright.
The relationship between health and partnership history
in adulthood: insights through retrospective information
from Europeans aged 50 and over
Jordi Guma
`Antonio D. Ca
´mara Rocı
´o Trevin
Springer-Verlag Berlin Heidelberg 2014
Abstract The association between health and partnership
status is a growing concern within the social sciences.
Some partnership situations exhibit positive effects on
health, while partnership breakdowns display negative
impacts. However, case studies show that these associa-
tions may change with age, due to potential sources of
heterogeneity within a population. The current analysis
explored this association over the adult life course (ages
30–64) of Europeans aged 50 years and older based on
retrospective information on health and partnership from
SHARELIFE (N=23,535 after data screening). The data
allowed us to control for socio-demographic covariates as
well as for individual infirmity, measured by childhood
health. We also considered contextual survival selection
effects by comparing 13 European countries for which pre-
adult mortality levels largely differed among the cohorts
involved (1907–1958). Discrete-time hazard analyses
examined the risk of suffering from a major episode of
poor health (self-reported) in adulthood as a function of
partnership history, using two approaches: a pooled model
and country-specific models. The results revealed no dif-
ferences between those who lived with a partner (first
union) and single individuals in terms of the retrospective
hazards of poor health. We hypothesize that this result
stems from the cumulative effect of survival selection on
individuals in advanced ages according to partnership sta-
tus. The results also partially point to the plausibility of a
contextual survival selection, which should be confirmed
by further research based on additional health indicators.
Keywords Partnership status Health Biographical
information Survival Selection SHARE
For the most part, previous research on the relationship
between partnership status and health has shown that living
with a partner is associated with better health outcomes
when measured in terms of mortality (Waite 1995; Val-
konen et al. 2004; Martikainen et al. 2005) or morbidity
(Joung et al. 1994; Liu and Umberson 2008; Hughes and
Waite 2009). Furthermore, some of these studies have
uncovered that partnership could determine mortality dif-
ferentials to a larger extent than socioeconomic status in
some circumstances (Martikainen et al. 2005).
Three main mechanisms behind partnership have been
emphasized as contributing to explain its health benefits:
(1) the reduction of risky behaviors and unhealthy habits
(Lillard and Waite 1995; Duncan et al. 2006), (2) the
development of a support network that can buffer the effect
of poor health episodes (Waite 1995), and (3) the increase
in material well-being associated with economies of scale
resulting from the addition of resources of both partners
and the possibility of task specialization (Lillard and Panis
1996). These processes do not influence males and females
identically. First, the benefits appear to be greater and
manifest more quickly among males. That is to say, men’s
health improves shortly after the beginning of a union,
whereas its effects on women’s health occur more
This study is part of Jordi Guma
`’s PhD within the Doctoral Program
in Demography from the Universitat Auto
`noma de Barcelona. A
previous version of the paper was developed as a Master’s Thesis
within the European Doctoral School of Demography.
Responsible Editor: H. Litwin.
J. Guma
`(&)A. D. Ca
´mara R. Trevin
Centre d’Estudis Demogra
`fics, Barcelona, Spain
Eur J Ageing
DOI 10.1007/s10433-014-0316-x
gradually. Secondly, women appear to benefit principally
from the increase in material well-being, whereas males
mainly benefit from the cessation or moderation of risky
behaviors and unhealthy habits (Waite 1995; Lillard and
Panis 1996; Duncan et al. 2006). In contrast, other part-
nership situations, as well as the transitions between them,
have been associated with negative effects on health, as
exemplified by the increases in depressive symptoms and/
or chronic morbidity among men and women who either
experienced a separation or divorce or became widows
(Simon 2002; Wade and Pevalin 2004; Hughes and Waite
2009). In addition, those engaged in subsequent unions
after an episode of separation did not completely recover
the original advantage they once had in terms of health
(Hughes and Waite 2009).
However, the health benefits of partnership among
mature and older adults have been questioned (i.e., benefits
of partnership with respect to singleness seem to moderate,
disappear, or even reverse at these ages) (Goldman et al.
1995; Regidor et al. 2001; Bardage et al. 2005). Some
studies explain this result as the effect of survival selection;
a major source of heterogeneity within populations, which
causes unexpected pattern of a given phenomenon (e.g.,
health and mortality) as a function of age (Vaupel et al.
1979; Vaupel and Yashin 1985). Survival selection may
originate on contextual and/or individual levels. Contex-
tually, it is associated with the living conditions of the
population, which strongly determine mortality levels
throughout the life course and particularly in childhood.
For instance, current populations that were exposed to hard
living conditions and high-mortality levels at pre-adult
ages are more likely to be selected in terms of survival.
Accordingly, these populations might exhibit better or less
disadvantaged than expected health outcomes at old ages
than populations with better living conditions and lower
mortality levels. At the individual level, survival selection
may be determined by a number of behavioral, socioeco-
nomic, and/or genetic factors. For instance, if singleness is
associated with risky health behaviors, then it is expected
that singles suffer from higher mortality levels and thus
become a more selected segment of the population when
reaching old age.
This work aims to explore the association between
health and partnership in adulthood, comparing individuals
who entered into their first union (either cohabitation or
marriage) and those who remained single at mature ages.
Biographical information on health status and partnership
status (a time-varying variable) provided by Europeans
aged 50 and over in the SHARELIFE survey is utilized for
this purpose. All 13 countries included in this survey
(Austria, Belgium, the Czech Republic, Denmark, France,
Germany, Greece, Italy, the Netherlands, Poland, Sweden,
Spain, and Switzerland) were analyzed both jointly and
specifically. These countries represent different mortality
backgrounds associated with diverse contexts of living
conditions for the cohorts that are analyzed in this study
(1907–1958), which indirectly permit us to approach the
influence of survival selection effects between these pop-
ulations. Another potential source of heterogeneity at the
individual level (i.e., individuals’ health trajectories) is
partly controlled by utilizing their health status in
Differences between these populations were also
noticeable with regard to the intensity and the timing of
some key aspects of the second demographic transition
(SDT) that took place from Northwestern to Southeastern
regions, mainly during the second half of the twentieth
century. SDT proposes that changes in a number of
demographic-related behaviors (e.g., the postponement of
childbearing and marriage, the diversification of family
forms, and the increase of divorce and cohabitation rates)
are associated not only with socioeconomic variables, but
also with large-scale changes in values (Lesthaeghe 1995).
