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Gender, Life Cycle Trajectories and their Determinants in the Portuguese Labour Market Graça Leão Fernandes Margarida Chagas Lopes Kiel, November 2002.

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Presentation on theme: "Gender, Life Cycle Trajectories and their Determinants in the Portuguese Labour Market Graça Leão Fernandes Margarida Chagas Lopes Kiel, November 2002."— Presentation transcript:

1 Gender, Life Cycle Trajectories and their Determinants in the Portuguese Labour Market Graça Leão Fernandes Margarida Chagas Lopes Kiel, November 2002

2 Main Purposes of the Research 1. To identify gender specificities in human capital life cycle trajectories. 2. To further analyse the leading determinants for women and men’s up-grading trajectories. 3. To take into account both supply and demand side variables and ascertain their relative impact upon those up-grading trajec- tories

3 Some previous results... - As to the Portuguese labour market, there is strong evidence that: - personnel policies and wage setting inside firms clearly penalise women; - being vertical occupational mobility easier for men, women in the same hierarchical levels than men have got to invest harder in human capital, as a general rule; - furthermore, in most cases – when they can afford to... - women seem to depict some sort of an ex-ante qualifying strategy, in order to try to overcome discrimination later on... Occupational statuses seems to improve with the nº of employments, therefore penalising also the less mobile, as women...

4 Theoretical Framework (1). We take as starting point, Weiss (1986) model of human capital growth rate along the life cycle : which allows both for the explicit influence of h (occupational-experience/job qualification../degree of workers’ skills utilisation by firm) and ∂ the obsolescence rate, which we’ll not take here into account.

5 Data Base A survey on 5,747 individuals’ ( 65% Men, 35% Women) life cycles, giving special attention to the number and characteristic of the jobs successively hold, including first job characteristics (e.g. activity sector). As to the individuals’ behaviour, a special concern was also given to human capital - formal schooling and vocational training – endowment, both at the moment of entry in the labour market and along the activity life. Relatively to firms variables, we could also report both wage setting policies and the intensity of the workers’ human capital utilisation, for each one of trajectories. Data on individual characteristics, such as sex, actual age as well as the age at the insertion moment, was also available.

6 Methodology (1) Variables used to define types of life cycle patterns: Formal schooling (f.s.) reinforcement and/or vocational training (v.t.) attendance along the life cycle (supply side); School outcomes and/or vocational training results utilization degree by each employment (demand-supply interaction); Influence exerted by schooling, vocational training or both on each employment wage setting policy (demand side).

7 Methodology (2) Qualification/skills life cycle patterns: - Two types of Up-grading trajectories: - B1: workers increased human capital (f.s. and/or v.t.) after entering labour market and the wage setting took into account - at least in one employment – those increasing strategies; B2: as to the workers, the same human capital strategies, but as to firms, we consider now the degree of utilisation/misusing of those strategies outcomes. - All other situations were considered as down-grading strategies.

8 Methodology (3) In order to characterise women and men’s up-grading trajectories, we carried both Bivariate Correlation and Discriminant Analyses; As to explanatory variables: - Sex; - Age at the moment of inquiry; - Age at labour market entry; - School level at labour market entry; - Sector of 1 st job; - Number of jobs along life cycle.

9 Results (1) As to the Correlation Analyses: - For identical human capital strategies from the supply side, the demand side (firms) response proved to shape much more effectively qualification trajectories throughout the degree of skills utilisation (B2), than by means of the corresponding effect upon wage setting (B1) (lack of H.C. endogeneisation…). Nevertheless, for both trajectories, initial f.s. endowment revealed a quite strong correlation, for both sexes.

10 Results (2) - As to Correlation Analyses – B2 trajectories: Corr.Co ef./ Trajecto ries B2 (Whole sample) B2 (W) B2 (M) Sex 0.084 ---- Age -0.086 -0.255* -0.040 Age entry LM 0.375**0.380**0.363** Initial Schoolin g 0.501**0.575**0.470** Activity Sector (1st. Employme nt) 0.243**0.311**0.230** Nº. Employme nts -0.244**-0.334**-0.213**

11 Results (3) - Discriminant Analysis for B2: Funct./ Tests Canonical Correlation Wilks’ Lambda Chi- Square sign. level % Cases Correctly Classi- fied F1(0,1) ( Whole sample) 0.5430.7050.00081.9 F2(0,1) (W) 0.6140.6230.00078.8 F3(0,1) (M) 0.5170.7320.00083.2 Statistical Tests Scores B2(0-downgrading; 1-upgrading)

12 Results (4): Functions/ / Coefficient F1(0,1) (whole Sample) F2(0,1) (W) F3(0,1) (M) Sex0.021----------- Age0.3090.1330.361 Age 00.1150.0170.155 School. 0 0.8650.8320.857 Sector 00.2290.3550.201 Nº Empls. -0.239-0.317-0.222 Standardised Canonical Discriminant Functions Coefficients

13 Conclusions - Irrespective of gender, workers’ HC utilisation by firms play a decisive role on the chance of having an up-grading or down-grading trajectory; - As predicted by HC theories, variables like age 0 and school. 0 have a positive significant impact on up-grading trajectories, specially for women – their ‘extra-costs’... - Older women can hardly have up-grading trajectories (age 0 and « K0...); - First job sector also exerts a significant impact ( from primary to tertiary, skill utilisation grows...) mostly for women – therefore, they become more restricted then men to this first employment and thereby less able to improve their occupational statuses; - But when facing a stronger instability ( a larger nº of employments), women become more penalised than men…

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