Published online: 26 Feb 2014.

Transcription

1 This article was downloaded by: [Jose Ignacio Gimenez-Nadal] On: 28 February 2014, At: 07:57 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Applied Economics Publication details, including instructions for authors and subscription information: Total work time in Spain: evidence from time diary data Jose Ignacio Gimenez-Nadal a & Almudena Sevilla b 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 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, , DOI: / To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

2 Applied Economics, 2014 Vol. 46, No. 16, , Total work time in Spain: evidence from time diary data Jose Ignacio Gimenez-Nadal a, * and Almudena Sevilla b a Economic Analysis, University of Zaragoza, Zaragoza, Spain b School of Business and Management, Queen Mary University of London, London, UK Using detailed time-use data from and 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 , compared to 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. ngimenez@unizar.es Taylor & Francis

3 Total work time in Spain: evidence from time diary data 1895 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 Gimenez- Nadal 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 to 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 and hours per week in total work in the period and , respectively, women devoted and 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 compared to 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; Gimenez- Nadal 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

4 1896 J. I. Gimenez-Nadal and A. Sevilla 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; 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

5 Total work time in Spain: evidence from time diary data 1897 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-NadalandMolina,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,whereε > 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 TW m = εw m and TW f = εw f, 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 TW m TW f = εw m εw f = ε(w m w f ). 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 (TW m *) and women (TW f *) 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 TW m =TW m * and TW f =TW f *. For ϕ between 0 and infinity, the social/ gender norm pulls optimal total work choice away from TW = εw towards TW*, and hence TW m ¼ αεw ð m TW f ¼ αεw f Þþð1 αþtw m þ ð1 αþtw f with the weight 0 α 1anddefined 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. Ifϕ =, α = 0, indicating that the social/gender norm is infinitely powerful and obligates individuals to choose TW m =TW m * and TW f =TW f *. If we focus on the gender differences in total work time, we obtain that TW m TW f = αε(w m w f )+(1 α)(tw m* TW f* ). 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 TW m TW f = ε(w m w f ). 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.

6 1898 J. I. Gimenez-Nadal and A. Sevilla Table 1. Time devoted to total work, Spain and Year Year Hours per week Mean SD Mean SD Men (0.28) (0.39) Women (0.20) (0.30) Diff p-value (<0.01) (<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. 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 (w m w f ) 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 (TW m *) and women (TW f *) 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 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 We examine diary data from Spain collected in and , 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 10- minute 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 2 We have also done the analysis including students in an alternative sample. Results are robust to the inclusion and are available upon request.

7 Total work time in Spain: evidence from time diary data 1899 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 and (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 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 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 3 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.

8 1900 J. I. Gimenez-Nadal and A. Sevilla 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 and 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 and , respectively, with these gender differences being statistically significant at standard levels. Specifically, men devote and hours per week to total work in and , while women devote and 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 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 , 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 and 19.4% in ; unemployment rates for women were around 16.% in and 20% in ), 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

9 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 Year Hours per week Mean SD Mean SD Diff. Cum Diff. p-value Paid work Men (0.36) (0.50) (<0.01) Women (0.26) (0.36) 0.36 Unpaid work Men 9.40 (0.14) (0.22) (<0.01) Women (0.19) (0.28) 3.57 Childcare Men 2.19 (0.06) 3.83 (0.14) (0.80) Women 5.22 (0.11) 6.79 (0.19) 1.57 Leisure Men (0.33) (0.46) (<0.01) Women (0.24) (0.35) 2.42 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 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 and , 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 and , 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 and , for individuals with a high school degree it is 4.54 and 7.22 hours per week in and , and for individuals with some college or more it is 3.51 and 3.15 hours per week in and , respectively. We also find a negative gradient between education and the increase in the gender gap between and In particular, the gender gap increased by 3.17 hours per week between and for low educated individuals, by 2.68 hours per week between and 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 ). 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

