employment dynamics

8/8/2019 Employment Dynamics

1/20

Employment dynamics of Greekmarried women

Michael Demoussis and Nicholas Giannakopoulos Department of Economics, University of Patras, Patras, Greece

AbstractPurpose This paper investigates the employment dynamics of Greek married women.Design/methodology/approach Longitudinal/panel data for the period 1995-2001 and dynamicdiscrete choice models are used for estimation purposes.Findings It is found that the probability of being employed is inuenced by observed individualcharacteristics (e.g. human capital, fertility and unearned income), while genuine state dependence andunobserved heterogeneity constitute major sources of observed serial persistence. The results show

that lagged employment affects current employment decisions in a systematic way, and that thenon-contemporaneous effects of unearned income and fertility correlate with unobservedheterogeneity. The estimated average partial effects reveal that an employed woman in t 2 1 has aprobability of being employed in t that is almost 50 percentage points higher than for a non-employedwoman.Practical implications The presence of state dependence and unobserved heterogeneity implythat the bridging of the female employment gap between Greece and its EU partners is expected tofollow a slow, long-term course.Originality/value Greek female labour force participation has been studied only under a staticanalytical framework. This is the rst study to investigate employment decisions of Greek marriedwomen in an inter-temporal setting.

Keywords Women, Employment, Greece

Paper type Research paper

1. IntroductionIt is almost certain that Greece will not be able to meet by 2010 the goal of a 60 per centfemale employment rate, which was set by the EU in 2000. Figure 1 presents femaleemployment rates in the EU and Greece for the period 1983-2003. Despite a steadyincrease in the Greek female employment rate (from 34 per cent in 1983 to around 45per cent in 2003), the EU-Greece gap remains relatively constant. There is, however,some cause for optimism. Figure 2 presents employment rates for Greek women andtheir EU counterparts, by age categories, for the years 1983, 1993 and 2003. It is evidentthat the employment rates of Greek women in younger age categories (from 25 to 44years old) reached the 60 per cent target in 2003. The gures are more encouraging forthe age group 25-34, which exhibits a smaller difference between the Greek and EU-15employment rates. Nevertheless, it can be clearly seen that after the age of 44 theemployment rate in Greece drops rather rapidly. To better understand the dynamics of

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0143-7720.htm

The authors thank Joan Daouli and two anonymous referees for valuable comments. They alsothank the participants of the conference European Labour Markets, Trade & Financial Flowsand Economic Growth, organised by the European Economics and Finance Society (EEFS),18-21 May, 2006, Heraklion, Greece. The usual disclaimer applies.

Employmentdynamics of

Greek women

423

Received 6 June 2006Revised 18 April 2007Accepted 19 June 2007

International Journal of ManpowerVol. 29 No. 5, 2008

pp. 423-442q Emerald Group Publishing Limited

0143-7720DOI 10.1108/01437720810888562


2/20

female employment in Greece, we investigate in this paper the inter-temporalemployment decisions of Greek married women[1].

The decision to participate in the labour market is typically analysed by economistswithin a standard utility maximisation framework, in which an individual isconsidered to trade off leisure and market goods subject to a full-income constraint(Mincer, 1962; Becker, 1965). Female labour supply and the corresponding

inter-temporal labour force participation decisions have been studied extensivelyboth in labour economics and in the applied econometrics literature. The accumulatedinternational empirical evidence suggests that wages and non-labour income are themajor predictors of this dynamic behavioural process (Blau, 1998; Goldin, 1989; Juhnand Murphy, 1997; Jacobsen, 1999; Blau and Kahn, 2007). Furthermore, theexamination of the dynamics of female labour market behaviour has identiedstrong persistence in participation decisions over time, i.e. correlated sequentialemployment decisions (Heckman, 1978; Hyslop, 1999). It has been observed thatindividuals who were employed, unemployed or collected social assistance in theprevious period are more likely to be observed to be in the same state in the immediatefuture (e.g. Heckman and Willis, 1977). This persistence is attributed to three majorfactors:

(1) unobserved heterogeneity, originating from differences in preferences;(2) state dependence, justied by human capital accumulation (Heckman, 1981),

inter-temporally non-separable preferences for leisure (Hotz et al., 1988) andsearch costs that vary across participation states (Eckstein and Wolpin, 1990);and

(3) serially correlated errors (Heckman and Willis, 1977; Heckman, 1981; Hyslop,1999).

Figure 1.Female employment ratesin EU-15 and Greece

IJM29,5

424


3/20

Figure 2.Female employment rates

in Greece and EU-15countries and their

differences


Greek women

425


4/20

It is well known that the typical female labour supply function is characterised bysubstantial discontinuities, which could result in a persistent out of labour forceparticipation status. Indeed, women are heterogeneous with regard to their tastes forwork, but this unobserved heterogeneity cannot explain participation interruptions.Instead, fertility and non-labour income have been identied as two of the mostimportant factors that could cause discontinuities in the inter-temporal labour supplyfunction. The accumulated empirical evidence suggests that the correlation betweenfertility and labour supply of women as well as between a womans non-labour incomeand her work behaviour is strongly negative. Given persistence, these participationinterruptions lower employment probabilities in subsequent periods. The observedemployment patterns of women worldwide provide ample evidence against treatingthe female population as a homogeneous one. This in turn implies that the use of cross-sectional data or pooled data, which assume a homogeneous population, are notappropriate for identifying the true relationship between employment decisions andthe major explanatory correlates. In the presence of heterogeneous populations, paneldata could provide more reliable estimates.

Major demographic developments that relate to female labour market behaviour inGreece concern decreased fertility rates, increased divorce rates, the entrance of mothers into the labour market and late childbearing. Female labour market behaviourhas also been inuenced by the gradual transformation of the general socioeconomicenvironment, from rural-agricultural to urban-industrial. Evidence from the LabourForce Survey of the National Statistical Service of Greece shows that the employmentrate of married women with one child increased by ve percentage points between1993 and 2000 (from 46 to 51 per cent), decreased by four percentage points for marriedwomen with two children (from 45 to 41 per cent) and remained relatively constant forwomen with three children (around 8 per cent). Despite the continuous increase infemale participation, Greek women occupy work places of low prestige, responsibility

and earnings. They are mainly employed in professions related to ofce work andretail sales. Compared to Greek men, women have increased their share, between 1995and 2000, in all professions (from 12.9 per cent to 18.1 per cent), in self-employment(from 21.4 per cent to 27.5 per cent), and in paid employment (from 37.4 per cent to 39.7per cent). Concerning this last category, female employment has increasedsubstantially in the public sector. Lastly, Greek women are let go more easily thanmen and a substantial gender wage gap exists in favour of men (Kanellopoulos, 1997;Cholezas and Tsakloglou, 2006).