The spread of these values differs across countries and is
related to both cultural and legislative aspects (for instance,
the official approval of divorce; Gonza
´lez and Viitanen
2009), which are important to understand the cross-national
partnership-related differentials observed among Europe-
ans aged 50 and older surveyed by SHARELIFE.
Microdata from the Survey of Health, Ageing and Retire-
ment in Europe (SHARE) were utilized. SHARE is a cross-
national panel survey conducted on individuals (intervie-
wees and their partners) aged 50 and over (Bo
et al. 2005; Schro
¨der 2011). The third wave of SHARE
(named SHARELIFE) was held between the autumn of
2008 and the summer of 2009, and it included retrospective
questions that permit a life-cycle approach to certain issues
such as health and partnership trajectories. The initial
sample size of interviewees aged 50?and their partners
was slightly reduced after discarding proxies (indirect
informants) and excluding missing cases of any of the
variables involved in the analysis and individuals who
experienced an event of poor health before turning 30.
Valid cases amounted to 89 % overall, ranging from 84 %
(Poland and France) to 94 % (Italy). The distribution of
cases according to age group and sex prior to and after the
data screening remained largely unchanged. The retro-
spective analysis of health and partnership spanned work-
ing ages (30–64) to avoid the potential biases related to
changes in socioeconomic status after retirement
Eur J Ageing
(Demakakos et al. 2008). The lower age boundary in this
analysis (age 30) responds to the aim of avoiding the effect
of age to mediate the relationship between partnership
status and health (i.e., younger individuals are more likely
to be single as well as to be less likely to experience poor
health episodes). At the age of 30, 82.5 % of individuals
were already in a partnership status other than single, and
this percentage rises slightly to 90.1 % at the age of 64.
Individuals younger than 64 at the time of the interview
were right-censored in the analysis.
Study variables
The health outcome (SHARELIFE item GL009) represents
whether the individual experienced ‘‘a distinct period
during which your health was poor compared to the rest of
your life’ over adulthood (ages 30–64) and in the case of
an affirmative answer, when this period occurred. The
prevalence of this event was 33.5 % among men and
37.1 % among women. Germany displayed the highest
prevalence (47.2 % among men and 46.8 % among
women) and Greece the lowest prevalence (17.0 % among
men and 20.2 % among women). Overall, in approximately
74 % of the cases, this event occurred between the ages of
30 and 64.
As with other self-reported health indicators, this item
captures both the objective and subjective dimensions of
health, which maintain a solid association for the most part
(Idler 1993). Therefore, it approaches the health status of
individuals in a more comprehensive manner (WHO 1946)
than more specific health indicators (i.e., whether the
individual suffers or has suffered from a certain disease
This indicator also shows a significant relationship with
other subjective health indicators such as the current self-
perceived health status at the time of the survey (62 % of
those who declared a poor health status experienced the
event of poor health, whereas the incidence of the event
among those who declared a good health status was 28 %;
the v
statistic was significant when both indicators were
cross-tabulated). That said, it must be acknowledged that
what constitutes a ‘distinct period’ of poor health may
differ from individual to individual as well as from country
to country.
Partnership history for each interviewee between the
ages of 30 and 64 (or alternatively until the age at time of
the interview) was re-constructed from retrospective
questions that recorded the occurrence of unions, separa-
tions, and widowhood. Technically, this information was
stored in a time-varying variable that was categorized as
Single (no previous union)
First union (living with a partner in a first union)
Interruption of the relationship (not living with a
partner after having experienced separation or divorce,
independent of the rank of the union)
Second or higher rank union (living with a partner in a
second or higher rank union).
The time-varying approach to partnership status as
practiced in this study prevents the artifact of the attenu-
ated effects of marital status on morbidity and mortality, as
Rendall et al. proposed in 2011. The first unions at the time
of the interview were clearly prevalent in all the countries
under analysis (overall, 75 % of cases among men and
60 % among women), but significant differences between
countries were observed for the other partnership situa-
tions. Southern European countries (Spain, Italy, and
Greece) together with Poland showed the lowest percent-
age of breakdowns (below 10 % for both sexes), whereas
Nordic countries (Denmark and Sweden) showed the
highest percentages for these situations (above 20 % in
both sexes). As expected, a lower percentage of widowers
(6.3 %) with respect to widows (21.9 %) was also found.
With regard to this, the main difference between countries
was found among widows ranging from 15.6 % (the
Netherlands) to 29.7 % (Austria).
Age at the time of the interview was collapsed into four
groups: 50–59, 60–69, 70–79, and 80 and over. This vari-
able controlled for two possible sources of bias in the time
of the occurrence of the event of interest, namely (1) a
choice set bias in that longer life increases the number of
potential episodes of poor health and consequently forced
the individual to prioritize health to a greater extent than
younger individuals; and (2) a recall bias in that longer life
may worsen the ability to remember and report past events
precisely. For instance, it might be the case that older
individuals prioritized more recent episodes, thus resulting
in an artifact of postponement of poor health episodes.
Childhood health was included to control for individual
infirmity in our analyses through the question, ‘Would you
say that your health during your childhood was in general
excellent, very good, good, fair or poor and/or not
Among other problems, a more specific set of items in this survey
(HS054) likely underestimates the periods or episodes of poor health
with respect to the indicator that was chosen for this work. For
example, the prevalence of a period of poor health is 21 % using
HS054, whereas the value is 40.4 % when GL009 is used. This
necessarily has to do with the different wording and different
conceptual nature of both items. Moreover, any period of poor health
in HS054 can be embodied by several illnesses, which provokes a
problem of ambiguity in the use of specific illnesses as health
indicators. Therefore, we believe that GL009 is a better technical
choice for our purposes, and its figures are more trustworthy taking
into account that SHARELIFE focuses on the population aged 50 and
over. Nevertheless, specific comments in the discussion section are
devoted to deal with the implications of this choice in our results.
Eur J Ageing
constant?’ These answers were grouped into two catego-
ries: good health (excellent, very good, and good) and poor
health (fair, poor, or ever poor).
Working status asked retrospectively about the changes
in a person’s labor situation throughout adulthood, which
was also treated as a time-varying variable in our analyses
(working/not working).