10 1902 J. I. Gimenez-Nadal and A. Sevilla Table 3. Time devoted to total work by groups of population Year Year Hours per week Mean SD Mean SD Panel A: Education Less than high education Men (0.64) (0.86) Women (0.36) (0.57) Diff p-value (<0.01) (<0.01) High school degree Men (0.38) (0.56) Women (0.29) (0.44) Diff p-value (<0.01) (<0.01) Some college and more Men (0.53) (0.68) Women (0.46) (0.57) Diff p-value (<0.01) (<0.01) Panel B: Children versus No Children Children <18 in hhld Men (0.41) (0.56) Women (0.30) (0.43) Diff p-value (<0.01) (<0.01) No children <18 in hhld Men (0.38) (0.53) Women (0.27) (0.40) Diff p-value (<0.01) (<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. 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 and , 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 and , while for those living in households without children under 18 it is 3.68 and 5.57 hours per week in and , 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

11 Total work time in Spain: evidence from time diary data 1903 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: T i ¼ α þ β 1 Female þ β 2 year 2009 þ β 3 X i þ β 4 Interactions i þ β 5 Z i þ ε i (1) where T i is the time devoted to total work by individual i, Female i takes value 1 if respondent i is female and value 0 otherwise, Year_2009 i takes value 1 if respondent i comes from the STUS and value 0 otherwise, X i 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, Interactions i includes a vector of interactions between the variables included in vector X i and the Female i and Year_2009 i variables and Z i 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 Thus, β 1 > 0 would indicate that compared to males, females in Spain devote more time to total work, while β 2 < 0 would indicate that the time devoted to total work by individuals in Spain has decreased between and However, Table 1 shows that the gender difference in the time devoted to total work has increased between and , and for this reason we include an interaction term between Female and Year_2009, which corresponds to β 3, 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 We observe that β 1 > 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, β 2 < 0 and is statistically significant at standard levels, indicating that for the reference category (males) the time devoted to total work decreased between and by 3.61 hours per week. However, β 3, 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 and Table 4. OLS regressions on the time devoted to total work Hours per week Analysis by gender Analysis by education Analysis by presence of children <18 Female 4.41*** 7.17*** 4.28*** (0.34) (0.80) (0.47) Year *** 2.01* 4.88*** (0.47) (1.12) (0.68) Female*Year *** *** (0.59) (1.33) (0.84) Secondary education 1.91*** 4.21*** 1.89*** (0.37) (0.77) (0.37) University education 3.48*** 6.20*** 3.41*** (0.43) (0.85) (0.43) Female * Secondary education 3.11*** (0.92) Female * University 3.32*** education (1.03) Secondary 2.12* education*year 2009 (1.29) University (0.97) education*year 2009 (1.37) Female * Secondary 1.61 education*year 2009 (1.57) Female * University 2.30 education*year 2009 (1.69) Children <18 in the 4.45*** 4.48*** 3.71*** household (0.35) (0.35) (0.58) Female*Children <18 in 0.22 the household (0.69) Children <18 in the 2.26** household*year 2009 (0.94) Female*Children <18 in 0.29 the household*year (1.18) 2009 Number of observations 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: T i ¼ α þ β 1 Female i þ β 2 Year 2009 i þ β 3 X i þ β 4 Interactions i þ β 5 Z i þ ε i, where T i is the time devoted to total work by individual i, Female i takes value 1 if respondent i is female and value 0 otherwise, Year_2009 i takes value 1 if respondent i comes from the STUS and value 0 otherwise, X i 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, Interactions i includes a vector of interactions between the variables included in vector X i and the Female i and Year_2009 i variables, and Z i 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 If we consider the decrease in the time devoted to total work by women between and

12 1904 J. I. Gimenez-Nadal and A. Sevilla (β 2 + β 3 ), 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 β 1 > 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 and , 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 Results are shown in Column (3) of Table 4, where the reference category is males with no children under 18 in the household in We observe that β 1 > 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, β 2 < 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 and by 4.88 hours per week. However, β 3, 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 and If we consider the decrease in the time devoted to total work by women between and (β 2 + β 3 ), 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 ( ), while women with children have decreased the time devoted to total work by 0.03 hours per week, ( ). 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 and , 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