Greek female labour force participation has been studied by several authors (Kottis,1990; Meghir et al., 1989; Magdalinos and Symeonidou, 1989; Kanellopoulos andMavromaras, 2002; Daouli et al., 2004; Nicolitsas, 2006). However, this is the rst studythat investigates employment decisions of Greek married women in an inter-temporalsetting. More specically, the aim of this paper is to explore the dynamics of therelationship between the employment decision and major economic and family-relatedindicators (i.e. human capital, fertility, non-labour income). We utilise a seven-year(1995-2001) longitudinal Greek sample from the European Community HouseholdPanel (ECHP) and dynamic discrete choice models for estimation purposes. We alsoincorporate relatively new methodological developments in order to identify the causesof persistence in observed participation rates. Along these lines, we investigate thepossible interaction between fertility and labour supply behaviour and between

IJM29,5

426


5/20

non-labour income and work preferences. The results obtained reveal that theemployment decisions of Greek married women are characterised primarily by strongstate dependence and to a lesser extent by unobserved heterogeneity.

The paper is organised as follows. In the next section, the data sources and variabledescriptives are presented and discussed. Also, in this section, the observedemployment sequences and the calculated raw transition employment probabilities arebriey discussed. Section 3 presents both the static and dynamic versions of the basiceconometric model. Section 4 reports and discusses the estimated results of the variousmodel specications. The paper concludes with section 5, which summarises the basicndings.

2. Data sources and descriptivesThe data utilised in this study were drawn from the Greek section of the EuropeanCommunity Household Panel (ECHP), a continuous longitudinal survey of individualsin private households in the member states of the European Union (Peracchi, 2002). We

focus on the survey years 1995-2001. Our sample consists of 711 women, aged between19 and 54 in the rst wave of the period analysed (1995), who were continuouslymarried in the period examined, and whose husband was present and reportedearnings from work in every sample year. We deleted observations with missing dataon any of the variables used in the analysis[2].

Table I contains summary information on the variables used in the analysis.Husbands annual earnings are expressed in constant 1995 drachmas[3]. It is evidentfrom the distribution of years worked during the period 1995-2001 that the observedannual employment behaviour of Greek married women is characterised by signicantpersistence (lower part of Table I). Namely, around a quarter (0.26) of women workedevery year of the sample period, while those who did not work at all comes to animpressive 53 per cent. Instead, the percentage of women who show some mobility in

and out of employment comes to only 21 per cent of the sample examined. In otherwords, almost 80 per cent of Greek married women appear to be persistently in or outof employment.

Comparing column 1 with column 2 in Table I, we see that women employed inevery year are better educated, in terms of completed years of schooling (three yearsmore), and more experienced. Women who were never employed in the examinedperiod shown in column 3 of Table I are substantially less educated and lessexperienced than those who are continuously employed[4]. Women who have made asingle transition from employment to non-employment (column 4) are slightly older(1.2 years), less educated and more experienced than the full-sample average. The samegroup of women have a smaller number of children in the age categories 3-17, reectingearly marriage and childbirth. Women with a single transition from non-employmentto employment (column 5) are younger and their husbands earn less than the sampleaverage. Finally, women who have experienced multiple transitions from employmentto non-employment and vice versa (column 6) are slightly younger, with lower thanaverage husbands earnings, and with fewer children.

The evidence presented in Table I reveals that the Greek case is different from thetypical West European paradigm but very similar to that observed in South EuropeanMediterranean countries (Boeri et al., 2005). However, the overall employment patternof Greek married women seems to be consistent with international experience. That is,


Greek women

427


6/20

F u l l

s a m p l e

C o n t i n u o u s

e m p l o y m e n t

C o n t i n u o u s

n o n - e m p l o y m e n t

S i n g l e t r a n s i t i o n

f r o m w o r k

S i n g l e t r a n s i t i o n

t o w o r k

M u l t i p l e

t r a n s i t i o n s

1

2

3

4

5

6

A g e ( 1 9 9 5 )

3 5 . 2

7

3 5 . 8

5

3 5 . 2

5

3 6 . 4

8

3 3 . 3

9

3 4 . 7

2

( 7 . 0

3 )

( 5 . 8

8 )

( 7 . 4

4 )

( 7 . 6

2 )

( 6 . 5

5 )

( 7 . 4

8 )

E d u c a t i o n a

1 0 . 8

5

1 3 . 5

6

9 . 7 6

9 . 7 2

1 0 . 6

0

1 0 . 1

2

( 4 . 5

1 )

( 4 . 0

6 )

( 4 . 2

2 )

( 4 . 4

0 )

( 4 . 7

5 )

( 3 . 8

9 )

E x p e r i e n c e b

1 2 . 0

3

1 5 . 7

9

9 . 6 9

1 7 . 0

0

1 2 . 2

6

1 2 . 5

7

( 1 0 . 2 9 )

( 7 . 3

7 )

( 1 1 . 1 3 )

( 8 . 2

4 )

( 1 0 . 4 7 )

( 9 . 0

4 )

N u m b e r o f c h i l d r e n i n 0 - 2 a g e g r o u p b

0 . 1 3

0 . 1

4

0 . 1 2

0 . 1 5

0 . 1 4

0 . 1 3

( 0 . 3

6 )

( 0 . 3

7 )

( 0 . 3

6 )

( 0 . 3

9 )

( 0 . 3

6 )

( 0 . 3

6 )

N u m b e r o f c h i l d r e n i n 3 - 5 a g e g r o u p b

0 . 2 0

0 . 2

0

0 . 2 1

0 . 1 3

0 . 2 3

0 . 1 7

( 0 . 4

4 )

( 0 . 4

4 )

( 0 . 4

4 )

( 0 . 4

0 )

( 0 . 4

8 )

( 0 . 3

8 )

N u m b e r o f c h i l d r e n i n 6 - 1 2 a g e g r o u p b

0 . 5 5

0 . 5

7

0 . 5 5

0 . 4 8

0 . 6 9

0 . 4 3

( 0 . 7

5 )

( 0 . 7

4 )

( 0 . 7

5 )

( 0 . 7

5 )

( 0 . 8

9 )

( 0 . 6

7 )

N u m b e r o f c h i l d r e n i n 1 3 - 1

7 a g e g r o u p b

0 . 4 4

0 . 4

7

0 . 4 4

0 . 3 3

0 . 4 6

0 . 4 0

( 0 . 6

9 )

( 0 . 7

1 )

( 0 . 7

0 )

( 0 . 6

0 )

( 0 . 7

0 )

( 0 . 6

6 )

H u s b a n d s e a r n i n g s / 1 0 0 , 0 0 0

3 4 . 2

6

3 4 . 8

9

3 6 . 0

1

3 2 . 6

7

2 9 . 5

8

2 7 . 3

7

( 2 2 . 2 9 )

( 1 9 . 7

4 )

( 2 5 . 2 0 )

( 1 5 . 9 6 )

( 1 3 . 4 6 )

