Female Labour France

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European Journal of Population 17: 235260, 2001. 2001Kluwer Academic Publishers. Printed in the Netherlands.

235

Labour Market Participation of French Women overthe Life Cycle, 19351990

MICHAEL GRIMM1 and NOL BONNEUIL21Dveloppement et insertion internationale (DIAL), 4 rue dEnghien, F-75010 Paris, France and

Institut dtudes politiques de Paris (E-mail: [email protected]);2Institut national dtudesdmographiques, 133 bld Davout, F-75980 Paris cedex 20, France and cole des hautes tudes ensciences sociales, Paris (E-mail: [email protected])

Received 1 August 2000; accepted in final form 15 December 2000

Grimm, M. and Bonneuil, N., 2001. Labour Market Participation of French Women over the LifeCycle, 19351990, European Journal of Population, 17: 235260.

Abstract. Employment histories with multiple spells and time varying covariates help identifydeterminants of labour market transitions of women in France between 1935 and 1990. Highereducated women were more likely to become inactive, but returned to work also more easily,especially when they added work experience. Being married, whether mother or not, induced arearrangement of time between staying at home and labour, in rendering exit from employmentmore likely and return from inactivity to employment less likely. Exits from employment wereless frequent for mothers of larger families, while return to employment decreased with the totalnumber of children, in spite of the growing financial needs of larger families. Transitions betweenemployment and inactivity increased with favourable economic conditions. However, involuntaryexits from employment were more probable during economic downturns.

Key words: female labour market transitions, female life cycle, multistate-multiepisodeduration model, retrospective survey

Grimm, M. et Bonneuil, N., 2001. Cycle de vie et activit fminine, France, 19351990,Revue Europenne de Dmographie,17: 235260.

Rsum. Les chroniques rtrospectives de la vie profession elle permettent didentifier certainsdterminants de la mobilit des femmes franaises entre emploi, inactivit et chmage de 1935 1990. Les femmes aux diplmes les plus levs quittent plus facilement leur emploi mais yretournent aussi plus facilement, surtout si elles ont acquis de lexprience professionnelle. Unefois maries, mme sans maternit, les femmes r-organisent leur temps entre travail et foyer, enaugmentant leur risque de quitter lactivit et en diminuant celui dy retourner quand elles sontinactives. Les mres de famille nombreuse vitent de quitter leur emploi, tandis que le retour

lemploi dcrot avec le nombre denfants, et bien que les besoins en ressources saccroissent.Lamlioration des conditions conomiques favorise les transferts entre emploi et inactivit.Cependant, la rcession occasionne des pertes demploi involontaires.

Mots cls: cycle de vie des femmes, enqute retrospective, mobilit sur le march du travailfminin, modle de dure multi-tats multi-pisodes


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236 MICHAEL GRIMM AND NOL BONNEUIL

Figure 1. Female labour participation rates. From 1962 to 1990, definition and data fromcensuses; for 1998, definition and data from theEnqute Emploi.Source: INSEE (1990, 1999).

1. Introduction

Female labour market participation in France has changed significantly in the pastdecades. The census shows that the participation rate of women aged over 15years in the labour force has risen from 36.3% in 1962 to 47.6% in 1998 (INSEE,1990, 1999). The increase was sustained at the end of the sixties and during theseventies. Figure 1 shows that the female labour force has grown mainly in the

intermediate age groups. Meanwhile, female participation, like mens, has declinedfor the younger and older age groups. Simultaneously, family structure has changeddramatically in France. The proportion of large families has decreased in favour ofone- or two-child families, and to the advantage of single mothers (Desplanques,1993).

Lry (1984), Vron (1988), Blanchet (1992), and Blanchet and Pennec (1993,1996) paid much attention to the interaction between female labour supply andfertility in France. All of them rejected a strict dependence between activity andfertility choice; instead they showed that female labour market participation hasbeen increasing for all family configurations. Most of these analyses for the Frenchmarket remain relatively static.

Female activity however, more than male activity, is characterized by flowsinto and flows out of the labour market. This is what Heckman and Willis (1977)and Heckman and Macurdy (1980) addressed, in following up the participationof women in the labour force. An increase of female activity means, under theassumption of a constant female adult population, that there are more entries intothan exits from the labour market. This implies a reduction of the mean duration of


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LABOUR MARKET PARTICIPATION OF FRENCH WOMEN 237

inactivity over the life cycle, and raises the question of the determinants of femalemobility.

Our research question is three-fold:

1. Why did female labour market participation take off, and what part of theobserved transformation was due to cohort effects? We shall thus search forlinks between this change and macroeconomic conditions, including improve-ment in educational attainment and age composition of the female laboursupply. Drobnic et al. (1999) for Germany and for the US found that femaleemployment after World War II increased mainly because of higher rates ofre-entry for younger cohorts after an employment interruption.

2. What about the transitions between employment, inactivity and unemploy-ment? Was one as likely to enter the labour market as one was to exit fromit? What difference did it make to be inactive or to be jobless when searchingfor a job?

3. How did personal characteristics influence labour market transitions over thelife cycle? Felmlee (1984), Klein and Braun (1995), Drobnic et al. (1999)confirmed the predictions of human capital theory: career women and highlyqualified women delay childbirth and return more rapidly to labour after child-birth. They also found high exit rates for highly educated women, who, due tohomogamy among highly educated, can often rely on wealthy husbands. Howdoes marriage alter female activity? Drobnic et al. (1999) confirmed that, forwomen, marriage increases exit from the labour market and decreases entryinto it. Even (1987) found that American married women became less likelyto return to work after the birth of their first children, and they did even lessas time spent in inactivity elapsed. Joesch (1994) pointed out the influenceof occupation, family income and the tax rate on the duration of the work

interruption after childbirth. Klein and Braun (1995), on German data, foundthat the depreciation of human capital overcompensates the reduction of familycare accompanying child aging, and that return to work decreases with durationelapsed in inactivity. Gustafsson et al. (1996) for Germany, Sweden and theUK, and Rnsen and Sundstrm (1996) for Sweden and Norway, comparedfamily policies on the return of women to work after the birth of their firstchildren. Gustafsson et al. found that British and German women are lesslikely than Swedish women to return to work after second or third childbirth,while they behave similarly after first childbirth. This disparity comes fromthe different welfare regimes. Educational attainment is also less influential ontransition rates where women benefit from paid maternity and child-care leave,and where low-income families receive financial aid (Gustafsson et al., 1996;Rnsen and Sundstrm, 1996; Drobnic et al., 1999).

