1. 1
Marriage, work and health: A cross national comparative study of the impact of welfare
regimes on gender-specific working hours and self-assessed health.
Abstract
This study is a cross-national comparative analysis of the observed differences in health among
married men and women using the Multinational Time Use Study (MTUS). Research on the effect
of marriage on health has largely maintained that there is a distinctly gender-specific gradient in
health outcomes, with men deriving far greater health benefits than women. One of the main
reasons provided for these differences is the disproportionate time spent by women on household
activities and providing care. This hypothesis has yet to be explicitly tested for time specific
usage. The purpose of this research is therefore to explore the proposed gender-specific effects of
marriage on self-assessed health; placing special emphasis on individual time spent on paid market
work, household work and childcare among men and women. We argue that social welfare
policies affects time allocation among married men and women differently across different
countries, and therefore accounts for differences in self-reported health. Consequently, cross-
national policy differences needs to be considered to fully explain the differential impact of
marital status on health.
Keywords: Marriage, Self-assessed health, Gender, Multinational Time Use Study
DRAFT
Authors: Kenisha S. Russell Jonsson1
, Nicholas Adjei2
, Gustav Öberg3
1
LCSR National Research University Higher School of Economics, Russian Federation/The Institute for Future Studies Stockholm
Sweden (ksruss@essex.ac.uk)
2
The Institute for Future Studies, Stockholm ,Sweden (nicholas.adjei@iffs.se/niad0545@student.su.se)
3
The Institute for Future Studies, Stockholm, Sweden(Gustav.oberg@iffs.se)

2. 2
Introduction
The existing literature on the social determinants of health has found that there is a wide
variation in the factors that affect individual health (Svedberg, Bardage et al. 2006, von dem
Knesebeck and Geyer 2007). Despite this, the majority of the work on the effects of marriage on
health claims that marriage is associated with better health (Holt-Lunstad, Birmingham et al. 2008,
St John and Montgomery 2009, Bookwala 2011, Dieperink, Hansen et al. 2012). In general, two
main hypotheses have been advanced to explain the advantageous effects of marriage: selection
and protection. The logic of these hypotheses states simply that healthy individuals are more likely
to marry and to remain married. The relationship between marriage and health is however
complex with some studies indicating that married individuals have better physical health (House,
Landis et al. 1988), that on average they enjoy better mental health (Gove and Tudor 1973,
Kiecolt-Glaser and Newton 2001) and that they live longer than unmarried individuals (Hu and
Goldman 1990). The mortality rates of married versus unmarried individuals cannot be entirely
explained by the selection of healthy individuals into marriage (Lillard and Panis 1996).
Nevertheless, an extended body of work has argued that the protective benefits of marriage are not
homogenous, with men deriving far greater benefits from marriage than women when several
health outcomes are tested (Gove and Tudor 1973, Kiecolt-Glaser and Newton 2001).
In an effort to further explore and explain the unequal benefits of marriage a strand of literature
which examines the impact of the social welfare state on health variation has been developed. The
results from these studies have largely indicated that welfare regimes are also an important
determinant of health (Zambon, Boyce et al. 2006, Bambra, Pope et al. 2009). Studies have for
example consistently shown that self-reported health varies by social welfare regime types
(Eikemo, Bambra et al. 2008, Bambra, Pope et al. 2009, Richter, Rathman et al. 2012). These
variations in health outcomes may be explained by the active roles of the state, the family and the
market in the welfare provisions (Esping-Andersen, 1990). In this regard, the welfare state plays a
decisive role in observed health outcomes in terms of how the state policies interact with family
structures and paid work (Hatland 2001).
A third strand of literature focuses on a lesser known research area, which is the effect of time
allocation on health. Psychosocial stressors are one of the main mechanisms driving this
relationship. The idea of psychosocial stressors implies that there are behavioural, social and
psychological factors which are external to an individual which impacts on physical functioning
(Martikainen, Bartley et al. 2002). This in turn creates a state of vulnerability, so that individuals
are more susceptible to illness. Essentially, we argue the time men and women allocate to various
tasks, may create feelings of stress and/or inhibits physical and social activities necessary for

3. 3
stress relief, and this stimulates the auto immune and neuroendocrine pathways negatively and
thereby increaes the risk of actual and perceived ill-health.
The relationship between the determinants of gender-specific differences in health discussed in
these three strands of literature remains understudied among married people. This is surprising
given the complex interrelationship between family structures and the considerable evidence
linking time usage to variations in institutional policies across various welfare regimes. For
instance, previous research demonstrates that there is a distinct link between work-family conflict4
and the policies instituted across various welfare regimes. Thus one could anticipate variations in
the level of this conflict based on national, individual and family circumstances (Crompton and
Lyonette 2006). In addition, it is not very far-fetched to argue that one of the main reasons for the
complexity in the relationship between family structure, welfare policies and its relationship to
health is the gender differences in time allocated to paid market work, unpaid household work and
child care.
Horwitz and White (1991:222) posits that marriage “…helps men by providing companionship
and household labour while not detracting from occupational rewards. Women, on the other hand,
either become isolated within the family if they do not work or suffer from role overload and
occupy inferior occupational positions if they do enter the labour force”. This argument suggests
that gender-work life balance and/or work conflict may be a contributing factor to time allocated
to various tasks and as a consequence a contributory factor to the observed gender-specific
inequality in health among married men and women. This explanation however does not fully
elucidate the variations in health among married men and women. This is therefore an important
avenue of examination given that the conflict which arises in the roles of women (i.e. between
paid market work and unpaid household work) has been hypothesized to be one of the main
factors driving these gender-specific inequalities in health among married people (Gove 1972).
To our knowledge the specific time which men and women allocate to their differing roles
based on national variation in welfare policies, has yet to be tested explicitly as an explanation for
these observed differences in health outcomes among married men and women.
Bearing in mind the gap in previous literature, this paper investigates the gendered impact on
self-assessed health among married individuals based on cross-national differences in time
allocated in paid market work, unpaid household work and childcare across differing welfare
regimes based on data from national time use surveys from five countries (Germany, Spain, Italy,
United Kingdom and the United States). The following questions are addressed: (1) what are the
factors which determine the allocation of time between men and women across different countries
and welfare regimes? (2) How does time allocated to paid market work, unpaid household work
4
A work-family conflict has been described as “the direct result of incompatible pressures from an individual’s work
and family roles” Roehling, P. V., P. Moen and R. Batt (2003). Spillover. in It’s about time: couples and careers. P. e.
Moen. Ithaca and London:, ILR Press..

4. 4
and childcare impact the self-assessed health of married men and women? (3) Do these effects
vary with gender and social welfare policies?
In an effort to examine these questions, we used seemingly unrelated regression models (SUR)
to determine time allocation by gender across different welfare regimes, while binary logit models
and the Blinder-Oaxaca decomposition method is used to examine health differences. These
approaches allow us to investigate individual and group heterogeneity in self-assessed health
among married men and women.
This study is important for a number of reasons. Firstly, we aim to contribute to the vast
literature on the observed gender-specific differences in health by placing special emphasis on the
differences in health among married men and women. Furthermore, through the adoption of a
rigorous statistical technique, which includes the use of cross-national data the comparability of
the results will be ensured. In addition, the use of the decomposition method means that we are
able to explain how and if the time allocated to both paid and unpaid work contributes to the
gender-specific differences in health among individuals. Finally, this question is of particular
importance because there is an acknowledged inequality in health between men and women, with
men reporting better health. Therefore by conducting these analyses, especially in a cross national
perspective, we may be more adequately equipped to address these issues and inform policy.
Previous literature: marriage and health
There are two main hypotheses which have been used to explain the health outcomes of
married individuals. These are selection and protection effects. These hypotheses posit simply that
healthy people are more likely to marry and stay married. However, as previously stated the
effects of marriage are unequal. The results from several empirical studies indicate that marriage
does not provide blanket protection and that the effects are not homogenous. For example,
evidence from several western countries demonstrate that married individuals who are in
relationships with high levels of distress, exhibit worse health than individuals in non-distressed
and supportive marriages (Burman and Margolin 1992, Carstensen 1992). Furthermore Bookwala
(2005) using a sample of individuals in their first marriage and aged 50+, indicated that the
behaviour of unsupportive and uncaring spouses outweighed positive spousal behaviour thus
contributing to lower physical health. Similar results have also been found among studies
conducted on non-western populations. One such study which examined the health benefits of
marriage across four East Asian countries- China, Japan, Taiwan, and the Republic of Korea-
indicated that marital satisfaction has greater impact in determining self-rated health outcomes
than marriage itself (Chung and Kim 2014).
Research has also examined the extent to which gender impacts health among married
individuals. Several studies demonstrate that the protective impact of marriage is stronger for men