( 1 5 . 4 0 )

E m p l o y m e n t

0 . 3 6

1

0

0 . 4 7

0 . 5 1

0 . 4 7

( 0 . 4

8 )

( 0 . 0

0 )

( 0 . 0

0 )

( 0 . 5

0 )

( 0 . 5

0 )

( 0 . 4

9 )

N u m b e r o f w a v e s i n e m p l o y m e n t c

Z e r o

0 . 5 3

1 . 0 0

O n e

0 . 0 3

0 . 1 1

0 . 1 9

0 . 1 3

T w o

0 . 0 4

0 . 2 8

0 . 1 7

0 . 2 1

T h r e e

0 . 0 4

0 . 2 4

0 . 0 9

0 . 2 0

F o u r

0 . 0 4

0 . 1 0

0 . 1 9

0 . 2 3

F i v e

0 . 0 3

0 . 1 0

0 . 1 3

0 . 1 4

S i x

0 . 0 3

0 . 1 7

0 . 2 3

0 . 0 9

S e v e n

0 . 2 6

1 . 0

0

O b s e r v a t i o n s i n e v e r y w a v e

7 1 1

1 8 3

3 7 6

2 9

5 3

7 0

N o t e s : S t a n d a r d d e v i a t i o n s i n p a r e n t h e s e s . S a m p l e s e l e c t i o n c r i t e r i a : m a r r i e d w o m e n , a

g e d 1 8 - 5

4 ( i n 1 9 9 5 ) , w i t h h u s b a n d p r e s e n t a n d r e p o r t i n g e a r n i n g s

f r o m w o r k i n e v e r y w a v e . a

A g e a t w h i c h s c h o o l i n g w a s c o m p l e t e d m i n u s s i x y e a r s .

b E C H P d o e s n o t c o n t a i n d a t a o n a c t u a l e x p e r i e n c e . T

h e e x p e r i e n c e

v a r i a b l e h a s b e e n c o n s t r u c t e d b y s u b s t r a c t i n g t h e a g e o f q u e s t i o n P E 0 3 9 H o w o l d w e r e y o u w h e n y o u b e g a n y o u r w o r k i n g l i f e ? f r o m

t h e a g e o f

q u e s t i o n P D 0 0 3 ( A g e ) o f t h e U D B - E

C H P d a t a b a s e . T

h i s v a r i a b l e i s k n o w n a s g e n e r i c e x p e r i e n c e . c C o l u m n p e r c e n t a g e s

Table I.Sample statistics

IJM29,5

428


7/20

human capital variables (e.g. education, work experience and the age prole) tend toincrease the probability of employment, while the presence of young children does notseem to affect signicantly participation or to lead women to non-employment in theperiod covered by the sample. Children, however, may play a role in employmenttransitions and work interruptions (i.e. through maternity leave arrangements). Indeed,while in terms of averages the presence of young children does not seem to affectparticipation signicantly or to lead women to non-employment, this does not seem tobe implied or supported by the between-group variations.

The apparent persistence of the employment decisions has several implications inrelation to the method of estimation. Since the overwhelming majority of Greekmarried women are either employed in all of the sample periods (183 out of 711) ornever employed (376 out of 711), it is clearly implied that the process underlying the 2 7

( 128) sequences is not independent over time, i.e. serial persistence is present. Thisserial persistence cannot be explained solely by unobserved heterogeneity. That is, if serial persistence had originated from a time-invariant personality trait, then onewould have observed different number of women across each class of sequences, butapproximately the same number of women in each sequence within a class, ceteris paribus [5]. For example, in the class of sequences in which women are employed a totalof four times during the sample period, the most prevalent sequences are those inwhich a woman is employed in either the rst four consecutive periods (1111000) or thelast four (0001111). This is in agreement with the argument put forward by Heckmanand Willis (1977) that multiple transitions occur with very low probability, andtherefore a model that allows for both unobserved heterogeneity and state dependencewill t the employment patterns better. Our study will attempt to distinguish betweenthese two sources of serial persistence in the data. The Greek case seems to conrmShaws (1994) nding regarding employment persistence over time, since the numberof women who enter the labour market tend to become continuous workers, replacing

continuous non-workers.In order to identify the patterns of persistence, for several characteristics of Greekmarried women, we have calculated raw transition probabilities of employment in thecurrent period conditional on lagged employment, i.e. Pr E t 1j E t 2 1 1, andtransition probabilities of non-employment in the current period conditional on laggednon-employment, i.e. Pr E t 1j E t 2 1 1. Overall, the non-employment incidence ischaracterised by a higher degree of persistence (0.95) compared to employmentincidence (0.92). In addition, we have calculated raw transition probabilities for severalexogenous variables, i.e. work experience and number and age of children. Ourndings seem to verify the argument provided by Eckstein and Wolpin (1989)according to which work experience leads to persistence of employment patterns of married women through its positive and persistent effect on wages. With regard to the

number and age of children in the household, our ndings do not indicate the presenceof any signicant differentiation in the aforementioned employment probabilities.

3. Econometric considerationsAs a rule, employment decisions at the micro level are investigated within both staticand dynamic frameworks. However, since employment decisions are of aninter-temporal character, which is a basic premise of the present paper, the use of static models, while consistent, cannot provide efcient and theoretically convincing


Greek women

429


8/20

results. Nevertheless, as a starting point and for comparison purposes, we estimate theemployment probabilities for married women in Greece using a simple cross-sectiondiscrete choice specication, i.e. pooling observations across the time period underconsideration. Then, recognising the dynamic nature of the employment decision andits inter-temporal character, we utilise a random effects probit model which is speciedand estimated in its restricted and unrestricted forms. The restrictions concern theassumed correlations between observed heterogeneity and individual unobservedspecic effects.

Labour market choices are usually represented by latent variable models, with thebinary dependent variable indicating participation in the labour market. Theemployment decision is assumed to follow a dichotomous choice specication, forwoman i in year t , which is described by an unobserved continuous latent variable y

*

it .This variable describes the underlying process that leads an individual to work, whichis in essence a process of utility maximisation under specic time and consumptionrealisations. Once the individual decides to participate in employment, a positiveamount of her available time will be supplied. Thus, the employment decision is adiscrete choice decision and statistical models that can handle the discreteness of thechoice will be applied. In the present application, a probit specication is adopted.Namely, the employment decision is modelled as a dummy variable yit , that takes avalue of 1 if y

*

it $ 0, implying that woman i is employed, and 0 otherwise. Assuming

that the unobserved characteristics eit , which affect employment decisions, arenormally distributed, the probit model takes the form:

Pr yit 1j xit F xit b; 1

where xit is a set of observed socio-demographic and economic characteristics of theindividual, b are coefcients to be estimated and F ( ) is the corresponding cumulativenormal distribution function for the so-called pooled probit. This empirical

specication provides a useful descriptive model for the discrete choice underinvestigation, given the exogenous nature of the covariates used for estimationpurposes.