Education (the highest educational level attained by the
interviewee) has two purposes in the analyses: (1) to
approach the socioeconomic status of individuals together
with their working status, and (2) to approach differences
in health risk behaviors (higher education is associated
with healthier behaviors) (Marmot 2005). Educational
levels across countries were harmonized using the Inter-
national Standard Classification of Education (ISCED
1997) by UNESCO, and the resulting variable grouped
some categories to obtain more robust cross-tabulations for
all countries: (1) First level of education includes no
studies completed and primary studies or first stage of basic
education (Levels 0 and 1 of ISCED); (2) Second level of
education: Second stage of basic education and secondary
education (Levels 2 and 3 of ISCED); and (3) Third level
of education: Post-secondary (non-tertiary) and all possible
levels of tertiary education (Levels 4-6 of ISCED).
Sex: men/women
Age (and age squared) in our models was a time-varying
variable that ranged from the age of 30 (first observation
for each individual) to the age of 64 (maximum) or,
alternatively, the age at time of the interview. Each indi-
vidual was observed as many times as the years passed
until the event (a distinct period of poor health) occurred or
until the age of 64 in the absence of an event. Age squared
was included in the models to better fit with age effects
beyond a linear function of age.
To ease the interpretation of our results, the analyzed
countries were grouped according to their infant mortality
rates during a decade of the inter-war period (1927–1938)
for which data are available for all countries (Chesnais
1 High mortality (thus high levels of potential survival
selection effect) includes countries with infant mortal-
ity rates above 100 per thousand at the beginning of the
period and mostly remained so over the period. These
countries are Poland, the Czech Republic,
Italy, and Greece.
2 Intermediate mortality (rates between 75 and 100 per
thousand): Austria, Belgium, France, Germany, and
3 Low mortality (rates systematically below 75 per
thousand): Sweden, Switzerland, and the Netherlands.
Accordingly, this group represents the lowest level of
potential survival selection at a contextual level
First, a descriptive analysis displays the percentage of in-
terviewees that experienced the event of poor health across
the range of ages studied (30–64). In this analysis, the
countries are sorted according to the mortality levels
defined above.
Second, survival analysis was utilized to measure the
risk of suffering from a distinct episode of poor health
(poor health hereafter) as a function of the partnership
status and controlling for the above-mentioned variables.
This analysis was performed through discrete-time hazard
models because the variables involved in the analysis
were interval censored (i.e., the exact time at which
every event occurred within 1 year was unknown).
Control variables were entered into the models as (1)
time-varying covariates (partnership status, working sta-
tus, and age) and (2) time-constant covariates (sex,
childhood health, age at the time of the interview, and
Two different model specifications are presented and
discussed. First, a pooled model was run to capture the net
differences in the hazard of poor health among countries as
well as to test the hypothesis of the attenuation or reversion
of a first-union advantage at mature and older ages. Sec-
ond, country-specific models explored the existence of
patterns of partnership-related determinants of poor health
hazard across countries (for instance, whether the direction
and the significance of first-union effects on the hazard of
poor health differ between countries).
The coefficients displayed report the relative difference
with respect to the reference group, whereby positive val-
ues indicate a higher hazard than the reference category
and vice versa. Only statistically significant coefficients
(significance \0.05) are commented and discussed in
Table 1displays that women exhibit a higher prevalence of
experiencing the event of poor health during the 30–64 age
range, particularly in Switzerland, Sweden, and Spain
(11.6, 9.6, and 6.9 % points of difference, respectively; the
The series depicted here are based on mortality data from the
former Czechoslovakia.
We included this country in the high-mortality group based on its
values from 1931 and onwards because Greek civil registration started
relatively late in 1925, and it was not fully enforced until 1931.
Eur J Ageing
total difference in the whole sample is 3.6). There are three
exceptions to this pattern: Austria, Germany, and the
In addition, younger groups at the time of the interview
(ages 50–59 and 60–69) display a higher prevalence, which
for the most part peaks at ages 60–69, whereas it bottoms
out among individuals aged 80 and over. Likewise, the
prevalence of poor health is invariably higher among
individuals that reported poor or unstable health during
their childhoods. Finally, by contrast, education does not
make any significant difference in most of the countries
analyzed, once other variables are controlled for.
Table 2displays the hazard coefficients (of poor health)
whereby country-specific differences are controlled for in a
pooled model. In this specification, the Netherlands was
taken as the reference country because it exhibited the
lowest infant mortality rate during the 1927–1938 period.
Eight of the twelve countries show a higher (and sta-
tistically significant) hazard of poor health compared to the
Netherlands. All four countries with lower hazards (the
Czech Republic, Greece, Italy, and Spain) belong to the
high infant mortality group, whereas only one of the five
countries so categorized (Poland) shows a relatively higher
hazard of poor health with respect to the Netherlands.
Looking at partnership status categories, when country-
specific differences were controlled for, none showed a
lower hazard of poor health than singleness. Indeed, two of
them (second union and separation-divorce) displayed
significantly higher hazards.
Poor or unstable health at pre-adult ages clearly
increases the risk of suffering from poor health during
adulthood. In addition, most of the socio-demographic
covariates included in the analysis displayed significant
effects. The hazard of poor health increases significantly
with age, although the assumption of linearity is refuted by
the significant coefficient of the age squared, whereas the
age at the time of the interview displays the opposite (and
expected) effect, in that being interviewed at older ages
likely reduces the probability of prioritizing a single dis-
tinct event of poor health within the range of ages 30–64.
Those who did not work between ages 30–64 show a higher
risk of experiencing poor health. Education does not dis-
play any significant effect when country-specific differ-
ences were controlled for, and no significant differences
between the hazards experienced by men and women are
The results from country-specific models (Table 3)
support the most substantial part of those provided in the
pooled model: there is no evidence of health advantages
associated with partnership (first unions) throughout
adulthood in these European countries among the individ-
uals who reached the age of 50. In reality, Spain and France
display significant differences (lower hazards) in favor of
single individuals. Regarding other partnership situations,
living in a second or higher rank union as well as a sepa-
ration or divorce show a similar pattern across countries:
positive coefficients that are significant in few cases
The interaction between country and partnership situations was
tested without obtaining significant results.
