Anna LovászInstitute of Economics

Hungarian Academy of SciencesJune 30, 2011.

Research AgendaOverall GWG fell from .31 to .18 following the

transition: mostly unexplainedCould 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

Statistics - overviewRelative Wage of Women 1986-2003

0

0.2

0.4

0.6

0.8

1

1986 1989 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Relative Wage

Source: Central Statistics Office

The gender wage gap in Hungary, 1986-2005

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) – 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

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 199680 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

Empirical StrategyStep 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 competitiongapjt = 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

Empirical Strategy – Measures of CompetitionMarket 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 IssuesUnion effect – constrain discrimination

Sample by union status2 stage procedure: gap estimate

Reweight in Step 2 using SE-s from Step 1Unobserved market characteristics

Industry FEsSelection bias: exit of low-skilled women

Worker controls, samples by skill levelIdentification: enough variation in

competitiveness within industries over time?

Identification: changes in competition over time

0.2

.4.6

.81

HH

I in

1998

0 .2 .4 .6 .8 1HHI in 1989

1989-1998Changes in Industry Concentration Ratios

Identification: changes in trade over time

0.2

.4.6

.81

Exp

ort

share

in 1

998

0 .2 .4 .6 .8 1Export share in 1989

1989-1998Changes in Industry Export Shares

Data descriptionWage and Earnings Survey: 1986, 1989, 1992-2005

Matched employer-employee datasetPanel in terms of firms, not workersWorker characteristics: gender, age, education,

occupation, potential experience, firm of employmentFirm data: employment, industry, region, ownership

shares

Sample restrictions:Firms with at least 20 employeesFirms with at least two male and two female workers in

dataExclude public sector

WES summary statistics

Year Observations Average real wagePercent female

1986 100,872 99,970.05 41.00

1989 118,326 114,509.8 40.88

1992 97,404 106,777.4 42.28

1995 106,902 101,165.9 41.55

1998 100,304 108,999.4 40.43

2001 111,396 122,909 41.16

2003 108,990 136,641.5 41.82

Results: gapjt = αt + β1CMkt + β2Nt + εjt

All industries Manufacturing

1 2 3 4

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 dummies Y Y Y YIndustry FE N Y N Y

Weighted Y Y Y YNumber of

observations9312 9312 5274 5274

R squared 0.378 0.597 0.407 0.562

Results: gapjt = αt + β1CMkt + β2Nt + εjt

All industries Manufacturing

1 2 3 4

Price Cost Margin-0.137**(0.051)

-0.104**(0.035)

-0.305**(0.075)

-0.074**(0.031)

Import penetration0.014

(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 dummies Y Y Y YIndustry FE N Y N Y

Weighted Y Y Y YNumber of obs. 9312 9312 5274 5274

R squared .453 .639 .495 .621

Results – by union status

Collective Wage Agreement

No Collective Wage Agreement

1 2 3 4

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 dummies Y Y Y YIndustry FE N Y N Y

Weighted Y Y Y YNumber of obs. 2231 2231 2846 2846

R squared 0.152 0.499 0.170 0.468

Results – by skill level

High skilled Medium and low skilled

1 2 3 4

1-HHI-0.064(0.036)

-0.044(0.037)

-0.094**(0.033)

-0.092*(0.043)

Import penetration0.272

(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 dummies Y Y Y YIndustry FE N Y N Y

Weighted Y Y Y YNumber of obs. 9289 9289 8741 8741

R squared 0.482 0.727 0.873 0.928

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!