Total work time in Spain: evidence from time diary data

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Applied Economics
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Total work time in Spain: evidence from time diary
data
a
b
Jose Ignacio Gimenez-Nadal & Almudena Sevilla
a
Economic Analysis, University of Zaragoza, Zaragoza, Spain
b
School of Business and Management, Queen Mary University of London, London, UK
Published online: 26 Feb 2014.
To cite this article: Jose Ignacio Gimenez-Nadal & Almudena Sevilla (2014) Total work time in Spain: evidence from time
diary data, Applied Economics, 46:16, 1894-1909, DOI: 10.1080/00036846.2014.887194
To link to this article: http://dx.doi.org/10.1080/00036846.2014.887194
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Applied Economics, 2014
Vol. 46, No. 16, 1894–1909, http://dx.doi.org/10.1080/00036846.2014.887194
Total work time in Spain: evidence
from time diary data
Jose Ignacio Gimenez-Nadala,* and Almudena Sevillab
a
Economic Analysis, University of Zaragoza, Zaragoza, Spain
School of Business and Management, Queen Mary University of London,
London, UK
Downloaded by [Jose Ignacio Gimenez-Nadal] at 07:57 28 February 2014
b
Using detailed time-use data from 2002–03 and 2009–10 for Spain, we analyse
changes in the time-allocation decisions of the Spanish population, with a focus
on the time devoted to total work. Consistent with prior literature, we document
that the concept of ‘iso-work’ (e.g. the time devoted to total work by gender is
equal) does not hold in societies with stringent gender roles, such as Spain.
Women devote more time to total work than men, and this difference has
increased throughout the period studied by 2 hours per week. The relative
increase in total work for women compared to men can be explained by a relative
increase in market work of 8 hours per week, coupled with a relative decrease in
nonmarket work of 6 hours per week, which have led Spanish women to devote,
relatively, 2 fewer hours to leisure per week in 2009–10, compared to 2002–03.
We propose social norms as a potential explanation of these empirical findings.
By uncovering how individuals allocate their time inside and outside the market
over a period of time, our results may improve our understanding of the dynamics
of economic change and welfare.
Keywords: time-use; gender; iso-work; social norms
JEL Classification: D13; J16; J22
I. Introduction
In this article, we analyse the differences in the time
devoted by men and by women to total work in Spain,
and how differences in total work have evolved during the
last decade. Differences in how individuals allocate their
time may have implications for well-being, as market and
nonmarket work activities are associated with lower levels
of affective well-being than leisure or civic/voluntary
activities (Kahneman et al., 2004; Kahneman and
Krueger, 2006; Krueger, 2007). We analyse Spain as
being representative of Mediterranean countries, characterized by the presence of rigid gender social norms
(Gimenez-Nadal et al., 2012b), with very rigid labour
market institutions (Amuedo-Dorantes, 2000; Fernández-
Kranz and Rodriguez-Planas, 2011) that foster the malebreadwinner model (Esping-Andersen, 1999; Lewis,
2009) and with a very limited public childcare sector for
children under age three (Gutierrez-Domenech, 2005). All
these factors may influence how the time men and women
devote to paid work, household production, and childcare
differ and evolve over time. By discovering how individuals allocate their time inside and outside the market over
a period of time, our results may improve our understanding of the dynamics of economic change and welfare.
Prior research has shown that, despite increases in
female labour-force participation, women continue to specialize in nonmarket work (Bittman, 1999; Bianchi et al.,
2000; Baxter, 2002). In most developed countries, men
spend about half of the time that women spend on
*Corresponding author. E-mail: [email protected]
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© 2014 Taylor & Francis
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Total work time in Spain: evidence from time diary data
housework and childcare activities (Gauthier et al., 2004;
Gimenez-Nadal and Sevilla, 2012), which suggests that
women may have added employment obligations to their
previously existing domestic responsibilities, giving rise
to a ‘second shift’ or ‘dual burden’ (Schor, 1991;
Hochschild, 1997), and that may help explain why, despite
reporting more satisfaction in other domains of life (Clark,
1997; Alesina et al., 2004), women consistently express
discontent with their leisure time (Robinson and Godbey,
1999; Bittman and Wajcman, 2000; Mattingly and
Bianchi, 2003; Sayer, 2005; Mattingly and Sayer, 2006;
Gimenez-Nadal and Sevilla, 2011).
The notion that women are working longer total hours
than men has recently been challenged by Hamermesh and
colleagues, who began to document ‘iso-work’ patterns
(Burda et al., 2008). In a recent paper, Burda et al. (2013)
analyse time diary data from 27 developed countries,
suggesting that gender equality in total work holds in
high-income and non-Catholic countries, dubbing this
apparent phenomenon ‘iso-work’. In Burda et al. (2008),
the authors show a gender difference in the time devoted to
total work in Italy of 40 minutes per day, and the authors
introduce the concept of social norms as a coordination
device between the total work of males and of females
(Burda et al., 2008, 2013). This article looks at Spain,
another Mediterranean country, as a way to test whether
this social norms hypothesis is applicable beyond Italy to
explain the cross-country differences in the gender gap in
total work. In particular, Sevilla (2010) and GimenezNadal et al. (2012b) have ranked Spain among the least
egalitarian developed countries regarding the distribution
of household labour, and as a result, women in Spain
specialize in these activities regardless of their relative
productivity or bargaining power (Sevilla et al., 2010).
We propose social norms as a possible explanation for the
observed gender gap in total work.
Using detailed time-use data from the period 2002–03
to 2009–10 for Spain, we find no evidence of ‘iso-work’.
The amount of time devoted to total work differs by
gender, since in Spain women devote more time to total
work than men. While men spent 52.60 and 47.51 hours
per week in total work in the period 2002–03 and
2009–10, respectively, women devoted 56.92 and 53.84
hours per week to total work in the same periods, leading
to a gender gap in total work favouring women of 4.5 and
6.5 hours per week, respectively. This result is similar to
that found by Burda et al. (2008) for the case of Italy.
We also document that the gender gap in total work
favouring women has increased during the analysed period by 2 hours per week. At the root of the increase in the
gender gap in total work favouring women, we find a
relative increase in market work of 8 hours per week,
coupled with a relative decrease in nonmarket work of 6
hours per week, which has led Spanish women to devote,
relatively, 2 fewer hours per week to leisure in 2009–10
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compared to 2002–03. In particular, for the case of men,
we find that the time devoted to paid work decreased by
8.56 hours per week, financing the increase in the time
devoted to unpaid work (2.25 hours per week), childcare
(1.64 hours per week) and leisure (4.66 hours per week).
For women, the time devoted to paid work held relatively
constant. Women experienced a decrease in the time
devoted to unpaid work (3.57 hours per week), which
financed the increase in the time devoted to childcare
(1.57 hours per week) and leisure (2.42 hours per week).
Such changes have fostered the gender gap in total work
and leisure over the period.
One possible explanation could be that the economic
crisis is behind this increasing gender gap in total work.
Gimenez-Nadal and Molina (forthcoming) have shown
that the associations between regional unemployment
rates and the uses of time of unemployed individuals in
Spain differ by gender, and thus it may well be that newly
unemployed men substituted a higher proportion of their
foregone market work with leisure, compared with household production, while newly unemployed women substituted a higher proportion of foregone market work with
household production, compared to leisure, leading to an
increase in the gender gap in total work favouring women.