Table 1 Prevalence of a poor health event within the range of ages 30–64 by sex, age at the time of the interview, childhood health, education,
and country
Sex Age at the interview Childhood health Education Total
80 and
over (%)
Poor or
variable (%)
level (%)
level (%)
level (%)
29.8 31.5 32.3 33.7 26.1 19.1 29.9 44.1 30.9 30.8 30.7 30.8
Greece 17.0 20.2 14.5 22.4 22.2 14.7 18.7 42.9 22.6 14.4 17.4 18.8
Italy 29.5 31.3 30.6 32.7 28.9 21.8 29.6 45.0 30.4 30.4 31.3 30.5
Poland 38.3 44.2 39.0 50.7 35.2 26.8 40.6 55.0 41.5 42.2 40.5 41.7
Spain 32.3 39.2 34.9 43.3 33.2 23.1 34.3 53.0 38.0 34.7 27.4 36.1
Austria 43.9 40.7 43.7 45.7 37.3 33.3 41.5 46.0 44.4 40.0 44.8 42.0
Belgium 34.2 38.2 38.7 41.1 33.3 21.5 35.1 52.5 33.5 36.8 38.1 36.4
Denmark 35.4 40.1 40.3 42.3 34.0 19.5 36.9 52.4 36.4 37.8 38.7 37.9
France 41.2 43.9 46.6 43.7 40.7 31.5 41.6 53.7 41.6 44.8 40.8 42.7
Germany 47.2 46.8 47.8 49.9 42.7 39.6 45.1 61.2 36.4 47.6 45.9 47.0
Netherlands 32.9 32.0 38.6 34.6 23.3 17.4 31.7 38.9 34.7 31.0 35.0 32.4
Sweden 35.6 45.2 45.6 44.4 36.0 27.8 39.4 61.4 40.0 42.7 39.9 40.9
Switzerland 31.8 43.4 39.9 41.6 33.2 33.0 36.4 57.0 37.1 38.5 38.5 38.3
Total 33.5 37.1 36.3 39.3 32.2 24.3 34.2 51.7 34.2 35.7 36.6 35.5
Eur J Ageing
(France and the Netherlands for second unions and Spain,
France, Germany, and Sweden in the case of separation or
divorce). Widowhood displays the most heterogeneous
effect across countries, with significant results only in
Age, age squared, age at the time of the interview,
childhood health, and working history show homogenous
effects within almost all of the countries analyzed, and
these effects are in accordance with those described in the
pooled model. Finally, this model specification helps
understand the tenuous and erratic effect of sex and edu-
cational level uncovered by the pooled model. Sex is sig-
nificant in five countries (Greece, Germany, Netherlands,
Sweden, and Switzerland), but while women exhibit a
higher hazard of poor health in Greece, Sweden, and
Switzerland, the opposite occurs in Germany and the
Netherlands. Education only shows an effect (lower hazard
for secondary education) in Greece and the Netherlands.
This study analyzed the hazard of experiencing poor health
(self-reported) over adulthood as a function of changes in
partnership history and a number of individual socio-
demographic characteristics in thirteen European countries.
The countries represent different paces and pathways into
the SDT, and they also embody different contexts of living
conditions among the cohorts analyzed (population aged
50?and born between 1907 and 1958). The latter might
imply different levels of potential health- and mortality-
driven selection effects.
Our results demonstrate that in this segment of the
European population, living in a first union throughout
adulthood (i.e., over ages 30–64) is not associated with a
meaningful advantage compared to those who remained
single, at least when health is measured by a comprehen-
sive indicator, such as the one utilized here. This result
supplements and enhances previous partial evidence from
case studies based on either cross-sectional or longitudinal
analyses, which made use of different health indicators. For
instance, in the urban Spanish region of Madrid, Regidor
et al. (2001) found higher survivorship rates among singles
(not cohabitating) aged 65?with respect to those living
with a partner. Goldman et al. (1995) reached similar
conclusions about mortality and disability from a short-
term longitudinal study (1984–1990) conducted with a
sample of Americans aged 70 and over. Bardage et al.
(2005) also found no difference in terms of self-rated
health among married and not married individuals aged
65–89 in study comparing Sweden, the Netherlands, and
Spain. A number of these studies hypothesized that the
absence of the (expected) protective effect of partnership
on health does not necessarily reflect the net effect of
partnership biography on health but rather some degree of
selection among individuals that have reached older adult
ages. This selection may work on two levels: individually
and contextually.
On the one hand, single individuals suffer from a higher
mortality risk (Waite 1995; Martikainen et al. 2005; Val-
konen et al. 2004; Joutsenniemi 2007), and as a conse-
quence, those who survive until mature and older ages may
Table 2 Coefficients of poor health hazards between ages 30 and 64
Coefficient significance
Country (Ref: Netherlands)
Czech Rep. 0.063
Greece -0.547***
Italy -0.092
Poland 0.329***
Spain 0.080
Austria 0.290***
Belgium 0.187**
Denmark 0.290***
France 0.324***
Germany 0.479***
Sweden 0.398***
Switzerland 0.252***
Partnership biography (Ref: Single)
First Union 0.119
Second Union 0.318***
Separation or divorce 0.387***
Widowhood 0.169
Childhood health (Ref: Good)
Poor or variable 0.405***
Working status (Ref: No working)
Working -0.479***
Age at the survey (Ref: 50–59)
60–69 -0.358***
70–79 -0.868***
80 and over -1.472***
Educational Level (Ref: First Level)
Second Level -0.049
Third Level 0.010
Age 0.194***
Age Squared -0.001***
Sex (Ref: Men)
Women -0.015
Constant -9.698***
Pseudo R-Square 0.045
Pooled countries specification
\0.001***; \0.01**; \0.05*; \0.1
Eur J Ageing
Table 3 Coefficients of country-specific poor health hazards between ages 30 and 64
Czech Rep. Greece Italy Poland Spain Austria Belgium Denmark France Germany Netherlands Sweden Switzerland
Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign.
