Post on 29-Dec-2015
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
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?