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Work and Time Use By Gender:A New Clustering of EuropeanWelfare SystemsLina Gálvez-Muñoz a , Paula Rodríguez-Modroño b &Mónica Domínguez-Serrano ca Department of Economics, University Pablo deOlavide, Carretera Utrera Km. 1, Seville, 41013, SpainE-mail: lgalvez@upo.esb Department of Economics, University Pablo deOlavide, Carretera Utrera Km. 1, Seville, 41013, SpainE-mail: prodmod@upo.esc Department of Economics, University Pablo deOlavide, Carretera Utrera Km. 1, Seville, 41013, SpainE-mail: mdomser@upo.es

Available online: 03 Nov 2011

To cite this article: Lina Gálvez-Muñoz, Paula Rodríguez-Modroño & Mónica Domínguez-Serrano (2011): Work and Time Use By Gender: A New Clustering of European WelfareSystems, Feminist Economics, 17:4, 125-157

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WO R K A N D TI M E US E BY GE N D E R:A NE W CL U S T E R I N G O F EU R O P E A N WE L F A R E

SY S T E M S

Lina Galvez-Munoz, Paula Rodrıguez-Modrono, andMonica Domınguez-Serrano

ABSTRACT

Using Harmonised European Time-Use Survey (HETUS) data, this study showshow care work that takes place outside the marketplace represents an essentialand distinctive part of national economies. Cross-national comparisons showpersistent patterns and differences in observed gender inequalities on totalworkload and care responsibilities. This country-by-country and group-by-groupanalysis is based on cluster methodology. The main finding is that includingtime use in gendered analyses of welfare regimes shows how unpaid care work isat the core of gender inequality in all countries. The results of this analysisindicate that Eastern European countries are very heterogeneous and aredistributed across three out of the four clusters obtained, a finding thatconstitutes a new departure point for analysis. Based on these findings, thisstudy makes public policy recommendations about the importance of time-usesurveys and how to improve the quality of care without decreasing women’swell-being and autonomy.

KEYWORDS

Gender, time-use surveys, welfare regimes, care regimes

JEL Codes: C8, J22

INTRODUCTION

Time-use data allow us to show how care work performed outside themarketplace represents an essential and distinctive part of nationaleconomies and to highlight how this work is unevenly distributed amongwomen and men. Cross-national comparisons among time-use surveysshow persisting patterns and differences in observed gender inequalities interms of total workload and care responsibilities. Women and men makeimportant choices in how to allocate their time, but their choices areconsiderably constrained by the various restrictions and social conditionsthey encounter, including the institutional environment in which they live.

Feminist Economics 17(4), October 2011, 125–157

Feminist Economics ISSN 1354-5701 print/ISSN 1466-4372 online � 2011 IAFFEhttp://www.tandf.co.uk/journals

http://dx.doi.org/10.1080/13545701.2011.620975

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Each society determines the distribution of welfare provisions, of whichcare is a central feature, among the state, the market, the family, andthe community in a different and dynamic way. The literature hascategorized the various institutional arrangements that contribute to thetotal sum of societal welfare in different ways, such as welfare regimes,the welfare triangle, welfare architecture, and the welfare diamond(Shahra Razavi 2007). In the welfare diamond, not only the state andthe market, but also civil society and the family, provide welfareconstituting the four vertices of the diamond. Razavi (2007) builds onthis idea to construct a care diamond, where the focus is on the diversityof sites in which care is produced and on societal decisions to privilegesome forms of care provision over others. As Razavi states:

the role of the state in the welfare architecture is of a qualitativelydifferent kind, compared to, say, families or markets, because thestate is not just a provider of welfare, but also a significant decisionmaker about the responsibilities to be assumed by the other three setsof institutions. (2007:20)

The societal and institutional conditions under which welfare ismade available encourage different types of family and genderrelations. Time-use data allow us to look for the differing genderimpacts of the combination of care provision between the differentvertices of the diamond, namely, the state, the market, the family, andcivil society.

Harmonised European Time Use Surveys (HETUS) were promoted byEurostat and carried out in the countries under consideration in this studyin the following years: Estonia (1999–2000), Italy (2002–3), Spain (2002–3),Belgium (1998–2000), France (1998–9), Germany (2001–2), Hungary(1999–2000), Poland (2003–4), the United Kingdom (2000–1), Finland(1999–2000), Norway (2000–1), Sweden (2000–1), Latvia (2003), Lithuania(2003), and Slovenia (2000–1). Prior to these surveys, comparative studiesin the European context faced limitations related to the differentmethodologies that reduced the sample of countries or cases.1 Currently,the existence of harmonized surveys across different countries allows us notonly to make cross-national comparisons but also to explore in depth thereasons for uneven time use between women and men. Countries differ intheir occupational structure, human capital, per-capita income, and fertilityrates, as well as in their institutional development, their welfare systems,and the various patterns of time use that combinations of welfare provisionimply.2 In this sense, Diane Sainsbury (1994) argues that variations acrosscountries should be the focal point for research because some systemsare comparatively more ‘‘woman-friendly,’’ to use a term coined by HelgaHernes (1987).3

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This contribution uses time-use data to examine the role of unpaid carework in different European welfare systems. Following Diane Elson (2000),we use the phrase ‘‘unpaid care work’’ to cover all the unpaid servicesprovided within a household for its members, including both care forchildren, the elderly, or the disabled and care for able-bodied adults,including housework. Using microdata from the Spanish survey, LinaGalvez-Munoz, Paula Rodrıguez-Modrono, and Monica Domınguez-Serrano (2008, 2010, 2011) show that the largest increase in women’sunpaid care work takes place when they start living with male companions(whether married or not), and this increase is much higher than theincrease that occurs when Spanish women have their first children. In thiscontribution, we extend this analysis to HETUS to examine the genderimpacts that result from differing combination of care provision betweenthe state, market, family, and civil society and explore in depth the reasonsfor uneven time use between women and men.

GENDERING WELFARE REGIMES THROUGH ATTENTIONTO UNPAID CARE WORK

The most influential analysis of welfare regimes is Gøsta Esping-Andersen(1990), in which politics and political institutions play the leading role.Esping-Andersen focuses on clusters of countries and dominantinstitutional patterns to characterize the relationship between the stateand the economy across advanced capitalist countries. He analyzes anddescribes decommodification – the capacity of people to achieve sociallyacceptable standards of living independent of market participation – andgroups countries into three different types of welfare regime: the social-democratic, exemplified by Sweden and Norway; the liberal, exemplifiedby the United States, the UK, Canada, and Australia; and the conservative-corporatist or status-based, such as Germany, France, and Italy.

Esping-Andersen (1990) characterized the social-democratic regime asemphasizing universalism; a strong role for the state; the integration ofsocial and economic policies; and full employment as primary objectives.These objectives are consistent with women’s high economic activity rates.Among liberal regimes, state interventions are clearly subordinate tomarket mechanisms, and these states show a relatively strong emphasis onmeans-tested programs. Individuals are constituted primarily as marketactors and are encouraged to seek welfare via the market. Finally, theconservative-corporatist type of regime links access to welfare rights to classand status through a variety of social insurances schemes and is deeplyconcerned with maintaining an organic hierarchical order. It has a strongcommitment to the maintenance of the traditional family and tends toprovide social services only when the family’s ability to cope is exhausted(Esping-Andersen 1990). We use the proposed threefold classification of

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welfare capitalism into social-democratic, liberal, and conservative-corporatist regimes as a starting-point to which we add a fourth category,namely, the Mediterranean or Latin regime. The last category ischaracterized by a low level of unionization, with welfare benefitsdependent on the labor market status and low social expenditures(Francis G. Castles and Deborah Mitchell 1990).