13 Total work time in Spain: evidence from time diary data 1905 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 compared to 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 gapintotalwork,asthetwostussurveysarecrosssection data sets composed of time-use diaries of individuals, covering one year, and thus we have insufficient timevariationtoidentifyhowtheallocationoftime evolves over the business cycle. (Only the ATUS has an annual periodicity.) We leave these issues for future research. References Aguiar, M. and Hurst, E. (2007) Measuring trends in leisure: the allocation of time over five decades, Quarterly Journal of Economics, 115, Aguiar, M. and Hurst, E. (2009) A summary of trends in American time allocation: , Social Indicators Research, 93, Aguiar, M., Hurst, E. and Karabarbounis, L. (2013) Time use during recessions, American Economic Review, 103, Ahn, N., Jimeno, J. F. and Ugidos, A. (2005) Mondays in the sun: unemployment, time use and consumption patterns in Spain, in The Economics of Time Use, Daniel, H. and Gerard, P. (Eds), Elsevier, Amsterdam. Alesina, A., Di Tella, R. and McCulloch, R. (2004) Inequality and happiness: are Americans and Europeans different?, Journal of Public Economics, 88, Alesina, A., Ichino, A. and Karabarbounis, L. (2011) Gender based taxation and the division of family chores, American Economic Journal: Economic Policy, 3, Alvarez, B. and Miles, D. (2003) Gender-effect in housework allocation: evidence from Spanish two-earner couples, Journal of Population Economics, 16, Alvarez, B. and Miles, D. (2006) Husbands housework time: does wives employment status make a difference?, Investigaciones Económicas, 30, Amuedo-Dorantes, C. (2000) Work Transitions into and out of involuntary temporary employment in a segmented market: evidence from Spain, Industrial and Labor Relations Review, 53, Baxter, J. (2002) Patterns of change and stability in the gender division of household labour in Australia, , Journal of Sociology, 38, Bianchi, S. M. (2000) Maternal employment and time with children: dramatic change or surprising continuity?, Demography, 37, doi: /dem Bianchi, S. M., Milkie, M., Sayer, L. et al. (2000) Is anyone doing the housework? Trends in the gender division of household labor, Social Forces, 79, Bianchi, S. M., Robinson, J. P. and Milkie, M. A. (2006) Changing Rhythms of American Family Life, Russell Sage, New York. Biddle, J. and Hamermesh, D. (1990) Sleep and the allocation of time, Journal of Political Economy, 98, Bittman, M. (1999) Now that the future has arrived: a retrospective of Gershuny s theory of social innovation, Social Policy Research Centre, Discussion Paper, No. 110, University of New South Wales, Kensington. Bittman, M., England, P., Folbre, N. et al. (2003) When does gender rump money? Bargaining and time in household work, The American Journal of Sociology, 109, Bittman, M. and Wajcman, J. (2000) The rush hour: the character of leisure time and gender equity, Social Forces, 79, Borra, C. (2009) Child care choices in Spain, Journal of Family and Economic Issues, 30, Brines, J. (1994) Economic dependency, gender, and the division of labor at home, American Journal of Sociology, 100,