The French social system is often believed to reconcile work and family care,because of efficient day-care centres; but France is also characterized by weaklydeveloped part-time work. It becomes thus instructive to compare French resultswith those of so-called breadwinner welfare-states such as Germany and Spain.


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Adam (1996) for Spain identified the influence of mans occupation on womanslabour supply after childbirth, whereas Giannelli (1996) for German womenconcluded only for a second-order role of these factors.

Courgeau (1990) incorporated migration decisions in family and career events.Hyslop (1999) advocated that only a dynamic framework is able to reveal somedirect link between fertility and activity decisions. Newman and McCulloch(1984), for Costa Rica, found that first childbirths are postponed for highlyeducated mothers, whatever the husbands education level, which plays a role onlyfor children of higher rank. Hotz and Miller (1988) on US data and Heckman andWalker (1990) on Swedish data found that high female wages delay childbirth andreduce fertility. Ermisch (1989) on UK data found that this effect is less influentialwhen child care is less expensive, and that husbands earnings increase the wifesfertility and tend to withdraw her from the labour market (also in Heckman andWalker, 1990).

Most of the literature is centered on specific periods of the female life cycle

and concerns sub-populations, such as mothers or married women. After Felmlee(1984) and Drobnic et al. (1999), and thanks to the retrospectiveEnqute Famille(Family Survey), we deal with women followed-up over their entire life cyclewhatever their family situations. The registered entries into and exits from thelabour market concern women who worked at least once, whatever their familysituations, and with no restriction to any age-specific period. Covariates are familystructure, marital status, age, education attainment, work experience, nationality,birth cohort, and macro-economic fluctuations.

After describing model and dataset, we shall clarify our conceptual debt tonewhomeeconomics and to the theory of human capital, which guided our selectionof explanatory variables. We shall then articulate the female mobility along four

transitions: from employment to inactivity or to unemployment, from inactivity orfrom unemployment to employment.

2. Data and method

2.1. THE DATA

TheEnqute Famille(EF) was carried out in March 1990 in connection with thepopulation census. 340,706 women, born between 1925 and 1971, selected ingeographical zones representing 1/50th of the population living in metropolitanFrance, filled out a questionnaire of four pages in addition to the census form. Thisquestionnaire contains information about the constitution of the womens families,

their matrimonial lives, their educations, their professional biographies, and theirhousings.The retrospective nature of the EF helps us to reconstruct the life courses of

women since birth for some specific events and characteristics. The survey includesonly women who were alive in 1990, and who had not emigrated abroad. Womendying relatively young before 1990 might have been ill more often and might


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subsequently have been more mobile as regards activity and inactivity. There isthus a bias toward underestimating the transition rates between employment andinactivity, more or less counter-balanced by a relatively early withdrawal fromthe labour force without return. Similarly, emigrated women include both inactivewomen following their partners and highly qualified career women. Another well-known difficulty inherent in retrospective surveys comes from memory errors(Auriat, 1991, 1996). We suspect that these errors concerning the existence of anevent and its correct dating are more related to women having experienced manyinterruptions, which would lead to an underestimation of the transition rates.

As regards occupational biography, the EF informs us about the date at firstemployment and the number of work interruptions that lasted at least two years.For each interruption, women declared the motive, the year of the beginning andthe year of the end of the interruption. The motive led us to distinguish betweenexits from employment into inactivity and those into unemployment. The latter arehere considered as involuntary exits. Unfortunately, we have no information on

transitions between unemployment and inactivity. We thus make the hypothesisthat each woman exiting from her employment remained in the state occupiedimmediately after this transition, until she re-entered employment or until theduration was censored.

The fact that we can take into account only interruptions of at least two yearsconstitutes no important drawback to our analysis, because we study entire lifecycles where events within two-year periods play only a relatively minor role.1

Filtering work interruptions of under two-year durations enables us to discardtemporal withdrawals due to a change of position, a move, or a childbirth.

Atkinson and Micklewright (1991) emphasized the importance of distin-guishing precarious from stable jobs and full-time from part-time work. Unfortu-

nately, the EF contains the corresponding information only for the last employmentand these distinctions are impossible to make for the previous periods of employ-ment. We thus retain the annual participation of women and we ignore their annualworking hours which are, according to Mincer (1962), the second dimension oflabour supply over the female life cycle. However, part-time work was rare inFrance during the period of study (Desplanques, 1993).

We first considered all women who declared to have worked at least once in theirlives and who remembered the corresponding dates.2 We thus discard women whohave never been active since they left school. This behaviour is beyond the scopeof our study, which is restricted to transitions of women who worked at least oncein their lives before 1990. We also dropped all observations where the describedlabour market history was inconsistent or where key explanatory variables were

missing. The sample contains about 82,000 women. Test theory warns us againstoverestimating the significance of some coefficients because standard errors tendto zero with so large a sample size, misleading toward undue significance. Behindthe significance of a coefficient found from the sample lies the question of therelevance of the effect for the population as a whole. A usual practice is to reduce


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artificially the number of individuals, starting with a small sample, and then enlar-ging it to the size where the effects of the different explanatory variables canbe made out and measured. If dividing the sample in two does not change theestimated coefficients importantly, the sample obtained provides an appropriateworking basis. We proceeded this way, to draw randomly a sub-sample of 40,882women, which is fifty percent of the initial sample. Another reason for reducingthe sample size is to lower the econometric difficulty of tied data individuals whoexperience identical failure times.

2.2. THE METHOD

The probability that an individual, who has occupied a state for a durationt, leavesit betweentand d t, is P(t T < t+ dt|T t), whereT texpresses the condi-tioning event that the state is still occupied at t. The hazard rate, or instantaneousfailure rateh, reads:

h(t) = limdt0

P (t T < t+ dt | T t)/dt. (1)

A detailed and comprehensive description of duration analysis can be found, forexample, in Cox and Oakes (1984), Lancaster (1990), or Courgeau and Lelivre(1992).