5. 5
than it is for women suggesting that the benefits of marriage are unequal, with married men
deriving greater health benefits (Gove 1972, Gove and Tudor 1973, Kiecolt-Glaser and Newton
2001) when compared to married women. Previous studies have indicated that married women
report higher rates of mental illness when compared to married men. In comparison, single and
divorced women reported lower rates of mental illness (Gove 1972). These results have been
replicated in more recent studies (de Jong Gierveld 2003). Moreover, these gendered differences
have been shown to extend across the life course.
Within the literature that has examined the impact of marital status on health and mortality in
older ages, the findings show that unmarried men are at particular risk of reporting worse health
and have higher risks of mortality when compared to women, and individuals who are unmarried,
divorced and widowed (Robards, Evandrou et al. 2012).
Nevertheless, one of the main criticism levelled at previous work which has examined gender-
specific differences in health among married men and women, is heavy focus on psychological
well-being and mental health. The focus on these outcomes dates back to earlier works (For e.g.
Durkheim 1897, Adler 1953, Gove 1972, Gove and Geerken 1977). The argument that has been
advanced by some researchers is that these measures are highly correlated with the way that
women express themselves. As such, the literature may have excluded vital measures related to
the health of men. Thus, some researchers remained unconvinced of the gendered impact of
marriage on health (Simon 2002, Liu and Umberson 2008). Therefore, for the purpose of this
work, self-assessed health which has seldom been utilized in the literature is used. This study is
also distinguished from others in the field because we account specifically for the time allocated to
paid work, unpaid unemployment and childcare by men and women across differing countries and
welfare regimes.
Accounting for health differences among married men & women
Overall the evidence supporting the gender-specific impact of marriage on health is
inconsistent, when a broader picture of the ways through which health benefits may be mediated
through marriage is considered. Though a plethora of alternate explanations exists for the
differential impact of health by gender among married individuals, for the purpose of brevity we
explore in brief the three alternate frameworks which may have of particular relevance to our
current research.
A factor which may have a mediating impact on the health outcomes of married men and
women is the number of hours that each individual spends carrying out various tasks in both the
private and public sphere. As discussed briefly above, this area of research is closely related to the
social welfare policies adopted across countries and may have a mediating impact on health. Yet,
our understanding of sources of correlation between these two lines of enquiry remains quite

6. 6
limited. To our knowledge only three studies have examined the relationship between time
allocation and health (Podor and Halliday 2012, Gimenez-Nadal and Molina 2013, Gimenez-
Nadal and Ortega-Lapiedra 2013). The results of these studies has indicated that time allocation,
specifically, the production of goods and services in the home is an integral component of welfare
and therefore integral to our understanding of the impact of time allocation on health (Podor and
Halliday 2012, Gimenez-Nadal and Molina 2013). Given that the research into time allocation and
health is in its infancy, there is clearly a lack of previous studies which has examined the
differential outcome in health by marital status for men and women. This is despite the fact that
the unequal division of paid and unpaid labour has been hypothesised to be one of the main factors
which contribute to the observed difference in health among men and women who are married.
While research interest in time allocation and health is relatively new, the closely related
literature on the association between social welfare policies and health is well beyond its infancy.
Researchers consistently agree that the varying characteristics of social welfare regimes are an
important determinant of health (Bambra 2006, Chung and Muntaner 2007, Eikemo, Bambra et al.
2008). It is argued that welfare provisions are a means of alleviating the socio-economic
differences which is a key mediator in health status. As such, welfare policies whose goal it is to
minimize these differences should have a significant impact on the association between socio-
economic status and health (Eikemo, Bambra et al. 2008).
The results of several empirical studies indicate strong variations in infant mortality rates by
welfare regimes (Bambra 2006, Chung and Muntaner 2007). Chung and Muntaner (2007)
demonstrated that 20% of the estimated cross national differences in infant mortality rates were
due to differences in welfare regimes. In addition, they found that 10% of estimated low birth
weight across countries could be explained by differences in welfare regime types. Furthermore,
when general health is considered as the outcome, results indicate that 10% of the differences in
self-assessed health could be linked to welfare regime characteristics (Eikemo, Bambra et al.
2008). Despite this evidence, there is some amount of debate in the literature as to the sources of
the variation in health by welfare regimes. A more in-depth discussion of this may be found in the
work of Bambra (2011).
The intersection between these two branches of the literature largely indicates that there is a
gender-specific patterning of time allocation based on welfare regime characteristics. The
evidence across the industrialised economies indicate that despite significant increases in paid
market work among women, there still remains a large gap in this respect compared to men. The
main reason for this is that marriage and childbirth are more likely disrupt women’s full
participation in the labour market. Although we have witnessed a reduction in the number of hours
women invest in unpaid household duties and small incremental changes in the time men invest in
household tasks (Gershuny and Sullivan 2003), women remain responsible for the majority of

7. 7
house and care work within the family. As a consequence, the gender imbalance in both paid and
unpaid work remains (Orloff 2002) thus leading several researchers to offer the variations in social
welfare policies as one of the mediating factors which could explain the gender-specific
differences in health among men and women. In order to fully examine differences in health by
social welfare regimes, we provide a brief description of the classification of the welfare regimes
alongside an overview of the policy variations across the countries used in this analysis.
Classifying Social welfare regimes
An established approach in examining the impact of social welfare policy variations in the
literature is to examine groups/clusters of countries with similar policy approaches. This approach
which was first developed by Esping-Anderson (1990), remains contested because there are
always individual country level characteristics which are atypical of these groups (Castles and
Mitchell 1993, Sainsbury 1996, Goodin 1999). Countries can also change their welfare systems in
ways that may impact their placing in the clusters over time (Scruggs and Allan 2008).
A strong critique of this early grouping of countries based on social welfare policies, by
Esping-Andersen was levelled by feminist sociologists. They argued that by using Esping-
Andersen’s decommodification index, the role of unpaid family contributions in the provision of
welfare is ignored through this gender blind perspective (Sainsbury 1996, O'Connor, Orloff et al.
1999, Arts and Gelissen 2002). As a consequence of these critiques, Esping-Andersen revised the
earlier classification, developing further the criteria for categorising welfare regimes and adding
the so-called “familialisation” concept. This concept takes into account the degree to which the
welfare system depends on family support. However, incorporating the concept of familialisation
into the earlier classification did not lead to substantial changes in the categorisation of the
countries (Bambra 2004).
Country grouping according to Siaroffs typology
In line with the continued critique of the Esping-Andersen classification, a number of scholars
have tried to empirically test the validity of the grouping of welfare states using, for instance,
cluster analysis. After considering the various classifications of social welfare regimes (Esping-
Andersen 1990, Castles and Mitchell 1993, Arts and Gelissen 2002) we have chosen to use
regime-clusters identified in the work of Allan Siaroff (1994). Siaroff identified four regime
categories, conservative, late female mobilization, liberal and social democratic welfare based on
two main concepts: female work desirability and family welfare orientation. Female work
desirability is meant to measure how attractive it is for women to join the paid labour market. This
measure of the incentive to join the paid labour market is based on: the female presence in the
labour force; gender differences in unemployment; female to male wage ratio; postsecondary
education attendance for women compared to men; and the number of women having

8. 8
administrative and managerial professions compared to men. Family welfare orientation is an
index meant to describe how strongly family welfare oriented the welfare state is. It is based on
measures of the general level of social security spending; family policy spending; public day-care
programs and the maternal and parental leave (Siaroff 1994). In line with these typologies, we
discuss in brief, features relevant to the classification of the five countries discussed in this paper
are classified into their respective clusters.
Italy
The estimated participation rate in the paid labour market among men is 62 percent and for
women it is 37 percent. Twenty three percent (23%) of employed women work part-time, for men
the corresponding figure is 5 percent (UNSD 2006). For mothers, the parental leave is 48 weeks
and they are allowed the equivalent of 25 weeks full pay. Among Italian fathers, the corresponding
figures are a total of 22 weeks of which none is paid (Ray, Gornick et al. 2009). Italy has a low
level of family welfare orientation and of the five countries it has the lowest female work
desirability except for Spain (Siaroff 1994).
Spain
Paid labour market participation for men is 67 percent and for women it is 43 percent. Sixteen
percent (16%) of employed women work part-time, however, only two percent of men has been
estimated to work part time (UNSD 2006). For mothers, parental leave is 156 weeks of which 16
are paid when converting the paid weeks in the Spanish system to full time labour market
participation. Spanish fathers are also eligible 156 weeks of parental leave, however, only two
weeks of these are paid in full (Ray, Gornick et al. 2009). Spain has therefore been characterised
as having a low level of family welfare orientation and is deemed to have the lowest female work
desirability of the five countries (Siaroff 1994).
UK
The labour participation for men has been estimated at 71 percent and 55 percent among women.
Of the women participating in the paid labour market, it has been estimated that forty percent
work part-time. Among men the corresponding figure is nine percent (UNSD 2006). Mothers are
allowed parental leave of 65 weeks, of which 12 are paid when converting the paid weeks in the
British system to a full time employment. British fathers are however allowed a total of 15 weeks
for parental leave, of which only two weeks are paid (Ray, Gornick et al. 2009). Of the five
countries that are discussed in this paper, the UK together with the USA, has a significantly higher
female work desirability than the other three countries and low levels of family welfare orientation
(Siaroff 1994).