14 1906 J. I. Gimenez-Nadal and A. Sevilla Budig, M. J. and Folbre, N. (2004) Activity, proximity, or responsibility? Measuring parental child care time, in Family Time: The Social Organization of Care, Folbre, N. and Bittman, M. (Eds), Routledge, New York. Burda, M., Hamermesh, D. and Weil, P. (2008) The distribution of total work in the US and EU, in Working Hours and Job Sharing in the EU and USA: Are Americans Crazy? Are Europeans Lazy?, Boeri, T., Burda, M. and Kramarz, F. (Eds), Oxford University Press, Oxford. Burda, M., Hamermesh, D. and Weil, P. (2013) Total work and gender: facts and possible explanations, Journal of Population Economics, 26, Carrasco, C. and Rodriguez, A. (2000) Women, families, and work in Spain: structural changes and new demands, Feminist Economics, 6, Clark, A. (1997) Job satisfaction and gender: why are women so happy at work?, Labour Economics, 4, Coltrane, S. (1989) Household labor and the routine production of gender, Social Problems, 36, 5, Coltrane, S. (2000) Research on household labor: modeling and measuring the social embeddedness of routine familywork, Journal of Marriage and the Family, 62, de Laat, J. and Sevilla, A. (2011) The fertility and women s labor force participation in OECD countries, Feminist Economics, 17, de la Rica, S., Dolado, J. J. and Llorens, V. (2008) Ceilings or floors?: Gender wage gaps by education in Spain, Journal of Population Economics, 21, de la Rica, S., Dolado, J. J. and Vegas, R. (forthcoming) Performance pay and the gender wage gap: evidence from Spain, Annals of Economics and Statistics. Esping-Andersen, G. (1999) Social Foundations of Postindustrial Economies, Oxford University Press, Oxford. EUROSTAT (2013) Statistics, Population and Social Conditions, European Commision, Brussels. Fahr, R. (2005) Loafing or learning?-the demand for informal education, European Economic Review, 49, Fernández, R. (2010) Does culture matter?, in Handbook of Social Economics, Vol. 1a, Benhabib, J., Bisin, A. and Jackson, M. (Eds), Elsevier, Amsterdam, pp Fernandez-Kranz, D. and Rodriguez-Planas, N. (2011) The parttime pay penalty in a segmented labor market, Labour Economics, 18, Fisher, K., Egerton, M., Gershuny, J. et al. (2007) Gender convergence in the American heritage time use study, Social Indicators Research, 82, Folbre, N. and Bittman, M. (2004) Family Time: The Social Organization of Care, Routledge, New York. Foster, G. and Kalenkoski, C. (2013) Tobit or OLS? An empirical evaluation under different diary window lengths, Applied Economics, 45, Frazis, H. and Stewart, J. (2012) How to think about time-use data: what inferences can we make about long- and shortrun time use from time use diaries?, Annals of Economics and Statistic, 105/106, Garcia, I. and Molina, J. A. (1998) Household labor supply with rationing in Spain, Applied Economics, 30, Garcia, I., Molina, J. A. and Montuenga, V. (2011) Gender differences in children: time allocation in five European countries, Feminist Economics, 17, Garcia, J., Hernandez, P. J. and Lopez-Nicolas, A. (2001) How wide is the gap? An investigation of gender wage differences using quantile regression, Empirical Economics, 26, Gauthier, A. H., Smeeding, T. M. and Furstenberg, F. F. (2004) Are parents investing less time in children? Trends in selected industrialized countries, Population and Development Review, 30, Gershuny, J. (2000) Changing Times, Work and Leisure in Post Industrial Society, Oxford University Press, Oxford. Gershuny, J. (2012) Too many zeros: a method for estimating long-term time-use from short diaries, Annals of Economics and Statistics, 105/106, Gershuny, J. (2013) National utility: measuring the enjoyment of activities, European Sociological Review, 29, Gimenez-Nadal, J. I., Marcen, M. and Ortega, R. (2010) How do children affect parents allocation of time?, Applied Economic Letters, 17, Gimenez-Nadal, J. I. and Molina, J. A. (2013) Parents education as a determinant of educational childcare time, Journal of Population Economics, 26, Gimenez-Nadal, J. I. and Molina, J. A. (forthcoming) Regional unemployment, gender, and time allocation of the unemployed, Review of Economics of the Household. Gimenez-Nadal, J. I., Molina, J. A. and Ortega, R. (2012a) Selfemployment and the work-life balance, Applied Economics, 44, Gimenez-Nadal, J. I., Molina, J. A. and Sevilla, A. (2012b) Social norms, partnerships and children, Review of Economics of the Household, 10, Gimenez-Nadal, J. I. and Ortega, R. (2010) Self-employment and time stress: the effect of leisure quality, Applied Economics Letters, 17, Gimenez-Nadal, J. I. and Sevilla, A. (2011) The time-crunch paradox, Social Indicators Research, 102, Gimenez-Nadal, J. I. and Sevilla, A. (2012) Trends in time allocation: a cross-country analysis, European Economic Review, 56, Greenstein, T. N. (2000) Economic dependence, gender, and the division of labor in the home: a replication and extension, Journal of Marriage and Family, 62, Guryan, J., Hurst, E. and Kearney, M. (2008) Parental education and parental time with children, Journal of Economic Perspectives, 22, Gutierrez-Domenech, M. (2005) Employment after motherhood: a European comparison, Labour Economics, 12, Gutierrez-Domenech, M. (2010) Parental employment and time with children in Spain, Review of Economics of the Household, 8, Hallberg, D. (2003) Synchronous leisure, jointness and household labor supply, Labour Economics, 10, Hamermesh, D. (2002) Timing, togetherness and time windfalls, Journal of Population Economics, 15, Hochschild, A. R. (1997) The Time Bind: When Work Becomes Home and Home Becomes Work, Metropolitan Books, New York. Juster, T. and Stafford, F. (1985) Time, Goods, and Well-Being, Institute for Social Research, The University of Michigan, Ann Arbor, MI. Kahneman, D. and Krueger, A. B. (2006) Developments in the measurement of subjective well-being, Journal of Economic Perspectives, 20, Kahneman, D., Krueger, A. B., Schkade, D. et al. (2004) A survey method for characterizing daily life experience: the day reconstruction method, Science, 306, doi: /science Kalenkoski, C. M. and Pabilonia, S. W. (2012) Time to work or time to play: the effect of student employment on homework, sleep, and screen time, Labour Economics, 19,