Partnership biography (Ref: Single)
First Union -0.089 0.205 0.189 0.051 0.597** 0.221 -0.242 -0.206 0.413* 0.192 0.023 0.176 0.034
Second or higher
0.292 0.433 0.465 0.159 0.740
-0.086 -0.047 0.592** 0.443
0.590** 0.263 0.033
Separation or divorce 0.094 0.315 0.696
-0.158 0.806* 0.559 -0.029 0.058 0.690** 0.617* 0.160 0.588* 0.234
Widowhood -0.074 0.056 0.394 0.172 0.931** 0.377 -0.419 0.121 0.404 0.316 0.435 -0.335 -0.305
Childhood health (Ref: Good)
Poor or variable 0.483** 0.815 0.319* 0.357* 0.445** -0.074 0.499*** 0.533** 0.322** 0.396** 0.230
0.532** 0.622***
Working status (Ref: No working)
Working -1.242*** 0.004 -0.303** -0.987*** -0.681*** -0.271 -0.553*** -0.824*** -0.350*** -0.481*** -0.399** -0.648*** -0.048
Age at the survey (Ref: 50–59)
60–69 -0.423*** -0.062 -0.339** 0.017 -0.409** -0.284 -0.417*** -0.415*** -0.538*** -0.431*** -0.505*** -0.393*** -0.271
70–79 -0.928*** -0.377** -0.718*** -0.673*** -1.169*** -0.839*** -0.845*** -0.992*** -1.038*** -0.881*** -1.117*** -0.833*** -0.864***
80 and over -1.429*** -0.991*** -1.224*** -1.148*** -1.728*** -1.547*** -1.589*** -1.777*** -1.635*** -1.317*** -1.835*** -1.672*** -0.926***
Educational Level (Ref: First Level)
Second Level 0.052 -0.422** 0.041 0.131 -0.133 -0.147 0.064 0.083 0.039 0.238 -0.296* 0.005 0.030
Third Level 0.096 -0.106 0.176 0.126 -0.340
0.051 0.190
0.134 -0.104 0.268 0.184 -0.047 0.143
Age 0.553*** 0.269*** 0.087* 0.419*** 0.161** 0.156* 0.140*** 0.243*** 0.144** 0.097* 0.199** 0.248*** 0.018
Age Squared -0.005*** -0.002** -0.001 -0.004*** -0.001
-0.001 -0.001* -0.002*** -0.001* -0.001 -0.001* -0.002*** 0.000
Sex (Ref: Men)
Women -0.121 0.253* -0.093 -0.025 -0.223 -0.155 -0.038 0.049 0.072 -0.196* -0.219* 0.270** 0.469***
Constant -16.828*** -13.546*** -7.657*** -14.125*** -9.430*** -8.148*** -7.582*** -10.044*** -8.471*** -7.152*** -8.797*** -10.882*** -5.681***
Pseudo R-Square 0.061 0.050 0.028 0.065 0.065 0.032 0.036 0.052 0.043 0.040 0.039 0.054 0.026
N1678 2645 2319 1601 1777 708 2532 1913 2037 1652 1911 1684 1078
\0.001***; \0.01**; \0.05*; \0.1
Eur J Ageing
represent a more select segment of the population. This
point was supported through independent hazard models
for each age group (not shown; available upon request) in
which the higher the age group (at the time of the inter-
view), the lower the risk of poor health of single individ-
uals in comparison with those who were in a first union.
Explanations other than the survival selection effect could
be proposed, but they are not very plausible in our opinion
(e.g., if there are mechanisms whereby married individuals
report the distinct period of poor health early more often
than singles). In addition, some authors have shown that
health influences the probability of individuals to enter into
a union (Joung et al. 1998; Brockmann and Klein 2004).
The retrospective data from SHARELIFE are insufficient
to fully consider reverse causality between partnership
status and health even though the interaction between
childhood health and partnership status was tested in this
analysis without having obtained significant results.
On the other hand, our results also point to some degree
of mortality-related selection at the country level because
four of the five countries categorized as high-mortality
countries during the first half of the twentieth century
(Greece, Italy, the Czech Republic, and Spain) show no
significant effect or even lower hazards of poor health with
respect to the low-mortality reference (the Netherlands),
once individual-level variables are controlled for. How-
ever, we acknowledge that this is an intuitive interpreta-
tion. This hypothesis is not supported by the hazard
coefficients based on specific illnesses reported in
SHARELIFE (item HS054; not shown and available from
authors upon request).
All types of data, whether cross-sectional, longitudinal,
or retrospective, the latter being the case of this study, have
proven to be influenced by survival selection when mature
and older ages are analyzed. As a consequence, the true
effect of partnership status on health among this specific
subpopulation cannot be precisely measured and the likely
survival selection should be of concern in all cases when
the relationship between any socio-demographic factor and
health is addressed. Only longitudinal data associated with
long-term follow-up can adequately measure the actual
magnitude of that selection effect. Although we applied a
survival analysis throughout adulthood (ages 30–64), in
this study, the selection of individuals with a better health
profile cannot be avoided, creating a potential effect on the
results. This effect is likely to be larger among those seg-
ments of the population that are, in principle, more exposed
to health-related disadvantages and for longer periods,
which seems to be the case of single individuals. By con-
trast, other partnership situations such as divorce or wid-
owhood are less likely to be affected by the survival
selection, or its effect is less intense due to a later start or
the temporary nature associated with those situations.
However, the influence of these situations on health found
in results must be taken with some caution due to the few
individuals included in these categories (i.e., separation or
divorce and second and subsequent unions).
Separation or divorce displays a negative effect on
health over adulthood, and the same is observed among
second or higher ranks of unions, thus confirming findings
from previous research (Joung et al. 1998; Hughes and
Waite 2009). The end of a relationship is stressful enough
in itself to have negative consequences on an individual’s
health. In fact, poor health status has been shown to be one
of the contributing factors for separation (Joung et al.
1998). In addition, it may imply a worsening of economic
status (separation or divorce usually reduces the purchasing
power of both partners; Andress et al. 2006), which toge-
ther with the expected consequences of aging may derive a
disadvantaged position within the marriage market before
an occasional new union. That is, these individuals become
less attractive and less competitive as a function of the
factors previously described.
Widowhood is usually associated with increasing
socioeconomic-related vulnerability, but in the current
study, only Spain shows a significant health disadvantage
associated with widowhood. In our opinion, several factors
might contribute to buffering the potential effect of wid-
owhood on the hazard of poor health: (1) the range of ages
is retrospectively analyzed (30–64 years) that reduces the
probabilities of widowhood, and (2) the set of provisions
from the welfare state that mitigates the negative effect of
this episode. Using SHARELIFE data, Biro
´(2013) pointed
out the complexity of the relationship between widowhood,
economic restrictions, and health with regard to different
welfare policies across European countries (i.e., the char-
acteristics of pension systems, the degree of female par-
ticipation in the labor market, and the development of
private annuity).