One of the strengths of feminist literature on welfare regimes has been itscross-national comparative dimension. However, despite the centrality ofcare for human welfare, the first wave of comparative social policy researchignored the importance of care (Razavi 2007). Feminists have criticizedEsping-Andersen’s (1990) framework for its failure to consider the role ofgender relations in shaping policy outcomes and the impact of the state ongender relations, inequality, and power (Sainsbury 1994; Ann Shola Orloff1996). Feminist scholars like Jane Lewis (1992) have highlighted the malebias in taking the so-called average industrial worker and the male-breadwinner-family model as the empirical point of departure. Lewis looksat ‘‘the way in which welfare states have treated women as wives andmothers, and as paid workers’’ (1992: 162). Lewis’s analysis constitutes agood reference for comparing countries in terms of the extent to whichthey have extended a male-breadwinner form of social care. The objectionsmay be grouped around three issues.

First, the family was ignored as the provider of well-being by comparativepolitical economy (Lewis 1992; Gøsta Esping-Andersen 2000). Yet, it iswithin the family that the bulk of unpaid care work is done and genderinequalities are more pronounced. Including the family in a welfaretriangle together with the state and the market allows for the considerationof unpaid care. Additionally, including the family in these analyses revealsthe distinctiveness of the Mediterranean model. Almudena Moreno-Mınguez (2007) describes the Mediterranean welfare regime ascharacterized by the existence of strong ‘‘familialism,’’ which is definedin the literature as the maintenance of intergenerational solidarity, thesurvival of the ‘‘male-breadwinner’’ model, weak institutional support tofamilies, a dual labor market model, an aging population, and women’slimited access to the labor market.

Second, decommodification is not necessarily a positive indicator forwomen in terms of autonomy. In fact, Esping-Andersen’s (1990) workconsiders class and citizenship as gender-neutral categories, just as itdefines the decommodification criterion as a gender-neutral indicator.However, decommodification does not necessarily indicate the autonomyof women, as paid work often provides them with a greater degree ofautonomy than unpaid care work. Ann Shola Orloff (1993) suggests twonew dimensions – access to work (or the right to be commodified) and thecapacity to form and maintain an autonomous household – for the analysisof welfare states to capture the effects of social policies on gender relations.

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In this sense, Julia S. O’Connor (1993) argues that the concept of personalautonomy or insulation from personal and/or public dependence mustsupplement the concept of decommodification or insulation from thepressures of the labor market, noting that the accessibility and range ofservices available determine the levels of both decommodification andpersonal autonomy.4 The concept of decommodification does not takeinto account the fact that demographic groups are not equallycommodified and have different access to the labor market. Moreover,the analytical framework of decommodification should include women’sdisproportionate unpaid care work burden (Orloff 1993).

Third, viewed through O’Connor’s (1993) concept of personal autonomy,the emphasis in Esping-Andersen’s research (1990) was on incometransfers instead of public services. Feminist social policy literature, on thewhole, rates the provision of public services for care-related needs morepositively than cash payments since it has several important advantagesfrom a gender-equality perspective. Public services simultaneously providecare and jobs for women that foster their financial autonomy.

These criticisms have resulted in the development of new and differentclassifications of welfare regimes. In one approach, Lewis (1992) analyzeswelfare regimes in reference to the strength or weakness of their levels ofcommitment to the male-breadwinner model. This methodology focuseson three different aspects: first, how women are treated in social securityand tax systems; second, the level of public service provision, especiallyconcerning childcare; and third, women’s position in the labor market.Lewis (1992) considers Sweden a ‘‘weak male-breadwinner or dual-breadwinner model,’’ given women’s high rates of labor forceparticipation, the good public provision of childcare services, andindependent payment of income tax by women and men, along withhigh marginal tax rates. She classifies the UK and Ireland as a ‘‘strongmale-breadwinner model’’ and France somewhere in between as a‘‘modified male-breadwinner model’’ (Lewis 1992). Although her clustersare somewhat different from Esping-Andersen’s (1990) analysis, hergrouping is not very far from his classification.

Sainsbury (1994) critiques Lewis’s (1992) typology for its failure to payappropriate attention to care. Sainsbury examines welfare regimes in termsof their similarity to one of two gendered ideals: the male-breadwinnermodel and the individual model in which both men and women areearners and carers, welfare benefits are targeted at individuals, and muchof care work is publicly paid and provided. Diane Sainsbury (1999)considers states to vary in terms of women’s eligibility for benefits based ontheir three primary social positions – that is, wives, mothers, and workers.Unlike other authors who assign specific countries to specific welfareregimes, Sainsbury considers that gender regimes intersect with welfareregimes, and vice versa.

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With the recent integration of Eastern European countries into the EU,we must allow for the possibility of additional regimes. In addition to theliberal, conservative, and social-democratic welfare regimes, Angelika vonWahl (2005) converts the care diamond into a care pentagon by includinga supranational level, the EU as an equal employment regime, whichreflects the expanding Europeanization of gender politics. EasternEuropean countries are in a process of economic and political structuralchange as part of their transition to the market economy that includes animportant withdrawal of the state (Susan Strange 2001),5 and they are thusfollowing different paths that existing models may not account for. The costof providing care has been increasingly shifting from the paid public sectorto the unpaid sector of family and civil society in Eastern Europeancountries.

DATA FROM HETUS

The data on time use in this contribution originate from HETUS, whichincludes fifteen national time-use surveys conducted between 1998 and2004. For a complete description of guidelines for the surveys, includingsample design and all definitions and explanations concerning the surveyforms, questionnaires, and the time diary, see Eurostat (2004a). HETUSuses a representative sample of individuals who completed a diary duringone weekday and one weekend day distributed over an entire year. Thesampled persons answered questions related to the individual and thehousehold. The national time-use survey (TUS) results are consideredrepresentative for the populations specified in the tables and figuresHETUS publishes.

The age limits and cohorts of the national surveys that make up HETUSare different; however, all of them included individuals between ages 20and 74, which is the sample used in our analysis. The HETUS activity-coding list was based on international practices and previous classificationsused in Europe, and it allows several groupings of activities depending onthe purpose of the analysis. Eurostat tested and revised the coding systemand index on the basis of time-use pilot surveys in eighteen Europeancountries, with the final system being completed in 2000. We include thefollowing grouping of main activities in our analysis:

. Paid work, which HETUS calls gainful work, includes main andsecond jobs and related activities, breaks and travel during workinghours, and job-seeking activities.

. Unpaid care work, which HETUS calls domestic work, includeshousework, child and adult care, gardening and pet care,construction and repairs, shopping and services, and household

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management.6 As noted earlier, we prefer to use a broad definition ofunpaid care work that includes all the unpaid work required formaintaining the household.

. Study indicates study at school and during free time.

. Travel refers to commuting and trips connected with all kinds ofactivities except for travel during working hours.

. Sleep includes sleep during night or daytime, the time spent fallingasleep, naps, and passive lying in bed because of sickness.

. Meals and personal care includes consuming seated meals, snacks,and drinks, dressing, personal hygiene, applying makeup, shaving,sexual activities, and personal healthcare.

. Free time includes all other kinds of activities not included above,including volunteer work and meetings, helping other households,socializing and entertainment, sports and outdoor activities, hobbiesand games, reading, watching television, and resting or doingnothing, as well as unspecified time use. Some of these activitiescould be included in care work for the society or for kin networks;however, this study only considers care work for people living in thehousehold.