15 Total work time in Spain: evidence from time diary data 1907 Katz, L. and Murphy, K. (1992) Changes in relative wages, : supply and demand factors, Quarterly Journal of Economics, 107, Klevmarken, N. A. (2005) Estimates of a labour supply function using alternative measures of hours of work, European Economic Review, 49, Knabe, A., Rätzel, S., Schöb, R. et al. (2010) Dissatisfied with life, but having a good day: time-use and well-being of the unemployed, Economic Journal, 120, Krueger, A. B. (2007) Are we having more fun yet? Categorizing and evaluating changes in time allocation, Brooking Papers on Economic Activity, 2, Lewis, J. (2009) Work-Family Balance, Gender and Policy, Edward Elgar, Cheltenham. Luengo-Prado, M. J. and Sevilla-Sanz, A. (2013) Time to cook: expenditure at retirement in Spain, Economic Journal, 123, Mattingly, M. J. and Bianchi, S. M. (2003) Gender differences in the quantity and quality of free time: the US experience, Social Forces, 81, Mattingly, M. J. and Sayer, L. C. (2006) Under pressure: gender differences in the relationship between free time and feeling rushed, Journal of Marriage and Family, 68, Appendix A: Demographic Weighting 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, 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 Robinson, J. P. and Godbey, G. (1999) Time for Life: The Surprising Ways Americans Use Their Time, Pennsylvania State University Press, University Park, PA. Sayer, L. C. (2005) Gender, time, and inequality: trends in women s and men s paid work, unpaid work and free time, Social Forces, 84, Schor, J. (1991) The Overworked American: The Unexpected Decline of Leisure, Basic Books, New York. Sevilla, A. (2010) Household división of labor and cross-country differences in household formation rates, Journal of Population Economics, 23, Sevilla, A., Gimenez-Nadal, J. I. and Fernandez, C. (2010) Gender roles and the division of unpaid work in Spanish households, Feminist Economics, 16, Stiglitz, J., Sen, A. and Fitoussi, J.-P. (2009) Report by the Commission on the Measurement of Economic Performance and Social Progress, The French Economic Observatory (OFCE), Paris. Tobin, J. (1958) Estimation of relationships for limited dependent variables, Econometrica, 26, doi: / Weichselbaumer, D. and Winter-Ebmer, R. (2005) A meta-analysis of the international gender wage gap, Journal of Economic Surveys, 19, 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 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 and , respectively) and that the proportion of households with at least one child under 18 has increased in the period compared to the period (from 42.98% to 47.26% of the sample in the periods and , respectively), consistent with the fact that the number of children under 18 in the households is larger in (0.75 children) compared to (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 compared to , 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).

16 1908 J. I. Gimenez-Nadal and A. Sevilla Table A1. Sum stats of demographic characteristics, by year of survey Demographic characteristics Year Year Male (0.35) (0.59) Age (0.08) (0.13) Secondary education (0.35) (0.59) University education (0.32) (0.53) Presence of children < (0.34) (0.59) Living in Couple (0. 27) (0. 39) Observations 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 Unpaid work Childcare 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 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 Unspecified Childcare, Child physical care and vigilance, Teaching child, Reading, playing talking with child, Accompanying child, Other specified childcare activities (continued)

17 Total work time in Spain: evidence from time diary data 1909 Table B1. Continued 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 and