9. 9
USA
Paid labour market participation estimates among men is 74 percent and for women it is 60
percent. Of those women that are employed 17 percent work part-time. In comparison, only 7
percent of men are engaged in part time work (UNSD 2006). Among mothers, the parental leave
allowance is approximately 12 weeks. However, unlike the other countries described here parental
leave payments are not mandatory for either women or men (Ray, Gornick et al. 2009). The USA
has the highest work desirability and the lowest family welfare orientation of the five countries
investigated in this study (Siaroff 1994).
According to Siaroffs typology, Germany is included in a conservative group that features welfare
states with a relatively high family welfare orientation but low female work desirability. Italy and
Spain are part of the cluster labelled late female mobilization, these states are predominantly
defined by having both low family welfare orientation and a low female work desirability. The
cluster containing the UK and the USA is labelled liberal. The characteristic features of these
states is a high degree of female work desirability and a low degree of family welfare orientation.
Present study
Given the above discussion, the present study has three objectives. The first is to examine the
question: what are the factors which determine the allocation of time among married men and
women, across different welfare regimes? The second objective follows from the first and seeks to
resolve the question: How does time allocated to paid market work, unpaid household work and
childcare impact the self-assessed health of married men and women? Our third and final
objective is to assess, if these effects vary with gender and across different welfare regimes.
Data and Measurement
Data
Data for this analysis was drawn from the Multinational Time Use Study (MTUS). This is a
rich data set containing information on the socio-economic and demographic background of
participants. Study participants, referred to as diarists, recorded the total time spent per day on 41
activities in 10 minute intervals (Fisher, Gershuny et al. 2012). In total MTUS offers harmonised
and contextual information from 38 countries, across six waves. Though it is possible to conduct
such studies using population based surveys derived from direct questions this information would
be subject to recall bias. On the other hand, using diary based time allocated data improves
reliability and accuracy of the information provided.

10. 10
The sample for this study was limited to respondents who are married at the time of the study
and who reported all 1440 minutes (24h) of activities during the day of the diary. In addition, after
considering the mean age for entering a first union across these countries, the mean age of first
birth and taking into consideration the stage of the life course which most individuals are involved
in balancing of family and work life, the age of study participants has been limited to individuals
aged between 20 and 60 years. The countries included in this analysis were United Kingdom
(survey year 2000 N=7337); United States (survey year 2003 N= 12,314); Spain (survey year
2000 N= 12,018); Italy (survey year 2002 N= 11,655); and Germany (survey year 2001
N=16,514).
Study limitations
The results of this study should be interpreted bearing in mind some limitations. One drawback
of time use data is the presence of zero time observations. This can be explained in one of two
ways: the first is that the individual has not undertaken a given task during the period that they
recorded the data or it may be that the given activity is irrelevant for that individual. For example,
there may be individuals who have recorded no childcare information. This may simply be due to
the fact that they have no children or during the day that they recorded. To correct for what is
essentially selection bias, the inverse mills ratio (IMR) is used in the models (Heckman 1979).
Coupled with the issue, there is an acknowledged limitation in using cross-sectional data. Using
measurements from a single time point means that it is not possible to conclusively argue the
directionality of the associations found. Thus, we are not able to conclusively state for instance
that the reason that some individuals work less hours is because they are in poor health or if it is
poor health that forces them to work less. Another shortcoming of this data is the fact that self-
assessed health may be endogenous to time allocated to various activities. This issue has been
discussed extensively in the labour supply literature (See for e.g. Podor and Halliday 2012,
Gimenez-Nadal and Molina 2013).
Variables
The primary outcome measure, self-assessed health, has been derived from the responses
provided to a single item, “How is your health in general; would you say that it is ….?” The
responses ranged from zero (poor) to three (very good). From this we created a binary measure,
“good heath” which took the value of “1” if individuals reported “good” or “very good” health
and value “0” if they reported having “poor” or “fair health” (Goryakin, Rocco et al. 2014). The
use of a single item measure of self-assessed health has been shown to be a robust measure of
morbidity and mortality in previous work (Idler and Benyamini 1997, Manderbacka, Lundberg et
al. 1999).

11. 11
The control variables that have been included as possible confounders have been chosen for
this study in line with the findings of the Commission on the Social Determinants of Health
(WHO, 2006). These are gender; educational level; employment status; occupation, age; and age
squared. Occupation was categorised according to the 2010 Standard Occupation Classification
(SOC) system (see Cosca and Emmel 2010). Additionally, we considered the time spent on three
groups of activities measured in hour per day. Unpaid household work: This includes all activities
related to household work such as cooking; gardening; shopping; washing; maintenance within the
house; repair works; bookkeeping; odd jobs and domestic travels. Child care: This encompasses
time spent on childcare and other care related activities for children in the household. Paid market
work: This includes time spent on both primary and secondary jobs as well as paid work at home.
Time allocated to these activities were estimated directly from the data and as stated
previously, this is based on the recorded time spent on various activities as specified by diarists.
Since it is not an easy task to classify activities that are considered to be unpaid household
activities an established method- “the third party criterion” was used. By this criterion, “if an
activity is of such a character that might be delegated to a paid worker, then that activity shall be
deemed productive” (Reid 1934:11) In other words, they are activities that people can pay others
to carry out on their behalf. Thus, activities such as cooking, washing, shopping and childcare are
considered productive while eating and sleeping are non-productive. One problem with this
criterion is that, student activities such as school classes and home work are classified as
unproductive; because one cannot pay someone to study on their behalf. Nonetheless, the use of
the “third party criterion” as a measure of evaluating unpaid housework has been widely accepted
(Wood 1997). Alongside this, there is a variable for the mean number of hours each individual
spends on each occupation, for number of children in each household under the age of 18 years
old and the overall household size. Descriptive statistics for the final sample for each country is
shown in Table 1 below.
Analytical Strategy
In order to examine the three research questions, we used seemingly unrelated regression models
(SUR) to determine time allocation by gender across different countries and by extension different
welfare regimes, while binary logit models and the Blinder-Oaxaca decomposition method is used
to examine health differences.
In the simplest terms, the SUR models seek to answer the question that given an individual has
several activities from which to choose to spend their time, which do they choose. In this case, we
examined how men and women determine to spend their time given that they have to choose
between paid market work, unpaid household work and childcare across all the countries. From a
theoretical perspective this model suggests that the decision to allocate time to unpaid market
work, unpaid household work and childcare is a simultaneous decision, as each individual needs to

12. 12
make tradeoffs to maximize their time. As such seemingly unrelated regressions (SUR), captures
the interdependence (i.e. the equations in M are linked through their error structures) of this
decision making process. The equations representing these models are displayed below:
iiii Xy   (1)
For i = 1 to M, where the matrices iy , iX and i are of dimension (T X 1), (T X Ki) and (Ki X 1),
respectively. The stacked system in matrix form is



















































MMM X
X
X
y
y
y
Y











2
1
2
1
1
1
1
2
1
00
00
00
=  X (2)
We then apply binary logit model. The goal in introducing the logit models is to (1) analyse the
data separately by gender (i.e. for females and males), as this will allow us to examine the effects
of all the independent variables together on self-assessed health; (2) perform a second set of
analyses with pooled data (i.e. for both males and females simultaneously), this will allow us to
assess the effect of self-assessed health after controlling for the possible confounding variables.
The binary logit model estimates the probability that the dependent variable is 1 (h=1) (Archer and
Lemeshow 2006).We used the logit command in STATA to fit the model and the estimates are the
coefficients with respect to logit (log of odds). More formally, the conditional probability of
experiencing the event can be expressed as:
)exp(1
)exp(
)|1(


x
x
xhpr

 (3)
In the third and final model, the Blinder-Oaxaca decomposition method is applied to data, as a
means of providing additional explanatory power. This method of analysis was introduced into the
field of economics by Blinder (1973) and Oaxaca (1973). The standard use for this approach is in
the study of wage differentials. This method essentially allows us to partition the mean health
differences between men and women into three components: “the endowments effect”, is due to
group differences in the vector of characteristics. The second component, is the “coefficient
effect”, this part of the model corresponds to differences in coefficients. The third and final part of
the model is the “interaction effect” that accounts for the simultaneous differences in endowments
and coefficients. The Blinder-Oaxaca decomposition method has been undertaken on all countries
in the study sample. The sex gap in the average values of the dependent variable, H, can be
expressed in the non-linear decomposition as follows using (Daymont and Andrisani 1984) “three-
fold decomposition” method.