Harmonized time-use surveys like HETUS are an important tool forintroducing nonmarket activities and work in economic analyses and forcomparing different countries. However, HETUS still has some limitations.7

For instance, the survey does not distinguish whether a respondent islooking after an adult because the adult is temporarily sick, elderly, ordisabled. Moreover, the survey underestimates unpaid care work. Therespondent can only write down two activities conducted at the same time,leaving out other tasks conducted simultaneously. For example, a womanmay be cooking, listening to the radio, and looking after a sick familymember, and she may not report the last activity, as it may be so natural forher to do this that she does not perceive it as a specific activity. A substantialproportion of time devoted to care is frequently included in leisureactivities for children, such as taking them to the park. Care of children alsoentails ‘‘being on call,’’ which tends to be underreported by the parent whois responsible.8

Despite the weaknesses of the HETUS, it allows us to advance, as SusanDonath (2000) says, our understanding of the so-called other economy,that is, the unpaid economy in which the direct production andmaintenance of human beings take place. Therefore, we first analyzetime-use data in fifteen European countries to evaluate pattern similaritiesin time use by gender. Second, we use cluster methodology to show theextent to which these patterns can be understood as resulting in distinctgroups of countries.

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GENDER PATTERNS IN THE TIME USE OF ADULTSACROSS SELECTED EUROPEAN COUNTRIES

Adding up paid and unpaid work time, the first common characteristic intime allocation by gender across the fifteen European countries is that, onaverage, women work longer each day than men. Only in Sweden do menand women spend the same time working. In Norway and the UK, womenwork only a few more minutes daily than men. Countries with the largestdiscrepancy of at least 1 hour of work per day between men and women arethe Eastern European and the Mediterranean countries, includingLithuania, Slovenia, Latvia, Estonia, Hungary, Italy, and Spain. In Spain,for example, women work 1 hour and 3 minutes longer per day than men,which indicates an annual difference of 383.25 hours. In Lithuania, womenwork an average of 8 hours per day, 1 hour and 5 minutes more thanLithuanian men who have the highest working hours among all EU men.

The higher female workload leads us to the second commoncharacteristic across the EU countries studied: European women haveless free time. In Lithuania, Italy, and Slovenia, this difference in free timeby gender is 1 hour per day (see Table 1). Note that we are consideringthe average of the time spent on each activity during working days,weekends, and holidays, thus creating an ‘‘average day,’’ during which, forexample, the amount of time spent working is far below that spent duringan ordinary working day. 9

The third common characteristic is that men continue to specialize in themarket economy, while women specialize in the nonmarket economy. Inthe fifteen EU countries under analysis, women generally work longerdoing unpaid care work and spend less time in the market than men (seeFigure 1). In all EU countries, fewer women than men work full time or atall in the market; thus, the average hours women spend on care exceed thetime they allocate to their paid jobs. The opposite phenomenon happenswith men. In all EU countries, men’s average remunerated working hoursfar exceed their hours of unpaid care work. Men usually work 2 hours morein the labor market than they do in care work, which is almost the sametime that women allocate in excess to unpaid care work instead of to theirpaid jobs in the market. Therefore, in general, patriarchal workspecialization still survives in the EU, something that must be taken intoconsideration as a central issue in macroeconomic models and policies.

HETUS shows that the greatest gender inequality currently lies not inpaid working time, as women have been substantially incorporated into thelabor market, but in the differences between the time women andmen spend on unpaid care work. This is explained by the focus of genderequality policies and women’s movements in Europe on improvingwomen’s access to the labor market rather than on achieving a moreegalitarian division of unpaid care work. It is also in accordance with

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Tab

le1

Tim

e-u

sest

ruct

ure

of

wo

men

and

men

ages

20–7

4(h

ou

rsan

dm

inu

tes

per

aver

age

day

)

Est

onia

(199

9–20

00)

Ital

y(2

002–

3)

Spai

n(2

002–

3)

Bel

giu

m(1

998–

2000

)

Fran

ce(1

998–

9)

Ger

man

y(2

001–

2)

Hu

nga

ry(1

999–

2000

)

Pol

and

(200

3–4)

UK

(200

0–1)

Fin

lan

d(1

999–

2000

)

Nor

way

(200

0–1)

Swed

en(2

000–

1)

Lat

via

(200

3)L

ithu

ania

(200

3)Sl

oven

ia(2

000–

1)

WO

ME

NW

ork

tota

l7:

287:

127:

016:

256:

476:

037:

167:

006:

396:

296:

256:

357:

258:

007:

40P

aid

wo

rk2:

261:

522:

061:

532:

171:

522:

182:

152:

242:

332:

382:

533:

293:

312:

42U

np

aid

care

wo

rk5:

025:

204:

554:

324:

304:

114:

584:

454:

153:

563:

473:

423:

564:

294:

58

Stu

dy

0:07

0:14

0:20

0:14

0:14

0:13

0:14

0:14

0:09

0:16

0:15

0:19

0:12

0:10

0:17

Tra

vel

1:06

1:14

1:05

1:19

0:54

1:18

0:51

1:06

1:25

1:07

1:11

1:23

1:20

1:04

1:02

Slee

p8:

358:

198:

328:

298:

558:

198:

428:

358:

278:

328:

108:

118:

448:

358:

24M

eals

and

per

son

alca

re

2:08

2:53

2:33

2:43

3:02

2:43

2:19

2:29

2:16

2:06

2:08

2:28

2:10

2:22

2:08

Fre

eti

me

4:36

4:08

4:29

4:50

4:08

5:24

4:38

4:36

5:04

5:30

5:51

5:04

4:09

3:49

4:29

ME

N Wor

kto

tal

6:23

5:50

5:58

5:53

6:10

5:41

6:12

6:23

6:28

6:04

6:26

6:40

6:50

6:55

6:34

Pai

dW

ork

3:35

4:15

4:21

3:15

3:48

3:20

3:32

4:01

4:10

3:48

4:04

4:11

5:00

4:46

3:54

Un

pai

dca

rew

ork

2:48

1:35

1:37

2:38

2:22

2:21

2:40

2:22

2:18

2:16

2:22

2:29

1:50

2:09

2:40

Stu

dy

0:05

0:11

0:18

0:15

0:15

0:15

0:14

0:14

0:08

0:13

0:12

0:14

0:09

0:09

0:13

Tra

vel

1:17

1:35

1:16

1:35

1:03

1:27

1:03

1:13

1:30

1:12

1:20

1:30

1:28

1:13

1:09

Slee

p8:

328:

178:

368:

158:

458:

128:

318:

218:

188:

227:

578:

018:

358:

288:

17M

eals

and

per

son

alca

re

2:15

2:59

2:35

2:40

3:01

2:33

2:31

2:23

2:04

2:01

2:02

2:11

2:10

2:25

2:13

Fre

eti

me

5:28

5:08

5:17

5:22

4:46

5:52

5:29

5:25

5:32

6:08

6:03

5:24

4:48

4:50

5:34

Sou

rces

:H

ET

US

(Eu

rost

at20

04b

,20

06).

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the historical situation of women and men. Before the economic,technological, and institutional changes called forth by globalizationprocesses, in some countries female employment was seen as a threat tomale employment. As a result, public policies or trade-union lobbies didnot favor the expansion of female employment but instead supported‘‘male breadwinner families’’ (Carmen Sarasua and Lina Galvez-Munoz2003). Parents and grandparents educate girls and boys to imitate theirgender-differentiated behavior regarding market and nonmarket work.Lina Galvez-Munoz, Monica Domınguez-Serrano, Yolanda Rebollo-Sanz,and Paula Rodrıguez-Modrono (2008) have found in Spain that boys andgirls ages 10–17 already exhibit a marked difference in time use in domesticactivities, showing the long period needed to change gender roles andstereotypes.

GENDER PATTERNS IN TIME USE BY EMPLOYED WOMENAND MEN ACROSS SELECTED EUROPEAN COUNTRIES

Across the EU countries studied, if we only take into account employedwomen and men, women’s total workload is still higher than that ofmen on an average day. In countries like Estonia, Italy, Spain, Hungary,Lithuania, and Slovenia, it is up to 1 hour longer (see Table 2). Onlyemployed Norwegian and Swedish women work a few minutes less per daythan employed men. Once again, in Mediterranean countries and Eastern

Figure 1 Working time of women and men ages 20–74 (hours and minutes peraverage day)Note: W and M refer to women’s and men’s hours, respectively.Sources: HETUS (Eurostat 2004b, 2006).