Additional analyses based on education and the presence of children under 18 in the household are consistent
with the existence of social norms that determine the
gender distribution of work. If we consider the education
level of individuals as a proxy for wages, we find a
negative gradient between wages and the gender gap in
total work and a negative gradient between education level
and the increase in the gender gap over time. These results
may indicate that, as wages increase, the differences
between men and women are less sensitive to marketbased forces (e.g. economic crisis) and that the impact of
gender stereotypes on behaviour decreases as wages
increase, reinforcing the idea that social norms are at the
root of the observed gender difference in total work. We
also find that the gender gap in total work is larger in
households with children under 18 compared to households with no children under 18, suggesting that for childcare, femininity norms dominate masculinity norms in
Spain (as argued by Sevilla et al., 2010). These results
all favour the social norms argument as an explanation for
the gender gap in total work favouring women in Spain.
More research on the channels through which social
norms operate is needed. All the described results are
maintained when we control for the observed heterogeneity of individuals.
In analysing a Mediterranean country, our work adds to
the existing literature on gender differences in the time
devoted to total work (Burda et al., 2008, 2013; GimenezNadal and Sevilla, 2012). We find that, unlike in other
developed countries, women in Spain spend more time in
total work than men. The nonexistence of ‘iso-work’ in
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Spain is consistent with the results of Burda et al. (2013),
who find that gender equality in total work does not hold in
predominantly Catholic countries. It is also consistent with
the results reported by Burda et al. (2008) for Italy, who
document a gender gap in total work favouring women.
Here we add evidence of the gender difference in total work
favouring women in a second Mediterranean country,
although we analyse two periods of time, indicating that
this difference is persistent over time. Compared to prior
results, including Aguiar and Hurst (2007) for the United
States and Gimenez-Nadal and Sevilla (2012) for other
developed countries, who find a decreasing trend in the
gender gap in total work in most countries, our results may
indicate that the time-use patterns of individuals could be
specific to Mediterranean countries, compared to other
developed countries. The fact that women devote more
time to total work, and less time to leisure, may indicate
that women obtain comparatively less well-being from their
daily activities, which may help to drive public policies.
Little research has examined time-allocation decisions
in Spain, and that is limited either to comparisons between
individuals (Ahn et al., 2005) or to specific activities,
including market work, housework, childcare and adult
care (Garcia and Molina, 1998; Alvarez and Miles, 2003,
2006; Gutierrez-Domenech, 2005, 2010; Borra, 2009;
Gimenez-Nadal and Ortega, 2010; Gimenez-Nadal et al.,
2010; Sevilla et al., 2010; Gimenez-Nadal et al., 2012a;
Luengo-Prado and Sevilla, 2013). These studies use one
cross-section of individuals from time-use surveys at one
point in time. In this study, we build on prior research by
analysing two cross-sections of time-use data 6 years apart
and by looking at the whole range of time-use activities.
Thus, the study of time-allocation decisions over a longer
period of time contributes to the study of trends in the
allocation of time in developed countries, including
the most recent work by Aguiar and Hurst (2007, 2009)
and Aguiar et al. (2013) for the United States and by
Gimenez-Nadal and Sevilla (2012) for several industrialized countries.
The remainder of the article is organized as follows.
Section II reviews the existing literature. Section III
describes the theoretical framework of social norms.
Section IV presents the data and the variables used in
our analysis. Section V shows trends in the individual
time-allocation decisions in Spain. Section VI describes
our econometric specifications and results and Section VII
sets out our main conclusions.
II. Background
Despite increases in female labour-force participation,
women continue to specialize in nonmarket work
(Bittman, 1999; Bianchi et al., 2000; Baxter, 2002;
J. I. Gimenez-Nadal and A. Sevilla
Gauthier et al., 2004; Gimenez-Nadal and Sevilla, 2012).
This has been said to produce an unequal division of home
labour, leading to a ‘second shift’ or ‘dual burden’ (Schor,
1991; Hochschild, 1997). In this context, prior literatures
have shown that, in several countries, women devote more
time to total work (the sum of paid and unpaid work)
relative to men (see Aguiar and Hurst (2007) for the case
of the United States and Gimenez-Nadal and Sevilla
(2012) for the case of Finland, France and the United
Kingdom), resulting in women having less leisure time
relative to men (Gimenez-Nadal and Sevilla, 2011). More
recently, Burda et al. (2013) have empirically analysed
gender differences in the time devoted to total work for a
pool of 27 developed countries, showing a negative relationship between GDP per-capita and gender differences
in total work and concluding that, in the rich non-Catholic
countries, men and women average about the same
amount of total work.
To explain this gender gap in total work favouring
women in middle- and low-income catholic countries – as
is the case of Spain – Burda et al. (2013) propose gender/
social norms. Social norms have been recently incorporated
into economic models (see Fernández (2010) for a discussion) of household division of labour (Coltrane, 1989,
2000; Brines, 1994; Greenstein, 2000; Alvarez and Miles,
2003; Bittman et al., 2003; Sevilla et al., 2010) and union
formation (Sevilla, 2010; de Laat and Sevilla, 2011;
Gimenez-Nadal et al., 2012b), and they consider that peer
pressure to conform to a common social norm for time
allocation makes time-use become more similar across
individuals. Alternatively, the Mediterranean countries (e.
g. Italy, Spain, Portugal) have been shown to be very
inegalitarian regarding the gender distribution of household
labour (Sevilla, 2010; Gimenez-Nadal et al., 2012b), possibly indicating that social norms in those countries foster a
more inegalitarian gender distribution of household labour.
Within this framework, several papers have directly
analysed gender differences in the distribution of household labour. Alvarez and Miles (2003) compare twoearner, married couples in Spain and show that the
unequal allocation of housework time persists after
observable characteristics are taken into account. The
authors interpret this finding as a gender role residual.
Garcia et al. (2011) analyse gender differences in childcare time in five European countries (Denmark, France,
Germany, Italy and Spain) and find that there is a gender
gap in childcare favouring mothers, with this being more
evident in the Mediterranean countries. Sevilla et al.
(2010) have analysed gender differences in both housework and childcare among Spanish couples and find
support for the existence of gender roles affecting housework. In contrast, they find that women specialize in
childcare activities regardless of their relative productivity or bargaining power, which can still be interpreted in
light of social norms. Social norms may also explain why
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Total work time in Spain: evidence from time diary data
women in Spain tend to specialize in basic childcare,
defined as those activities aimed at fulfilling the essential
needs of children, while men show a relative specialization in educational childcare (Gutierrez-Domenech,
2010; Gimenez-Nadal and Molina, 2013).1
Spain has a lower female labour-force participation rate
compared to Northern European countries (EUROSTAT,
2013), with a rate of around 58%. Spain also has an
inflexible labour market, where part-time employment is
quite rare (Fernández-Kranz and Rodríguez-Planas, 2011)
and the implementation of family-friendly policies is very
limited (Carrasco and Rodriguez, 2000). As for the implementation of family-friendly policies, the portion of gross
domestic product (GDP) devoted by the government to
gender-equality policies has increased from 0.5% in 1998
to 1.1% in 2005, although this portion is still the lowest in
the EU (EUROSTAT, 2013). Such policies include the
‘baby-check’ (€2500) and the Spanish law ‘Ley para la
igualdad efectiva de hombres y mujeres 2007/3’. Such
limitation on the use of family-friend policies may be a
reflection of the social norms of the country, which indicate that work for mothers is not considered a priority in
society. Differences in the roles of men and women in
Spanish society are also reflected in the way men and
women react to unemployment; while women devote
more time to household production when they become
unemployed, unemployment has a lesser impact on the
time devoted to this activity by men (Gimenez-Nadal and
Molina, forthcoming). Within this framework, Spanish
mothers with children have significant difficulties coping
with market and household responsibilities, adding market
work time to their household responsibilities.