Yet, the simple pooled probit model does not take into consideration the panelnature of the data, and thus the dynamic nature of the employment decision. Thisshortcoming causes a substantial loss in potential explanatory power. With repeatedobservations for every woman in the sample, the important issue of unobservedtime-invariant heterogeneity arises. This issue is typically handled by introducing arandom (unobserved) effect term ci in the error structure, i.e. eit ci uit . The term ci is assumed to capture differences among women (e.g. tastes and preferences foremployment), caused by unobserved factors. Therefore, the random effects probitmodel takes the form:

Pr yit 1j xit ; ci F xit b ci ; 2

where ci is the unobserved time-invariant individual effect. We assume exogeneitybetween ci and xit , which implies that conventional maximum likelihood estimation(MLE) methods, alongside the Gaussian quadrature procedure, can be used for theconsistent estimation of b and s 2c (Butler and Moftt, 1982; Greene, 2003, p. 692)[6].Further, the variance of the idiosyncratic error term ( u it ) is assumed to be unitary and,thus the models variance due to unobserved heterogeneity is given by

IJM29,5

430


9/20

r s 2c=s 2c 1. The presence of unobserved heterogeneity requires rejection of the

null hypothesis that r 0 (Wooldridge, 2002).Equation (2) is a typical uncorrelated random effects model, and is based on the

strong assumption of exogeneity between unobserved time-invariant individualcharacteristics and observed individual characteristics, both time-variant andinvariant. A more exible specication of the model requires the inclusion(imposition) of an assumed correlation structure between ci and xit . Mundlak (1978)and Chamberlain (1984) suggest that the correlation between the two terms can behandled by making ci a function of the time averages of the independent correlatesused in the analysis. Along the same lines, Hyslop (1999) suggests that, in a standardrandom effects probit model, unobserved heterogeneity could be modelled empiricallyby making it a function of the non-contemporaneous effects of the independentcorrelates. In his case, the variables allowed to be correlated with the unobservedheterogeneity term, in the employment decisions of US married women, were variousfertility indicators and transitory income. The latter is proxied by the deviations of wifes non-work income from time-average income (permanent income). We adoptHyslops suggestion and make ci a function of leads and lags of the fertility and thetransitory income variables, as follows:

ci XT

s0

d 1 sno: of children 0-2 is d 2 sno: of children3-5 is

d 3 sno: of children6-12 is d 4 sno: of children 13-17 is

XT 2 1

s0d 4 strans income is h i ; 3

where h i is independent of the xs, and distributed normally with zero mean andvariance s 2h . Using this specication, a test of the exogeneity of fertility and currentnon-work income with respect to employment decisions can be performed by testingthe overall statistical signicance of the variables in equation (3).

Heckman (1981) and Hyslop (1999) argue further that employment decisions arecharacterised by rst-order Markov properties, and thus the issue of persistence needsto be addressed. Persistence refers to the relationship of the probability of employmentin year t to past employment realisations, i.e. t 2 1. The presence of persistence istypically tested by introducing in equation (2) a state-dependent variable, i.e. laggedvalues of the dependent variable.

Assuming that observations start at t 0, the dynamic model for t 1; . . . ; T becomes:

Pr yit 1j yit 2 1; xit ; ci F g yit 2 1 xit b ci ; 4

where ci is again assumed to be exogenous to the contemporaneous covariates of themodel. The existence of state dependence (a component of persistence) requires therejection of the null hypothesis that g 0, while simultaneously controlling forunobserved heterogeneity (Wooldridge, 2002).


Greek women

431


10/20

The preceding approach, however, encompasses the initial conditions problem,which refers to the treatment of employment status at t 0, i.e. yi 0 [7].

In statistical terms, the initial conditions problem refers to the event that thosewomen observed in employment at t 0 may not constitute a random sample. Severaleconometric procedures have been proposed for incorporating the initial conditions in adynamic response approach (Heckman, 1981; Orme, 1997; Wooldridge, 2005). Heckman(1981) suggests that a solution of the problem requires the conditioning of the jointdistribution of yi 0 on all outcomes. This method requires a rich set of pre-sampleinformation, to be used together with the rst sample-period characteristics in order topredict the initial conditions. Wooldridge (2005) proposes an alternative estimatorwhich is based on the conditioning of the distribution on the initial period value and thecovariates used in the analysis. We adopt Wooldridges approach and we model thedensity of employment indicators ( yi 1 , . . . , yiT ) on ( yi 0 , xi ). Empirically this procedure isimplemented by a model for yi 0 given xi and ci where ci c0 c1 yi 0 c2 xi h i , assuggested in Mundlak (1978). Therefore, the random effects probit model, with statedependence and controls for the initial value yi 0 , becomes:

Pr yit 1j yi 0; yit 2 1; xit ; h i F c0 c1 yi 0 g yit 2 1 xit b c xi h i : 5

The above model can be estimated by standard random effects probit software (e.g.Stata or Limdep). The empirical specication adopted in this paper has been usedextensively in related applied research of inter-temporal labour force participationdecisions for a number of countries[8].

The estimation results of equation (5) are in fact point estimates, and thus not veryrelevant or directly usable for policy analysis. Instead of point estimates, averagepartial effects (APE) are required in order to determine the relative signicance of thecoefcient under investigation (Wooldridge, 2005). In a pooled probit specicationthese partial effects can be identied through the calculation of marginal effects at thesamples means. However, in random effects discrete choice models, a consistentestimator of APE concerning, for example, the lagged employment dummy indicator inequation (5) is estimated by:

APE N 2 1X N

i 2 1

F g 0 ba x0 c0a c1a yi 0 c2a xi

2 N 2 1X N

i 2 1

F ba x0 c0a c1a yi 0 c2a ^ xi ;

6

where the subscript a denotes that the MLE parameter estimates should be multipliedby 1= ffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 s 2h q and x0 are sample means averaged across i and t [9]. Equation (6) isappropriate for the estimation of the APE of discrete variables. The APE of acontinuous variable is obtained by using its average value across i (Gannon, 2005;Stewart, 2007). The corresponding standard errors of the APE are computed using theDelta method (Wooldridge, 2002, 2005).

