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

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Presentation on theme: "CEILINGS AND FLOORS: GENDER WAGE GAPS BY EDUCATION IN SPAIN Sara de la Rica *, Juan J. Dolado * * & Vanesa Llorens ** * & Vanesa Llorens ** * ( * ) UPV."— Presentation transcript:

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

2 Motivation Gender wage gaps: ln(W m /W f ) (W m - W f )/ W f 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

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6 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

7 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, W i (i=1,2) are chosen by the firm. Workers do not quit if W i - 0. No wage renegotiation nor outside wage offers (monopsony) F m ( )>F f ( ) G( )= U[0, ]; f m ( ) =U[0, m ]; f f ( ) =U[0, f ]; f > m

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9 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: W A 2 =, W B 2 = 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)

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

11 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.

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16 Different QR by gender and by education [Tables 2 a-d] i.H-group Higher returns to experience ( ), being married ( ), supervisory role ( ), (Men) Higher returns for public sector, size>20, OC4-6 (Women) ii.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)

17 GENDER GAP DECOMPOSITION (Oaxaca-Blinder)

18 MM decomposition Draw θ-th quantile from U[0,1] Estimate β m (θ) Draw x f and construct β m (θ) x f. Repeat N=100 times Construct counterfactual gap ( M=250 times): β m (θ) x f - β f (θ) x f = (β m (θ) - β f (θ)) x f. Returns

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

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23 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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