13. 13
CECEHH WM  (4)
)}|()|({ iWiWWiMiMW
HEHEE   (5)
)}|()|({ iWiWWiWiWM
HEHEC   (6)
)}|()|({)}|()|({ iWiWWiWiWMiMiMWiMiMW
HEHEHEHECE  
(7)
Where, H= health status; β = estimates of (X); M= men; W=women. The first term represents the
part of the sex gap that is due to group differences in the distribution of X, the endowments effects
(E) and the second term represents the part due to the differences in the group processes
determining the levels of H, coefficient effect (C). The last term is the difference arising from the
interaction of the endowment and coefficient (CE).

14. 14
Table 1: Descriptive statistics: Descriptive statistics for the final sample
Germany Italy Spain UK USA
Men Women Men Women Men Women Men Women Men Women
Variable Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
Proportion SRH=0 0.229 0.420 0.230 0.421 0.287 0.452 0.337 0.473 0.196 0.397 0.199 0.399 0.170 0.376 0.183 0.387 0.080 0.271 0.072 0.258
Proportion SRH=1 0.771 0.420 0.770 0.421 0.713 0.452 0.663 0.473 0.804 0.397 0.801 0.399 0.830 0.376 0.817 0.387 0.920 0.271 0.928 0.258
Household work hours 2.586 2.456 4.113 2.535 1.683 2.103 4.814 2.641 1.584 2.008 4.114 2.408 2.682 2.562 4.453 2.565 2.924 2.886 4.136 3.007
Childcare hours 0.326 0.762 0.560 1.125 0.412 0.909 0.713 1.351 0.364 0.895 0.716 1.372 0.359 0.898 0.705 1.384 0.727 1.397 1.009 1.597
Management 4.600 4.154 2.855 3.480 4.802 4.471 3.744 3.756 5.568 4.485 4.259 3.888 4.089 4.555 2.848 3.858 4.828 4.649 3.689 4.248
Service 5.079 4.266 3.222 3.676 4.156 3.855 2.614 3.238 4.958 4.322 3.958 3.547 3.229 3.622 2.400 3.427 3.709 4.148 3.422 3.925
Sales 4.604 4.205 3.147 3.551 3.999 4.029 2.696 3.513 6.021 4.319 4.245 3.721 4.154 4.392 2.040 3.192 4.861 4.651 3.573 3.946
Natural resources construction 4.930 4.216 3.452 3.712 4.283 4.368 2.640 3.567 5.756 4.507 4.786 3.995 4.240 4.696 2.293 3.583 4.817 4.644 3.692 4.108
Transport 7.387 4.152 3.385 3.510 6.565 4.205 3.214 3.346 7.230 3.975 4.559 3.515 3.459 4.314 1.569 2.973 5.383 5.193 3.721 4.580
Military 4.295 4.534 2.340 3.196 4.358 4.403 2.763 3.432 5.797 4.352 3.273 4.102 3.897 4.616 0.730 1.409 5.057 5.823 4.897 4.469
Self employed . . . . 4.638 3.744 2.820 3.233 5.785 4.705 3.250 3.480 4.902 4.669 1.239 2.431 4.992 4.672 3.630 4.020
age 42.689 9.676 41.830 9.100 43.607 8.303 41.729 8.593 44.244 8.621 41.839 8.611 43.739 9.539 42.545 9.814 42.093 9.105 41.344 9.265
Proportion Primary educ. 0.073 0.260 0.106 0.308 0.114 0.318 0.087 0.281 0.178 0.382 0.151 0.358 0.333 0.471 0.357 0.479 0.076 0.265 0.052 0.221
Proportion Secondary educ. 0.469 0.499 0.593 0.491 0.782 0.413 0.769 0.422 0.564 0.496 0.527 0.499 0.383 0.486 0.365 0.482 0.236 0.425 0.230 0.421
Proportion Tertiary 0.458 0.498 0.301 0.459 0.104 0.305 0.144 0.352 0.258 0.438 0.322 0.467 0.284 0.451 0.278 0.448 0.688 0.463 0.719 0.450
Household size 2 (proportion) 0.239 0.427 0.332 0.471 0.132 0.338 0.171 0.377 0.119 0.324 0.149 0.356 0.300 0.458 0.319 0.466 0.176 0.381 0.217 0.412
Household size 3-4 (proportion) 0.628 0.483 0.574 0.495 0.724 0.447 0.719 0.449 0.692 0.462 0.687 0.464 0.540 0.499 0.528 0.499 0.601 0.490 0.595 0.491
Household size 5-6 (proportion) 0.133 0.339 0.094 0.292 0.144 0.351 0.110 0.313 0.189 0.391 0.164 0.370 0.160 0.367 0.153 0.360 0.223 0.416 0.188 0.391
Childles (Proportion) 0.421 0.494 0.435 0.496 0.429 0.495 0.490 0.500 0.365 0.481 0.393 0.489 0.421 0.494 0.444 0.497 0.224 0.417 0.278 0.448
1-3 children (Proportion) 0.535 0.499 0.529 0.499 0.515 0.500 0.473 0.499 0.582 0.493 0.562 0.496 0.462 0.499 0.447 0.497 0.602 0.489 0.586 0.493
3+ children 0.044 0.206 0.036 0.187 0.056 0.230 0.037 0.189 0.053 0.223 0.045 0.207 0.117 0.322 0.109 0.312 0.173 0.379 0.136 0.342

15. 15
Descriptive Results
Table 1 above, provides a descriptive statistics for the final sample used. As expected, the
average age of married men is higher than that of married women in all countries. The majority of
men and women reported having good health in all countries, with only marginal differences in
self-assessed health between men and women.
When time allocated to various activities are considered by gender and country, time allocated
to unpaid household work among women in Germany, the UK and US, is almost twice the time
allocated by men. The difference in time spent on unpaid household work is even more
pronounced in Italy and Spain, where time allocated to household work is almost three times
higher for women. Even though women allocate more time to childcare than men, the difference is
marginal.
With regards to occupation, more time is allocated to paid work in the production, transport
and moving occupation than any other occupation for men in all countries except the UK where
those in the natural, construction and maintenance occupation allocates more time to paid work.
On the hand, women in management positions in Italy and United Kingdom allocates more time to
paid work whiles those in the production, transport and moving occupation allocates more time to
paid work in Germany, United States and Spain. There were no individuals categorised as self-
employed in Germany.
When education is considered, besides the US, where the majority of the sample has a tertiary
education, the largest share of the sample are people with secondary education. Men and women
who have 1-3 children have the largest proportion with regards to the number of children in the
sample for all countries. In all countries, for both men and women, household size of 3-4 is the
largest in the sample.
Mean time allocated by country and activity
Table 2 shows the descriptive statistics of the daily hours spent on unpaid household work,
childcare and paid work used. Men spend more time on paid work compared to women in all
countries. Women on the other hand spend more time on unpaid work and childcare than men
across all countries. Total work is almost equal for men and women in Germany, United Kingdom
and United States. In contrast, married women in Spain and Italy spend more time working when
considering all three activities. Spain and USA had the longest working hours of the five countries in the
year 2000 (Goldstein 1997). This and the fact that we have an age-span of 20-60, the ages where
people are most active on the labour market, may contribute to the respondents in Spain reporting
more paid work per day than in the other countries.