Before analysing the data with explanatory variables, it is convenient to estimatehazard rate functions to see how the transition rate varies over time since entry intothe state. We use the non-parametric technique of life-tables (Kaplan and Meier,1958) to estimate the hazard rate as:

h(ti) = di /n i, (2)

wherediis the total number of individuals experiencing the event at the same dateti , divided by the total number of individuals ni at risk before ti. The estimatedtransition rate gives thus the probability of leaving a state in yeartto another state,given survival in the origin state until the beginning of yeart.

In the discrete-time competing risk Cox model (Cox, 1972, 1975), the under-lying density function of duration Tis assumed to be semi-parametric. Everyonein the sample is postulated to be subject to a baseline risk h0(t)to experience anevent. The baseline risk is then multiplied by a factor depending on the explanatoryvariables xj k(t). There is no assumption about the functional form of the baselinehazard, so that the hazard rate, hj k(t,xj k(t)), giving the probability that a womanmoves from the labour market state j to the labour market state k, knowing thatshe was in the initial state juntil t, and that the vector of explanatory variables isxjk (t)evaluated att, reads:

hj k(t,xjk (t)) = h0,j k(t) exp(xj k(t)j k), (3)

wherej kindicates the vector of coefficients to be estimated. Time tis measured asindividual time: the clock for each woman is set to zero each time she enters a new


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state. Correspondingly, duration Tis measured when this woman leaves the state.The explanatory variables xj k (t)could either be constant in time, like educationor nationality, or be time-varying, like age, family structure or marital status.3

The solution of the Cox model requires the use of partial likelihood methods. Wedistinguish four different transitions: from employment to inactivity, from employ-ment to unemployment, from inactivity to employment, and from unemploymentto employment. The consideration of competing risks can be managed by analysingeach type of event separately and treating the others as exits from the risk set(censored observations). This relies on the assumption that these exits are non-informative for the event under study, and subsequently, that the model applies tothe individuals having not experienced the event under study yet, at the end of theobservation period (right-censored data4). In Appendix 1, we explain how to treatrepeated events and handle correlations of variables across spells.

3. Research hypotheses and variables

Women are observed from their first entry into employment. Consequently eachindividuals spell-sequence starts with paid work. Either individuals remain in thisstate and are censored in 1990 (date of the survey) or they exit employment andenter inactivity or unemployment. Then women can either re-enter employmentor remain inactive or unemployed, respectively. All women are followed in thismanner until their censoring in 1990. In order to avoid a bias due to retirement,spells are censored when women reach sixty years of age.

Many variables are likely to influence the hazard rates under study: age, maritalstatus, family structure (number and age of children), male and female wages,family income, family allowances, total number of activity interruptions already

experienced, nationality, birth cohort, and macro-economic fluctuations.In demographic analysis, time effects are generally split into cohort, period, and

age effects (Hobcraft et al., 1982). Age is supposed to capture the effects due to lifecycle, such as family, career, attitude to life, etc. Most of these factors are directlyintroduced into our model through variables such as marital status, family structure,and work experience. Age here serves as a control variable of residual influences.Nine dummy variables representing quinquennial birth cohorts are introduced tocapture cohort effects.

According to the new home economics (Becker, 1973, 1991), single menand women are considered as negotiators who try to maximize their gains frommarriage. Theoretically, these gains are maximal if each partner executes the tasks

for which he or she has a comparative advantage, so that one partner specializesin the production of market goods and the other in home production. In manysocieties, women invest in the sort of human capital that increases their productiv-ities at home and men invest in the sort of human capital that increases theirproductivities on the job. In such an environment, marriage influences positivelythe transition hazard from employment to inactivity and negatively the transition


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Table I. Family configurations obtained with the classification

No of child. Age class of thei-th child Variable

1st child (youngest) 2nd child 3rd child 4th child

0 Ref.

1 04 FAMI1

1 58 FAMI2

1 919 FAMI3

2 07 412 FAMI4

2 815 1119 FAMI5

2 1619 20 and + FAMI6

3 010 414 719 FAMI7

3 1119 1423 17 and + FAMI8

4 and + 012 416 719 1021 FAMI9

4 and + 1319 20 and + 20 and + 20 and + FAMI10

1 and + 20 and + (20 and +) (20 and +) (20 and +) FAMI11

Note: See Table V (Appendix 2) for percentages over time.

from inactivity to employment. However, this traditional picture is different forthe younger birth cohorts. We distinguish three different marital statuses: single,married, and divorced. Concerning the labour supply of women, the date of separa-tion would be a more interesting explanatory variable than the date of divorce, butit is not available.

Family care is sometimes indicated through a variable for the total number ofchildren at home, and a variable for the age of the youngest child. This specification

assumes additivity of these two variables and neglects the age intervals betweenthe children. We combine here the total number and the ages of children. As thenumber of categories is too large, we identify twelve typical family configura-tions using cluster analysis5 presented in Table I. Less frequent configurations areassigned to the closest class in the sense of Euclidean distance.6 The distributionwithin each class was checked to be Gaussian. For example, a household includingtwo children aged one and three years old is assigned to the category one 07 yearold plus one 412 year old child (FAMI4). Some family configurations concernonly older birth cohorts, such as adult children: in these cases, family structuremeasures a joint effect of family and birth cohort. The family structure variable isre-evaluated for each woman at each period.

Young children represent high time costs and raise the reservation wage oftheir mothers so that young children in the household are likely to acceleratetheir exit from work and to deter them from returning to employment. Thiseffect should wane as the child grows older. This is less due to direct time coststhan to the decreasing cost of alternative supervision and care (Browning, 1992),because mothers would keep infants with them at home and would be more tired


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by infants than by older children. How the total number of children influencesfemale labour market mobility is difficult to say. On the one hand, the necessaryinvestment in time increases with the number of children, because children aretime-intensive goods. On the other hand, the financial needs of the family drivewomen toward participating in the labour market. However, children and labourmarket participation may be chosen simultaneously.

A drawback of the dataset is the absence of male and female wages as well asfamily allowances, the combination of which undoubtedly constitutes the majorpart of the opportunity cost of inactivity. Female wage has to be approximatedby human capital variables which are only part of the story. Family allowanceschanged frequently between 1945 and 1990 (about 25 major modifications),depending on the number and the age structure of children, on the income andsometimes on the professional status and sector of employment of the parents(Martin, 1998). However, having no information on family income prevented usfrom reconstructing individual family allocations and using them as covariates.