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Tab

le2

Tim

eu

seo

fem

plo

yed

wo

men

and

men

(ho

urs

and

min

ute

sp

erav

erag

ed

ay)

Est

onia

Ital

ySp

ain

Bel

giu

mFr

ance

Ger

man

yH

un

gary

Pol

and

UK

Fin

lan

dN

orw

aySw

eden

Lat

via

Lit

huan

iaSl

oven

ia

WO

ME

NT

otal

wor

k8:

128:

268:

147:

408:

056:

448:

298:

357:

227:

286:

547:

278:

459:

138:

34P

aid

wo

rk4:

084:

354:

453:

484:

333:

334:

354:

373:

544:

073:

283:

555:

375:

494:

10U

np

aid

care

wo

rk

4:04

3:51

3:29

3:52

3:32

3:11

3:54

3:58

3:28

3:21

3:26

3:32

3:08

3:24

4:24

Stu

dy

0:05

0:04

0:12

0:05

0:02

0:19

0:08

0:09

0:12

0:13

0:18

0:10

0:09

0:06

0:13

ME

N Tot

alw

ork

6:55

7:20

7:23

7:13

7:29

6:36

7:29

7:55

7:27

7:23

6:58

7:33

8:02

8:07

7:35

Pai

dw

ork

4:55

6:10

6:03

4:58

5:39

4:44

5:20

6:02

5:33

5:24

4:46

5:10

6:36

6:28

5:11

Un

pai

dca

rew

ork

2:00

1:10

1:20

2:15

1:50

1:52

2:09

1:53

1:54

1:59

2:12

2:23

1:26

1:39

2:24

Stu

dy

0:05

0:03

0:08

0:05

0:03

0:11

0:05

0:08

0:09

0:08

0:10

0:07

0:05

0:03

0:09

Sou

rces

:H

ET

US

(Eu

rost

at20

04b

,20

06).

WORK AND TIME USE BY GENDER

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European countries, employed women’s total work time is higher than theEuropean average.

Women have increased their presence in the labor market, but theyhave not been able to reduce their unpaid work at home in equalproportion, thus bearing the double burden of paid and unpaid work.The main duty of women continues to be the care of everyone in theirfamilies, thus limiting their ability to achieve financial autonomy throughpaid work. After sleeping, unpaid care work constitutes women’s mainactivity in Italy, Estonia, Hungary, Slovenia, Spain, Poland, France, andLithuania, where women spend a daily average of 5 hours on this work. Incontrast, in Sweden, Norway, Finland, and Latvia, women allocate anaverage of 3 hours and 50 minutes to unpaid care work. Although in somecountries there are emerging new social values that are redefiningmasculinity, for the most part men have fewer incentives to involvethemselves in unpaid care work. However, men no longer seem resistantto having another paid worker in the family, mainly because of changes inconsumption patterns and the transformation that has taken place inlabor markets since the 1970s. This transformation entailed greateremployment flexibility but also better job opportunities for women in theservice economy, which resulted in increased wages and alteredopportunity costs for women. Governments have not objected either, asthey benefit from the increase in revenues from taxation of labor incomes,without experiencing a sizeable increase in the average cost of socialtransfers as a percentage of total public expenditure.

In fact, evidence shows that an unequal distribution of unpaid care worktranslates into a greater amount of work for women than for men evenamong those households in which women contribute more income to thefamily budget (Julie Brines 1994; Lina Galvez-Munoz 1997; George A.Akerlof and Rachel E. Kranton 2000). Longitudinal analyses using time-usesurveys show that despite the increase in women’s participation in the labormarket, men’s work time in the household has changed very little inpast decades (Mark Aguiar and Erik Hurst 2007). Empirical evidencedemonstrates that women do more and men do less unpaid care work thanexpected; this holds true both when using a Beckerian model of thehousehold or when employing different bargaining models (Pierre-AndreChiappori 1988, 1992). Therefore, social norms appear to be betterexplanatory variables for the division of labor than purely economicobjectives.

However, the difference between employed women and men is smallerthan the difference between all women and men ages 20–74, suggestingthat gender inequalities are somewhat reduced – though insufficiently –when women enter the labor market and their bargaining power increases.In fact, entering the market reduces the time spent in unpaid care work forboth women and men, but at different rates. On average, women engaged

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in paid work across Europe spend 50 minutes less per day on unpaid workthan women who are not in paid work, whereas men in paid work spendonly 21 minutes less per day than men who are not in paid work.

Across the EU countries, there are a variety of ways in which employedwomen have externalized the unpaid care work that is not assumed by men.In the Scandinavian countries, the development of a large welfare state hasbeen associated with an extension of the provision of care by the publicsector. Moreover, Sweden was the first country to include the daddy quotas,which consist in reserving at least one month of the total parental leaveavailable per child for the father.10

In Eastern European countries, the low income levels and highunemployment rates explain the long total working hours for bothemployed men and women, as well as households’ lack of demand forpaid domestic workers. In Italy and Spain, unpaid care work is externalizedto the market due to the low wages of an increasing population ofwomen immigrants (Lina Galvez-Munoz and Oscar Marcenaro 2008).11 Inaddition, grandmothers play an important role in childcare.

Figure 2 shows how the total work of employed individuals continues tobe unevenly distributed by gender. Men spend an average of three-quartersof their time on paid work, whereas paid work accounts for only 55 percentof women’s total working time, with an average of 2 hours more than meninvested in unpaid care work across all EU countries. Table 3 clearly showsthe gender difference in the distribution of time between paid work and

Figure 2 Structure of working time of employed women and men (hours andminutes per average day)Note: W and M refer to women’s and men’s hours, respectively.Sources: HETUS (Eurostat 2004b, 2006).

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Tab

le3

Gen

der

dif

fere

nti

alin

tim

e-u

sest

ruct

ure

of

emp

loye

dw

om

enan

dm

eno

nan

aver

age

day

Est

onia

Spai

nIt

aly

Bel

giu

mFr

ance

Ger

man

yH

un

gary

Pol

and

UK

Fin

lan

dN

orw

aySw

eden

Lat

via

Lit

huan

iaSl

oven

ia

Wor

kto

tal

1.19

1.12

1.15

1.06

1.08

1.02

1.13

1.08

0.99

1.01

0.99

0.99

1.09

1.14

1.13

Pai

dw

ork

0.84

0.79

0.74

0.77

0.81

0.75

0.86

0.77

0.70

0.76

0.73

0.76

0.85

0.90

0.80

Un

pai

dca

rew

ork

2.03

2.61

3.30

1.72

1.93

1.71

1.81

2.11

1.82

1.69

1.56

1.48

2.19

2.06

1.83

Stu

dy

1.00

1.50

1.33

1.00

0.67

1.73

1.60

1.13

1.33

1.63

1.80

1.43

1.80

2.00

1.44

Tra

vel

0.94

0.99

0.88

0.87

0.93

0.96

0.89

0.93

0.97

0.99

0.93

0.96

0.95

0.87

0.93

Slee

p1

0.99

11.

031.

031.

021.

021.

021.

031.

021.

031.

031.

011.

011.

01M

eals

and

per

son

alca

re

0.96

0.98

0.95

1.01

0.99

1.07

0.94

11.

11.

061.

031.

140.

980.

950.

96

Fre

eti

me

0.83

0.82

0.8

0.89

0.81

0.93

0.8

0.83

0.93

0.91

0.95

0.92

0.81

0.76

0.8

Not

e:T

he

gen

der

dif

fere

nti

alis

wo

men

’sti

me

div

ided

by

men

’sti

me.