III. Theoretical Framework
Our theoretical framework is based on Burda et al. (2008)
who establish a social norm for leisure, defined as the residual
of total work (sum of paid and unpaid work). Considering
that the demand for leisure of any agent depends linearly
and negatively on the wage rate, L = 1 ‒εw, and that the
amount of time available per day is normalized to 1, the
amount of time devoted to (total) work is defined as TW =
εw, where ε > 0 measures the sensitivity of work to the wage
rate ‘w’. Our focus differs from these authors in that we focus
on total work given our interest in explaining the gender gap
in this activity, while they focus on leisure and develop a
model to explain ‘iso-work’.
Given our focus on gender differences in total work, we
focus on the time devoted to leisure by both men and
women, which can be represented as TWm = εwm and
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TWf = εwf, where ‘m’ and ‘f’ signify male and female,
respectively, and where we assume that the sensitivity of
work to the wage rate ‘w’ is equal across genders in each
country. If we focus on the gender differences in
total work, we obtain that TWm ‒ TWf = εwm ‒ εwf =
ε(wm ‒ wf). The literature has developed in recent decades
to explain the empirical pattern of a gender gap in wages
favouring males, which would indicate that males in every
country devote more time to work (and less time to leisure)
relative to females.
However, empirical evidence has shown that in certain
countries, males devote about the same or less time to total
work (Gershuny, 2000; Gauthier et al., 2004; Burda et al.,
2008, 2013; Gimenez-Nadal and Sevilla, 2012). Thus, we
consider the existence of a social/gender norm in the
country that influences individual time in total work,
defined as the mean time men (TWm*) and women
(TWf*) should devote to total work. We measure the
strength of the social norm regarding total work as 0 ≤ ϕ
≤ ∞, where ϕ = 0 indicates that there is no binding social/
gender norm and thus agents choose TW = εw, whereas
when ϕ = ∞, the social/gender norm is infinitely powerful
and obligates individuals to choose TWm = TWm* and
TWf = TWf*. For ϕ between ‘0’ and infinity, the social/
gender norm pulls optimal total work choice away from
TW = εw towards TW*, and hence
TWm ¼ αðεwm Þ þ ð1 αÞTWm
TWf ¼ α εwf þ ð1 αÞTWf with the weight 0 ≤ α ≤ 1 and defined as α ¼ 1=1 þ fε. We
establish a negative relationship between α and ϕ. If ϕ = 0,
α = 1, indicating that there is no binding social/gender
norm and individuals choose freely their total work time
according to TW = εw. If ϕ = ∞, α = 0, indicating that the
social/gender norm is infinitely powerful and obligates
individuals to choose TWm = TWm* and TWf = TWf*.
If we focus on the gender differences in total work time,
we obtain that TWm ‒ TWf = αε(wm ‒ wf) + (1 ‒ α)(TWm*
‒ TWf*). If α = 1, we could consider that there is no social/
gender norm, and thus the gender difference in total work
can be explained by TWm ‒ TWf = ε(wm ‒ wf). Sevilla
(2010) uses questions included in the ‘family and changing social norms module’ of the 1994 and 2002
International Social Survey Program to classify countries
according to their egalitarianism regarding social norms
and, in Table 1, (p. 233) classifies Sweden and Norway as
the most egalitarian countries. According to this ranking,
these two countries would be those closer to α = 1 compared to Spain; social/gender norms would have the
1
Although we focus here on social/gender norms as an explanation of the time devoted to childcare activities, prior research has shown
that several factors affect the time devoted to this activity, as does time involvement in labour supply, wages, nonlabour income and
individual and household characteristics. See Garcia et al. (2011) for an in-depth review of these factors.
J. I. Gimenez-Nadal and A. Sevilla
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Table 1. Time devoted to total work, Spain 2002–03 and
2009–10
Year 2002–03
Year 2009–10
Hours per week
Mean
SD
Mean
SD
Men
Women
Diff.
p-Value
52.60
56.92
(0.28)
(0.20)
47.51
53.84
(0.39)
(0.30)
−4.32
(<0.01)
−6.33
(<0.01)
Notes: The sample is restricted to include nonretired/nonstudent
individuals between the ages of 21 and 65 (inclusive). Total work
is measured in hours per week; see Table B1 for definitions of
time-use categories. Demographic weightings proposed by Katz
and Murphy (1992) and used by Aguiar and Hurst (2007) and
Gimenez-Nadal and Sevilla (2012) are used to ensure a constant
representation of types of individuals and days of the week.
Differential computed as the time devoted to total work by men
minus the time devoted to total work by women. p-Value of
difference in parentheses.
has decreased, perhaps as a consequence of the economic
crisis, which makes social norms more important in explanatory terms. Alternatively, the loss of income induced by
individual unemployment implies that households must
reduce their expenses, which may increase the family
working time. Gimenez-Nadal and Molina (forthcoming)
have shown that the associations between regional unemployment rates and the uses of time of the unemployed in
Spain differ by gender, and thus it may well be that the
newly unemployed men substituted a higher proportion of
their foregone market work with leisure, compared with
household production, while the newly unemployed
women substituted a higher proportion of foregone market
work with household production, compared to leisure,
leading to an increase in the gender gap in total work
favouring women.
IV. Data and Variables
smallest effect and thus the gender differences in total
work would be mainly explained by the gender gap in
wage rates. But these countries also have a small gender
gap in wage rates (Weichselbaumer and Winter-Ebmer,
2005), which would lead to a very small gender gap in
total work as (wm – wf) would be close to zero, evidence
shown in Gimenez-Nadal and Sevilla (2012) for the case
of Norway (Table 4, p. 1435). Thus, this model is able to
explain the empirical evidence observed in Northern
countries.
On the other hand, Sevilla (2010) classifies Spain as an
inegalitarian country regarding social norms, which would
indicate under this framework that α < 1 and that social/
norms have an effect on the time devoted to total work.
Despite that Spain has been shown to have a high gender
gap in wage rates (Garcia et al., 2001; de la Rica et al.,
2008, forthcoming), which would lead to men doing more
total work relative to women, we find that men, in fact,
devote less time to total work, which would indicate that
social norms are deeply entrenched in Spain (ϕ ~ ∞), or
that the mean times men (TWm*) and women (TWf*)
should devote to total work according to those social/
gender norms are very different, favour women, or both.
This model would be easily applicable to different subgroups of population by allowing gender/social norms on
total work to vary between groups, which would be consistent with our empirical finding that the gender gap in
total work favouring women is different for different
groups of population (e.g. education level, presence of
children). Furthermore, the fact that the gender gap in
total work favouring women has increased over the period
examined may be an indication that the gender wage gap
2
We examine diary data from Spain collected in 2002–03
and 2009–10, and the two surveys cover from the 4th term
of the first year to the 3rd term of the second year. In these
surveys, a diary is completed on selected days by respondents who are 10 years old, or older, and is divided into 10minute intervals where the respondent records a main
activity, a secondary activity carried out simultaneously
with the primary activity (if any), whether the activity was
performed in the company of a child, another member of
the household, or another adult, and where the activity
took place. An extensive literature confirms the reliability
and validity of diary data and their superiority over other
time-use surveys based on stylized questions, asking
respondents to estimate time in activities on a ‘typical day’
(Juster and Stafford, 1985; Robinson and Godbey, 1999;
Bianchi et al., 2006; Kalenkoski and Pabilonia, 2012). In
the labour supply literature, for example, Klevmarken
(2005) argues that information on actual hours of work
from time-use surveys is more relevant than normal hours
or contracted hours generally reported in stylized questions.
The author shows that time-use data yields much smaller
estimates of wage-rate effects compared to measures of
normal hours of work, which may have important implications for tax policy design, among others.
For the sake of comparison with prior studies (e.g.
Aguiar and Hurst, 2007) and to minimize the role of
time-allocation decisions that have a strong inter-temporal
component over the life cycle, such as education and
retirement, we restrict the sample used throughout our
analysis to nonretired/nonstudent individuals between
the ages of 21 and 65 (inclusive), so results should be
interpreted as being ‘per working-age adult’.2 We use the
We have also done the analysis including students in an alternative sample. Results are robust to the inclusion and are available upon
request.