Although individuals’ working status shows a significant
effect on the hazard of poor health, it is not possible to
hypothesize about the underlying mechanisms because
poor health itself is a determinant of working status (De
Lange et al. 2005). The control exerted in our models
through the information on childhood health status does not
solve this problem because no direct causal association
between health at childhood and working status during
adulthood can be ascertained through our data. A similar
reasoning applies to the educational level (Ross and Wu
1996), although in this case, no significant effect is
observed in our results.
Finally, it is important to note that no significant dif-
ferences in the risk of poor health are observed between
men and women in eight of the thirteen countries analyzed
once the remaining socio-demographic variables are con-
trolled for. In the five countries, where sex differences are
Eur J Ageing
statistically significant, they do not point univocally: in
Greece, Sweden, and Switzerland, there is a lower hazard
of poor health among men, whereas in Germany and the
Netherlands, the opposite is observed. This finding is
interesting to us because it would invite a supplement and
revision to the so-called ‘‘sex health-survival paradox’
(Oksuzyan et al. 2010) on the basis of retrospective data.
Acknowledgments This study uses data from SHARELIFE release
1, as of November 24, 2010. The collection of SHARE data has been
primarily funded by the European Commission through the fifth
framework programme (project QLK6-CT-2001- 00360 in the the-
matic programme Quality of Life), through the sixth framework
programme (projects SHARE-I3, RII-CT- 2006-062193, COMPARE,
CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812)
and through the seventh framework programme (SHARE-PREP,
211909 and SHARE-LEAP, 227822). Additional funding from the
U.S. National Institute on Aging (U01 AG09740-13S2, P01
AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and
OGHA 04-064, IAG BSR06-11, R21 AG025169) as well as from
various national sources is gratefully acknowledged.
Andress H-J, Borgloh B, Bro
¨ckel M, Giesselmann M (2006) The
economic consequences of partnership dissolution: a compara-
tive analysis of panel studies from Belgium, Germany, Great
Britain, Italy, and Sweden. Eur Sociol Rev 22(5):533–560
Bardage C, Pluijm SM, Pedersen NL, Deeg DJ, Jylha
¨M, Noale M,
Blumstein T, Otero A (2005) Self-rated health among older
adults: a cross-national comparison. Eur J Ageing 2:149–158
´A (2013) Adverse effects of widowhood in Europe. Adv Life
Course Res 18(1):68–82
¨rsch-Supan A, Karsten H, Ju
¨rges H (2005) A new comprehensive
and international view on ageing: introducing the ‘Survey of
Health, Ageing and Retirement in Europe’. Eur J Ageing
Brockmann H, Klein T (2004) Love and death in Germany: the
marital biography and its effect on mortality. J Marriage Fam
Chesnais J-C (1986) La transition de
´mographique: e
´tapes, forms,
implications e
´conomiques. Etude de series temporelles
(1720–1984) relatives a
`67 pays. Presses Universitaires de
France, Paris
De Lange AH, Taris TW, Kompier M, Houtman I, Bongers P (2005)
Different mechanisms to explain the reversed effects of mental
health on work characteristics. Scand J Work Environ Health
Demakakos P, Nazroo J, Breeze E, Marmot M (2008) Socioeconomic
status and health: the role of subjective social status. Soc Sci
Med 67:330–340
Duncan G, Wilkerson B, England P (2006) Cleaning up their act: the
effects of marriage and cohabitation on licit and illicit drug use.
Demography 43(4):691–710
Goldman N, Korenman S, Weinstein R (1995) Marital status and
health among the elderly. Soc Sci Med 40(2):1717–1730
´lez L, Viitanen T (2009) The effect of divorce laws on divorce
rates in Europe. Eur Econ Rev 53:127–138
Hughes M, Waite L (2009) Marital biography and health at mid-life.
J Health Soc Behav 50:344–358
Idler EL (1993) Age differences in self-assessments of health: age
changes, cohort differences, or survivorship? J Gerontol
Joung I, Van de Mheen H, Stronks K, Van Poppel F, Mackenbach J
(1994) Differences in self-reported morbidity by marital status
and by living arrangement. Int J Epidemiol 23(1):91–97
Joung I, Dike H, Stronks K, Van Poppel F, Mackenbach J (1998) A
longitudinal study of health selection in marital transitions. Soc
Sci Med 46(3):425–435
Joutsenniemi K (2007) Living arrangements and health. Dissertation,
University of Helsinki
Lesthaeghe R (1995) The second demographic transition in western
countries. In: Mason KO, Jensen AM (eds) Gender and family in
industrialized countries. Clarendon Press, Oxford, pp 17–62
Lillard L, Panis C (1996) Marital status and mortality: the role of
health. Demography 33(3):313–327
Lillard L, Waite L (1995) Til death do us part: marital disruption and
mortality. Am J Sociol 100(5):1131–1156
Liu H, Umberson D (2008) The times are a changin
´: marital status
and health differentials from 1972 to 2003. J Health Soc Behav
Marmot M (2005) Social determinants of health inequalities. Lancet
Martikainen P, Martelin T, Nihtila
¨E, Majamaa K, Koskinen S (2005)
Differences in mortality by marital status in Finland from 1976
to 2000: analyses of changes in marital-status distributions,
socio-demographic and household composition, and cause of
death. Popul Stud 59(1):99–115
Oksuzyan A, Brønnum-Hansen H, Jeune B (2010) Gender gap in
health expectancy. Eur J Ageing 7(4):213–218
Regidor E, Calle M, Domı
´nguez V, Navarro P (2001) Mortalidad
´n caracterı
´sticas sociales y econo
´micas: Estudio de mortal-
idad de la Comunidad Auto
´noma de Madrid. Med Clin
Rendall MS, Weden MM, Favreault MM, Waldron H (2011) The
protective effect of marriage for survival: a review and update.
Demography 48:481–506
Ross C, Wu CH (1996) Education, age, and the cumulative advantage
in health. J Health Soc Behav 37(1):104–120
¨der M (2011) Retrospective data collection in the survey of
health, ageing and retirement in Europe. SHARELIFE Method-
ology. Mannheim
Simon W (2002) Revisiting the relationships among gender, marital
status, and mental health. Am J Sociol 107:1065–1096
Valkonen T, Martikainen P, Blomgren J (2004) Increasing excess
mortality among non-married elderly people in developed
countries. Demogr Res 2:305–330
Vaupel JW, Yashin AI (1985) Heterogeneity’s ruses: some surprising
effects of selection on population dynamics. Am Stat
Vaupel JW, Manton KG, Stallard E (1979) The impact of heteroge-
neity in individual frailty on the dynamics of mortality.