16. 16
Table 2: Descriptive statistics: Mean time allocated by country and activity
Mean hours spent in Unpaid work Childcare Paid work Total work
Germany
Whole Sample 3.340 0.441 3.868 7.650
Men 2.585 0.325 4.711 7.622
Women 4.113 0.559 3.006 7.678
Italy
Whole Sample 2.906 0.529 3.911 7.347
Men 1.682 0.411 4.478 6.573
Women 4.813 0.712 3.027 8.554
Spain
Whole Sample 2.534 0.496 5.215 8.246
Men 1.584 0.364 5.783 7.732
Women 4.114 0.716 4.269 9.100
UK
Whole Sample 3.609 0.540 3.207 7.357
Men 2.681 0.359 4.182 7.222
Women 4.453 0.704 2.322 7.480
USA
Whole Sample 3.500 0.861 4.237 8.599
Men 2.923 0.726 4.807 8.457
Women 4.136 1.009 3.609 8.755
Note: Time activities are measured in hours per day
Determinants of time allocation (SUR Models)
Across all the countries the coefficient of the estimates for daily hours of work spent on paid
labour market activities, unpaid household work and child care activities for the SUR models are
shown in Appendix 1-5. The results concern the differences in time allocated to these tasks for
selected variables for men and women.
If we look at the countries individually, by gender and given tasks there are only marginal
differences in time allocation. Consider for example, among the US sample (appendix 1) the
biggest determinants of time allocated to paid market work for both men and women is mean
hours spent in various occupation. In addition, having an above secondary education also has a
strong negative impact on paid market work. Meanwhile, age and household size has a positive
effect on unpaid housework. Among men, the number of hours spent on housework increased with
age.
In the UK (appendix 2), the mean number of hours allocated by women to their occupations
has the strongest impact on paid labour market activities, unpaid household work and child care
activities. Among men age, time allocated to various occupation, having one to three children in
the household is the biggest determinant of time allocated to child care. These factors seemingly
have little or no impact on the time allocated by men to paid work or other unpaid household
tasks.

17. 17
For the German sample (appendix 3), from the regression on the time allocated to paid market
work, the estimates indicate that the number of children under 18 in the household and the
household size are the biggest determinants of time allocated to paid market work for both men
and women. Age also seems to be an important factor among men. If one considers unpaid
household work, women’s age, the household size and the occupational roles of women. For men
if they had 3 or more children under the age of 18 and they worked in the military these led
increases in their contribution in the time allocated to household tasks. However, if the size of the
household surpassed 5 or more individuals this leads to a decrease in the time allocated to
household tasks. One can see from the results table that the number of children under 18, in the
household had the biggest impact on time allocated to child care. In general, when all three
activities are considered simultaneously, German women were more likely to allocate their time to
housework when compared to all other countries.
Spain (appendix 4) presents a contrast to Germany. As the level of education among Spanish
women increases, estimates show a strong decrease in the number of hours allocated to
housework. However, the time and type of occupation a woman hold increases the number of
hours spent on housework. Among Spanish men, education impacted all three outcomes from the
SUR models. As educational level increased among men, there is a significant decline in paid
market work, and increases in their contribution to household work and child care. These factors
are however impacted negatively by the type and time allocation by various occupations.
Similar to Spain, higher levels of education predicts a reduction in time allocated to housework
among Italian (appendix 5) women. However, among Italian women unlike Spanish women, time
allocated to child care also decreases with educational level. In addition, the type and time
allocated to various occupation leads to significant declines in the number of hours spent on
housework. Declines based on occupation are also evident for paid work and child care, but these
are not significant. Educational level contributed to significant declines in time allocated to all
outcomes examined among Italian men. However the type and time allocated to various
occupations was inconsistent.
Overall, the results indicate that the presence of and the number of children in the household is
the most consistent predictor of how both men and women allocate their time. This is true for all
countries in the analysis. Concerning men, we observe that the presence of and the number of
children positively impacts the number of hours allocated to childcare. There are however some
noteworthy cross-national differences when time allocation is examined among women. For
instance in Italy, Spain and the US, the estimates from the regression on the number of hours
allocated to childcare indicate that time allocation increases significantly among women as the
number of children increases. Across these three countries, we observe that this increase in
childcare activities usually reflects a decline in the number of hours of paid work and housework.

18. 18
Among women, time allocation for childcare and unpaid household labour increased with the
presence and the number of children in Germany and the UK, while showing a simultaneous
decrease in participation in paid labour market activities.
Regression Results
The results of the pooled binary logit regression models are presented in Table 3, while Table 4a
(women) and Table 4b (men) provides the results for the gender-specific models. In general, we
observe cross-national differences in the effects of time allocated to unpaid household work,
childcare and paid work on self-assessed health.
There is a significant negative association between housework and health status in Italy and a
significant positive association in the UK. The estimates from the model further indicated a
significant and negative association between childcare and health among married people Germany
and a non-significant negative association for UK. For the rest of the countries the association is
positive but not significant.
Notes: Standard errors in parenthesis. *** Significant at the 99% level, ** significant at the 95% level, *significant at the 90% level. Regressions
include sex, age, female, age square, education dummies (ref.: below secondary), the number children under 18 in the household (ref.: childless),
household size dummies (ref.: two), Inverse Mill Ratio (housework, childcare & paid work). Time activities are measured in hours per day. Selection
bias corrected using the Mills-ratio.
Management work has a significant negative effect on health in all countries except in the UK.
In the UK, management work is positively related to health. Service work in Spain Italy and
Germany is positively related to health while we observe a negative association in the UK and the
United states. Sales and office work in the UK, Spain, Germany and the US has significant
negative effect on health. For Italy the association is positive. Self-employment is positively
related to health in Spain. There is a positive association between natural resource, maintenance
and construction work in Spain UK and the US while the effect is negative in Italy and Germany.
Production, transport and material moving in Spain, Italy and Germany is negatively associated
with health, the opposite is true for the UK and US. The results indicate that military
Table 3. Binary logit regression (Pooled)
Spain Italy Germany United Kingdom United States
Variables
Housework-0.022(0.02) -0.037***(0.01) -0.022(0.02) - 0.037***(0.01) -0.014(0.01) 0.048***(0.02) -0.001(0.02)
Childcare 0.055(0.04) 0.021(0.02) -0.082***(0.03) -0.016(0.04) 0.003(0.03)
Occupation (mean hours)
Management -0.042***(0.01) -0.041***(0.01) -0.037***(0.01) 0.033**(0.01) -0.068***(0.02)
Service 0.343***(0.04) 0.180***(0.02) 0.134***(0.04) -0.119***(0.04) -0.613***(0.03)
Sales & Office Work -0.005(0.02) 0.049***(0.01) -0.024**(0.01) -0.027(0.02) -0.400***(0.02)
Natural,Construction& Maintenance 0.147***(0.02) -0.077***(0.01) -0.587***(0.02) 0.076***(0.02) 0.142***(0.02)
Prodution, Transport & material
moving
-2.156***(0.05) -0.098***(0.02) -2.139***(0.06) 0.198*(0.12) 0.152**(0.07)
Military Specialisation -0.620***(0.04) -0.068***(0.02) 0.348***(0.02) -0.083(0.06) 0.266***(0.06)
Self Employed non professionals 0.406***(0.05) 0.023(0.02) _ 0.046(0.03) 0.036(0.03)
Observations 12,018 11,655 16,432 7,377 12,314
Psuedo R2
0.557 0.071 0.347 0.177 0.286