Human capital is positively associated with labour market participation and withshort work interruptions. Total human capital is supposed to reflect the poten-tial wage of an individual. When work is interrupted, the higher the potentialwage, the higher the opportunity cost. Mincer and Polachek (1974) decomposethis opportunity cost (or the full wage) into: (a) the market wage, (b) the presentvalue of the reduction in future earnings caused by the increased depreciation ofhuman capital during a career interruption, and (c) the present value of the lossin future earnings due to the missing accumulation of new professional experi-ence. Following Mincer and Ofek (1982), we assume that highly qualified womenreturn rapidly to work after an interruption, but this flow will decrease quickly asinactivity lasts, because the longer the interruption, the greater the decline in poten-

tial wages. We approximate here total human capital by educational attainment andprofessional experience. In absence of a better indicator, education is measuredby the highest diploma obtained by the woman: no diploma, 7 primary education,certificate of apprenticeship, baccalaurat(A-level), and baccalauratplus twoyears or more. Concerning human capital accumulated through work experience,we follow Mincer (1974) and Becker (1975) in considering the total number ofyears at work of the woman.

To capture the correlation across multiple spells, we also control for the numberof episodes of the same type already experienced by the woman. Women who haveoften interrupted their careers in the past may be more likely to experience furtherinterruptions, especially when interruptions result from unemployment.

Foreign women in France have lower labour market participation rates than

women of French nationality. The same holds true if we control for fertility differ-ences (Desplanques, 1993). In order to take this difference into account, we testthree dummy variables for nationality (at birth): French, foreign, and non-declared.

Beside individual characteristics, transitions may depend on macro-economicfluctuations, which we capture through the utilization rate of production capacities.


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This variable is re-evaluated in the model at each period, thanks to the time seriespublished by the Institut national de la statistique et des tudes conomiques(INSEE) and covering the entire observation period of our study (Villa, 1994;INSEE, 1996). The female unemployment rate is available only since 1962. Wesuspect that an increase of the utilization rate of production capacities makestransitions from inactivity and from unemployment back to employment easier.We also suspect a positive effect on transitions from employment to inactivity,because women would interrupt their work more easily when re-entry seems lesscomplicated. In contrast, involuntary exits from employment should be more likelyduring economic downturns.

Tables III, IV, and V (Appendix 2) present the data.

4. Results

4.1. THE HAZARD RATE FUNCTIONS

Kaplan-Meier hazard rate estimates show that the risk of leaving the labour marketis maximal after six years of employment (Figure 2a). This suggests that mostwomen accumulated a certain professional experience before a work interruption.The curves stratified by transition rank show that the second and the third employ-ment spells were, on average, shorter than first employment spells (Figure 2a,left-hand side). Employed women faced a higher risk of switching to inactivitythan of leaving their employments involuntarily (Figures 2a and 2b). The risk ofbecoming unemployed was maximal after about two years of activity (Figure 2b).For inactive women, the return to work was most probable at the beginning ofthe spell, after two years out of the labour market, which is the required minimal

duration of the registered work interruptions in our dataset. Then the risk decreases,stabilizes for a duration between six and twenty-three years and, finally, decreasesagain (Figure 2c, right-hand side). This risk was higher than that of an exit fromemployment to inactivity or to unemployment (Figures 2a and 2b), but smallerthan that of a return to work, given that the woman was unemployed (Figure 2d).For the latter, re-entry became more and more difficult as time went by, thoughthe risk follows no clear pattern after an unemployment spell of over ten years(Figure 2d). Some women here counted as unemployed had actually switched toinactivity, while others had switched from inactivity to unemployment.

The risk patterns computed through the non-parametric method would apply toeach individual if the population would be homogenous, which is not true. The

next section shows the effects of individual characteristics on the transition rates.

4.2. USING THE COX MODELS

The estimated coefficients of the Cox models are presented in Table II.


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(a) from employment to inactivity

(b) from employment to unemployment

(c) from inactivity to employment

(d) from unemployment to employment

Figure 2. Hazard rate functions for the four transition types.


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TableII.Estimatedregressioncoefficientsforthetransitionsfromemploymenttoinactivityandunemploymentandfor

thetransitionsfrominactivityandunemploymenttoemployment

Variables

Exitfrom

employment

Reentrytoemployment

toinactivity

tounempl.

frominactivity

fromunempl.

Spellrank

first

0

(Ref.)

0

(Ref.)

0

(Ref.)

second

0.277

(0.058)

0.404

(0.126)

0.423

(0.046)

third

0.996

(0.104)

1.158

(0.192)

0.340

(0.118)

Childrena(def.seeTable1)

nochildren

0

(Ref.)

0

(Ref.)

0

(Ref.)

FAMI1

0.693

(0.033)

0.144

(0.063)

0.410

(0.131)

FAMI2

0.477

(0.082)

0.267

(0.077)

0.166

(0.210)

FAMI3

0.472

(0.085)

0.258

(0.075)

0.014

(0.178)

FAMI4

0.243

(0.038)

0.186

(0.061)

0.574

(0.139)

FAMI5

0.627

(0.086)

0.170

(0.070)

0.094

(0.187)

FAMI6

0.272

(0.138)

0.272

(0.099)

0.094

(0.265)

FAMI7

0.077

(0.049)

0.323

(0.063)

0.434

(0.182)

FAMI8

0.499

(0.114)

0.246

(0.078)

0.067

(0.273)

FAMI9

0.512

(0.064)

0.322

(0.065)

0.407

(0.247)

FAMI10

0.054

(0.127)

0.267

(0.093)

0.253

(0.298)

FAMI11

0.007

(0.097)

0.083

(0.085)

0.063

(0.171)


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TableII.Continued

Variables

Exitfro

memployment

Reen

trytoemployment

toinactivity

tounempl.

from

inactivity

fromunempl.

Maritalstatus

single

0

(Ref.)

0

(Ref.)

0

(Ref.)

married

1.664

(0.034)

0.330

(0.085)

0.392

(0.048)

divorced

1.616

(0.080)

0.354

(0.139)

0.138

(0.075)

Age

0.003

(0.011)

0.239

(0.025)

0.110

(0.013)

Age2/100

0.089

(0.017)

0.184

(0.032)

0.158

(0.017)

Educationalattainment

primaryeducation

0

(Ref.)

0

(Ref.)

0

(Ref.)