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Total work time in Spain: evidence from time diary data
demographic weights proposed by Katz and Murphy
(1992) to ensure a constant representation of types of
individuals and days of the week. These demographic
weights have been used in other studies of time-use trends
(Aguiar and Hurst, 2007, 2009; Gimenez-Nadal and
Sevilla, 2012). (Appendix A provides a detailed description of how demographic weights are computed.)
Although the classification of time-use activities may
change over time and certain activities disappear as new
activities emerge (just as in the case of expenditurediary categories in expenditure surveys), our broad
classification of activities into leisure, paid work,
unpaid work and childcare provides a good basis to
run meaningful comparisons over time. Although the
number of activity codes decreased between the period
2002–03 and 2009–10 (from 177 to 115 activity codes),
these changes affect the codes for main and secondary
work (these have been aggregated), arts and hobbies
(these have been aggregated) and unspecified activities
(fewer details are given for such activities). However,
both surveys share the same sampling method (random
stratified sampling) and have the same selection criteria
(individuals who are 10 years old or older), so both are
directly comparable. (See http://www.ine.es/prodyser/
micro_emptiem.htm for detailed information on the
methodology of the surveys.)
The conceptualization of time-use categories is usually
driven by a systematic, principle-driven approach of distinguishing means versus ends. The so-called third person
criterion, for example, excludes from the definition of
leisure any activity that might be carried out by some
third party without losing the intended utility for the final
consumer. Unfortunately, the third person criterion involves
questionable assumptions, such that the enjoyment derived
from work can legitimately be ignored and that all leisure is
enjoyable. However, one quarter of the time that would be
considered leisure according to the conventional implementation of the third person criterion, and one third of what
would conventionally be considered work, is unexpectedly
placed by the diarists (Gershuny, 2013). Certain activities,
such as sleeping, eating, personal and medical care or
resting, do not fall comfortably into the means versus
ends classification. These activities cannot be purchased
in the market, but they may not be considered leisure in
the sense that they are necessary for life.
Nonetheless, some variation in the time spent in these
activities may result from conscious choice. Biddle and
Hamermesh (1990) show that sleep time responds
3
1899
to economic incentives such as the wage, while
Hamermesh (2002) and Hallberg (2003) show that couples tend to synchronize their leisure activities. The
decreasing marginal utility of sleep (and of other consumption activities) is indeed shown by Gershuny (2013)
using (subsequent) diary reports of enjoyment. Similarly,
many of the tasks constituting childcare can be purchased
in the market and so could be conceptualized as a part of
unpaid production (Aguiar and Hurst, 2007; Fisher et al.,
2007; Guryan et al., 2008). However, parents report that
time spent with their children is among their more enjoyable activities, especially when compared with other standard home-production activities (Juster and Stafford,
1999; Robinson and Godbey, 1999; Kahneman et al.,
2004; Kahneman and Krueger, 2006; Krueger, 2007;
Guryan et al., 2008).
Rather than trying to resolve this debate on theoretical
grounds, we adopt an empirical approach and follow
Aguiar and Hurst (2007) in the definition of time-use
categories. Following these authors, we consider the following categories (see Appendix B, Table B1 for an overview of the activities included in our definition of
activities):
Paid work includes all time spent working in the paid
sector on main jobs, second jobs and overtime, including
any time spent working at home plus time spent commuting to/from work.
Unpaid work includes any time spent on meal preparation and cleanup, doing laundry, ironing, dusting,
vacuuming, indoor household cleaning, indoor design
and maintenance (including painting and decorating),
time spent obtaining goods and services (i.e. grocery
shopping, shopping for other household items, comparison shopping) and time spent on other home production,
such as home maintenance, outdoor cleaning and vehicle
repair.
Childcare includes all time devoted to childcare as
primary activity (e.g. feeding and food preparation for
babies and children; washing, changing babies and
children; putting children and babies to bed or getting
them up; babysitting; medical care of babies and
children; reading to or playing with babies and children; helping children with homework; supervising
children).3
Leisure includes activities such as watching television,
sports, general out-of-home leisure, gardening and pet
care, and socializing and coincides with the definition of
Leisure Measure 2 in Aguiar and Hurst (2007).4
There is a concern, however, that childcare reported as primary activity significantly underestimates total childcare time (Bianchi, 2000;
Budig and Folbre, 2004; Folbre and Bittman, 2004), as it does not take into account other time that parents spend supervising children.
We acknowledge that our results rely on this simpler definition of childcare. See Sevilla et al. (2010) for a full description of the different
definitions of childcare.
4
Fahr (2005) includes reading journals and newspapers as ‘informal education’, although describes such activities as ‘part of daily leisure
time’, as do we.
1900
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V. Trends in Total Work
We first analyse the time devoted to total work – as the
sum of the time devoted to Paid work, Unpaid work and
Childcare – in the two periods, for both men and women,
to see to what extent we find evidence of ‘iso-work’.
Table 1 shows the time devoted to total work by men
and women, the difference in the time devoted by men
and women to this activity and the p-value of that difference, for both the 2002–03 and 2009–10 surveys. We find
no evidence of ‘iso-work’ as women devote 4.32 and 6.33
more hours per week to total work compared to men in
2002–03 and 2009–10, respectively, with these gender
differences being statistically significant at standard
levels. Specifically, men devote 52.60 and 47.51 hours
per week to total work in 2002–03 and 2009–10, while
women devote 56.92 and 53.84 hours per week to total
work in the same periods. The nonexistence of ‘iso-work’
in Spain is consistent with the results by Burda et al.
(2013), who find that gender equality in total work does
not hold in predominantly Catholic countries. This is also
consistent with the results reported by Burda et al. (2008)
for another Mediterranean country, Italy.
One of the factors proposed by Burda et al. (2013) to
explain cross-country differences in the gender gap in total
work is that of social norms, and for the case of Spain, it
appears to be a plausible explanation. Sevilla (2010) uses
data from the International Social Survey Program to build
a country egalitarian index aimed at measuring differences
in the gender distribution of household labour and finds
that Spain ranks among the least egalitarian developed
countries. Alternatively, Gimenez-Nadal et al. (2012b)
construct a traditionality index aimed at measuring differences in the gender distribution of household labour,
based on the time devoted to childcare by men and
women in European countries, also finding that Spain
ranks among the least egalitarian countries regarding the
distribution of household labour. This evidence is consistent with the fact that Spain has entrenched social norms
favouring the unequal gender distribution of household
labour. Specifically, Sevilla et al. (2010) analyse the time
devoted to housework and childcare activities by men and
women in Spain and find that women in Spain specialize
in this type of activity regardless of their relative productivity or bargaining power, and whereas masculinity
norms seem to dominate housework time decisions in
Spain, for childcare it seems that femininity norms dominate masculinity norms in Spain. Thus, we propose social
norms as a possible explanation for the observed gender
gap in total work.
Additionally, we observe that the difference in the time
devoted to total work between men and women in Spain
has increased by 2 hours, with this difference being statistically significant at the 99% level, indicating that the
gender gap in total work increased over the period. One
J. I. Gimenez-Nadal and A. Sevilla
possible explanation could be that the economic crisis is
behind this increasing gender gap in total work. However,
we cannot fully analyse the effect of the economic crisis
on the gender gap in total work. Aguiar et al. (2013) use
the American Time-Use survey (ATUS), for the period
2003–10, for a comprehensive analysis of time-use prior
to and during the recent US recession, allowing the
authors to document how the allocation of time evolves
over the business cycle. The two STUS surveys are crosssection data sets composed of time-use diaries of individuals, covering one year, but we do not have sufficient
time variation to identify how the allocation of time
evolves over the business cycle.