4. Estimation resultsTable II presents the estimation results of the pooled (equation (1)), random effects(equation (2)) and correlated random effects (equation (4)) models. All specications

IJM29,5

432


11/20

P o o l e d

R a n d o m e f f e c t s ( R E )

C o r r e l a t e d r a n d o m e f f e c t s

( C R E )

C o e f c i e n t

M E

C o e f c i e n t

M E

C o e f c i e n t

M E

C o n s t a n t

2 6 . 6 5 7

2 1 2

. 7 0 3

2 1 2

. 9 1 1

( 0 . 5

7 5 * * * )

( 1 . 8

3 6 ) * * *

( 2 . 0

7 2 ) * * *

E d u c a t i o n

0 . 0 8 9

0 . 0 3 2

0 . 2 3 5

0 . 0 4 1

0 . 2 3 8

0 . 0 4 0

( 0 . 0

0 4 ) * * *

( 0 . 0

0 1 )

( 0 . 0

1 5 ) * * *

( 0 . 0

0 6 )

( 0 . 0

1 9 ) * * *

( 0 . 0

0 6 )

E x p e r i e n c e

0 . 0 4 3

0 . 0 1 5

0 . 1 0 3

0 . 0 1 8

0 . 0 9 9

0 . 0 1 6

( 0 . 0

0 2 ) * * *

( 0 . 0

0 0 )

( 0 . 0

1 3 ) * * *

( 0 . 0

0 3 )

( 0 . 0

1 3 ) * * *

( 0 . 0

0 3 )

A g e

0 . 3 1 5

0 . 1 1 3

0 . 5 4 1

0 . 0 9 6

0 . 5 6 0

0 . 0 9 4

( 0 . 0

3 0 ) * * *

( 0 . 0

1 1 )

( 0 . 0

9 5 ) * * *

( 0 . 0

2 2 )

( 0 . 1

0 2 ) * * *

( 0 . 0

2 2 )

A g e s q u a r e d

2 0 . 0 0 4

2 0 . 0 0 1

2 0 . 0 0 7

2 0 . 0 0 1

2 0 . 0 0 8

2 0 . 0 0 1

( 0 . 0

0 0 ) * * *

( 0 . 0

0 0 )

( 0 . 0

0 1 ) * * *

( 0 . 0

0 0 )

( 0 . 0

0 1 ) * * *

( 0 . 0

0 0 )

N u m b e r o f c h i l d r e n i n 0 - 2 a g e g r o u p

2 0 . 1 3 8

2 0 . 0 4 9

2 0 . 5 2 5

2 0 . 0 9 3

2 0 . 5 4 3

2 0 . 0 9 1

( 0 . 0

6 3 ) * *

( 0 . 0

2 1 )

( 0 . 1

4 9 ) * * *

( 0 . 0

2 9 )

( 0 . 1

6 2 ) * * *

( 0 . 0

3 0 )


2 0 . 1 6 0

2 0 . 0 5 7

2 0 . 3 5 1

2 0 . 0 6 2

2 0 . 3 5 5

2 0 . 0 5 9

( 0 . 0

5 1 ) * * *

( 0 . 0

1 8 )

( 0 . 1

3 4 ) * * *

( 0 . 0

2 5 )

( 0 . 1

4 9 ) * *

( 0 . 0

2 6 )

N u m b e r o f c h i l d r e n i n 6 - 1 2 a g e g r o u p

2 0 . 1 7 6

2 0 . 0 6 3

2 0 . 3 3 8

2 0 . 0 6 0

2 0 . 3 4 9

2 0 . 0 5 8

( 0 . 0

3 0 ) * * *

( 0 . 0

1 0 )

( 0 . 1

0 1 ) * * *

( 0 . 0

2 0 )

( 0 . 1

1 8 ) * * *

( 0 . 0

2 1 )

N u m b e r o f c h i l d r e n i n 1 3 - 1

7 a g e g r o u p

2 0 . 0 6 4

2 0 . 0 2 3

2 0 . 2 1 3

2 0 . 0 3 7

2 0 . 2 3 8

2 0 . 0 4 0

( c o n t i n u e d )

Table II.Estimation results of the

static probit models


Greek women

433


12/20

P o o l e d

R a n d o m e f f e c t s ( R E )

C o r r e l a t e d r a n d o m e f f e c t s

( C R E )

C o e f c i e n t

M E

C o e f c i e n t

M E

C o e f c i e n t

M E

( 0 . 0

3 2 ) * *

( 0 . 0

1 1 )

( 0 . 0

9 1 ) * *

( 0 . 0

1 7 )

( 0 . 1

0 1 ) * *

( 0 . 0

1 8 )

P e r m a n e n t i n c o m e

2 0 . 0 1 4

2 0 . 0 0 4

2 0 . 0 3 0

2 0 . 0 0 5

2 0 . 0 4 2

2 0 . 0 0 7

( 0 . 0

0 1 ) * * *

( 0 . 0

0 0 )

( 0 . 0

0 7 ) * * *

( 0 . 0

0 1 )

( 0 . 0

0 9 ) * * *

( 0 . 0

0 1 )

T r a n s i t o r y i n c o m e

2 0 . 0 0 3

2 0 . 0 0 1

2 0 . 0 0 8

2 0 . 0 0 1

2 0 . 0 1 2

2 0 . 0 0 2

( 0 . 0

0 1 ) *

( 0 . 0

0 0 )

( 0 . 0

0 2 ) * * *

( 0 . 0

0 0 )

( 0 . 0

0 3 ) * * *

( 0 . 0

0 0 )

O b s e r v a t i o n s

4 , 9 7 7

4 , 9 7 7

4 , 9 7 7

L o g - l i k e l i h o o d

2 2 , 6 6 0 . 3 2

2 1 , 3 4 5 . 1 8

2 1 , 3 1 6 . 6 2

P s e u d o R

2

0 . 1 8 3

O v e r a l l s i g n i c a n c e

1 , 1 9 1 . 0 5 * * *

3 7 2 . 2 3 * * *

3 8 3 . 8 3 * * *

r

0 . 8 8 1

0 . 8 7 2

W a l d t e s t f o r H

0 : C R E 0

T r a n s i t o r y i n c o m e

1 1 . 3

8 * *


1 6 . 2

8 * *


8 . 3 6

N u m b e r o f c h i l d r e n i n 6 - 1

2 a g e g r o u p

4 . 1 8

N u m b e r o f c h i l d r e n i n 1 3 - 1 7 a g e g r o u p

4 . 9 6

N o t e s : S t a n d a r d e r r o r s i n p a r e n t h e s e s . A s t e r i s k s i n d i c a t e s t a t i s t i c a l s i g n i c a n c e a t

* 1 0 p e r c e n t , * *

5 p e r c e n t ,

* * * 1 p e r c e n t . A

l l m o d e l s i n c l u d e y e a r ,

r e g i o n a l , h

e a l t h

, m e m b e r s h i p i n c l u b - p a r t y d u m m i e s . M a r g i n a l e f f e c t s e v a l u a t e d a t s a m p l e m e a n s . r r

e p r e s e n t s t h e p r o p o r t i o n o f t h e m o d e l s v a r i a n c e d u e

t o u n o b s e r v e d h e t e r o g e n e i t y

. S t a n d a r d e r r o r s o f A P E w e r e c o m p u t e d b y t h e D e l t a m e t h o d ( W o o l d r i d g e , 2

0 0 5 )

Table II.

