Anna Lovász Institute of Economics Hungarian Academy of Sciences June 30, 2011.

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,

Statistics - overview Source: Central Statistics Office

The gender wage gap in Hungary, Source: estimation using WES dataset

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

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 percent produced by private sector by 1998 (GKI) Exports expanded from 9170 million current USD in 1989 to million current USD in 2003 (WTO)  Use changes to identify effect of increased competition on firm-level gender wage gap

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

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

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?

Identification: changes in competition over time

Identification: changes in trade over time

Data description Wage and Earnings Survey: 1986, 1989, 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

WES summary statistics YearObservationsAverage real wage Percent female ,87299, ,326114, ,404106, ,902101, ,304108, ,396122, ,990136,

Results: gap jt = α t + β 1 CM kt + β 2 N t + ε jt All industriesManufacturing HHI ** (0.018) ** (0.025) * (0.054) * (0.056) Import penetration 0.094** (0.036) (0.032) 0.129** (0.027) (0.032) Export share (0.041) ** (0.043) ** (0.048) ** (0.048) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of observations R squared

Results: gap jt = α t + β 1 CM kt + β 2 N t + ε jt All industriesManufacturing 1234 Price Cost Margin ** (0.051) ** (0.035) ** (0.075) ** (0.031) Import penetration (0.034) (0.036) (0.091) (0.063) Export share (0.032) (0.045) * (0.026) (0.046) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of obs R squared

Results – by union status Collective Wage Agreement No Collective Wage Agreement HHI * (0.022) (0.063) ** (0.024) (0.054) Import penetration (0.053) (0.042) (0.057) (0.053) Export share (0.072) (0.091) ** (0.049) (0.082) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of obs R squared

Results – by skill level High skilledMedium and low skilled HHI (0.036) (0.037) ** (0.033) * (0.043) Import penetration (0.157) (0.051) 0.386** (0.073) (0.035) Export share (0.209) (0.056) ** (0.069) (0.054) Year dummiesYYYY Industry FENYNY WeightedYYYY Number of obs R squared

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?

Thank you!