0

(Ref.)

nodiploma

0.090

(0.025)

0.015

(0.076)

0.103

(0.029)

0.188

(0.105)

certf.ofapprenticeship

0.015

(0.028)

0.061

(0.080)

0.083

(0.032)

0.044

(0.107)

baccalaureat(A-level)

0.062

(0.039)

0.246

(0.109)

0.114

(0.045)

0.112

(0.142)

bac.plustwoyearsormore

0.193

(0.046)

0.765

(0.155)

0.172

(0.055)

0.226

(0.195)

Workexperience(years)

0.006

(0.006)

0.019

(0.011)

0.014

(0.004)

0.021

(0.007)

Birthcohort

192529

0

(Ref.)

0

(Ref.)

0

(Ref.)

0

(Ref.)

193034

0.064

(0.032)

0.756

(0.134)

0.003

(0.032)

0.046

(0.182)

193539

0.148

(0.037)

1.233

(0.150)

0.052

(0.041)

0.203

(0.198)


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TableII.Continued

Variables

Exitfromemployment

Reentry

toemployment

toinactivity

tounempl.

frominactivity

fromunempl.

194044

0.179

(0.041)

1.416

(0.174)

0.092

(0.047)

0.553

(0.214)

194549

0.270

(0.039)

2.266

(0.158)

0.048

(0.044)

0.353

(0.190)

195054

0.297

(0.038)

2.798

(0.161)

0.205

(0.046)

0.407

(0.194)

195559

0.304

(0.039)

3.352

(0.166)

0.503

(0.050)

0.777

(0.195)

196064

0.329

(0.047)

4.029

(0.175)

0.766

(0.064)

0.844

(0.212)

196571

0.597

(0.085)

4.138

(0.209)

0.972

(0.123)

0.950

(0.268)

Nationality

French

0

(Ref.)

0

(Ref.)

foreign

0.164

(0.038)

0.284

(0.099)

notdeclared

0.195

(0.094)

0.046

(0.238)

Util.rateofprod.capac.

1.174

(0.285)

8.565

(0.925)

2.014

(0.689)

4.958

(2.432)

Numberofspells

49,218

49,218

10,276

1,161

Numberofevents

10,287

1,171

7,778

675

df

33

22

31

25

Significantat5%.Standarderrorsinparentheses.Waldtests,seeTableVI(Appendix3).

aSomefamilyconfigurationsconcern

onlyolderbirthcohorts,suchasadultchildren:inthesecases,family

structuremeasuresajointeffectoffamilyandbirthcohort.


15/26


Transition from employment to inactivity

Women having interrupted their careers in the past had a significantly higher riskto exit from employment into inactivity than women who had not experienced a

first work interruption yet.Mothers of very young children (FAMI1, FAMI4) faced higher risks of volun-

tary exits from the labour market than women without children. However, thisceased to hold true for families of more than two children (FAMI7, FAMI9). Thissuggests that the financial needs of larger families kept mothers at work in spite ofthe presence of young children in the household. Regardless of the total number ofchildren, the risk of work interruptions decreased as children grew older (FAMI2,FAMI3, FAMI5, FAMI6, FAMI8). In exiting from work, having adult children wasnot different than having no children at all.

Married women were more likely to interrupt their working careers than singlewomen, suggesting that a large part of couples reproduced the traditional family

roles, where the husband works and the wife stays at home. This result complieswith other empirical studies (Felmlee 1984; Drobnic et al., 1999). The Wald testshows no significant difference between married and divorced (not remarried)(see Appendix 3).

Exit from work is a decreasing, concave function of age. Educational attainmentand work experience had only a minor effect on voluntary exits from employment.Having no diploma induced a lower risk, and having a very high diploma a higherrisk of interrupting work, compared with women with primary education.

The cohort effects suggest that younger birth cohorts were less likely than olderbirth cohorts to cease or to interrupt their working careers significantly.

Foreign women faced higher risks of voluntary exit from employment thanFrench women.

Favourable macro-economic conditions, measured by the utilization rate ofproduction capacities, had a positive effect on the exit rate, suggesting that womenstopped their jobs more easily, when a later re-entry was more likely.

Transition from employment to unemployment

As before, the total number of unemployment durations already experienced by thewoman is an important determinant of the transition. Active women having alreadyexperienced unemployment were 1.5 times more likely to become unemployedthan those who had not. For women at risk of a third spell of unemployment, therelative risk was over 3 times higher.

Being married reduced the risk of losing ones job, whereas being divorcedincreased it. Similar effects of marriage on the risk of becoming unemployed werefound by Visser (1992) for men, but it has not been confirmed for women.

The risk of unemployment increased with age, but decelerated. Educationattainment had a significant influence on involuntary exits from employment.Having a baccalaurat (A-level) reduced the risk by about 25% compared to


16/26


women of primary education. Having any university diploma or equivalent reducedthe risk by 55%. This result confirms those of Bonnal and Fougre (1990). Workexperience (duration of the current and the preceding work spells) reduced the riskof unemployment, but the effect of this variable is only significant at 10%.

The estimates indicate strong cohort effects, even when age and diploma arecontrolled for. The risk of unemployment increases sharply with the year of birth.If we assume that the utilization rate of production capacities captures businesscycles, the measured cohort effects would reflect the dramatic growth of unemploy-ment which in particular affected younger birth cohorts since the seventies. Thiscomplies with the Easterlin hypothesis (1973), according to which the unemploy-ment of the baby-boom cohorts is linked to their relatively larger sizes. However,Riboud (1987) denied this hypothesis for the French case, arguing that the increasein school enrolments has more than compensated for demographic changes.

Foreign women faced a higher unemployment risk,ceteris paribus, than Frenchwomen. This result is also in line with the finding of Bonnal and Fougre (1990).

As expected, involuntary exits from employment were favoured duringeconomic downturns. An increase by one percentage point of the utilization rateof production capacities decreased the risk of becoming unemployed by about onepercent.

Family structure variables did not improve significantly the goodness-of-fit ofthe model.

Transition from inactivity to employment

The spell rank again influenced the transition; women who had had a spell ofinactivity had shorter work interruptions in average than women who experienced

their first inactivity spell.Women with only one child were more likely to return to work compared

to women without children, regardless of the childs age (FAMI1FAMI3). Incontrast, women with two or more children, one of whom is very young (FAMI4,FAMI7, FAMI9), had longer work interruptions on average. According to Blanchetand Pennec (1993), the incompatibility between children and labour market parti-cipation is negligible if women are mothers of only one child, but it increases withparity.