19. 19
specialization work is associated with worse health for Spain and Italy. In Germany and the US we
see a positive association for health and military specialization work.
Notes: Standard errors in parenthesis. *** Significant at the 99% level, ** significant at the 95% level, *significant at the 90% level. Regressions
include sex, age, age square, education dummies (ref.: below secondary), the number children under 18 in the household (ref.: childless), household
size dummies (ref.: two), Inverse Mill Ratio (housework, childcare & paid work). Time activities are measured in hours per day. Time activities are
measured in hours per day. Selection bias corrected using the Mills-ratio.
We find a similar but smaller association between health and housework for men compared to
the pooled results. Housework is associated with poorer health among men in Italy. We can
observe a negative association between childcare and health in Germany. In the rest of the
countries the effects, positive or negative, are insignificant. Working as a manager is associated
with poor health in all countries except in the UK where it is associated with better health. Paid
work by sales and office workers is positively associated with health in Germany, Italy and the US
whiles we observe a negative association in Spain. Paid service work in Germany and Italy is
associated with better health while the opposite is true in Spain and US. Natural resources,
construction and maintenance work in Spain positively associated with health as contrary to
Germany and UK. Meanwhile we observe a positive association between working in production,
transport and material moving and health in the UK and a negative association in Spain, Italy and
Germany. Working as a Self-employed man in Spain and US is associated with better health
whiles in Italy and UK the opposite is true. Military specialization occupation work is associated
with better health among men in US and Germany. Contrary, we find a negative association in
Spain and UK.
Notes: Standard errors in parenthesis. *** Significant at the 99% level, ** significant at the 95% level, *significant at the 90% level. Regressions
include sex, age, age square, education dummies (ref.: below secondary), the number children under 18 in the household (ref.: childless), household
size dummies (ref.: two), Inverse Mill Ratio (housework, childcare & paid work). Time activities are measured in hours per day. Time activities are
measured in hours per day. Selection bias corrected using the Mills-ratio.
Table 4a. Binary logit regression (Men)
Spain Italy Germany United Kingdom United States
Variables
Housework 0.023(0.03) -0.025*(0.01) -0.010(0.02) 0.037(0.03) 0.005(0.05)
Childcare 0.056(0.07) 0.030(0.04) -0.125**(0.05) 0.029(0.07) -0.007(0.11)
Occupation (mean hours)
Management -0.144***(0.02) -0.043***(0.01) -0.166***(0.02) 0.115***(0.02) -0.615***(0.07)
Service -0.292***(0.10) 0.103***(0.03) 2.022***(0.10) 0.000(0.00) -1.827***(0.18)
Sales & Office Work -0.170***(0.03) 0.069***(0.02) 0.119***(0.01) -0.028(0.03) 0.422***(0.08)
Natural,Construction& Maintenance 0.282***(0.02) -0.012(0.01) -0.890***(0.03) -0.160***(0.02) 0.104(0.09)
Prodution, Transport & material moving -1.616***(0.07) -0.159***(0.02) -0.249***(0.05) 0.460***(0.13) 0.379(0.36)
Military Specialisation -1.061***(0.06) 0.009(0.03) 0.125***(0.02) -0.979***(0.09) 0.578***(0.19)
Self Employed non professionals 0.685***(0.06) -0.039*(0.02) _ -0.572***(0.05) 0.464***(0.14)
Observations 7,503 7,101 8,309 3,496 6,456
Pseudo R2
0.681 0.048 0.405 0.344 0.896
Table 4b. Binary logit regression (Women )
Spain Italy Germany United Kingdom United States
Variables
Housework -0.113***(0.04) -0.046***(0.02) -0.016(0.03) 0.063(0.04) 0.010(0.03)
Childcare 0.047(0.06) 0.005(0.03) -0.040(0.06) 0.135(0.10) 0.002(0.06)
Occupation (mean hours)
Management -0.060*(0.03) -0.053***(0.02) - 0.033(0.03) 0.470***(0.06) 0.248***(0.04)
Service 0.565***(0.07) -0.116***(0.02) -0.293***(0.09) -0.570***(0.09) -0.361***(0.04)
Sales & Office Work 0.056*(0.03) -0.058***(0.02) 0.086***(0.03) -0.304***(0.06) -0.492***(0.04)
Natural,Construction& Maintenance -0.786***(0.06) -0.094***(0.03) -1.204***(0.08) -0.592***(0.10) 0.178***(0.05)
Prodution, Transport & material moving -1.658***(0.07) 1.091***(0.08) -2.220***(0.08) 2.923(268.24) -0.276(0.18)
Military Specialisation 0.480**(0.24) -0.815***(0.08) 0.014(0.06) 2.216(1.65) 2.542(188.78)
Self Employed non professionals 0.883***(0.16) 0.272***(0.03) _ 0.586***(0.17) 0.425***(0.09)
Observations 4,515 4,554 8,123 3,865 5,858
Pseudo R2
0.699 0.153 0.702 0.790 0.578

20. 20
Regarding the result for women, we observe somewhat similar patterns in the association
between housework and health as in the pooled model. Housework activities are associated with
poor health in Italy and Spain, meanwhile time spent on childcare is not significant in any country.
For those in top management positions, we observe a positive effect of work on health in US and
UK and a negative effect in Italy and Spain. Service work in Spain is positively related to health.
In all other countries the association is negative. We also observe a negative association between
work and health among sales and office workers in Italy, UK and US and a positive association in
Spain and Germany. Self-employed work has a positive association with health in Spain, Italy,
UK and US. We find a negative association with health for work in military specialization
occupation for women in Italy and a positive association for Spain but insignificant associations in
the other countries.
In summary, if we compare the results across all models and countries by gender, we observe
that poor health outcomes for women are associated with time allocated to unpaid housework in
the late female mobilization countries. With regards to childcare in the late female mobilization
countries, the effect is positive and non-significant for both men and women. However, we cannot
find a clear pattern for men and women with regards to paid work for those countries.
For the liberal welfare states, we see no clear difference between men and women in the effect
of time allocation on paid work and health outcomes. Paid work in the US has a more positive
effect on the health for men than women whiles we see a more inconsistent association in United
Kingdom for both men and women.
The estimates from the model regarding the conservative welfare state demonstrate a similar
positive and negative association between paid work, childcare and health for men and women.
Table 5: Blinder-Oaxaca Decomposition
Health Endowments Coefficients Interaction
United Kingdom
Men 0.830*** (0.01)
-0.017*(0.01) 0.039*** (0.01) -0.009(0.01)
women 0.817*** (0.01)
Gap 0.013 (0.01)
United states
Men 0.920*** (0.00)
-0.065*** (0.01) 0.032*** (0.00) 0.024*** (0.01)
women 0.928*** (0.00)
Gap -0.008* (0.00)
Germany
Men 0.771*** (0.00)
-0.020*** (0.01) 0.023*** (0.01) -0.002 (0.01)
women 0.770*** (0.01)
Gap 0.001 (0.01)
Italy
Men 0.713*** (0.01)
0.048*** (0.01) -0.025* (0.01) 0.027* (0.02)
women 0.663*** (0.01)
Gap 0.050*** (0.01)
Spain
Men 0.804*** (0.00)
women 0.801*** (0.01)
Gap 0.003(0.01) -0.039***(0.01) 0.006(0.01) 0.037***(0.01)
Notes: Standard errors in parenthesis. *** Significant at the 99% level, ** significant at the 95% level, *significant at the 90% level

21. 21
The results in Table 5 shows the aggregate differences in self- assessed health based on the
explanatory variables used in the model. We used the three-fold decomposition to decompose the
differences into the components described above. The estimates on the differences are reported as
the probability of having good self-assessed health for men and women. Overall, we observed
little difference in self- assessed health between men and women.
The self-assessed health gap in the Oaxaca decomposition seems to be in favour of married
men in all countries except the US. In the US married women have better health as compared to
married men. Italy reported the largest gap of self-assessed health between men and women. We
noticed that the gender self-assessed health gap is primarily due to the effects on the coefficient
rather than the endowments in percentage terms in UK, Germany and Spain. For the US and Italy,
a substantial share of the gender gap is due to the differences in endowments or characteristics.
CONCLUSION
The overarching goal of this paper is contribute to the wider literature which has sought to
explain the gender-specific inequalities in health by focusing specifically on health differences
among married men and women based on time allocated to paid work, unpaid household work and
childcare. Though knowledge on gender inequality and health is growing, there remains many
unanswered questions. This paper has addressed several of the gaps in the current literature
through an examination of cross-sectional time allocation data from national time use surveys
across five countries and three welfare regimes. We argue simply, that, the institutional policies as
defined by a given welfare regimes will either increase or decrease the difference in time spent
among married individuals on the various required tasks. We posit therefore that the relationship
between time allocation, based on the variation in institutional policies across welfare regimes and
health outcomes as it relates to relationship status is an understudied area and may provide an
insight into the causes of the gender-specific differences in health. This study contributes new
information about the role of time allocation among men and women, and the possible impact of
residing in different welfare regimes on the gender-specific effect of marriage on health.
In order to examine the hypothesized gendered outcomes among married men and women, we
asked three question. The first question explored the factors which determine the allocation of
time between men and women across different countries and welfare regimes? Time allocation
was estimated using seemingly unrelated regression, across all countries analysed in the data the
estimates from these models indicates that the presence of and the number of children in the
household is the most consistent predictor of how both men and women spend their time. Among
men, the presence of and the number of children positively impacts the number of hours allocated
to childcare. There are however some noteworthy cross-national differences when time allocation