IJM29,5

434


13/20

include controls for year, region of residence, health status and membership of clubsand political parties. As a rule, club and party members have a wider social networkand thus a higher employment probability. The results of the static models (pooled andrandom effects) are very similar in terms of effect direction and statistical signicance,while direct comparisons in terms of effect magnitudes cannot be performed. So,women with higher education and experience are more likely to be employed, a typicalinverse U-shaped age-employment prole[10] is clearly identied, while the number of children in all age categories negatively and signicantly affects the employmentdecisions of married women in Greece. We can also see that the decomposition of wifesnon-work income into its permanent and transitory components exerts a negative andstatistical signicant impact on current employment decisions. Table II also presentsthe estimation results of the correlated random effects model specication. This moreexible specication incorporates an assumed relationship between the unobservedheterogeneity term and the asynchronous effects of fertility and transitory non-workincome. The estimated coefcients of the basic human capital variables do not seem todiffer in any substantive way from those obtained from the simple random effectsmodel. Most importantly, the results provide evidence against the hypothesis that therandom effects are uncorrelated with the fertility and non-work income variables (LRtest). In particular, the random effects seem to be correlated with thenon-contemporaneous transitory non-work income effects and with the past andfuture experiences concerning the number of children, but only in the 0-2 age category.These results are conrmed by separate Wald tests, presented in the bottom part of Table II.

Turning now to the estimation results of the corrected for initial conditions dynamicrandom effects probit model (equation (5)), several points are worth making (seeTable III). The models variance due to unobserved heterogeneity has beensubstantially reduced from around 88 per cent to 41 per cent. This impressive

reduction implies that in a dynamic specication, without correction for the initialemployment status, the estimated coefcient of the lagged dependent variableencompasses elements of spurious state dependence. After controlling for initialconditions (i.e. possible cause of unobserved heterogeneity) and for observedheterogeneity indicators, the coefcient of lagged employment status provides anestimate of the extent of true state dependence. Thus, the estimated coefcient of lagged employment clearly indicates that employment in the previous year isassociated with a higher probability of employment in the current year andfurthermore, that this is a genuine association. Finally, Table III also presents theestimated average partial effects of the random effect specications[11]. The relativeimportance of initial participation and lagged participation status is profound[12,13].

The predicted probabilities of the above models can be compared with theraw/observed density points of the employment incidence of Greek married women.These probabilities are portrayed in Figure 3. It becomes quite evident that thedynamic random effects model ts the data more adequately than any other version. Incontrast, the pooled probit model provides an inadequate t since it fails toapproximate the high frequencies observed at the two tails of the [0, 1] distribution of employment status. Specically, the pooled probit model underestimates the rawemployment sequences at the right tail of 0 and at the left tail of 1. It also seems toover-identify the distribution of employment incidence in the range of density points


Greek women

435


14/20

0.20-0.70, while the highest concentration appears to lie in the interval 0.10-0.30). Thisinadequate performance is apparently the cost of ignoring unobserved heterogeneity,which was found to be signicant in the present study. Turning now to the simplerandom effects probit model, we observe that non-employment is predicted by themodel satisfactorily but the opposite is true for employment. It seems that the simple

random effects probit model provides a smooth distribution of predicted probabilitiesof female employment in the Greek labour market, which however is unrealistic.Finally, Figure 3 conrms that true state dependence and unobserved heterogeneitycan indeed adequately explain the employment decisions of married women in Greece.

5. SummaryAs Greece is searching for ways to close the gap in the female employment rate thatseparates its from its EU partners, understanding how Greek married women form

Coefcient APE

Constant 2 5.818 (1.521)* * *

Lagged participation 1.770 0.568(0.112)* * * (0.036)

Education 0.070 0.021(0.013)* * * (0.003)

Experience 0.020 0.006(0.006)* * * (0.001)

Age 0.211 0.063(9.078)* * * (0.023)

Age squared 2 0.003 2 0.001(0.001)* * * (0.000)

Number of children in 0-2 age group 2 0.334 2 0.101(0.190)* (0.057)

Number of children in 3-5 age group 2 0.151 2 0.045

(0.173) (0.052)Number of children in 6-12 age group 2 0.220 2 0.066(0.136)* (0.041)

Number of children in 13-17 age group 2 0.125 2 0.037(0.112) (0.033)

Permanent income 2 0.013 2 0.004(0.004)* * * (0.001)

Transitory income 2 0.004 2 0.001(0.002)* (0.000)

Observations 4,266Log likelihood 2 829.53r 0.414x

2 test 1,029.64 * * *

Notes: Asymptotic standard errors in parentheses. Asterisks indicate statistical signicance at *10per cent, * *5 per cent, * * *1 per cent. Year, regional, health, membership are included in club-partydummies. r represents the proportion of the models variance due to unobserved heterogeneity.Standard errors of APE were computed by the Delta method (Wooldridge, 2005)

Table III.

Estimation results of thedynamic random effects(DRE) probit model, withcorrection for initialconditions

IJM29,5

436


15/20

their decisions with regard to employment has important implications for effectivepolicymaking. This paper examined the inter-temporal employment decisions of Greekmarried women, using panel data for the period 1995-2001 and various discrete choiceprobit models for estimation purposes. We employed a balanced panel of marriedwomen aged 19-54 at the rst wave of the survey (1995). The results of the econometricestimation and the comparisons of predicted and actual probabilities of employmentshow that the most appropriate model for estimating employment probabilities of married women in Greece is a dynamic random effects probit specication, correctedfor initial conditions. The empirical approach adopted in this paper follows closely thework of Hyslop (1999).

The results obtained suggest that observed individual heterogeneity does inuencethe probability of employment in any given year. Observed heterogeneity primarilyconcerns basic human capital variables such as education, age and work experience.These factors, as expected, affect the probability of employment positively. On theother hand, serial persistence seems to characterise the dynamics of femaleemployment decisions. The results suggest that the observed persistence is causedby unobserved heterogeneity and genuine state dependence. The former is usuallyattributed to unobserved time-invariant individual traits, which may relate toidiosyncratic attitudes towards labour market participation and work. However, in thepresent study, unobserved heterogeneity was found to correlate strongly with theasynchronous effects of fertility and transitory non-work income. More specically,there appears to be a strong association between the number of children in the 0-2 agegroup and non-work transitory income with unobserved work propensity. With regardto the presence of state dependence, this could be explained as originating from theadjustment costs of changing employment status. For example, skills accumulatedthrough work experience raise the probability of working in the future since the

Figure 3.Densities for actual and

predicted employmentprobabilities foralternative models


Greek women

437


16/20

opportunity cost of shifting into non-employment increases through time (Heckman,1981). Similarly, xed costs of entering the labour force make future employment morelikely for individuals already in the labour market (Eckstein and Wolpin, 1990). Thereduced employment rates of Greek women after the age of 35 could be attributed tostate dependence originating from maternity-related work interruptions. Longmaternity leaves, for example, interrupt the process of human capital accumulation,may cause depreciation of the accumulated human capital stock and increase searchand re-entering costs. Fertility, which is approximated by the number of children indifferent age groups, seems to affect employment decisions negatively, but only thenumber of children in the 0-2 age category is found statistically to affect theemployment decision in a dynamic framework. This, in turn, may be the result of maternity leave arrangements/legislation. Wifes non-work income, decomposed intoits permanent and transitory components, seems to affect employment negatively, withits permanent component being more important.