Demography 16:439–454
Wade T, Pevalin D (2004) Marital transitions and mental health.
J Health Soc Behav 45:155–170
Waite L (1995) Does marriage matter? Demography 32(4):483–507
World Health Organization (WHO) (1946) Preamble to the Consti-
tution of the World Health Organization as adopted by the
International Health Conference, New York, 19–22 June
Eur J Ageing
... Moreover, it is unclear how survival selection or survey attrition affects or biases any of the observed developments of the 'marriage benefit' with age (Rendall et al. 2011;Gumà, Cámara and Treviño 2015). Alternatively, inconsistencies of results may come from actual variations and historical changes in mortality and health by period, country, and cohort (Murphy, Grundy and Kalogirou 2007). ...
... To date, most datasets do not allow analysing both childhood health and old age health, and marital life course of the same individuals within one single research design. One study that included measurements of health in early life in a research design on partnership/marital status and health was conducted by Gumà, Cámara and Treviño (2015). They used the retrospective SHARELIFE data of elderly Europeans to examine the relationship between partnership history and self-reported health at age 30 to 64. ...
... (cf. alsoGumà, Cámara and Treviño 2015).Moreover, reporting errors of the marital life histories may have reduced the quality of the results. As presented in detail in Chapter III.2. and IV.1.2., inconsistencies were discovered between marital status reports in regular SHARE panel waves and marital history reports in the retrospective SHARELIFE data. ...
The research project is motivated by the well-known health and longevity advantage of married persons over the unmarried. In times of population ageing and changing marital behaviour, an investigation of the marriage-health nexus by applying a life course perspective is relevant. Instead of merely considering current marital status, the study compares marital biographies and the association with health in old age. Using the Survey of Health, Ageing and Retirement in Europe (SHARE), I investigate cross-country differences of marital and partnership biographies of a sample of more than 22,000 Europeans above age 50, from 14 European countries. I discuss national differences and similarities of the following components of the marital life course: current marital status, number of marriages, sequencing and timing of marital transitions, duration of marital status, number, type and timing of non-marital partnerships. In a second step, I analyse the association of the marital life course with health status in later life, using objective indicators of physical and cognitive health (grip strength, expiratory air flow, memory, verbal fluency). I address possible selection effects by adjusting for health and cognitive status during childhood. Results obtained by cross-sectional regressions show that married Europeans have better physical and cognitive health outcomes, compared to the never married and the separated. Widowed Europeans are disadvantaged in terms of cognitive health, but not physical health. With respect to marital biography, marriage before age 20 and remarriage are related to health deficits in old age, whereas length of a marriage shows no effect. Also multiple marital losses seem to affect physical health negatively. A longer period of widowhood is related to a decrease in cognitive health – regardless of current marital status. The latter finding is also supported by longitudinal fixed-effects regressions that draw on over 55,000 observations from 17 European countries and Israel.
... Research has recently focused on understanding the consequences of divorce and separation for former spouses' well-being (see Gumà et al., 2015 for a recent contribution for European countries and Clark et al., 2008 for an analysis on the impact of life and labour market events on well-being in Germany). While there is general consensus in recognizing that marital dissolution can potentially generate uneasiness in an individual's life (Amato, 2000), scholars also recognize the need to better understand the heterogeneity of outcomes according to individuals' and couples' characteristics (Amato, 2010), its evolution over time (Bauer et al., 2015;Clark and Georgellis, 2013;Rudolf and Kang, 2011), and the channels driving such detrimental effect. ...
We investigate the happiness variations associated with divorce by drawing data from a retrospective panel dataset based on the third wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) and covering 14 European countries. This dataset proposes as a powerful tool to control for reporting style heterogeneity in happiness self-evaluations. Indeed, in addition to individual fixed-effects, we control for full migration trajectories in order to remove bias in well-being evaluations produced by cross-country heterogeneity in the cultural norms and societal values individuals have been exposed during their life-cycle. Happiness is found to increase in the period after divorce for both men and women. We show that this pattern goes through a decrease in stress and financial hardship.
... The bulk of the literature on family histories and health has focused on a single aspect, such as, partnership [24,25] and fertility [5,6]. ...
Full-text available
Objectives We examine to what extent the effect of early-life conditions (health and socioeconomic status) on health in later life is mediated by educational attainment and life-course trajectories (fertility, partnership, employment). Methods Using data from the Survey of Health, Ageing and Retirement in Europe (N = 12,034), we apply, separately by gender, multichannel sequence analysis and cluster analysis to obtain groups of similar family and employment histories. The KHB method is used to disentangle direct and indirect effects of early-life conditions on health. Results Early-life-conditions indirectly impact on health in later life as result of their influence on education and family and employment trajectories. For example, between 22% and 42% of the effect of low parental socio-economic status at childhood on the three considered health outcomes at older age is explained by educational attainment for women. Even higher percentages are found for men (35% - 57%). On the contrary, the positive effect of poor health at childhood on poor health at older ages is not significantly mediated by education and life-course trajectories. Education captures most of the mediating effect of parental socio-economic status. More specifically, between 66% and 75% of the indirect effect of low parental socio-economic status at childhood on the three considered health outcomes at older age is explained by educational attainment for women. Again, higher percentages are found for men (86% - 93%). Early-life conditions, especially socioeconomic status, influence family and employment trajectories indirectly through their impact on education. We also find a persistent direct impact of early-life conditions on health at older ages. Conclusions Our findings demonstrate that early-life experiences influence education and life-course trajectories and health in later life, suggesting that public investments in children are expected to produce long lasting effects on people’s lives throughout the different phases of their life-course.
As the number of single (unpartnered) individuals continues to rise, researchers across various disciplines have started to pay more attention to single individuals' lives. Yet, compared to the accumulated knowledge about experiences within romantic relationships, there is far less known about various experiences within singlehood. For singlehood research to grow in both quantity and quality, it is essential that research findings are critically evaluated both in terms of robustness of the evidence and validity of the inferences. In this paper, we review three broad approaches researchers have taken to understand singlehood that centered on (a) between‐group status (i.e., single vs. in a relationship) differences, (b) within‐person status differences, and (c) within‐group variability among singles. With a focus on well‐being as an outcome, we illustrate how each approach provides unique insights into singlehood and what caveats there are in interpreting results derived from each approach. Finally, we identify questions or methods that have not been extensively explored within each approach and offer suggestions for future research directions.