22. 22
is examined among women. For instance in Italy, Spain and the US, the estimates from the
regression on the number of hours allocated to childcare indicate that time allocation increases
significantly among women as the number of children increases. Across these three countries, we
observe that this increase in childcare activities usually reflects a decline in the number of hours of
paid work and housework. Among women, time allocation for childcare and unpaid household
labour increased with the presence and the number of children in Germany and the UK, while
showing a simultaneous decrease in participation in paid labour market activities.
The second research question examined in this study was, how does time allocated to paid
market work, unpaid household work and childcare impact the self-assessed health of married men
and women? The results indicated that based on the allocation of time to paid work, unpaid
household work and childcare, there are very small difference in the self-assessed health of
married men and women across the various countries and welfare regimes. One could argue that
these small differences, could be explained by the fact that we have considered married people in
general instead of looking at couples. By looking at couples we may be able to more accurately
predict the effect of time allocation on each spouse. Thus an interesting avenue for future research
could be the re-examination of this question using couple data. Another possible explanation for
the small differences in the self-assessed health among married men and women in this data is
because time was divided into three main tasks. If one considers total time, this may have a
stronger impact on reported health.
The results from the third and final research question, asked if the effects of health and time
allocation vary with gender and across welfare regimes. The estimates from the models tested
indicated that the determinants of time allocation is distinctly gender-specific, whereby married
women across all countries allocated a smaller proportion of their time to paid work, while
increasing the mean number of hours spent on unpaid household work and childcare once they
have children. Time allocated to these activities has however not been explained by differences
based on the institutional setting. For the most part, we cannot discern a general pattern among the
various welfare regime-clusters; although based on Siaroffs typology countries have been
classified based on similarities, such as the level of social security spending, family policy
spending, and rates of labor market participation for men and women. Spain and Italy (late female
mobilization countries) offer the only clear examples of a pattern of behavior based on the welfare
typology. Among women in both countries, their total working hours are significantly longer than
that of the total working hours of the men. Another similarity between the late female mobilization
countries, is that time spent on household work is related to significantly negative self-assessed
health for women. This finding suggests that women
On the other hand, despite the lack of a clear pattern of time usage based on welfare typology
we find strong evidence of cross national differences in the size and effect of the relationship

23. 23
between self-assessed health and time allocation decisions. We were however not able to explain
these differences based on the models tested. To do so, would require country level variables and
the application of a fixed or multilevel model.
The overall the results of this study indicated also that based on time allocation there are very
small differences in the self-assessed health of married men and women. These small differences
may be explained by the fact that we have considered married people in general, instead of
examining these outcomes for couples. By using couple data we may be able to more accurately
predict the effect of time allocation on each spouse.
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27. 27
Appendix 1.Results of Seemingly Unrelated Regressions- The determinants of time allocation among men and women (USA)
Women Men
Variables Paid Market Work Housework Childcare Paid Market Work Housework Childcare
Age 0.085(0.05) 0.077*(0.04) -0.041 (0.02) -0.057(0.06) 0.097**(0.04) 0.004 (0.02)
Age squared -0.001*(0.00) -0.000(0.00) 0.001** (0.00) 0.001(0.00) 0.001*(0.00) -0.000(0.00)
Education
Below Secondary (Ref.)
completed secondary 0.268(0.40) -0.131(0.20) 0.017 (0.13) -0.041(0.25) 0.017(0.21) 0.092(0.11)
Above Secondary -0.532*(0.27) -0.157(0.20) 0.014 (0.19) -0.381(0.27) 0.119(0.37) 0.200 (0.18)
Occupation (mean hours)
Management(Ref.)
Service -0.411*(0.17) -0.344(0.18) -0.058(0.06) -0.999*(0.42) 0.167(0.20) 0.091(0.09)
Sales & Office -0.051(0.14) -0.402*(0.17) -0.044 (0.07) -0.022(0.20) -0.089(0.12) -0.059 (0.07)
Natural Resources, Construction, & Maintenance 0.511(0.35) 0.282(0.17) -0.027 (0.12) 0.018(0.67) 0.078(0.10) -0.036(0.11)
Production, Transportation, & Material Moving . . . 0.295(0.69) 0.207(0.42) -0.139 (0.24)
Military Specific 0.535(0.79) -0.144(0.54) 0.315(0.27) 0.363(0.54) 0.544*(0.21) -0.001(0.14)
Self Employed -2.198*(0.94) -0.407(0.25) -0.029 (0.10) -0.074(0.36) -0.069(0.19) 0.073(0.11)
Children in Household
Childless (Ref.)
1-3 children -0.126(0.24) -0.509*(0.25) 1.361***(0.15) 0.090(0.32) 0.005(0.20) 0.866***(0.17)
3+ Children -0.241(0.42) -0.355(0.39) 1.394***(0.13) -0.033(0.46) -0.095(0.38) 1.131***(0.12)
Household Size
Two (Ref)
Three/Four -0.196(0.30) 0.296(0.18) -0.062 (0.10) -0.336(0.33) 0.007(0.18) -0.003(0.11)
Five or more 0.165(0.38) 0.591*(0.26) 0.046 (0.12) -0.262(0.44) 0.129(0.26) -0.153(0.11)
Mills Ratio Paid Work 10.917*(4.60) 1.660(4.37)
Mills Ratio Household Work 1.630**(0.52) 0.846(0.86)
Mills Ratio Childcare 1.299***(0.38) 0.620(0.42)
R2 0.008 0.021 0.216 0.005 0.012 0.118
Observations 5840 6456
Notes: Dependent variables (i.e. time spent on housework, childcare & paid work) are measured in daily hours. Standard errors in parenthesis. *** Significant at
the 99% level, ** significant at the 95% level, *significant at the 90% level.
Appendix 2. Results of Seemingly Unrelated Regressions- The determinants of time allocation among men and women (UK)
Women Men
Variables Paid Market
Work
Housework Childcare Paid Market
Work
Housework Childcare
Age 0.078(0.05) -0.003(0.04) 0.031(0.03) 0.135(0.08) 0.019(0.04) 0.064** (0.02)
Age square -0.001(0.00) 0.001(0.00) 0.001* (0.00) -0.002(0.00) -0.000(0.00) -0.000* (0.00)
Education
Below Secondary (Ref.)
completed secondary 0.070(0.13) 0.109(0.10) 0.032 (0.05) -0.236(0.18) 0.046(0.11) -0.062 (0.04)
Above Secondary -0.066(0.16) -0.079(0.12) -
0.352***(0.07)
-0.127(0.22) -0.062(0.13) -0.037 (0.04)
Occupation (mean hours)
Management(Ref.)
Service -0.277(0.21) 0.524**(0.17) 0.181* (0.08) . . .