However, we can link our evidence presented here with
prior evidence on the behaviour of the unemployed and
unemployment rates in Spain. Gimenez-Nadal and Molina
(forthcoming) show that regional unemployment rates are
associated with changes in the uses of time of the unemployed, and while higher unemployment rates are associated with more time devoted to household production by
women, it is associated with an increase in the time
devoted to leisure by unemployed men. Given that the
increase in unemployment rates between the two survey
periods was much larger for men than for women (unemployment rates for men were around 8.4% in 2002–03 and
19.4% in 2009–10; unemployment rates for women were
around 16.% in 2002–03 and 20% in 2009–10), it may
well be that the newly unemployed men substituted a
higher proportion of their foregone market work with
leisure, while the newly unemployed women substituted
a higher proportion of foregone market work with household production, leading to an increase in the gender gap
in total work favouring women.
We have additionally analysed the gender gap and the
evolution of the gender gap for the time devoted to the
main time-use categories: Paid Work, Unpaid work,
Childcare and Leisure (see Table 2). We observe that
decreases in paid work financed the increases in unpaid
work, childcare and leisure for men, while decreases in
unpaid work financed the increases in childcare and leisure for women. Men increased the time devoted to unpaid
work and childcare activities by 2.25 and 1.64 hours per
week, respectively, while the time devoted to paid
declined for men an average of 8.56 hours per week.
Women increased the time devoted to childcare activities
by 1.57 hours per week, while the time devoted to paid
work declined for women an average of 3.57 hours per
week. Hence, we find that men relatively decreased the
time devoted to paid work by 8.20 hours per week, while
they relatively increased the time devoted to unpaid work
and childcare by 5.89 hours per week, showing that the
relative decrease in paid work for men has not been
compensated for by their relative increase in unpaid
work and childcare, leading to a relative increase in the
gender gap in total work favouring women of around
Total work time in Spain: evidence from time diary data
1901
Table 2. Trends in leisure, paid work, unpaid work and childcare, men and women
Year 2002–03
Hours per week
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Paid work
Men
Women
Unpaid work
Men
Women
Childcare
Men
Women
Leisure
Men
Women
Mean
Year 2009–10
SD
Mean
SD
Diff.
Cum Diff.
p-Value
42.56
18.34
(0.36)
(0.26)
34.00
17.98
(0.50)
(0.36)
−8.56
−0.36
−8.20
(<0.01)
9.40
34.69
(0.14)
(0.19)
11.65
31.12
(0.22)
(0.28)
2.25
−3.57
5.82
(<0.01)
2.19
5.22
(0.06)
(0.11)
3.83
6.79
(0.14)
(0.19)
1.64
1.57
0.07
(0.80)
113.15
108.93
(0.33)
(0.24)
117.81
111.35
(0.46)
(0.35)
4.66
2.42
2.24
(<0.01)
Notes: The sample is restricted to include nonretired/nonstudent individuals between the ages of 21 and 65 (inclusive). Leisure, paid
work, unpaid work and childcare are measured in hours per week, see Table B1 for definitions of time-use categories. Demographic
weightings proposed by Katz and Murphy (1992) and used by Aguiar and Hurst (2007) and Gimenez-Nadal and Sevilla (2012) are used
to ensure a constant representation of types of individuals and days of the week. Differential computed as the time devoted to the
reference activity in 2009 minus the time devoted to the reference activity in 2003. Cum.Diff. is measured as the change in the time
devoted to the reference activity by men minus the change in the time devoted to the reference activity by women, p-value of the
cumulative difference in parentheses.
2 hours. Finally, leisure evolved unequally for men and
women in Spain over the period, as leisure increased for
men by 4.66 hours per week and for women by 2.42 hours
per week, between 2002–03 and 2009–10, consistent with
the relative decrease in total work of 2 hours for men.
Heterogeneous effects
We now analyse the distribution of the time devoted to
total work by men and women in different socio-demographic groups to see to what extent the evidence obtained
is against or in favour of the social norms hypothesis.
Despite this is not a complete analysis, as we cannot
fully analyse how social norms condition the gender gap
in total work (since we do not have information on the role
of men and women in society or the desired distribution of
household labour), if we obtain evidence in favour of the
social norms hypothesis, this would improve our understanding of the possible channels through which social
norms operate and will foster future analysis on how social
norms are created and operate in society.
Panel A in Table 3 shows the total time devoted to total
work in both 2002–03 and 2009–10, the gender difference
and the p-value of the difference according to the level of
education of individuals. We consider three educational
levels: less than high school, high school degree and some
college and more. Comparing the gender gap present in
the three levels of education, we find a negative gradient
between the gender gap in total work favouring women
and education of individuals. In particular, the gender gap
in total work for individuals with less than a high school
degree is 6.13 and 9.30 hours per week in 2002–03 and
2009–10, for individuals with a high school degree it is
4.54 and 7.22 hours per week in 2002–03 and 2009–10,
and for individuals with some college or more it is 3.51
and 3.15 hours per week in 2002–03 and 2009–10, respectively. We also find a negative gradient between education
and the increase in the gender gap between 2002–03 and
2009–10. In particular, the gender gap increased by 3.17
hours per week between 2002–03 and 2009–10 for low
educated individuals, by 2.68 hours per week between
2002–03 and 2009–10 for medium educated individuals,
and held relatively constant for highly educated
individuals.
If we consider education of the individuals as a proxy
for their wages, we can speculate about the existence of a
negative gradient between wages and the gender gap in
total work. As argued by Burda et al. (2013), social norms
are associated with ‘a social stigma attached to female
participation in market activities... rise in wages reduce
the impact of gender stereotypes on behaviour. In that
respect, development makes men and women behave,
ceteris paribus, in increasingly similar ways (pp. 251–2).
As wages increase for women, the impact of gender
stereotypes on behaviour decreases, which would be consistent with the decrease in the gender gap in total work.
Thus, the negative gradient between education – and
wages – and the gender gap in total work is consistent
with a social norms theory. Additionally, we find a negative gradient between education and the increase in the
gender gap that may indicate that, as wages increase, the
J. I. Gimenez-Nadal and A. Sevilla
1902
Table 3. Time devoted to total work by groups of population
Year 2002–03
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Hours per week
Panel A: Education
Less than high education
Men
Women
Diff.
p-Value
High school degree
Men
Women
Diff.
p-Value
Some college and more
Men
Women
Diff.
p-Value
Panel B: Children versus No Children
Children <18 in hhld
Men
Women
Diff.
p-Value
No children <18 in hhld
Men
Women
Diff.
p-Value
Year 2009–10
Mean
SD
Mean
SD
47.03
53.16
(0.64)
(0.36)
41.43
50.73
(0.86)
(0.57)
(0.38)
(0.29)
47.75
54.97
(0.53)
(0.46)
51.64
54.79
(0.41)
(0.30)
52.80
60.13
(0.38)
(0.27)
43.06
48.63
53.39
57.93
55.34
58.85
56.81
62.05
49.18
52.86
−6.13
(<0.01)
−4.54
(<0.01)
−3.51
(<0.01)
−5.24
(<0.01)
−3.68
(<0.01)
−9.30
(<0.01)
−7.22
(<0.01)
−3.15
(<0.01)
−7.33
(<0.01)
−5.57
(<0.01)
(0.56)
(0.44)
(0.68)
(0.57)
(0.56)
(0.43)
(0.53)
(0.40)
Notes: The sample is restricted to include nonretired/nonstudent individuals between the ages of 21 and 65 (inclusive). Total work is
measured in hours per week, see Table B1 for definitions of time-use categories. Demographic weightings proposed by Katz and Murphy
(1992) and used by Aguiar and Hurst (2007) and Gimenez-Nadal and Sevilla (2012) are used to ensure a constant representation of types
of individuals and days of the week. Differential computed as the time devoted to total work by men minus the time devoted to total work
by women. p-value of difference in parentheses.
difference between men and women is less sensitive to
market-based forces (e.g. economic crisis), reinforcing the
idea that social norms are at the root of the observed
gender difference in total work.
