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CEILINGS AND FLOORS: GENDER WAGE GAPS BY EDUCATION IN SPAIN

CEILINGS AND FLOORS: GENDER WAGE GAPS BY EDUCATION IN SPAIN. Sara de la Rica * , Juan J. Dolado * * & Vanesa Llorens ** * ( * ) UPV & IZA ( ** ) UCIII & CEPR & IZA ( *** ) LECG. Motivation. Gender wage gaps: ln(W m /W f )  (W m - W f )/ W f

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CEILINGS AND FLOORS: GENDER WAGE GAPS BY EDUCATION IN SPAIN

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  1. CEILINGS AND FLOORS: GENDER WAGE GAPS BY EDUCATION IN SPAIN Sara de la Rica *, Juan J. Dolado* * & Vanesa Llorens** * (*) UPV & IZA (**) UCIII & CEPR & IZA (***) LECG

  2. Motivation • Gender wage gaps: ln(Wm/Wf)(Wm- Wf)/ Wf • Traditional: At the mean vs. New: At the quantiles • Recent evidence about Glass Ceilings in Sweden (Albrecht et al., 2003) • Southern vs. Central & Northern Europe

  3. Composition effect by Education • Glass Ceiling (H-Group): High female participation rate (80% vs 85%) Lower job stability (Lazear and Rosen, 1990) leads to lower promotion opportunities and higher wages (PUZZLE) • Glass Floor (L-Group): Low female participation rate (48% vs 68%) Statistical discrimination at the bottom of the wage distribution

  4. INTERPRETATIVE MODELS • L-Group • Ability for men and women: , c.d.f. G() • Need of training in period 1 (2 periods) • Productivity: 1, 0< 1 <1 (period 1), 2 , 1 < 1< 2 (period 2) • Firms know  at the begining of period 2 • Workers receive a disutility shock  with c.d.f. F() after wages in period 1 & 2, Wi (i=1,2) are chosen by the firm. Workers do not quit if Wi - 0. • No wage renegotiation nor outside wage offers (monopsony) • Fm()>Ff() • G()= U[0,]; fm() =U[0,m]; ff() =U[0,f]; f> m

  5. H-Group (Lazear and Rosen, 1990) • A model of job ladders: A (no training), B (training) • A: ,  ; B: 1 , 2  • Firms pay competitive wages in period 2: WA2= , WB2= 2  • Cut-off points to allocate to B: *f> *mLess women are promoted but conditional on being promoted they should be earn higher wages • Explanations:(i) Different ability distribution (Mincer and Polacheck, 1974), (ii) Different outside offers (Booth et.al., 2003), (iii) Different competing skills (Gneezy et al., 2003, Babcock and Laschever, 2003)

  6. Data • ECHP (1999) • H-Group: 721 (Men), 558 (Women) • L-Group: 1585 (Men), 626 (Women) • Quantile Regressions (QR) Buchinsky (1998), Koenker and Basset (1978)

  7. Covariates -Exp (age), marital st., tenure, children age, Sec. Edn (L-W) , type of contract, immigrant, public, firm size, supervisory role, region, size local council, occupations.

  8. Different QR by gender and by education [Tables 2 a-d] • H-group • Higher returns to experience (), being married (), supervisory role (), (Men) • Higher returns for public sector, size>20, OC4-6 (Women) • L-group • Higher returns for experience (), being married and supervisory role () (Men) • Higher returns to tenure () (Women) , • Higher returns for public sector, permanent contract, secondary attainment, public sector (Women)

  9. GENDER GAP DECOMPOSITION (Oaxaca-Blinder)

  10. MM decomposition • Draw θ-th quantile from U[0,1] • Estimate βm (θ) • Draw xf and construct βm (θ) xf . Repeat N=100 times • Construct counterfactual gap ( M=250 times): βm (θ) xf - βf (θ) xf = (βm (θ) - βf (θ)) xf . Returns

  11. PANEL & STAT. DCN. • ECHP waves (1994-01) to follow workers in their jobs over time. • Follow approach in Farber & Gibbons (1996) • Interact Tenure* Female • RESULT: Only Positive & Significant for L-group.

  12. CONCLUSIONS • New finding: Glass Floors • Due to statistical dcn. in countries with low participation of L-women. • Further research: - Other alternatives for H-group (stress leaves) - Endogenize Participation (with S. de la Rica and C. Gª-Peñalosa…in progess) - Academic women-economists (with M. Almunia and F. Felgueroso)

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