The ndings and results of the present study are not particularly encouraging withrespect to the policy objective of bridging by 2010 the EU-Greece gap in femaleemployment rates. In other words, the Greek female employment rate will most likelycontinue to rise, albeit at a slow speed and along its existent long-term trend. Theobserved human capital variables are not expected to change drastically in the nextve years; they are characterised by their very nature by gradual change, and thustheir effect on employment is expected to be modest. On the other hand, the presence of true state dependence works against a faster transition between the two states, i.e.employment and non-employment. Indeed, women tend to be either workers ornon-workers over their lifetime (Shaw, 1994).

The results obtained could be helpful in formulating a more appropriate policy forexpanding female labour force participation and employment in Greece. Obviously, thecauses of observed slow employment transitions need to be addressed. In this

framework, the appropriate policy should provide a structure of incentives that willmotivate Greek women to revisit their initial labour supply decisions, by altering themajor determinants of these decisions, i.e. shadow wages, market wages, sector of employment, type of employment, working hours and schedules, etc. Future researchon these issues is warranted. For example, changing gender role attitudes (Fortin, 2005)and family culture (Algan and Cahuc, 2005) and their effects on the structure of wagesneed to be investigated. Similarly, issues related to the demand side of the market suchas part-time versus full-time work, exible working schedules, overtime workarrangements and the like, also need to be brought into the analysis of femaleemployment decisions in Greece.

Notes1. In the presence of high and variable unemployment rates, the terms participation and

employment are not synonymous. According to the Greek Labour Force Survey, the femaleunemployment rate in Greece in the period 1990-2005 uctuated between 10 per cent and 18per cent. Nevertheless, the unemployment rate for Greek married women in the age group25-54 was about 7 per cent in the period 1998-2005. These facts suggest that age andmarriage are inter-related with several labour market outcomes.

2. A husband is considered to be a participant in the labour market if he reports positive annualearnings from work. Furthermore, we condition on the husbands employment status in

IJM29,5

438


17/20

order to avoid specication of joint employment decisions, i.e. collective family bargainingmodels. Our approach is similar to that of Hyslop (1999).

3. Wifes non-work income refers to the husbands labour earnings. Handling of any other typeof income (e.g. income from assets) requires a different theoretical approach, i.e.incorporating into the analysis life-time investment and other nancial decisions, which isbeyond the goals of the present paper (Hotz et al., 1988).

4. Women in the status continuous non-employment could have been employed in thepre-sample period (the ECHP covers the period 1995-2001). Thus, women in the statuscontinuous non-employment could have reported zero or non-zero work experience (in thepre-sample period).

5. A complete list of employment sequences is available from the authors upon request.

6. It should be noted that apart from MLE, a growing number of studies employ maximumsimulated likelihood (MSL) estimation methods (Stewart, 2007).

7. The problem of initial conditions arises in a discrete stochastic process when seriallycorrelated unobservables generate the process (Heckman, 1981, p. 194).

8. See for example, Phimister et al. (2002) for Canada, Knight et al. (2002) for Australia, Gannon(2005) studying disability and labour supply aspects in the Irish case, Lee and Tae (2005) inKorea, Viitanen (2005) analysing informal elderly care and female labour force participationacross 13 EU countries, and Michaud and Tatsiramos (2005) exploring employmentdynamics in six EU countries.

9. See Peters (2005) on the calculation of APE over the whole period covered by the sample andWooldridge (2005) on the calculation of APE at a specic point in time.

10. The employment-age curve reaches a maximum at the age of 35 in all model specications.

11. We have also estimated the model without controls for initial conditions. The resultingestimate of the marginal effect of the lagged dependent variable was 0.85. When controls forinitial conditions were included the resulting marginal effect was, as expected, reduced to0.56. These ndings verify the argument that models without controls for initial conditionsintroduce a substantial amount of spurious state dependence. Furthermore, a likelihood ratiotest (LR 147:86), comparing the models with and without initial conditions, indicated thesuperiority of the latter version.

12. The presence of serial persistence in female employment data is a common phenomenon. Forexample, Michaud and Tatsiramos (2005), who examined employment outcomes of marriedwomen in six European countries using the ECHP data base and dynamic binary choicemodels, found that the APE of lagged participation was 0.454 in The Netherlands, 0.438 inFrance, 0.221 in Italy, 0.257 in Spain, 0.279 in Germany and 0.301 in the UK. Thus, theestimated in the present study APE of 0.56 is more or less in line with international evidence.

13. A sensitivity analysis investigating the applicability of the generic measure of experienceand whether the overall ndings are affected by this choice was also carried out. Twoadditional model specications were estimated, i.e. one without any proxy for experience andone where we controlled for the age of entry in the labour force. The comparisons reveal thatfor all practical purposes the observed changes in the estimated coefcients are onlymarginal. This is not surprising, since in a dynamic analysis of employment decisionsexperience captures primarily taste, attachment and work propensity effects. Thus, thecontemporaneous effect of experience becomes less important as compared to the staticmodel because the pre-sample work history is already incorporated in the initial conditions(see also Heckman, 1981).


Greek women

439


18/20

ReferencesAlgan, A. and Cahuc, P. (2005), The roots of low European employment: family culture?, IZA

Discussion Papers 1683, Institute for the Study of Labor, Bonn.Becker, G.S. (1965), A theory of the allocation of time, Economic Journal , Vol. 75, pp. 493-517.Blau, D.F. and Kahn, L.M. (2007), Changes in the labor supply behavior of married women:

1980-2000, Journal of Labor Economics , Vol. 25, pp. 393-438.Blau, F.D. (1998), Trends in the well-being of American women, 1970-1995, Journal of Economic

Literature , Vol. 36, pp. 112-65.Boeri, T., del Boca, D. and Pissarides, C. (2005), Women at Work: An Economic Perspective ,

Oxford University Press, Oxford.Buttler, J.S. and Moftt, R. (1982), A computationally efcient quadrature procedure for the

one-factor multinomial probit model, Econometrica , Vol. 50, pp. 761-4.Chamberlain, G. (1984), Panel data, in Griliches, Z. and Intriligator, M.D. (Eds), Handbook of

Econometrics , Vol. 2, Elsevier Science, Amsterdam.Cholezas, I. and Tsakloglou, P. (2006), Gender earnings differentials in the Greek labour

market, mimeo, Athens University of Economics and Business, Athens.Daouli, J.J., Demoussis, M. and Giannakopoulos, N. (2004), Participation of Greek married

women in full-time paid employment, South Eastern Europe Journal of Economics , Vol. 2No. 2, pp. 19-33.