In agreement with Drobnic et al. (1999), married women had longer workinginterruptions than single or divorced women.

The return to work increased with age, but decelerated. The introduction of

educational attainment and work experience provides a significant improvementof the explanatory power of the model. A high diploma and work experience,accumulated in the past, increased the risk of returning to work compared withwomen with primary education and less work experience. According to humancapital theory, there exists a negative correlation between the opportunity cost ofstaying outside the labour market and the duration of work interruption.


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The birth-cohorts born after 1950 faced a higher risk of returning to work thanthe generations born before the Second World War, even after controlling for familystructure and education. This result, together with the finding for the transitionin the opposite direction (younger birth cohorts were less likely than older birthcohorts to cease or to interrupt their careers), suggests that stronger attraction tothe labour market of younger birth cohorts was one of the most important causesof the rise of female participation to the labour market in the last decades.

As expected, re-entry into the labour market seems to have been easier underfavourable economic conditions.

Transition from unemployment to employment

The fact that the population at risk actually consisted of unemployed and inactivewomen makes it difficult to carry out reliable estimations. As described in section 2,we have no information on transitions between unemployment and inactivity. With

data from theEnqute Emploi

(Employment Survey), LHorty (1997) showed thatabout 15% of the individuals in the early seventies who were unemployed a givenyear were inactive the following year. This rate decreased regularly to about 10%in 1990. The rate in the opposite direction was about 0.5% in 1970 and rose to 2%in 1990. We thus consider the population at risk as a group of women who losttheir employment involuntarily, a part of whom switched to inactivity, but was stillcounted as unemployed (with the already mentioned qualification that we studyonly interruptions exceeding two years).

Meanwhile, Table II shows that having a very young child (FAMI1, FAMI4,FAMI7) exerted a negative effect on the risk of returning to work. This result couldbe explained by demand effects (small children deter the re-entry into employ-ment), or by supply effects (mothers of small children prefer to stay at home).

In the second case, women should be counted as inactive. It could be that someunemployed women used the period of unemployment to constitute their families,thus delaying their returns to work.

Having a baccalaurat (A-level) or higher facilitated the re-entry into paidwork compared with women of primary education. In contrast, having no diplomadecreased the risk of returning to work. These estimates are only significant atthe 10% level, but in agreement with Bonnal and Fougre (1990) and Joutard andWerquin (1992).

Exit from unemployment was easier for younger cohorts. Wald tests show thatwe can distinguish cohorts born between 1925 and 1939 from those born between1940 and 1954 and from those born between 1955 and 1971 (see Appendix 3).

The effect of the utilization rate of production capacities is hardly significant.Spell rank, marital status, and nationality do not improve significantly thegoodness-of-fit of the model.


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5. Conclusion

The retrospective data from the French Enqute Familleoffered us the opportunityto build a dynamic model of the French female labour force participation.

More recent cohorts were less likely to leave their jobs and, after an interruption,more likely to return to work: this phenomenon reveals that the increase in femaleactivity in the last 50 years has been underlied by the tighter attachment of youngerbirth cohorts to activity. In agreement with Drobnic et al. (1999) for American andGerman labour markets, it extends to all women, across all differences in educationor family configuration. The coefficients for the cohort variables in Table II reflect agradual transition along successive generations. Younger cohorts, better educated,were also more active and their work interruptions were shorter.

Macroeconomic fluctuations are significant and have expected signs: ineconomic growth, employed women were more likely to leave activity forinactivity and less likely to do the same for unemployment. For our period,economic growth, which often meant downsizing rather than reduction of unem-ployment, benefited to inactive women who could find a job more easily, and leftno particular advantage to long-term unemployed women (hardly significant coef-ficient in Table II). Women were more likely to interrupt their professional careersif a later re-entry seemed easier. Similar results were found by Giannelli (1996) forGerman women.

According to human capital theory and empirical studies of other coun-tries (Joesch, 1994 for the USA; Klein and Braun, 1995 for Germany; Rnsenand Sundstrm, 1996 for Scandinavia; Drobnic et al., 1999 for a comparisonGermany/USA), better education and work experience favour the return to work.Opportunity costs of a work interruption increase for highly qualified women, whosubsequently return more rapidly to activity. The attractiveness of highly qualified

jobs comes also from their intrinsic interest, beyond the pecuniary aspect.Meanwhile, we also found that educational attainment was associated with a

higher propensity to exit from employment: less educated women were less likelyto interrupt their careers, but when they did it, the interruptions lasted longer.Drobnic et al. (1999) for Germany and the US found the contrary. Our resultindicates that opportunity costs were relatively low for short interruptions, not onlybecause of the maternity leave (which is relatively short in France compared tosome other European countries), but also, as predicted by Mincer and Ofek (1982),because human capital depreciates when inactivity lasts.

As expected in the new home economics, marriage induced a reallocation of lifetime between staying at home and activity, in connection or not with motherhood:

exit from employment rose for married women and re-entry declined.Infants favoured voluntary exits from employment, testifying for theirdemanding much time-consuming care and affection. Like German and Americanrates (Klein and Braun, 1994; Giannelli, 1996; Drobnic et al., 1999), unlike Spanishones (Adam, 1996), French rates of female re-entry into work at the time whenchildren reach school age had been increasing. One child is not enough to prevent


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the mother from returning to work, but the more children, the less likely this returnbecomes. This is what Blanchet and Pennec (1993) noticed, that the incompatibilitybetween children and labour market participation in France was negligible for one-child mothers, but that it increased with parity. However, comparing the exit hazardrate out of employment for mothers of three or more children to mothers of lessthan three suggests that financial needs tended to drive mothers of large familiesback to work. For mothers staying at home, children should not be interpretedas a constraint, because the risk of returning to work did not decrease with thetotal number of children. The fact that these mothers stopped activity after thefirst childbirth reveals that they actually realized a family project. The same wasfound by Joesch (1994) who studied the return to activity of American mothersafter childbirth: after controlling for work status during pregnancy, the birth orderwas no longer significant. Whithout controlling for work status, parity reduces therisk of return. Furthermore, mothers of adult children behaved like women withoutchildren.

