Female access to the labor market and wages over transition

MotivationResearch goals

Women on the labor market in transitionData and methodology

Empirical resultsConclusions

Female Access to the Labor Market and WagesOver Transition

A Multicountry Analysis

Karolina Goraus Joanna Tyrowicz

Faculty of Economic SciencesUniversity of Warsaw

XXVIII National Conference of Labour EconomicsRome, 27-28 September 2013

Karolina Goraus, Joanna Tyrowicz Female Access to the Labor Market and Wages Over Transition




Outline

1 Motivation

2 Research goals

3 Women on the labor market in transition

4 Data and methodologyResearch methodData sources

5 Empirical resultsFirst stage: analysis of micro-level dataSecond stage: analysis of obtained gender gaps estimates

6 Conclusions





What is the reason of gender wage differentials?

Gender wage gaps

Italy 2005 15.7% Picchio, Mussida, 2010Italy 2008 24.16% Nopo, Daza, Ramos, 2011

Czech Republic 1998 30% Jurajda, 2001Czech Republic 2008 35.19% Nopo, Daza, Ramos, 2011





Major contributions in international analysis of gender gaps

Weichselbaumer and Winter-Ebmer (2007)meta-analysis of 263 papers exploring gender wage gaps in 62countriesincomparability of estimators

Nopo, Daza and Ramos (2011)attempt to estimate gender wage gaps for 63 countries withthe same methodologyhuge differences in gender gaps between countriesunknown reasons





Context of transition

Brainerd (2000)inequalities grew in transitionthe changes affected women adversely, contributing to thewidening gender gaps

”Do Markets Favor Women’s Human Capital More ThanPlanners?” (Munich, Svejnar, Terrell, 2004)

gender wage gap in Czech Republic decreased from 33% inyear 1989 to 25% in year 2002although women have higher returns on education in themarket system, similar changes have been observed for men,thus it is not a reason of declining gender wage gap





Raw wage gap vs. ”discrimination” in Poland





Research goals

1 Investigating the position of women on the labor market intransition

2 Analysing the international variation in gender gaps usingcoherent estimators

3 Exploring differences between CEEC and Western Europe, aswell as within the group of transition economies

4 Verifying if higher FLFP leads to lower discrimination





Different trends in FLFP in CEEC and Western Europe

Total sample Advanced Transition

Time 0.397*** 0.668*** -0.443***(0.0365) (0.0402) (0.0456)

Observations 631 338 203R2 0.167 0.465 0.336No of countries 42 18 15Note: panel fixed effect robust estimator. Constant included.Data source: ILO. Transition: Albania, Belarus, Bulgaria, Czech Republic, Estonia,Hungary, Latvia, Lithuania, Poland, Romania, Russia, Slovakia, Slovenia, Ukraine.Advanced: Austria, Belgium, Denmark, Finland, France, E & W Germany , Greece,Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and UnitedKingdom. Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1





Different trends in FLFP in CEEC and Western Europe





Share of highly educated females & FLFP





Share of females with children under 5 years & FLFP





Mean age of active female & FLFP





Research methodData sources

How to measure discrimination?






Research method

Oaxaca-Blinder decomposition

yM − yF = βM(xM − xF ) + (βM − βF )xF

Decomposition of Nopo

δ = δM + δX + δA + δF

δM - can be explained by differences between „matched” and„unmatched” malesδX - can be explained by differences in the distribution ofcharacteristics of males and females over the common supportδA - unexplained part of the gapδF - can be explained by differences between „matched” and„unmatched” females






Varius sources of micro-level data

National censuses (acquired from Integrated Public UseMicrodata Series International)

International Social Survey Program

Living Standard Measurement Surveys of The World Bank

National Labor Force Surveys

European Union Labor Force Survey

European Community Household Panel






Data on transition countries

Country LFS EU LFS Census LSMS ISSP

Albania 2002-2005Armenia 2001Belarus 1999Bosnia & H. 2001-2004Bulgaria 2000-2008 1995, 1997, 2001, 2003 1993-1995Croatia 1997-2008Czech R. 1998-2008 1993-1995Estonia 1997-2008 1992-1995Hungary 1997-2008 1970, 1980, 1990, 2001 1989-1995Kyrgyzstan 1993, 1996-1998Latvia 1998-2008 1995Lithuania 1998-2008 1995Poland 1995-2010 1997-2008 1987, 1991-1995Romania 1997-2008 1977, 1992, 2002Russia 1991-1995Serbia 2002-2004, 2007Slovakia 1998-2008 1995Slovenia 1996-2008 2002 1991-1995Tajikistan 1999, 2003, 2009






Data on benchmark countries

Country EU LFS ECHP ISSP

Austria 1995-2008 1995-2001 1989-1995Belgium 1992-2008 1994-2001Denmark 1992-2008 1994-2001Finland 1995-2008 1996-2001France 1993-2008 1994-2001Germany 2002-2008 1994-2001 1989-1995Greece 1992-2008 1994-2001Ireland 1999-2008 1994-2001 1989-1995Italy 1992-2008 1994-2001 1989- 1995Netherlands 1996-2008 1994-2001 1989-1995Norway 1996-2008 1989-1995Portugal 1992-2008 1994-2001Spain 1992-2008 1994-2001 1993-1995Sweden 1995-2008 1997-2001 1994-1995Switzerland 1996-2008 1987UK 1992-2008 1994-2001 1989-1995





First stage: analysis of micro-level dataSecond stage: analysis of obtained gender gaps estimates

Empirical analysis

Two stages1 Obtaining comparable measures of gender discrimination in

employment rates and wages (∆A) - Nopo (2008)decompositions.