Eckstein, Z. and Wolpin, K.I. (1989), Dynamic labour force participation of married women andendogenous work experience, Review of Economic Studies , Vol. 56 No. 3, pp. 375-90.

Eckstein, Z. and Wolpin, K.I. (1990), On the estimation of labor force participation, job searchand job matching models using panel data, in Weiss, Y. and Fishelson, G. (Eds), Advancesof Theory and Measurement of Unemployment , Macmillan Press, London, pp. 209-13.

Fortin, M.N. (2005), Gender role attitudes and the labour-market outcomes of women acrossOECD countries, Oxford Review of Economic Policy, Vol. 21 No. 3, pp. 416-38.

Gannon, B. (2005), A dynamic analysis of disability and labour force participation in Ireland1995-2000, Health Economics , Vol. 4 No. 9, pp. 925-38.Goldin, C. (1989), Life-cycle labor-force participation of married women: historical evidence and

implications, Journal of Labor Economics , Vol. 7 No. 1, pp. 20-47.Greene, W.H. (2003), Econometric Analysis , 5th ed., Pearson Education Prentice-Hall, Upper

Saddle River, NJ.Heckman, J. (1978), Simple statistical models for discrete panel data developed and applied to

test the hypothesis of true state dependence against the hypothesis of spurious statedependence, Annals de INSEE, Paris , Vol. 30/31, pp. 227-69.

Heckman, J.J. (1981), The incidental parameters problem and the problem of initial condition inestimating a discrete-time data stochastic process, in Manski, C.F. and McFadden, D. (Eds),Structural Analysis of Discrete Data with Econometric Application , MIT Press, Cambridge,MA, pp. 179-95.

Heckman, J.J. and Willis, R. (1977), A beta-logistic model for the analysis of sequential laborforce participation by married women, Journal of Political Economy , Vol. 85 No. 1,pp. 27-58.

Hotz, V.J., Kydland, F. and Sedlacek, G. (1988), Intertemporal preferences and labor supply, Econometrica , Vol. 56 No. 2, pp. 335-60.

Hyslop, D. (1999), State dependence, serial correlation and heterogeneity in intertemporal laborforce participation of married women, Econometrica , Vol. 67, pp. 1255-94.

IJM29,5

440


19/20

Jacobsen, P.J. (1999), Labor force participation, The Quarterly Review of Economics and Finance , Vol. 39, pp. 597-610.

Juhn, C. and Murphy, K.M. (1997), Wage inequality and family labor supply, Journal of Labor Economics, Vol. 15, pp. 72-97.

Kanellopoulos, C. (1997), Public-private wage differentials in Greece, Applied Economics , Vol. 29No. 8, pp. 1023-32.

Kanellopoulos, C. and Mavromaras, K. (2002), Male-female labour market participation andwage differentials in Greece, Labour , Vol. 16, pp. 771-801.

Knight, S., Harris, M. and Loundes, J. (2002), Dynamic relationships in the Australian labourmarket: heterogeneity and state dependence, Economic Record , Vol. 78, pp. 284-98.

Kottis, A.P. (1990), Shifts over time and regional variation in womens labor force participationrates in a developing economy, Journal of Development Economics , Vol. 33, pp. 117-32.

Lee, M.-J. and Tae, Y.-H. (2005), Analysis of labour participation behaviour of Korean womenwith dynamic probit and conditional logit, Oxford Bulletin of Economics and Statistics ,Vol. 67 No. 1, pp. 71-91.

Magdalinos, M. and Symeonidou, H. (1989), Modeling the fertility-employment relationship:simultaneity and misspecication testing, European Journal of Population , Vol. 5,pp. 119-43.

Meghir, C., Ioannides, I. and Pissarides, C. (1989), Female participation and male unemploymentduration in Greece: evidence from the Labour Force Survey, European Economic Review ,Vol. 33, pp. 395-406.

Michaud, P.-C. and Tatsiramos, K. (2005), Employment dynamics of married women in Europe,IZA Discussion Paper No. 1706, Institute for the Study of Labor, Bonn.

Mincer, J. (1962), Labor force participation of married women: a study of labor supply, Aspectsof Labor Economics , Princeton University Press, Princeton, NJ.

Mundlak, Y. (1978), On the pooling of time series and cross section data, Econometrica , Vol. 46No. 1, pp. 69-85.

Nicolitsas, D. (2006), Female labour force participation in Greece, Bank of Greece Economic Bulletin, Vol. 12, Bank of Greece, Athens.

Orme, C.D. (1997) The initial conditions problem and two-step estimation in discrete panel datamodels, mimeo, School of Economic Studies, University of Manchester, Manchester.

Peracchi, F. (2002), The European Community Household Panel: a review, Empirical Economics, Vol. 27 No. 1, pp. 63-90.

Peters, B. (2005), Persistence of innovation: stylised facts and panel data evidence, developmentand comp systems, Working Paper No. 0511021, Centre for European Economic Research,Mannheim.

Phimister, E., Vera-Toscano, E. and Weersink, A. (2002), Female participation and labor marketattachment in rural Canada, American Journal of Agricultural Economics , Vol. 84 No. 1,pp. 210-21.

Shaw, K. (1994), The persistence of female labor supply: empirical evidence and implications, Journal of Human Resources , Vol. 29 No. 2, pp. 348-78.

Stewart, M.B. (2007), The inter-related dynamics of unemployment and low pay, Journal of Applied Econometrics , Vol. 22 No. 3, pp. 511-31.

Viitanen, T.K. (2005), Informal elderly care and female labour force participation acrossEurope, ENEPRI Research Report No. 13, July, European Network of Economic PolicyResearch Institutes, Brussels.


Greek women

441


20/20

Wooldridge, M.J. (2002), Econometric Analysis of Cross Section and Panel Data , The MIT Press,London.

Wooldridge, M.J. (2005), Simple solutions to the initial conditions problem in dynamic, nonlinearpanel data models with unobserved heterogeneity, Journal of Applied Econometrics ,

Vol. 20 No. 1, pp. 39-54.

About the authorsMichael Demoussis is Professor of Economics in the Department of Economics at the Universityof Patras, Greece. He holds a PhD from the Department of Economics and Business, NorthCarolina State University, USA. His research focuses on consumer theory and labour economics.Michael Demoussis is the corresponding author and can be contacted at: [email protected]

Nicholas Giannakopoulos is an adjunct lecturer in the Department of Economics at theUniversity of Patras, Greece. He holds a PhD from the Department of Economics of theUniversity of Patras, Greece. His main research interests include labour economics andsubjective outcomes.

IJM29,5

442

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

employment dynamics

Documents