Human aging is a global problem that will have a large socioeconomic impact. A better understanding of aging can direct public policies that minimize its negative effects in the future. Over many years, several longitudinal studies of human aging have been conducted aiming to comprehend the phenomenon, and various factors influencing human aging are under analysis. In this review, we categorize the main aspects affecting human aging into a taxonomy for assisting data mining (DM) research on this topic. We also present tables summarizing the main characteristics of 64 research articles using data from aging‐related longitudinal studies, in terms of the aging‐related aspects analyzed, the main data analysis techniques used, and the specific longitudinal database mined in each article. Finally, we analyze the comprehensiveness of the main databases of longitudinal studies of human aging worldwide, regarding which proportion of the proposed taxonomy's aspects are covered by each longitudinal database. We observed that most articles analyzing such data use classical (parametric, linear) statistical techniques, with little use of more modern (nonparametric, nonlinear) DM methods for analyzing longitudinal databases of human aging. We hope that this article will contribute to DM research in two ways: first, by drawing attention to the important problem of global aging and the free availability of several longitudinal databases of human aging; second, by providing useful information to make research design choices about mining such data, e.g., which longitudinal study and which types of aging‐related aspects should be analyzed, depending on the research's goals. WIREs Data Mining Knowl Discov 2017, 7:e1202. doi: 10.1002/widm.1202 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Full-text available
In this article, we analyze how the marital biography is affecting mortality in Germany today (N = 12,484). We find support for temporal selection into marriage for both genders, but the effect is stronger for men. In addition, protection through marriage results from long-term accumulation of survival advantages and from the attenuation of higher mortality risks that occur immediately after a transition into or out of a marriage. Moreover, women are more likely to keep survival advantages from previous marriages and to forget about survival disadvantages from divorces and widowhood.
Introduction The linking between the social determinants and the health status of the people has been recognized for a very long time, although the last three decades has witnessed the rebirth of interest for this association. The number of scientific publications rose, the contents of which showed the social inequalities in health, hence their validity turns them into a mandatory topic in the field of public health. Objective To review the relevant information on the social determinants and inequalities in health policies. Data sources Literature and document review on-line and in electronic format about this topic. The subject headings such as social determinants, and health inequality and inequity were used for search in websites and pages from well-known centers and institutions. Data synthesis Brief historical account on the relationship between the health status of the people and their social conditions; assessments and critical comments on prestigious researchers ́ approaches. Those methods for the study of health inequalities were summarized and exemplified with the situation of these research works in Mexico. Conclusions More comprehensive and good evidence-supported processes of research on the social determinants are required. Also, it is necessary to recognize the importance of these research works to make decisions to overcome health problems using approaches that guide us to their determinants, but not only from the medical care viewpoint.
I investigate the relationship between widowhood and the financial situation among women aged 50 and above in Europe. The results of the paper are based on the Survey of Health, Ageing and Retirement in Europe, and its retrospective third wave (SHARELIFE). Using retrospective data makes it possible to analyze the dynamics of the adverse effects of widowhood. I estimate both the short run and long run effects of widowhood on financial circumstances, health, and labor force status. I argue that not only the lack of the deceased husband's income, but also the worse health condition and earlier retirement of widows contribute to the unfavorable financial conditions, although these indirect effects are small. I also analyze the role survivors’ pensions have in mitigating the adverse effects of widowhood, and provide evidence for varying compensating effects of survivors’ pensions in the European countries analyzed.
Background To estimate the effect of marital status, household size, employement status, educational level and occupation on mortality. Subjects and method About 3,100,000 persons 24 years and older resident on first of May, 1996 in Autonomous Community of Madrid. For the next 19 months information on individuals who died were obtained by linking Mortality Register and 1996 Population Census. Results Except for household size, the effect on mortality of characteristics analysed was higher in individuals aged 25 to 44 years than in other group of age. In general, married persons had the lowest mortality, except for men older than 64 years where the lowest mortality was found in the never married category. Men aged 45 to 64 years who lived alone had higher mortality than those who lived with other people, while mortality in people aged older than 64 years increased with household size. Inactive persons had higher mortality than active people. Higher education level and higher skilled occuoation were associated with lower mortality, except for women aged 45 to 64 years. Conclusion This study has identified population groups associated with increased risk of mortality. Monitoring the trend of mortality in those groups will make possible to found the rational basis to implement social and health programmes.
The paper analyses the economic consequences of partnership dissolution in different institutional settings. Belgium, Germany, Great Britain, Italy, and Sweden are selected as representatives of four prototypical models of family support (market model, extended family model, male breadwinner model, dual earner model). It is assumed that these four types of family support create specific dependencies within the family, which in case of separation or divorce may have negative economic consequences for the weaker partner. The central question is how much economic autonomy is granted to the weaker family members within each of the four models. Following a thorough discussion of the institutional setting in each of the selected countries, it is assumed that economic autonomy is highest in Sweden and lowest in Italy with Belgium, Germany, and Great Britain ranging in between. Using a cross-national data set of separations developed by the authors from national household panels in these five countries, a large number of partnership dissolutions are studied over time. The observation period is long enough to distinguish short- from long-term consequences of partnership dissolution and in doing so to add to previous comparative research. Using multivariate panel data models it is shown that (i) adjusted household income is affected for both genders; however more negatively for women than for men, (ii) the income decline is highest in Italy and lowest in Sweden, and (iii) British and German women recover rather quickly from the negative economic effects of separation. Sweden stands out as the country with the highest gender equalities with respect to post-separation incomes. However, the model does not convince without having a blemish: in the long run both Swedish men and women have to deal with long-lasting financial consequences after separation, which do not appear to the same extent in any of the other countries.
We analyze the effect on divorce rates of the legal reforms leading to "easier divorce" that took place in Europe during the last four decades. We construct a 54-year-long panel and exploit the different timing and nature of the reforms in divorce laws across countries. The reforms range from countries that legalized divorce to the introduction of no-fault grounds and unilateral divorce. We estimate that the introduction of no-fault, unilateral divorce increased the divorce rate by about 0.6, a sizeable effect given the average rate of 2 divorces per 1000 people in 2002.