Panel B in Table 3 shows the total time devoted to total
work in both 2002–03 and 2009–10, the gender difference
and the p-value of the difference, according to whether
there are or not children under 18 in the household. We
find a positive relationship between the presence of children under 18 in the household and the gender gap in total
work. Specifically, the gender gap in total work for individuals living in households with children under 18 is 5.24
and 7.33 hours per week in 2002–03 and 2009–10, while
for those living in households without children under 18 it
is 3.68 and 5.57 hours per week in 2002–03 and 2009–10,
respectively. Thus, we find a positive relationship between
the presence of children under 18 in the household and the
gender gap in total work favouring women, indicating that
children foster the gender gap in total work. This result is
consistent with Sevilla et al. (2010), who argue that for
childcare, femininity norms dominate masculinity norms
in Spain, which is consistent with the larger gender gap in
total work favouring women in households with children
under 18.
VI. Econometric Analysis
We estimate OLS regressions on the time devoted to total
work. However, since we observe a small but significant
proportion of ‘zeros’ for total work (3.69% of the individuals, which accounts for 1523 observations, reported no
time devoted to this activity), there can be some controversy regarding the selection of alternative models, such
as that of Tobin (1958). According to Frazis and Stewart
(2012), OLS models are preferred in the analysis of timeallocation decisions, and Gershuny (2012) argues that
traditional diary studies can still produce accurate
Total work time in Spain: evidence from time diary data
estimates of mean times in activities for samples and
subgroups. Foster and Kalenkoski (2013) compare the
use of tobit and OLS models in the analysis of the time
devoted to childcare activities, finding that the qualitative
conclusions are similar for the two estimation methods.
We estimate the following equation by OLS
regressions:
Ti ¼ α þ β1 Female þ β2 year 2009
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þ β3 Xi þ β4 Interactionsi þ β5 Zi þ εi
(1)
where Ti is the time devoted to total work by individual ‘i’,
Femalei takes value ‘1’ if respondent ‘i’ is female and
value ‘0’ otherwise, Year_2009i takes value ‘1’ if respondent ‘i’ comes from the STUS 2009–10 and value ‘0’
otherwise, Xi is a vector of variables that includes
dummy variables for secondary and university education
and the presence of children under 18 in the household of
respondent, Interactionsi includes a vector of interactions
between the variables included in vector Xi and the
Femalei and Year_2009i variables and Zi includes additional demographic variables (age, age squared divided by
100 and whether respondent lives in couple or not) and
dummy variables to control for the day of the week (Ref.:
Saturday).
The female dummy is included to measure gender differences in the time devoted to total work, while the year
dummy is included to measure differences in the time
devoted to total work between 2003 and 2009. Thus, β1
> 0 would indicate that compared to males, females in
Spain devote more time to total work, while β2 < 0 would
indicate that the time devoted to total work by individuals
in Spain has decreased between 2002–03 and 2009–10.
However, Table 1 shows that the gender difference in the
time devoted to total work has increased between 2002–03
and 2009–10, and for this reason we include an interaction
term between Female and Year_2009, which corresponds
to β3, to determine whether the gender difference in total
work favouring females has increased during the period.
Column 1 in Table 4 shows the results of estimating
Equation 1 including an interaction term between Female
and Year_2009. In this regression, the reference category
is males in 2003. We observe that β1 > 0 and is statistically
significant at standard levels, indicating that comparing
males and females in 2003, there is a gender gap in total
work favouring females of 4.41 hours per week.
Additionally, β2 < 0 and is statistically significant at standard levels, indicating that for the reference category
(males) the time devoted to total work decreased between
2002–03 and 2009–10 by 3.61 hours per week. However,
β3, corresponding to the interaction between Female and
Year_2009, is positive and statistically significant at standard levels, indicating a differential decrease in the time
devoted to total work for women between 2002–03 and
1903
Table 4. OLS regressions on the time devoted to total work
Hours per week
Female
Analysis
by
gender
4.41***
(0.34)
Year 2009
−3.61***
(0.47)
Female*Year 2009
2.38***
(0.59)
Secondary education
1.91***
(0.37)
University education
3.48***
(0.43)
Female * Secondary
–
education
–
Female * University
–
education
–
Secondary
–
education*Year 2009
–
University
–
education*Year 2009
–
Female * Secondary
–
education*Year 2009
–
Female * University
–
education*Year 2009
–
Children <18 in the
4.45***
household
(0.35)
Female*Children <18 in –
the household
–
Children <18 in the
–
household*Year 2009 –
Female*Children <18 in –
the household*Year
–
2009
Number of observations 41 200
Analysis
by
education
Analysis by
presence of
children <18
7.17***
(0.80)
−2.01*
(1.12)
1.84
(1.33)
4.21***
(0.77)
6.20***
(0.85)
−3.11***
(0.92)
−3.32***
(1.03)
−2.12*
(1.29)
(0.97)
(1.37)
1.61
(1.57)
−2.30
(1.69)
4.48***
(0.35)
–
–
–
–
–
–
4.28***
(0.47)
−4.88***
(0.68)
2.59***
(0.84)
1.89***
(0.37)
3.41***
(0.43)
–
–
–
–
–
–
–
–
–
–
–
–
3.71***
(0.58)
0.22
(0.69)
2.26**
(0.94)
−0.29
(1.18)
41 200
41 200
Notes: The sample is restricted to include nonretired/nonstudent
individuals between the ages of 21 and 65 (inclusive). Total work
is measured in hours per week, see Table B1 for definitions of
time-use categories. Demographic weightings proposed by Katz
and Murphy (1992) and used by Aguiar and Hurst (2007) and
Gimenez-Nadal and Sevilla (2012) are used to ensure a constant
representation of types of individuals and days of the week.
We estimate the following equation: Ti ¼ α þ β1 Femalei þ
β2 Year 2009i þ β3 Xi þ β4 Interactionsi þ β5 Zi þ εi , where Ti is
the time devoted to total work by individual ‘i’, Femalei takes
value ‘1’ if respondent ‘i’ is female and value ‘0’ otherwise,
Year_2009i takes value ‘1’ if respondent ‘i’ comes from the
STUS 2009–10 and value ‘0’ otherwise, Xi is a vector of variables that includes dummy variables for secondary and university education, and the presence of children under 18 in the
household of respondent, Interactionsi includes a vector of interactions between the variables included in vector Xi and the
Femalei and Year_2009i variables, and Zi include additional
demographic variables (e.g. age, age squared divided by 100,
and whether respondent lives in couple or not) and dummy
variables to control for the day of the week (Ref.: Saturday).
*Significant at the 90% level; **Significant at the 95% level;
***Significant at the 99% level.
2009–10. If we consider the decrease in the time devoted
to total work by women between 2002–03 and 2009–10
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1904
(β2 + β3), we observe that the decrease is 1.23 hours per
week. Comparing the decrease in the time devoted to total
work for men (3.61) and for women (1.23), we observe
that it has decreased more for men, leading to an increase
in the gender gap in total work favouring women of 2.38
hours per week. This result is consistent with results
shown in Table 1.
We next consider the heterogeneous effects analysed
in Section V (education level and the presence of children)
to see whether results are consistent. Thus, we first
consider the differential effects of education on the gender
gap in total work. In doing so, we estimate Equation (1)
where we include the following interactions: Female*
Year_2009, Female*Secondary education, Female*
University education, Secondary education*Year_2009,
University education*Year_2009, Female*Secondary
education*Year_2009 and Female*University education*
Year_2009. Results are shown in Column (2) of Table 4.