The spell rank was found to explain an important part of transition rates, indic-ating a certain consistency: women having interrupted their careers in the past weremore likely to leave their jobs for inactivity.

Economic downturns, low human capital, unemployment spells, belonging toa recent birth cohort, being divorced, and being a foreigner increased the risk ofbecoming unemployed. These results comply with other analyses of the Frenchlabour market (Bonnal and Fougre, 1990; Joutard and Werquin, 1992).

Acknowledgements

This work was done during Michael Grimms visiting the Institut national

dtudes dmographiques in Summer 1999 and benefited from discussions at NolBonneuils seminar at the cole des hautes tudes en sciences sociales, as well asfrom his presentation at the Young Economists Conference held at Oxford inMarch 2000.

Appendix 1

Repeated eventsOver the life cycle of women, the same event can occur several times. A woman can, forexample, experience two or three employment spells, interrupted by periods of inactivityor unemployment. The logical way to model repeatable events is to use a multiple-spell

hazard model, which consists in treating thesspells belonging to a specific individual, butconcerning different spell ranks r , as stemming from different individuals. Coxs partiallikelihood can then be written as:

LP() =

sr=1

ni=1

exp(x i (tir ))lR(tir )

exp(x l (tir )), (4)


20/26


where r indexes the rank, and i the individual. The vector xi contains the covariates ofindividual i evaluated at time tir , and R(tir ) is the population at risk at tir , the set ofindividualsl who have not transited or been censored before or at tir . The multiple-spellhazard model assumes that the effects of the explanatory variables and of time do not

vary over the subsequent spells. Furthermore, the occurrence of an individuals subsequentevent will not be independent of prior events. If the first and second employment spellswere brief, we speculate that the third will also be so. To handle the correlation betweensubsequent spells, we have to condition the hazard rate on the individuals previous history(spell rank, working experience, age, etc.). Heckman and Singer (1984a), Heckman andWalker (1990), and Willett and Singer (1995) have discussed and advocated this approach.

A major part of unobserved heterogeneity should come from the rank of spell. Wetested the sensitivity of our results to the pooling of spell ranks in running the Cox modelseparately for each spell. The differences are minor. Introducing spell ranks as a covariateinto the pooled Cox model permits thus to partially control the dependence between spellranks. The remaining unobserved heterogeneity is hard to be taken into account in presentCox modelling with time varying covariates and multiple spells (Heckman and Singer,

1984a, 1984b; Horowitz, 1999). In addition, in a parametric estimation, the estimates arevery sensitive to the chosen distribution of unobservables and of the conditional dura-tion (Heckman and Singer, 1984a, 1984b; Horowitz, 1999), weakening the usefulness ofunobserved heterogeneity.

Appendix 2

Description of the data sample

Table III. Static variables

Variable Percentages Variable Percentages

Birth cohort Educational attainment

192529 19.5 no diploma 26.0

193034 14.2 primary education 37.1

193539 8.9 certf. of apprenticeship 20.5

194044 7.1 baccalaureat(A-level) 9.8

194549 9.6 bac.plus two years or more 6.6

195054 11.0 Nationality at birth

195559 11.5 French 91.1

196064 10.2 foreign 7.5

196571 8.1 not declared 1.5

Residence (in 1990) Mean age at first employmenta 17.6 (4.20)

Paris (region) 14.3Province 85.7

aStandard deviation in parentheses.


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Table IV. Dynamic variables

Variable Percentagesa

1950 1960 1970 1980 1990

Family configuration(def. see Table 1)

no children 72.6 37.1 28.6 22.8 15.1FAMI1 16.9 13.7 7.9 7.6 5.4FAMI2 0.7 4.9 2.7 2.3 1.6FAMI3 0 4.4 7.4 4.3 3.3FAMI4 7.2 16.9 11.0 9.7 8.4FAMI5 0 2.6 7.8 5.8 5.6FAMI6 0 0 2.3 3.1 2.4FAMI7 2.0 10.7 9.8 6.6 8.2FAMI8 0 0 4.3 5.7 4.4FAMI9 0.6 9.7 13.3 6.0 4.2

FAMI10 0 0 1.6 5.7 2.8FAMI11 0 0 3.3 20.1 38.6

Marital Statussingle 60.1 26.7 20.6 15.8 12.8married 39.6 72.1 77.4 80.8 82.1divorced and not remarried 0.3 1.2 2.0 3.4 5.1

Mean age (years)b 29.0 (2.92) 32.1 (5.47) 36.6 (8.91) 41.5 (11.9) 44.6 (13.7)

aThe values concern only the women who have already occupied an employment in or before thespecified year.bStandard deviation in parentheses.

Table V. Description of the spellsa

Spell Number of Mean duration Exit to Exit to Exit toindividuals (years)b inactivity unemployment employment

1st employment 40,882 22.19 (14.81) 9,120 878 1st inactivity 9,117 10.19 (8.60) 6,9301st unemployment 874 5.05 (4.15) 5182nd employment 7,448 13.12 (10.89) 1,022 240 2nd inactivity 1,019 6.68 (5.25) 7602nd unemployment 239 4.97 (3.46) 1283rd employment 888 9.08 (8.92) 145 53 3rd inactivity 140 5.92 (4.14) 883rd unemployment 48 5.88 (5.92) 29

aThe small differences between the number of exits from one state and the corresponding numberof entries into another state have their origin in: (i) the censoring of women at age 60, which can

cause work interruptions of less than two years, not taken into account; (ii) exits from employmentshortly before 1990, also causing working interruptions of less than two years, which are not takeninto account.bStandard deviation in parentheses.