28. 28
Sales & Office -0.416**(0.14) 0.370***(0.11
)
-0.006(0.05) -0.005(0.25) -0.047(0.14) -
0.219***(0.06)
Natural Resources, Construction, &
Maintenance
-0.100(0.24) 0.224(0.19) 0.487***(0.09) 0.370(0.20) -0.099(0.12) 0.115** (0.04)
Production, Transportation, & Material Moving . . . 0.361(0.67) -0.516(0.39) 0.313* (0.14)
Military Specific . . . -0.006(0.69) 0.158(0.41) 0.335* (0.14)
Self Employed . . . -0.086(0.31) 0.037(0.19) 0.161** (0.06)
Children in Household
Childless (Ref.) . . .
1-3 children -0.373(0.19) 0.234(0.15) 0.662***(0.07) -0.294(0.26) 0.205(0.15) 0.327***(0.05)
3+ Children -1.087**(0.37) 0.759*(0.31) 0.157 (0.16) -0.511(0.50) -0.099(0.31) 0.031(0.11)
Household Size
Two (Ref)
Three/Four -0.010(0.18) 0.302*(0.14) 0.230***(0.06) 0.117(0.26) 0.175(0.16) -
0.277***(0.06)
Five or more 0.477(0.32) 0.319(0.25) 0.187 (0.12) 0.140(0.45) 0.609*(0.30) -0.084 (0.08)
Mills Ratio Paid Work 3.188***(0.17) 5.293***(0.32)
Mills Ratio Household Work -0.015(0.21) 2.128***(0.36
)
Mills Ratio childcare 2.283***(0.16) 1.764***(0.19)
R2 0.130 0.041 0.309 0.087 0.031 0.157
Observations 3680 3496
Notes: Dependent variables (i.e. time spent on housework, childcare & paid work) are measured in daily hours. Standard errors in parenthesis. *** Significant at
the 99% level, ** significant at the 95% level, *significant at the 90% level.
Appendix 3. .Results of Seemingly Unrelated Regressions- The determinants of time allocation among men and women (Germany)
Women Men
Variables Paid Market
Work
Housework Childcare Paid Market
Work
Housework Childcare
Age 0.043(0.04) 0.857***(0.16) 0.002 (0.03) 0.195***(0.05) 0.128***(0.03) 0.085***(0.02)
Age squared -0.001(0.00) -0.002***(0.00) -
0.001***(0.00)
-0.003***(0.00) -
0.001***(0.00)
-
0.001***(0.00)
Education
Below Secondary (Ref.)
Completed secondary 0.011(0.19) 5.449***(1.40) 0.696***(0.17) 0.091(0.33) 0.199(0.32) -0.072 (0.05)
Above Secondary -0.022(0.31) 2.011***(0.60) 0.674***(0.16) -0.062(0.40) 0.081(0.28) 0.005 (0.15)
Occupation (mean hours)
Management(Ref.)
Service . . . 0.232(0.55) -0.627(0.60) -0.192 (0.17)
Sales & Office 0.149(0.11) 2.979***(0.78) -0.126** (0.04) 0.105(0.13) -0.239(0.19) -0.031(0.07)
Natural Resources, Construction, &
Maintenance
0.472(0.31) 2.182**(0.69) -
0.782***(0.18)
0.140(0.19) -0.133(0.11) -0.050(0.03)
Production, Transportation, & Material Moving -0.330(0.50) 1.166***(0.32) -0.255 (0.13) 0.261(1.50) -0.849(0.81) -0.072 (0.10)
Military Specific -0.517**(0.20) 0.964***(0.21) -0.194 (0.11) 0.100(0.28) 0.228(0.17) -0.043 (0.03)
Self Employed
Children in Household
Childless (Ref.)
1-3 children -0.415***(0.12) 10.156***(2.50) -0.161 (0.21) -0.310(0.17) 0.296(0.41) 0.400***(0.06)
3+ Children -0.481(0.29) 11.852***(2.90) 0.635***(0.09) -0.427(0.33) 0.571(0.88) 0.241***(0.06)
Household Size
Two (Ref)
Three/Four -0.152(0.20) -7.398***(2.01) 0.261***(0.05) 0.177(0.53) -0.802*(0.33) 0.060 (0.15)
Five or more -0.232(0.33) -
10.464***(2.87)
0.419***(0.07) 0.289(0.63) -1.204(0.81) 0.101 (0.17)
Mills Ratio Paid Work 5.345**(1.85) 4.464(2.57)
Mills Ratio Household Work -
25.554***(6.54)
-0.785(2.26)
Mills Ratio childcare -3.643***(0.92) 0.369 (0.75)
R2 0.020 0.087 0.152 0.028 0.019 0.112
Observations 6154 6453
Notes: Dependent variables (i.e. time spent on housework, childcare & paid work) are measured in daily hours. Standard errors in parenthesis. *** Significant at
the 99% level, ** significant at the 95% level, *significant at the 90% level.
Appendix 4. .Results of Seemingly Unrelated Regressions- The determinants of time allocation among men and women (Spain)
Women Men
Variables Paid Market
Work
Housework Childcare Paid Market
Work
Housework Childcare

29. 29
Age 0.067(0.06) 0.076*(0.04) -
0.208***(0.05)
0.043(0.06) 0.003(0.04) -
0.130***(0.02)
Age squared -0.001(0.00) -0.000(0.00) 0.002***(0.00) -0.001(0.00) -0.001* 0.001***(0.00)
Education
Below Secondary (Ref.)
completed secondary -0.078(0.19) -
0.476***(0.13)
0.050 (0.06) -0.188(0.16) 1.198**(0.44) 0.062* (0.03)
Above Secondary -0.132(0.21) -
0.717***(0.15)
0.370** (0.14) -0.484*(0.20) 2.266**(0.83) 0.293***(0.06)
Occupation (mean hours)
Management(Ref.)
Service -0.465*(0.23) 0.304(0.17) -0.170* (0.07) -0.432(0.47) 2.158**(0.67) -0.093 (0.09)
Sales & Office -0.322*(0.15) 0.336***(0.10) -0.126* (0.05) 0.156(0.18) 0.106(0.09) -0.047 (0.03)
Natural Resources, Construction, &
Maintenance
0.369(0.26) 0.875***(0.20) -0.174(0.09) 0.227(0.15) -0.497*(0.22) -
0.138***(0.03)
Production, Transportation, & Material Moving -0.970*(0.39) 1.118***(0.18) -0.141 (0.10) -0.174(0.53) -
2.981**(1.09)
-
0.249***(0.06)
Military Specific 0.105(0.89) 0.533(0.52) 1.215***(0.26) -0.267(0.29) 0.931**(0.34) 0.073 (0.05)
Self Employed . . . 0.259(0.48) -0.837*(0.40) -0.227*(0.10)
Children in Household
Childless (Ref.) . . .
1-3 children -0.162(0.18) -0.015(0.10) 0.777***(0.09) 0.009(0.16) 0.123(0.09) 0.273***(0.05)
3+ Children -0.770*(0.35) -0.368(0.29) 1.330***(0.11) -0.054(0.32) -
0.605**(0.23)
0.605***(0.06)
Household Size
Two (Ref)
Three/Four -0.275(0.21) 0.189(0.16) 0.605***(0.06) 0.047(0.20) -
0.941**(0.35)
0.268***(0.04)
Five or more -0.004(0.27) 0.722***(0.16) 0.351***(0.08) 0.103(0.25) -1.840*(0.73) 0.053(0.05)
Mills Ratio Paid Work 5.439***(1.41) 3.734***(1.13)
Mills Ratio Household Work 1.911***(0.56) -6.755*(3.00)
Mills Ratio childcare 0.076(0.28) -0.275(0.16)
R2 0.016 0.066 0.279 0.011 0.015 0.166
Observations 4491 7503
Notes: Dependent variables (i.e. time spent on housework, childcare & paid work) are measured in daily hours. Standard errors in parenthesis. *** Significant at
the 99% level, ** significant at the 95% level, *significant at the 90% level.
Appendix 5. .Results of Seemingly Unrelated Regressions- The determinants of time allocation among men and women (Italy)
Women
Men
Variables
Paid Market
Work Housework Childcare Paid Market Work Housework Childcare
Age 0.045(0.06) -0.083(0.07) -0.041 (0.05) -0.132*(0.07) 0.102**(0.03) 0.083***(0.02)
Age squared -0.000(0.00) -0.001(0.00) 0.001* (0.00) 0.001(0.00) -0.001*(0.00) -0.001** (0.00)
Education
Below Secondary (Ref.)
completed secondary -0.138(0.22) -3.800***(1.02) -0.272** (0.10) -0.261(0.18) -0.763(0.50) -0.150***(0.04)
Above Secondary -0.879(0.63) -3.844***(1.01) -0.317 (0.25) -0.760**(0.24) -1.679(1.03) -0.521***(0.11)
Occupation (mean hours)
Management(Ref.)
Service -0.809(0.81) -0.600(0.35) 0.084 (0.06) -0.576(0.31) -1.641(1.01) -0.097 (0.06)
Sales & Office -0.625(1.16) -1.575**(0.56) -0.297*(0.13) -0.447(0.77) -0.833(0.51) -0.122** (0.04)
Natural Resources, Construction, &
Maintenance
-0.630(1.35) -1.327*(0.64) 0.012 (0.08) -0.186(0.88) -0.100(0.18) 0.018(0.03)
Production, Transportation, & Material
Moving
-0.838*(0.35) -1.895*(0.86) -0.012 (0.12) -0.132(2.70) 0.872(0.67) 0.329***(0.09)
Military Specific -0.701(1.07) 1.359***(0.36) -0.317(0.18) -0.109(0.86) 0.046(0.16) 0.079 (0.06)
Self Employed -0.827(0.74) -1.443*(0.63) 0.044(0.10) -0.663(0.52) -0.310(0.18) 0.105* (0.05)
Children in Household
Childless (Ref.)
1-3 children -0.051(0.31) -1.241**(0.43) 0.463* (0.20) 0.018(0.44) -0.102(0.09) 0.626***(0.08)
3+ Children . . . -0.081(0.68) -0.579(0.43) 0.344***(0.06)
Household Size
Two (Ref) . . .
Three/Four -0.194(0.27) -1.073**(0.35) 1.062***(0.05) 0.183(0.18) 0.330(0.31) 0.178** (0.06)
Five or more -0.092(0.25) -3.012***(0.80) 1.212***(0.09) 0.301(0.65) 0.812(0.65) 0.383***(0.05)
Mills Ratio Paid Work 2.264(3.82) 3.908(6.58)
Mills Ratio Household Work 8.412***(2.17) 6.655(3.42)
Mills Ratio childcare 1.322** (0.50) 1.995***(0.25)
R2 0.033 0.066 0.251 0.018 0.010 0.147
Observations 4386 7101

30. 30
Notes: Dependent variables (i.e. time spent on housework, childcare & paid work) are measured in daily hours. Standard errors in parenthesis. *** Significant at
the 99% level, ** significant at the 95% level, *significant at the 90% level.