Sou

rces

:H

ET

US

(Eu

rost

at20

04b

and

2006

).

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unpaid care work. Therefore, if we understand work as an activity thatincorporates all the necessary tasks for the support of human life, wecannot speak of underutilizing women’s labor but rather overexploiting it.

Although regimes are not constant over time, since socioeconomicenvironments undergo frequent changes that give rise to new demands bysocial forces, the gender division of labor is very slowly modified, showingdifferences in past institutional developments. In all countries studied,women work fewer hours in the market than men. In general, employedEuropean women spend less time than employed men in all activitiesexcept for unpaid care work, to which they devote an average of twicemen’s time. Even in countries like the UK or Sweden, where the total worktimes for men and women are approximately equal, women devote asmaller amount of time to paid work. Italian women are the ones thatspend more time on unpaid care work in comparison with men – that is,three-and-a-half times the amount men spend. Other countries where thesegender differences are also high are Spain, Latvia, Lithuania, and Estonia.

EUROPEAN WELFARE SYSTEMS AND UNPAID CARE WORK:A CLUSTER ANALYSIS

In this section we conduct a cluster analysis to study how Europeancountries aggregate together by similarities in time use and care regimes.Cluster analysis is a statistical tool that allows us to group together a set ofobservations in clusters or groups based on the differences (or distance)and similarities between the observations according to preset variables.The objective is thus to divide the original set of observations into groupsof homogeneous elements with notable differences from the rest ofthe groups. In this case, the technique presents the advantage of allowingus to group countries together according to a multitude of variablessimultaneously.

Our procedure for the analysis is as follows. This study is based on analysisof HETUS, which includes time-use data for fifteen European countries.When selecting variables, we must choose the variables that best describethe underlying structures but also limit their number.12 We are interestedin classifying European countries using gendered patterns of time,especially regarding paid work and unpaid care work. To do so, wedefine all variables as the ratio between the time men and women devotedto the same category. We consider the following categories: time devoted topaid work, studies, unpaid care work,13 travel, personal care (eating mealsand other activities), sleep, and leisure.

In cluster analysis, the nonstandardization of the original variables canaffect the results; but in certain cases, it can be useful to reflect thequalitative or conceptual aspects of the original data that would be lost withstandardization.14 Therefore, it is necessary to address this issue carefully

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on a case-by-case basis. In this study, we work with ratios that are relativelyhomogeneous, and so we have decided not to standardize them.

The central point of our procedure is to determine the similarity ordistance between observations. In statistical methods, equivalence is usuallymeasured in terms of distance between pairs of objects to build a symmetriccomparison matrix. Therefore, in cluster analysis, objects (in this case,countries) with smaller distances between their variables are more similarthan those with larger distances, and thus are included within the samecluster. The selection of the distance measure depends on the kinds ofvariable employed, which in our case are interval variables. We thusdecided to use the square Euclidean distance, which measures geometricdistance as if the observations were points in a p-dimensional space formedby variables. Let n be the observations measured along k variables. Then theEuclidean distances are:

d ¼Xp

k¼1

ðxik � xjkÞ2 !1=2

Once the similarities between variables have been identified, it is necessaryto group them by applying the most useful statistical method. In this study,we first performed a hierarchical cluster analysis using the Ward method asa selection instrument to determine the optimal number of clusters.15

We also used other methods, such as the average linkage between groupsmethod and the single linkage and complete linkage methods, and theresults were similar each time. During the second stage, once the numberof clusters and their initial means were established, we performed ak-means cluster analysis. There is no general, unbiased, valid criterion todefine the optimal number of clusters, but according to the results ofthe hierarchical cluster analysis and the descriptive findings, it seemsappropriate to choose four different groups, as shown in Figure 3.

A dendogram (Figure 3) is the most common type of graph to representthe nearness among the terms of our analysis (countries, in our case),generating automatically logical groups of terms. Two countries (or more)are grouped in one cluster when they have the closest values of all thevariables that are considered in the analysis. Thus, departing from all thecountries individually, Figure 3 shows the successive groups progressivelyjoining in clusters such that the final cluster contains all the analyzedcountries. Each vertical line represents a new cluster, which is formed bythe countries that correspond to every horizontal line. In our case, weestablish the division at the first grouping of countries, which is the onewith the highest consistency levels inside the clusters. In Figure 3, it is thepoint distinguished by the vertical, gray color line, where four differentgroups would be obtained.

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Table 4 presents the resulting groups of countries. Cluster 1 is composedof Estonia, Spain, and Italy; cluster 2 contains Belgium, France, Germany,Hungary, Poland, and the UK; cluster 3 groups Finland, Norway, andSweden; and cluster 4 includes Latvia, Lithuania, and Slovenia. The firststriking result compared to previous welfare regime analyses is that theinclusion of Eastern European countries leads to their distribution acrossdifferent groups. Contrary to Esping-Andersen (1990) or some feministstudies such as Lewis (1992), our analysis groups countries not regardingideal types or public policy similarities but using time-use data, which allowsus to capture the interaction between paid and unpaid work and thedifferent opportunities women and men face regarding institutionalsettings and labor market conditions.

In our analysis, the Mediterranean countries and Estonia (Cluster 1)display high internal consistency and well-differentiated characteristicsdistinct from the rest of the countries. Cluster 3, Nordic countries, andCluster 4, Eastern countries, are also very homogeneous. Cluster 2 appearsto be the most heterogeneous, which means that this group of countries hasmore differences within the cluster, but it is also the cluster containing thelargest number of countries. We present here all data and results of theanalysis before discussing each cluster in depth.

The analysis of variance (ANOVA) provides a statistical test of whetherthe means of several groups are equal. Table 5 shows how each variablecontributes to cluster solutions by providing the results of the Fishertest (F ) and its level of statistical significance. Variables with high F values

Figure 3 Hierarchical cluster analysis

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are those that most distinguish the clusters. Therefore, in this analysis, thevariable that contributes the most to distance between clusters is time spentin unpaid care work, while the variable that contributes the least is timespent in personal care. This means that unpaid care work is the mostimportant variable in the division of countries in the different groups, as itis the time-use activity in which countries differ the most. Gender inequalityin time use in Europe is mostly due to the various degrees of participationin care work by the market, the state, the community, and the family and,within the latter, women and men. The different options in the distributionof care costs among the market, state, community, and family have led toa variety of situations regarding women’s employment, demographicbehaviors, and household and female strategies used to cope with thisdouble burden; for instance, the degree to which women work part time.

Three Eastern European countries – Slovenia, Latvia, and Lithuania –form Cluster 4. As pointed out in the studies by Christy Glass and Eva Fodor(2007), Rianne Mahon and Fiona Williams (2007), and Steven Saxonberg

Table 5 Analysis of variance (ANOVA)

F-ratio Significance level of F

Paid work 7,253 .006Study 2,787 .091Unpaid care work 86,919 .000Travel 3,568 .051Personal care 1,888 .190Leisure 3,431 .056Sleep 19,620 .000

Table 4 Clusters of countries

Estonia Cluster 1ItalySpain

Belgium Cluster 2FranceGermanyHungaryPolandUK

Finland Cluster 3NorwaySweden

Latvia Cluster 4LithuaniaSlovenia

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and Dorota Szelewa (2007), which focus on gender, state, and society andthe dynamics of change in the post-communist era, there is not a singlepost-communist model. Instead, the stresses and strains of transition haveled post-communist societies to different paths and outcomes. We findthree former communist countries in Cluster 4. The other EasternEuropean countries are grouped inside other clusters: Estonia is inCluster 1, together with the Mediterranean countries, and Hungary andPoland are in Cluster 2.

