1. 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) – w m M – w f F – 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 w m - w f = MPL m – MPL f + [d/M + dF/M 2 ] = (MPL m – MPL f ) + 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: lnw ijt = α t + β t X ijt + δ jt FE it + ε ijt X ij = worker characteristics (education, potential experience, occupation) FE i = female dummy variable δ jt = residual within-firm wage gap = upper bound for discrimination Step 2: Testing the effect of competition gap jt = δ jt = α t + β 1 CM kt + β 2 N t + ε jt CM kt : competition measures in industry k at year t N t : additional controls (year dummies, region dummies, industry FE) Becker’s implication: β 1 < 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 YearObservationsAverage real wage Percent female 1986100,87299,970.0541.00 1989118,326114,509.840.88 199297,404106,777.442.28 1995106,902101,165.941.55 1998100,304108,999.440.43 2001111,396122,90941.16 2003108,990136,641.541.82

14. Results: gap jt = α t + β 1 CM kt + β 2 N t + ε jt All industriesManufacturing 1234 1-HHI -0.075** (0.018) -0.081** (0.025) -0.133* (0.054) -0.117* (0.056) Import penetration 0.094** (0.036) 0.012 (0.032) 0.129** (0.027) 0.057 (0.032) Export share -0.056 (0.041) -0.160** (0.043) -0.169** (0.048) -0.186** (0.048) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of observations 9312 5274 R squared0.3780.5970.4070.562

15. Results: gap jt = α t + β 1 CM kt + β 2 N t + ε jt All industriesManufacturing 1234 Price Cost Margin -0.137** (0.051) -0.104** (0.035) -0.305** (0.075) -0.074** (0.031) Import penetration 0.014 (0.034) 0.055 (0.036) -0.095 (0.091) -0.020 (0.063) Export share -0.018 (0.032) -0.042 (0.045) -0.059* (0.026) -0.056 (0.046) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of obs. 9312 5274 R squared.453.639.495.621

16. Results – by union status Collective Wage Agreement No Collective Wage Agreement 1234 1-HHI -0.046* (0.022) 0.061 (0.063) -0.115** (0.024) -0.101 (0.054) Import penetration -0.079 (0.053) 0.021 (0.042) 0.013 (0.057) -0.005 (0.053) Export share -0.108 (0.072) -0.038 (0.091) -0.161** (0.049) -0.070 (0.082) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of obs. 2231 2846 R squared 0.1520.499 0.1700.468

17. Results – by skill level High skilledMedium and low skilled 1234 1-HHI -0.064 (0.036) -0.044 (0.037) -0.094** (0.033) -0.092* (0.043) Import penetration 0.272 (0.157) -0.019 (0.051) 0.386** (0.073) 0.023 (0.035) Export share -0.390 (0.209) -0.098 (0.056) -0.368** (0.069) -0.165 (0.054) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of obs. 9289 8741 R squared 0.4820.7270.8730.928

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!