We observe that β1 > 0 and is statistically significant at
standard levels, indicating that, comparing males and
females with primary education in 2003, there is a gender
gap in total work favouring females of 7.17 hours per week.
Additionally, we observe that secondary and university
education is associated with more time devoted to total
work by men, as their coefficients are positive (4.21 and
6.20 hours per week, respectively) and statistically significant. Female*Secondary education and Female*University
education are both negative and statistically significant at
standard levels, indicating that the gender gap in total work
favouring women in 2003 is larger if we compare males
and females with primary education (7.71 hours per week)
than for males and females with secondary (4.06 hours per
week) and university education (3.85 hours per week). If
we consider the change in the gender gap in total work
between 2002–03 and 2009–10, we observe that there
have been no changes in total work across educational
levels, as Female*Year_2009, Secondary education*
Year_2009, University education*Year_2009, Female*
Secondary education*Year_2009 and Female*University
education*Year_2009 are nonstatistically significant at
standard levels. Thus, our results suggest that the gender
gap in total work favouring females is larger in the loweducation group, consistent with the hypothesis that social
norms lie at the root of this gender gap. However, educational differences cannot explain the increase in the gender
gap in total work favouring females during the period.
We now analyse the gender difference in total work,
considering the presence of children under 18 in the
household. In doing so, we estimate Equation (1)
where we include the following interactions: Female*
Year_2009, Female*Children <18 in the household,
Children <18 in the household*Year_2009 and
Female*Children <18 in the household*Year 2009.
Results are shown in Column (3) of Table 4, where the
reference category is males with no children under 18 in
J. I. Gimenez-Nadal and A. Sevilla
the household in 2003. We observe that β1 > 0 and is
statistically significant at standard levels, indicating that
comparing males and females with no children in 2003,
there is a gender gap in total work favouring females of
4.28 hours per week. Additionally, we observe that the
presence of children under 18 is associated with more time
devoted to total work by men, as its coefficient is positive
(3.71 hours per week) and statistically significant, while
we find no differential effect by gender of the presence of
children on the time devoted to total work as
Female*Children <18 in the household is nonstatistically
significant.
Additionally, β2 < 0 and is statistically significant at
standard levels, indicating that for the reference category
(males without children) the time devoted to total work
decreased between 2002–03 and 2009–10 by 4.88 hours
per week. However, β3, corresponding to the interaction
between Female and Year_2009, is positive and statistically
significant at standard levels, indicating a differential
decrease in the time devoted to total work for women
without children between 2002–03 and 2009–10. If we
consider the decrease in the time devoted to total work by
women between 2002–03 and 2009–10 (β2 + β3), we
observe that the decrease is 2.29 hours per week.
Comparing the decrease in the time devoted to total work
for men (4.88) and for women (2.29), we observe that it has
decreased more for men, compared to women, leading to an
increase in the gender gap in total work favoring women of
2.59 hours per week. This result is consistent with results
shown in Table 1. Finally, considering Children <18 in the
household*Year_2009 and Female*Children <18 in the
household*Year 2009, we find that the decrease in total
work is smaller and with no difference between males and
females with children under 18 in the household. Males
with children have decreased the time devoted to total work
by 2.62 hours per week (–4.88 +2.26), while women with
children have decreased the time devoted to total work by
0.03 hours per week, (–4.88 + 2.59 + 2.26). These results
show that the gender gap in total time favouring women has
increased over time by 2.29 hours per week and that this
increase has been present in individuals both with and
without children under 18 in the household. Thus, we
cannot argue that the existence of children is at the root of
the increase in the gender gap in total work favouring
women, consistent with the results in Table 3.
VII. Conclusion
In this article, we analyse the time-allocation decisions of
individuals in Spain using the Spanish Time-Use Survey
for the years 2002–03 and 2009–10, with a focus on
gender differences in the time devoted to total work and
how such gender differences have changed over time. We
find no evidence of ‘iso-work’ in Spain, since women
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Total work time in Spain: evidence from time diary data
devote more time to total work than men throughout the
period. We propose social norms to explain the gender gap
in total work in Spain, which seems to be supported by the
prior literature, and additional analyses based on education and the presence of children under 18 in the household are consistent with the existence of the social norms
that underlie the gender distribution of work. We also
document that the gender gap in total work favouring
women has increased during the analysed period by 2
hours per week. At the root of this increase in the gender
gap, we find a relative increase in market work of 8 hours
per week, coupled with a relative decrease in nonmarket
work of 6 hours per week, leading Spanish women to
devote 2 fewer hours per week to leisure in 2009–10
compared to 2002–03. One explanation could be that the
economic crisis is behind this increasing gender gap in
total work.
To the extent that leisure time is a good indicator of
quality of life (Stiglitz et al., 2009), this issue is relevant.
Our results indicate that the well-being of women in
Spain is relatively lower relative to men, as prior studies
have found that leisure ranks among the most enjoyable
activities, while household production and market work
rank among the least enjoyable activities (Kahneman et
al., 2004; Kahneman and Krueger, 2006; Krueger, 2007;
Knabe et al., 2010). Family policies that challenge the
existing gender structure, such as paternity leave policies or gender-based taxation schemes with higher marginal tax rates for men (Alesina et al., 2011), may
constitute a good starting point for successfully shifting
the household division of labour in a more egalitarian
direction.
One limitation of our analysis is that our data is a
cross-section of individuals, and it does not allow us to
identify differences in the time devoted to total work net
of (permanent) individual heterogeneity in preferences.
This is particularly important in our context, as it could
be that social norms on preferences for work and leisure
differ by gender, and men in Spain have a greater preference for leisure time and a lesser preference for work.
At present, there are no panels of time-use surveys currently available. Alternative data sets with a panel data
structure, such as the British Household Panel Survey or
the Panel Study of Income Dynamics, which also have
information on housework time, could be used to investigate this topic. Stylized questions on housework time
have been confirmed as being less reliable than the diary
information used here (Juster and Stafford, 1985;
Robinson and Godbey, 1999). Moreover, these surveys
do not provide information about other uses of time, such
as childcare. A second limitation is that we cannot fully
analyse the effect of the economic crisis on the gender
gap in total work, as the two STUS surveys are crosssection data sets composed of time-use diaries of individuals, covering one year, and thus we have insufficient
1905
time variation to identify how the allocation of time
evolves over the business cycle. (Only the ATUS has an
annual periodicity.) We leave these issues for future
research.
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Appendix A: Demographic Weighting
surveys to ensure the data is representative of the total
population. We adjust these weights so that each day of
the week and each survey are equally represented in the
overall sample.
Table A1 shows summary statistics for the demographic characteristics used to compute the demographic constant weights. We observe that individuals
sampled in 2009–10 are older (1.62 years of difference) and with a higher level of education (29.72%
and 26.69% of individuals with more than secondary
education in the period 2009–10 and 2002–03, respectively) and that the proportion of households with at
least one child under 18 has increased in the period
2009–10 compared to the period 2002–03 (from
42.98% to 47.26% of the sample in the periods
2002–03 and 2009–10, respectively), consistent with
the fact that the number of children under 18 in the
households is larger in 2009–10 (0.75 children) compared to 2002–03 (0.67 children). As argued by
Gimenez-Nadal and Sevilla (2012), such differences
may condition changes in the allocation of time. For
instance, more children in the household may imply
that survey respondents must devote more time to
childcare activities. Also, the higher educational level
of the population may imply that they devote more
time to market work activities in 2009–10 compared to
2002–03, given their higher opportunity cost of time
(Becker, 1965). Thus, in our analysis, we must control
for the uncertainty in the population shares of different
demographic categories and we must use the demographic weight proposed by Katz and Murphy (1992).