Note that Cluster 4 is characterized by a gross domestic product (GDP)per capita below the EU average, low public social expenditures, and arelatively stable labor market with unemployment and activity rates for bothwomen and men close to the EU average during the period studied. Inthese countries – Slovenia, Latvia, and Lithuania – there are no importantdisparities in terms of gender behavior in the labor market, a fact thatreflects similar education levels, and the average age of mothers havingtheir first child is lower than in Cluster 1. However, the total work burden ofboth women and men in Cluster 4 is the highest in Europe, as low publicsocial expenditures mean that domestic work cannot be externalized, andmost unpaid care work lies within the family. As a result, some feministstudies on post-communist responses to gender-related policies constitutean important counterpoint to the defamilializing trends taking place acrossthe western and southern regions of Europe (Glass and Fodor 2007, Mahonand Williams 2007, Saxonberg and Szelewa 2007).

The members of Cluster 2 – Belgium, France, Germany, Hungary, Poland,and the UK (including countries with a conservative welfare regime such asBelgium, France, and Germany) – share many characteristics with those inCluster 3 in terms of both labor market and educational levels (althoughthese are lower and more uneven). As with Cluster 3, these countries havefewer gender inequalities in leisure time (Table 3). This fact, coupled with thelower difference between the time women and men spend on unpaid carework (though, as in the rest of Europe, this time is higher for women),indicates that women’s total workload is smaller. In this regard, the case ofFrance should be noted, as it reflects a situation that is different from that ofthe rest of the countries in the group. In France, women do considerablymore unpaid care work than men and thus have less leisure time.

It is important to explain why Eastern European countries such as Polandand Hungary are grouped in Cluster 2 with the continental conservativecountries and why a country such as the UK, considered a neoliberal type inmost welfare analyses, is in this group. Eva Fodor’s (2005) study shows howthe societies of Hungary and Poland were radically transformed in 1989–90as their state-socialist political regimes collapsed and their economies wereliberalized. As part of this transformation process, about one-third of alljobs were lost, and the state welfare systems were dismantled. Since 1989, asthe real value of wages has declined, social benefits have been cut back

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and have lost their value, and gender inequalities have increased. Post-communist political regimes placed women back in their kitchens, definingwomen’s function exclusively in terms of raising children and occupyingpositions from which they could best support their working husbands(Marilyn Rueschemeyer 1998). In fact, in Poland, the cost of childcare hasbecome a serious problem. During the 1990s, the number of kindergartenswas reduced by about one-third; and if this has not resulted in an overalllower proportion of children attending these institutions, it is only becauseof the radical fall in fertility (Fodor 2005). The situation is better inHungary, which has more kindergartens, greater parental benefits, anduniversal provisions, as well as some means-tested programs. Thus,following Fodor (2005), globalization and liberalization did not threatenHungarian and Polish women’s positions to the extent that had beenexpected, simply because they were in a relatively advantageous labormarket position when these social transformations began. In addition, thedeclining yet still persistent remains of a strong state-socialist welfaretradition helped alleviate some of the burden of the transition, unlike inother parts of the world. Starting positions thus matter when assessing theeffects of development.

As for the UK, we could argue that if other neoliberal countries – such asthe US or Australia – were in the sample, Britain could have been groupedwith them. Other explanations relate mainly to its path dependence. Afterthe Second World War, the UK developed a welfare system similar to thatused in continental European countries. Although it was one of the firstcountries to move towards a neoliberal regime, its experience shows that ittakes time to change gender orders. The inclusion of the UK in Cluster 2also shows how past institutional changes determine present characteristics.As Lewis found in her study, the UK exhibited the ‘‘strong male-breadwinner model,’’ while France was somewhere in between, with a‘‘modified male-breadwinner model’’ (1992).

Different patterns of time distribution persist among the various groupsof countries, proving that both welfare systems and family policies(especially those established a few decades ago) have developed inparticular ways, much like per-capita income levels, which differ betweenWestern and Eastern Europe, and institutional arrangements or othervariables dependent on country-specific economic development paths.These various degrees of development continue to affect women’s ability todevelop their personal capabilities, including their capability to enter thelabor market on equal terms with men. Women entering the labor marketand staying in it throughout their life cycle seem to be key aspects of a moreequitable distribution of total work time and unpaid care work. However,this is possible only with a considerable public expenditure in social servicesand incentives for men to share unpaid care work in their families, as is thecase in Nordic countries. Meanwhile, as shown in the Mediterranean

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countries, the ‘‘economic gift’’ is greatest when the family acts as asubstitute for the state.16

The most obvious differences are between Cluster 3 – the Scandinaviancountries, which have the smallest gender difference in the use of time –and Cluster 1 – the Mediterranean nations of Spain and Italy as well asEstonia, which still show a very uneven distribution of activities betweenmen and women. As Table 6 shows, women’s economic activity rates inCluster 3 are higher than in other groups of countries, although there is ahigh presence of part-time employment among women in Norway andSweden. Women’s part-time employment is also high in certain countries inCluster 2 (Belgium, Germany, and the UK), though it is low in two othercountries in Cluster 2 (Hungary and Poland) and quite high in the othercountry in this cluster, France. On the contrary, in Eastern European andMediterranean countries, part-time employment is quite low due partly tolow household incomes that make part-time employment an undesirableoption for women reconciling childcare and labor. Usually, data fromOrganisation for Economic Co-operation and Development (OECD)countries show that the percentage of women who have part-time jobsincreases when women live in couples; this percentage increases even more

Table 6 Activity and employment rates of women and men ages 25–74, 2006(percentages)

Clusters

Labor marketactivity rates

Employmentrates

Unemploymentrates

Part-timeemployment

rates

Involuntarypart-time

employmentrates

Women Men Women Men Women Men Women Men Women

1 Estonia 68.4 78.9 65.2 74.5 4.7 5.6 10.4 3.8 22.6Italy 46.2 71.0 42.8 67.9 7.4 4.3 26.2 4.2 NASpain 54.6 77.3 49.0 73.2 10.2 5.3 22.1 3.3 32.4

2 Belgium 55.9 71.3 51.5 66.8 7.9 6.2 41.9 6.9 11.9France 62.4 73.2 57.3 68.4 8.2 6.6 29.8 5.0 28.4Germany 61.0 74.3 55.0 67.0 9.7 9.6 48.2 8.6 19.1Hungary 52.7 69.1 49.1 64.9 6.9 6.1 5.6 2.5 24.0Poland 55.5 72.3 48.4 64.4 12.8 10.9 11.8 5.9 27.3UK 62.6 77.2 60.4 74.2 3.6 4.0 42.3 7.8 5.9

3 Finland 67.3 73.1 62.8 68.8 6.6 5.8 15.2 7.1 33.5Norway 70.3 79.2 68.6 77.1 2.5 2.7 41.4 10.2 19.2Sweden 71.1 78.6 67.5 74.6 5.1 5.0 38.4 10.1 21.5

4 Latvia 64.4 77.4 61.1 72.0 5.2 6.9 7.9 4.4 31.9Lithuania 64.4 74.8 61.1 70.8 5.1 5.4 12.1 7.8 32.7Slovenia 62.8 73.9 58.9 70.9 6.2 4.1 8.8 4.9 8.0

Source: Eurostat (2005).

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so when they have dependent children (OECD 2007).17 The problems withpart-time jobs are low wages, fewer possibilities for promotion, and thelower accumulation of human capital and experience than in other jobs.

Cluster 1 is formed by Estonia and the Mediterranean countries of Italyand Spain, which is a surprising clustering. The two Mediterraneancountries share important characteristics related to gender issues in theirlabor markets, since they present the greatest difference in terms of activityand employment rates by gender and the lowest employment rates ofwomen. They also have the highest level of women’s unemployment, whilemen in these countries have unemployment rates that are lower than othergroups of countries, although these rates has started to be reversed with theeffects of the 2008–10 financial crisis. The rates of women’s part-timeemployment are low compared with Northern European countries, butaround 80 percent of all part-time contracts are occupied by women.

