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Anna Lovász Institute of Economics Hungarian Academy of Sciences June 3 0 , 201 1 .

Competition and the Gender Wage Gap: New Evidence from Linked Employer-Employee Data in Hungary , 1986-2005. Anna Lovász Institute of Economics Hungarian Academy of Sciences June 3 0 , 201 1. Research Agenda. Overall GWG fell from .31 to .18 following the transition: mostly unexplained

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Anna Lovász Institute of Economics Hungarian Academy of Sciences June 3 0 , 201 1 .

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  1. Competition and the Gender Wage Gap: New Evidence from Linked Employer-Employee Data in Hungary, 1986-2005 Anna Lovász Institute of Economics Hungarian Academy of Sciences June 30, 2011.

  2. Research Agenda • Overall GWG fell from .31 to .18 following the transition: mostly unexplained Could changes in competitive environment faced by firms have led to a fall in discrimination? • Becker (1957): increased product market competition leads to lower employer taste discrimination in the long run • Empirical opportunity: • Rapid liberalization of markets in Hungary • Large linked employer-employee dataset, 1986-2005

  3. Statistics - overview Source: Central Statistics Office

  4. The gender wage gap in Hungary, 1986-2005 Source: estimation using WES dataset

  5. Motivation: Becker’s Model of Employer Taste Discrimination • Employers derive personal disutility (d) from hiring a higher ratio of women: U(π,F/M) = π – d(F/M) = f(M+F) – wmM – wfF – d(F/M)  • Discriminating employers (d>0) hire a lower than profit-maximizing ratio of females, at a lower wage than men with equal characteristics wm - wf= MPLm – MPLf + [d/M + dF/M2] = (MPLm – MPLf) + gender gap • Implications: • The more competitive a market, the less employers are able to discriminate, since discrimination is costly • An increase in product market competition leads to lower discrimination in the long run

  6. Hungary as test? • Rapid liberalization of trade, prices, entry into markets: • Number of registered economic organizations: 391 thousand in 1990 to 1.1 million in 1996 • 80 percent produced by private sector by 1998 (GKI) • Exports expanded from 9170 million current USD in 1989 to 43394 million current USD in 2003 (WTO) Use changes to identify effect of increased competition on firm-level gender wage gap

  7. Empirical Strategy • Step 1 : Estimation of gender wage gap: worker and firm WES data • For each firm j in each year t: lnwijt = αt + βtXijt + δjtFEit + εijt • Xij = worker characteristics (education, potential experience, occupation) • FEi = female dummy variable  • δjt = residual within-firm wage gap = upper bound for discrimination • Step 2: Testing the effect of competition gapjt=δjt = αt + β1CMkt + β2Nt + εjt • CMkt: competition measures in industry k at year t • Nt: additional controls (year dummies, region dummies, industry FE) Becker’s implication: β1 < 0

  8. Empirical Strategy – Measures of Competition • Market concentration (1-HHI) • 3 digit industry level, Tax Authority Data on firm revenue from sales • 0=monopoly, 1=perfectly competitive • Export share (export sales/sales) • 3 digit industry level, Tax Authority Data on firm revenue from sales and exports • 0=no export, 1=all export • Import penetration (import/sales+import-export) • 3 digit industry level, Customs Authority Data on imports, Tax Authority Data on firm revenue from sales and exports • 0=no import, 1=all import • Price Cost Margin (profits/sales) • 3 digit industry level, Tax Authority Data on firm revenue from sales All increase with competition

  9. Empirical Strategy – Estimation Issues • Union effect – constrain discrimination • Sample by union status • 2 stage procedure: gap estimate • Reweight in Step 2 using SE-s from Step 1 • Unobserved market characteristics • Industry FEs • Selection bias: exit of low-skilled women • Worker controls, samples by skill level • Identification: enough variation in competitiveness within industries over time?

  10. Identification: changes in competition over time

  11. Identification: changes in trade over time

  12. Data description • Wage and Earnings Survey: 1986, 1989, 1992-2005 • Matched employer-employee dataset • Panel in terms of firms, not workers • Worker characteristics: gender, age, education, occupation, potential experience, firm of employment • Firm data: employment, industry, region, ownership shares • Sample restrictions: • Firms with at least 20 employees • Firms with at least two male and two female workers in data • Exclude public sector

  13. WES summary statistics

  14. Results: gapjt = αt + β1CMkt + β2Nt + εjt

  15. Results: gapjt = αt + β1CMkt + β2Nt + εjt

  16. Results – by union status

  17. Results – by skill level

  18. Conclusion • Results support Becker’s implication: increased competition led to a fall in the gender wage gap • Magnitude: observed change in competition explains roughly 26% of fall in gap • Remaining issues: • Selection bias? • Import results?

  19. Thank you!

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