We report trends over the last decade, holding constant
the demographic composition of the sample, following
Aguiar and Hurst (2007) and Gimenez-Nadal and Sevilla
(2012). Specifically, we divide the sample into demographic cells defined by five age groups (21–29, 30–39,
40–49, 50–59 and 60–65), three education categories
(less than high school, high school degree and some
college and more), two gender categories (male and
female), whether there is a child under 18 in the household and whether respondent lives in couple or not. We
do not create separate cells distinguishing child status for
respondents aged 60–65, due to the small number that
have children present in the home. To calculate the constant weights used for our demographic adjustments, we
pool the two surveys and compute the percentage of the
population that resides in each demographic cell for each
survey. Following Katz and Murphy (1992), we use these
fixed weights to calculate weighted means for each activity in each year.
Since our analysis is based on gender, means for
each subsample (males and females) are calculated in
a similar manner with the weights scaled to sum to one.
In particular, we calculate the percentage of men who
reside in each demographic cell (according to age
range, education, presence of children and living in
couple), with these percentages summing to one for
men. The same applies for women. When pooling the
surveys to compute the percentage of the population in
each of our cells, we use the weights provided by the
J. I. Gimenez-Nadal and A. Sevilla
1908
Table A1. Sum stats of demographic characteristics, by year of survey
Demographic characteristics
Year 2002–03
Male
Age
Secondary education
University education
Presence of children <18
Living in Couple
Observations
49.16
40.65
51.69
26.69
42.98
68.89
Year 2009–10
(0.35)
(0.08)
(0.35)
(0.32)
(0.34)
(0. 27)
29 075
49.06
42.27
46.39
29.72
47.26
75.12
(0.59)
(0.13)
(0.59)
(0.53)
(0.59)
(0. 39)
12 219
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Notes: The sample is restricted to include nonretired/nonstudent individuals between the ages of 21 and 65
(inclusive). Male, Secondary education, University education and Presence of children <18 are measured in
percentage points. SDs in parentheses.
Appendix B: Classification of Activities
Table B1. Classification of time-use activities
Paid work
The following not at home: Unspecified job, Regular work in main job, Break time and coffee break, Training during the
main job, Unspecified activities related with main job, Lunch break, Other activities related with main job; The following
if not at home and not travelling: Job seeking; The following at home: Unspecified job, Regular work in main job, Break
time and coffee break, Training during the main job, Regular work in second job, Break time and coffee break in second
job, Training during second job, Unspecified activities related with main job, Lunch break, Job seeking, Other activities
related with main job, Home managements, Home managements by Internet, Other home managements, Regular work in
second job, Break time and coffee break in second job, Training during second job, Unspecified studies, Unspecified
activities related with University/School, Class, course and conferences, Other activities related with University/school,
Travel related to main job, Travel related with second job, Travel from/to work, Travel from/to school/University, Travel
during free time for studies; The following if mode of transport recorded: Job seeking; Homework and library, Free time
study, Computing-programming, Information search
Unpaid
work
Unspecified culinary activities, Food preparation, Pastry making, Dish washing, Preserving, Other specified culinary
activities, Unspecified activities related with house and family, Unspecified house maintenance, Cleaning dwelling,
Cleaning yard, Heating and water supplying, Different organization tasks, Other specified house maintenance,
Unspecified activities related with caring and dressmaking, Laundry, Ironing, Other specified activities related with
caring and dressmaking, Unspecified construction and repair activities, Home construction, renewals, Home repairs,
Home equipment construction, repairs and maintenance, Vehicles maintenance, Other specified construction and repair
activities, Help to adult members of household, Unspecified helps to other households; The following to helps to other
households: Helps in culinary activities, Helps in house maintenance, Helps in gardening and animal care, Helps in
construction and repairs, Helps in shopping and services, Helps in work and agriculture, Helps in childcare, Helps to
adults in other households, Unspecified informal helps, Unspecified Shopping and services, Shopping, Commercial and
administrative Services, Other specified shopping and services, Travel related with house activities, Travel related with
shopping and services, Travel related with childcare, Travel related with adult people care of the family
Childcare
Unspecified Childcare, Child physical care and vigilance, Teaching child, Reading, playing talking with child,
Accompanying child, Other specified childcare activities
(continued )
Total work time in Spain: evidence from time diary data
1909
Table B1. Continued
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Leisure
Unspecified gardening and animal care activities, Gardening, Domestic animal care, Pet care, Walking dog, Other activities
related to gardening and pet care, Picking mushrooms, etc..., Dressing and toilet, Imputed personal and household care
(own computation), Unspecified personal care, Other Unspecified personal care, Other specified personal care, Personal
services, Travel related with personal care, Unspecified meals and drinks, Principal meals and drinks, Not principal
meals and drinks, Appetizer, Afternoon snack, Other not principal meals and drinks, Unspecified sleep, Sleep, Sick in
bed, Imputed sleep (own computation), Travel related to organizational work, Travel related to informal help to other
households, Travel related to participatory activities, Travel related to social activities, Travel related to cultural and fun
activities, Travel related to hobbies, Travel related to location changes, Driving for pleasure, Travel with Unspecified
purpose, Travel related to sport and cultural outdoor activities, Imputed time away from home (own computation),
Unspecified cultural and fun acts, Art expositions and museums, Library, Other specified fun and cultural activities,
Travel related to sport and cultural outdoor activities, Unspecified sport or outdoor activities, Unspecified physical sport,
Running, Cycling, skiing or skating, Ball games, Gymnastics, Fitness and bodybuilding, Aquatic games, Other specified
physic exercises, Unspecified productive exercise, Hunting and fishing, Other specified productive exercise, Activities
related to Sports, Sports events, Walking, Travel with Unspecified purpose, Unspecified volunteer work and meetings,
Unspecified organizational work, Work for an organization, Volunteer work through an organization, Other specified
organizational work, Unspecified participatory activities, Meetings, Other specified participatory activities, Cinema,
Unspecified concerts and theatre, Theatre, Classic music concerts, opera, ballet, Modern music and other music concerts,
Parties, Unspecified social and fun activities, Unspecified social relations, Family social life, Visiting and receiving
visits, Other specified social relations, Unspecified meals and drinks, Principal meals and drinks, Not principal meals and
drinks, Appetizer, Afternoon snack, Other not principal meals and drinks, Listening to unspecified radio or music,
Listening to radio, Unspecified mass media, Watching unspecified television or video, Watching television, Watching
video, Listening records, tapes, etc..., Homework and library, Free time study, Computing-programming, Information
search, Unspecified reading, Reading books, Unspecified reading press, Reading newspapers, Reading magazines,
Reading press by internet, Other specified readings, General passive leisure, Passive leisure, Activities related to the
Time-Use Survey, Activities related to other surveys, Unspecified use of time, Other specified free time, Phone
conversations, Unspecified social and fun activities, Unspecified social relations, Family social life, Visiting and
receiving visits, Other specified social relations, Handcraft and producing textiles, Unspecified games and hobbies,
Unspecified art hobbies, Visual arts, Plastic arts, Photography, Cine, Other visual arts, Unspecified performing arts,
Musical hobbies, Theatre, Other performing arts, Literary arts, Other specified artistic hobbies, Unspecified hobbies,
Collectionism, Unspecified information with computer, Electronic mail, Chatting by Internet, Other communications
with computer, Other computing hobbies, Correspondence, Mobile phone information, Unspecified mobile phone
communication, Messages with mobile phone, Other communications with mobile phone, Other specified hobbies,
Unspecified games, Alone games, Society games, Computing games, Gambling, Other specified games, Activities
related to Time Use Survey, Activities related to other surveys
Source: Spanish Time-Use Survey 2002–03 and 2009–10.