Estonia, on the other hand, does present economic, labor, anddemographic characteristics that bring it closer to the Eastern Europeancountries than to the Mediterranean ones. It has higher levels of women’slabor market activity and employment than Italy and Spain, and its part-timeemployment rate of women is more similar to that of Lithuania and Latviathan the Mediterranean countries. However, Estonian women’s paid workinghours are closer to those in the Mediterranean countries than to the EasternEuropean countries. One reason for including Estonia in the cluster with thelargest gender differences in time use may be the fact that, according toInternational Monetary Fund (IMF) reports, Estonia is one of the EasternEuropean economies that made the fastest transition to a market economy inthe 1990s, which involved dismantling public services (Jack Reardon 1996). Inaddition, Estonia was the last Eastern European country to adopt andimplement the EU legislation on the equality between women and men (seealso Rene Weber and Gunther Taube [1999] and Adalbert Knobl, AndresSutt, and Basil Zavoico [2002]). The rapid process of transition to a capitalistsystem has not been accompanied by public policies to counteract thenegative impact on women’s double burden, and it has thus likely producedthe present gender inequalities in time use, causing Estonia to be in thecluster with the largest gender differentials in time use.

Even if families continue to be the main provider of care and replacean important part of the state’s functions, it seems that the great differencesthat persist in the distribution of unpaid care work between women andmen in the Mediterranean countries still represent the primary barrierto Spanish and Italian women’s equal access to the labor market. Indeed,the family continues to function as the main care supplier in thesesocioeconomic systems.18

Gender inequality in unpaid care work leads to women’s low economicactivity and fertility rates in Mediterranean and some Eastern Europeancountries (see Tables 6 and 7). Table 7 shows some selected indicators

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Tab

le7

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of

pu

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ster

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ster

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1.94

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1.24

1.78

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1.84

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Sou

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Eu

rost

at(2

009)

.

WORK AND TIME USE BY GENDER

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Tab

le8

Nu

mb

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ffu

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emp

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ages

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kin

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cies

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ner

ated

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118

9.9

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16,

130.

456

5.4

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791

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6.2

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37,

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656

8.9

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9

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um

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561

6.3

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72.9

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23.7

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nce

4,39

2.9

7,31

6.7

456.

544

5.5

77.6

112.

199

8.9

1,40

6.7

Ger

man

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69,

546.

413

3.6

296.

610

6.4

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816

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294.

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225.

315

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240.

516

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216.

719

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UK

3,58

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737.

598

8.9

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348.

168

1.6

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nd

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861

9.6

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62.4

No

rway

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924

3.8

66.4

94.3

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588

6.1

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525

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428

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nia

261.

833

5.6

40.9

21.8

NA

NA

12.7

11.8

Sou

rce:

Eu

rost

at(2

005)

.

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of provision for children. There are considerable differences in publicexpenditure on families and children, income maintenance benefits in theevent of childbirth, formal care for children, and informal care that mustbe provided by the family.

As Table 7 shows, and by taking into account Vicenc Navarro’s analysis(2005, 2007) of EU social expenditures, countries in Clusters 1 and 4 andPoland and the UK in Cluster 2 present low social expenditures whencompared with the other groups and countries. In this sense, MonicaThrelfall, Christine Cousins, and Celia Valiente (2005) argue thatdependency and social solidarity are the results of this spare expenditure inthe so-called Mediterranean gendered social policy regime. As a result,women are dependent on men’s incomes, whereas men are dependenton women’s unpaid care work. The process of the individualizationof citizenship and family rights has been much slower in the twoMediterranean countries. This is evidenced by the belated and limiteddevelopment of individual paternity rights to substitute for family rights andthe continued use of joint tax returns, which, according to Paloma de Villota(2009), represent instances of heavy fiscal gender discrimination and thusdiscourage the labor market participation of the second family earner, who isusually a woman. These countries still carry the legacy of totalitarian regimes,and family matters have remained within the private sphere by reducing theimplementation of public policies that affect the family. This lack of policiesrelated to families has reinforced the perpetuation of the traditional familyand of the family’s caretaking burden, together with women’s low labormarket activity rates (Rosanna Trifiletti 1999).

Social expenditure, along with many other public policies, influencesindividuals’ choices, particularly women’s, about their labor marketparticipation and the ways in which individuals balance the competingdemands of their professional and personal lives. Table 8 shows, as anexample, one of the differences that still exist in EU countries’ policies forreconciliation of work and family life: the various national legal rights toemployees’ family-related leave. Notably in Table 8, it is always women whotake time off for family sickness or emergencies. Table 8 further showscountries’ different possibilities for taking time off for family reasons:special leave days remunerated, special leave days not remunerated, otherschemes, or no possibility of leave. There are differences in the schemes bycountry, though they do not perfectly correspond to the four clusters.

CONCLUSIONS

Time-use data allow us to show how unpaid care work that takes placeoutside the marketplace represents an essential and distinctive part of theeconomy. Cross-national comparisons show the persistent patterns anddifferences in observed gender inequalities on total workload and care

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responsibilities. We used the HETUS, an international time-use study offifteen countries, to conduct a country-by-country and group-by-groupanalysis based on cluster methodology.

European countries show similarities and differences regarding time useby gender. The first similarity is that women perform the bulk of unpaidcare work across all countries; this is why it is so important for feministscholars to advance visibility and measurement of unpaid care work. Incontrast, across all countries studied, men specialize in paid work, resultingin important differentiated consequences, especially in capabilities andautonomy, for men and women. In addition, among all fifteen countries weanalyzed, except for Sweden, women work more total hours than men,since the time women spend in paid work is more than the time men spendin unpaid care work. Consequently, women have less free time than men,and this difference is likely to affect their capabilities and well-being.

We examined welfare regimes from a gender perspective to explore howdifferent combinations in the provision of welfare between the state, themarket, the family, and civil society support different models of family andgender relations, and differences in gendered patterns of time use. Wehave grouped the European countries, for which we have harmonized TUSdata, in four clusters according to gender inequality in time use. We foundthat the countries are grouped in a slightly different way than the standardused for the original clusters by Esping-Andersen (1990) and other genderregime classifications, such as that created by Lewis (1992), which did notconsider time-use data nor include Eastern European countries.

Different institutional settings and labor market structures are associatedwith the different clusters. The countries with the greatest inequalities in thedistribution of unpaid care work are those showing the lowest women’s labormarket activity rates, namely, Spain, Italy, and Estonia. However, some welfaresystems are oriented towards the provision of state services and benefits forthe majority of the population. For example, Sweden, Norway, and Finlandshow high activity rates among women, which account for the most egalitariandistribution of unpaid care work and total work time by gender in our survey.

These findings allow us to build some public policy recommendationsabout the importance of time-use surveys and the need to improve thequality of care without decreasing women’s well-being and autonomy. Thenature and extent of the choices women and men may face in approachinggender equality depend on how public policies address the issue of unpaidcare work and to what extent women are able to achieve financialautonomy through their participation in the labor market.

In addition, our results show how the recent restructuring of welfarestates does not need to follow the path of reprivatization and redistributionof care by channeling it toward the family and the market because this pathcould intensify women’s work and important inequalities regarding familyincome. In fact, an economic crisis plus the associated recovery measures

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should be designed to prevent an increase in women’s workload. Rather,sustainable structural adjustments require investments in care and equality.