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Appendix 3

Wald tests of pairwise equality between regression coefficients

Table VI. Wald tests of pairwise equality between the regression coefficients reported in Table II

Test Pvalues Test Pvalues

EI EU IE UE EI EU IE UE

Spell rank FAMI7 vs 9 0+ 0.981 0.922

Rank 2 vs 3 0+ 0+ 0.478 FAMI7 vs 10 0.854 0+ 0.582

Children (def. see Table I) FAMI7 vs 11 0.479 0.003 0.024

FAMI1 vs 2 0+ 0.061 0.260 FAMI8 vs 9 0.911 0+ 0.317

FAMI1 vs 3 0+ 0.061 0.025 FAMI8 vs 10 0.002 0.810 0.611

FAMI1 vs 4 0+ 0+ 0.257 FAMI8 vs 11 0+ 0+ 0.636

FAMI1 vs 5 0+ 0.659 0.111 FAMI9 vs 10 0+ 0+ 0.671

FAMI1 vs 6 0+ 0.180 0.068 FAMI9 vs 11 0+ 0.003 0.077

FAMI1 vs 7 0+ 0+ 0.897 FAMI10 vs 11 0.691 0+ 0.272

FAMI1 vs 8 0+ 0.164 0.227 Marital status

FAMI1 vs 9 0+ 0+ 0.991 marr. vs div. 0.512 0+ 0+

FAMI1 vs 10 0+ 0.176 0.613 Educational attainment

FAMI1 vs 11 0+ 0.007 0.014 No dipl. vs certf. 0.001 0.595 0+ 0.214

FAMI2 vs 3 0.962 0.906 0.460 No dipl. vsbac. 0.500 0.042 0+ 0.045

FAMI2 vs 4 0+ 0+ 0.062 No dipl. vsbac.+ 0+ 0+ 0+ 0.041

FAMI2 vs 5 0.167 0.179 0.773 Certf. vsbac. 0.063 0.104 0.515 0.299

FAMI2 vs 6 0.183 0.959 0.408 Certf. vsbac.+ 0+ 0+ 0.116 0.182

FAMI2 vs 7 0+ 0+ 0.276 bac.vsbac.+ 0+ 0.003 0.359 0.604

FAMI2 vs 8 0.870 0.800 0.758 Birth cohortFAMI2 vs 9 0.706 0+ 0.419 193034 vs 3539 0.030 0.001 0.257 0.415

FAMI2 vs 10 0.003 0.998 0.799 193034 vs 4044 0.006 0+ 0.065 0.016

FAMI2 vs 11 0+ 0+ 0.342 193034 vs 4549 0+ 0+ 0.273 0.103

FAMI3 vs 4 0+ 0+ 0.002 193034 vs 5054 0+ 0+ 0+ 0.062

FAMI3 vs 5 0.144 0.185 0.621 193034 vs 5559 0+ 0+ 0+ 0+

FAMI3 vs 6 0.182 0.883 0.779 193034 vs 6064 0+ 0+ 0+ 0+

FAMI3 vs 7 0+ 0+ 0.041 193034 vs 6571 0+ 0+ 0+ 0.001

FAMI3 vs 8 0.834 0.876 0.781 193539 vs 4044 0.505 0.265 0.453 0.105

FAMI3 vs 9 0.664 0+ 0.123 193539 vs 4549 0.006 0 + 0.049 0 .439

FAMI3 vs 10 0.002 0.923 0.399 193539 vs 5054 0.001 0+ 0+ 0.301

FAMI3 vs 11 0+ 0+ 0.809 193539 vs 5559 0.001 0+ 0+ 0.004FAMI4 vs 5 0+ 0+ 0.016 193539 vs 6064 0.001 0+ 0+ 0.003

FAMI4 vs 6 0+ 0+ 0.015 193539 vs 6571 0.001 0+ 0+ 0.006

FAMI4 vs 7 0+ 0.001 0 .466 194044 vs 4549 0.050 0 + 0.010 0 .326


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Table VI. Continued

Test Pvalues Test Pvalues

EI EU IE UE EI EU IE UE

FAMI4 vs 8 0+ 0+ 0.073 194044 vs 5054 0.009 0+ 0+ 0.473

FAMI4 vs 9 0+ 0.003 0.514 194044 vs 5559 0.007 0+ 0+ 0.269

FAMI4 vs 10 0.015 0+ 0.299 194044 vs 6064 0.004 0+ 0+ 0.178

FAMI4 vs 11 0.010 0.205 0.001 194044 vs 6571 0+ 0+ 0+ 0.138

FAMI5 vs 6 0.018 0.272 0.516 194549 vs 5054 0.521 0+ 0.002 0.742

FAMI5 vs 7 0+ 0+ 0.134 194549 vs 5559 0.431 0+ 0+ 0.009

FAMI5 vs 8 0.319 0.271 0.930 194549 vs 6064 0.236 0+ 0+ 0.005

FAMI5 vs 9 0.219 0+ 0.257 194549 vs 6571 0+ 0+ 0+ 0.011

FAMI5 vs 10 0+ 0.271 0.618 195054 vs 5559 0.869 0+ 0+ 0.011

FAMI5 vs 11 0+ 0.002 0.449 195054 vs 6064 0.516 0+ 0+ 0.006

FAMI6 vs 7 0.159 0+ 0.076 195054 vs 6571 0.001 0+ 0+ 0.014

FAMI6 vs 8 0.160 0.779 0.637 195559 vs 6065 0.615 0+ 0+ 0.617FAMI6 vs 9 0.091 0+ 0.133 195559 vs 6571 0.001 0+ 0+ 0.384

FAMI6 vs 10 0.181 0.958 0.333 196064 vs 6571 0.003 0.441 0.094 0.579

FAMI6 vs 11 0.061 0+ 0.906 Nationality

FAMI7 vs 8 0+ 0+ 0.226 foreign. vs not dec. 0+ 0.347

Notes: E = Employment, I = Inactivity, U = Unemployment.

Notes

1 In France between 1979 and 1990, the maternity leave was 16 weeks for the first and second child.For each additional child this period was extended to 26 weeks. Before 1979, the maternity leave was14 weeks.2 Employment in terms of the EF (and the French census) does not exactly correspond to the termsof the ILO, because it is based on spontaneous individual declaration. A trainee, for example, maybe counted as employed in the EF and inactive by the ILO.3 Introducing time-dependent covariates eliminates the property of proportionality between thehazard rates of different individuals, because variables do not change in general in the same wayfor all individuals. It follows that the relationship between the hazard rates cannot remain constant intime. However, the violation of the hypothesis of proportional hazards does not hinder the estimationof the vectorj k(Cox and Oakes, 1984).4 We have no left-censored data, because the EF is retrospective since birth.5 Using the Ward criterion.6

EDi =m

j=1(xj xij)2

, where xjis the age of the j-th child and xij the non-weightedaverage age of thej-th child of the i-th family configuration.7 No diploma concerns mainly elderly women who attended five years of schooling, but failed toobtain the primary education diploma.


24/26


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