HETUS data show that although degrees of gender inequality vary, there isnot yet an economic model that is equal and nondiscriminatory from afeminist perspective. Therefore, innovations in the way a society faces carehave yet to be undertaken. So far, our results show that policymakers still needto allocate a substantial budget to social services, equal opportunity policies,education, and other positive actions to truly reduce gender inequality,especially in relation to men’s nontransferable parental leave. A clearpreference for investment in social services instead of direct transfers isrequired. As such, awareness and education must play a key role in changingsocial norms and the traditional conception of the family. In addition, we mustthink globally, exchanging best practices so as to allow both developed anddeveloping countries to build a more gender-equal welfare system. In order toadvance along these lines, it is necessary to have homogeneous and well-designed TUS to measure care economies and to allow for new cross-nationalcomparative studies. TUS need to be continuous over time, to captureevolution and cultural changes, and to add other socioeconomic variables andindicators aiming to obtain a full picture of the so far socially unsustainableeconomic development in terms of gender equity. Moreover, it is necessary toovercome the gender bias that is deeply entrenched in social protectionsystems and to make citizenship truly inclusive. This is the only way to advancein the analysis of the other economy, where the direct production andmaintenance of human beings take place.

Lina Galvez-MunozDepartment of Economics, University Pablo de Olavide

Carretera Utrera Km. 1, Seville 41013, Spaine-mail: lgalvez@upo.es

Paula Rodrıguez-ModronoDepartment of Economics, University Pablo de Olavide

Carretera Utrera Km. 1, Seville 41013, Spaine-mail: prodmod@upo.es

Monica Domınguez-SerranoDepartment of Economics, University Pablo de OlavideCarretera Utrera Km. 1, Seville 41013, Seville, Spain

e-mail: mdomser@upo.es

ACKNOWLEDGMENTS

We thank the referees and guest editors for all their comments andsuggestions, which have been very useful in improving this study. Previous

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versions benefited also from comments by participants at the 2007 IAFFEconference in Bangkok; the UNDP Expert Group Meeting on ‘‘UnpaidWork, Economic Development and Human Wellbeing’’ in New York; theworkshop on ‘‘Unpaid Work, Time Use and Public Policy’’ in Washington;and at the second Spanish Feminist Economics Conference in Zaragoza.All views and errors are our own. This research has been made possiblethrough the economic support of the national R&D project, ‘‘Time UseAnalysis of Spanish Labor’’ (ECO2008-05325) and the regional R&Dproject, ‘‘The Care Economy in Andalucia’’ (P09-SEJ-4833).

NOTES1 This problem has occurred through the European Community Household Panel

(ECHP), the Multinational Time Use Study (MTUS), and the HETUS; an importanteffort to compile time-use data has been made by the Economic and Social ResearchCouncil (ESRC) and its ESRC Research Centre on Micro-Social Change (MISOC).See Jutta M. Joesch and Katharina Spiess (2006) for a European time-use study onchild care.

2 One existing study (Jonathan Gershuny and Oriel Sullivan 2003) uses time-use data ina comparison of welfare regimes, but it only considers time spent in paid work. Inaddition, the sample of countries in this study is limited, and they are characterized interms of different predetermined welfare systems.

3 Anette Borchorst (2011) has recently challenged the status of Nordic countries as‘‘women-friendly’’ with regard to aging populations and economic globalization.Increasing multiculturalism has made the previously fairly homogeneous Nordiccountries much more diverse and gender equality a contested issue.

4 According to O’Connor (1993), comparative research on the welfare state wasconcentrated on the relationship between the labor market and the state, with littleattention given to the family aspect of this tripartite relationship. The concept ofpersonal autonomy remedies this imbalance. The absence of involuntary economicdependence does not imply the absence of interdependence. While interdependenceimplies choice, equality, and reciprocity, involuntary economic dependence impliesan unequal power relationship and the absence of choice.

5 Strange (2001) uses this term to explain the progression of neoliberal policies fromthe 1980s.

6 A list of the subactivities included in unpaid care work is available, upon request, fromthe authors.

7 For a study on some weaknesses of HETUS, see Cristina Carrasco and MaribelMayordomo (2005).

8 The 1997 Australian Time Use Survey includes a category called ‘‘minding children’’(Dennis Trewin 1999).

9 The average day is an average for the whole group of respondents across the wholeyear, which means that all persons are included, whether they have performed thisactivity or not, and all days of the week as well as working and holiday periods areincluded. Although the average day is an abstract measure and does not describeconcrete everyday life at the individual level, it is a proper indicator of time use at theaggregate level and enables comparisons between countries and population groups.

10 For the Swedish case, see Anita Nyberg (2004). John Ekberg, Rickard Eriksson, andGuido Friebel (2004) found that after the Swedish government included anontransferable first month for fathers in 1995, daddy quotas increased considerably

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in the short term. Rickard Eriksson’s (2005) analysis of the introduction of a secondnontransferable month in Sweden for fathers shows an increase not only in theamount of leave fathers request, but also in requests by mothers, since the reformcoincided with an increase in the parental leave by one month. For a comparativeanalysis on parental leaves, see Carmen Castro-Garcıa and Marıa Pazos-Moran (2008).

11 A study on family networks in Southern Spain has shown that in-kind networkscontinue to be essential for the sustainability of economic and social systems (JoseAntonio Fernandez-Cordon and Constanza Tobıo 2006). A key factor is the loweconomic activity rates of grandmothers. However, this situation will change in thecoming decades, as the new grandmothers will still be working in the labor marketwhen their grandchildren are born.

12 If the number of variables is very high, it will cause an exponential increase in thenumber of clusters during the calculations needed to define them.

13 We include time spent on childcare and the care of dependents in the category‘‘unpaid care work.’’ We had performed a previous cluster analysis to the onepresented here, including an isolated variable on ‘‘childcare’’ instead of consideringit inside the variable ‘‘unpaid care work.’’ To do this, we defined a new variable aswomen’s time spent in childcare as a proportion of men’s time spent in childcare. Theresults were not congruent because Lithuania shows a very high ratio in this variable,which distorts the analysis, due to the fact that there are almost no men who devotedtime to this activity (only 1 minute per average day).

14 The most common form of standardization is reducing the average of the variable toevery observation and dividing this result by its standard deviation. The resulteliminates differences of value between variables that arise due to differences on thescale in which the variables are measured. Therefore, when differences of scale do notexist, it is not necessary to carry out the standardization.

15 There are basically two types of algorithm for this purpose: hierarchical andnonhierarchical. Hierarchical algorithms are based on clustering in successive stagesaccording to the rigorous inclusion of elements in each subgroup. Within anystructure of clusters obtained, we can produce a greater degree of homogeneity with alarger number of groups. Nonhierarchical methods work in the reverse way. Thenumber of clusters to be identified is predetermined, and so the procedure consists ofa single classification of k objects that act as the initial estimation of the center of eachcluster. This center can be specified by the researcher or determined by thealgorithm. Generally, the algorithm used is the k-means algorithm, which seeks toallocate items to those clusters with relatively close centroids. The centroid is thep-dimensional point resulting from averaging the values of cluster objects for eachvariable.

16 The ‘‘economic gift’’ is the mechanism by which unpaid women’s services allow mento develop their professional careers while limiting careers for women. Wives who donot work in the labor market not only assume all of the housework but also committhemselves to promoting the careers of their spouses, for example, by agreeing tomobility, helping them in their social lives, or arranging their agenda (Olwen Hufton1997).

17 The countries covered in the OECD analysis are Australia, Austria, Belgium, Canada,the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland,Ireland, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, New Zealand,Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey,the UK, and the US.

18 Among the countries included in the multicountry ISSP94 household survey, Joost deLaat and Almudena Sevilla-Sanz (2006) conclude that more children are born inhouseholds with high gender inequality in time allocation and women’s participation

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in the labor market is low. However, consistent with the presence of socialexternalities, women in countries with less egalitarian views where men participateless in home production are less able to combine having children with market work,leading to lower average fertility rates (Joost de Laat and Almudena Sevilla-Sanz2011).

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