2 Using gender gap estimates as explained variables, whereascountry characteristics as explanatory variables. Identify thecorrelates (better yet: determinants) of the stark differentialsin measured ∆A.






Results of Nopo decomposition for Poland

Characteristics ∆ ∆A ∆M ∆F ∆X % M % KDemographic 10% 20% 0% 0% -10% 99 97+ Occupation 10% 20% 0% 0% -10% 96 93+ Sector 10% 20% -1% -1% -9% 92 92+ Public 10% 21% 0% -1% -10% 99 95+ Informal 10% 21% 0% 0% -10% 99 97+ Tenure 10% 21% 0% -1% -10% 99 95All 10% 19% -2% -1% -6% 65 74






Descriptive statistics of obtained estimates

matched M matched F raw gap ∆M ∆F ∆A ∆X

Participation gap, N=628Mean 99% 99% 34% 0.2% 0.2% 16% -1%Minimum 81% 60% -7% -14% -26% 0.5% -16%Maximum 100% 100% 138% 25% 7% 43% 36%

Wage gap, N=187Mean 71% 77% 18% 2% 1% 15% 0.3%Minimum 2% 3% -6% -60% -55% -90% -37%Maximum 99% 99% 138% 111% 74% 62% 51%






Distribution of gender gaps






Focus on the transition context

Three types of measures:1 years from transition2 EBRD Reform Index (1 - 4)

privatizationenterprise restructuring

3 indexes of the Cingranelli-Richards (CIRI) Human RightsDataset (1 - 3)

Women’s Economic Rights indexWomen’s Social Rights index






Years from transition

Year of transformation No. of country/sets

Albania 1992 4Armenia 1991 1Bosnia & Herzegovina 1993 4Bulgaria 1991 16Croatia 1992 13Czech Republic 1989 14Estonia 1991 12Hungary 1989 25Kyrgistan 1992 4Latvia 1991 12Lithuania 1991 11Poland 1989 32Romania 1990 15Russia 1991 4Serbia 1993 3Slovakia 1989 12Slovenia 1991 18Tajikistan 1992 3






Percentage of females matched in the wage gapdecompositions






Adjusted wage gap All % matched over 30 % matched over 30, EBRD evaluated % matched over 30

Female participation rate -1.472*** -3.984*** -3.514*** -2.290** -3.957*** -7.373*** -7.425***(0.7429) (0.9029) (1.7181) (1.2658) (1.8100) (1.1104) (1.1344)

x Transition 2.535*** 5.052*** 35.309 27.701 5.862 7.512*** 7.847***(1.0092) (1.2196) (44.8532) (31.1209) (47.3125) (1.2782) (1.3919)

Years from transition 0.100*** 0.062 0.039 0.084 0.108 -0.0001 0.022(0.0348) (0.0450) (0.1315) (0.0916) (0.1339) (0.0441) (0.0451)

squared -0.004*** -0.001 -0.001 -0.006 -0.002 0.002 0.002(0.0015) (0.0019) (0.0061) (0.0045) (0.0065) (0.0019) (0.0019)

x Transition -0.078** -0.033 9.375 4.897 0.357 0.078 0.097**(0.0422) (0.0523) (11.0341) (7.3705) (11.0925) (0.0544) (0.0561)

squared x Transition 0.004*** 0.001 -0.090 -0.044 -0.003 -0.003 -0.003**(0.0015) (0.0019) (0.1075) (0.0718) (0.1080) (0.0019) (0.0019)

% of females with tertiary education -0.631 -0.672 4.970 21.863*** -0.447 -0.342 -0.562(0.7991) (0.8489) (11.2638) (8.7870) (11.5959) (0.7725) (0.7904)

Mean age of active female 0.069*** -0.003 -0.058 0.068 -0.056 0.018 0.002(0.0219) (0.0292) (0.1014) (0.0765) (0.1074) (0.0268) (0.0271)

% of HH with children under 5 0.867*** 3.021*** 5.932*** 13.399*** 4.577* 7.253*** 7.592***(0.3461) (1.2163) (2.8513) (2.5122) (2.9900) (1.6902) (1.7312)

EBRD - large scale privatization 0.676**(0.3577)

EBRD - small scale privatization 2.614***(0.4538)

EBRD - governance and enterprise restructuring -0.131(0.4477)

CIRI - womens’ economic rights 0.341***(0.1191)

CIRI - womens’ social rights 0.128**(0.0773)

Constant -2.948*** 0.220 -37.186 -32.481 1.608 -1.797* -0.375(0.8577) (1.1951) (46.3010) (31.1332) (46.5176) (1.1598) (1.2762)

Observations 196 163 54 54 54 160 160R-squared 0.383 0.505 0.570 0.775 0.518 0.601 0.390






Adjusted participation gap AREG EBRD evaluated All All EBRD evaluated

Year from transition -0.005*** -0.005* -0.007*** -0.008*** -0.006*** -0.004** -0.002(0.0007) (0.0031) (0.0030) (0.0026) (0.0022) (0.0023) (0.0038)

squared 0.0002*** 0.0002** 0.0002*** 0.0002*** 0.0002*** 0.0002* 0.000(0.00001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

x Transiton -0.007*** -0.011 -0.003 -0.000 -0.007*** -0.008*** -0.011(0.0013) (0.0314) (0.0301) (0.0305) (0.0025) (0.0026) (0.0334)

squared x Transition -0.0002*** -0.0002 -0.0002 -0.0002 -0.0002** -0.0002 -0.0002(0.00003) (0.0003) (0.0003) (0.0003) (0.0001) (0.0001) (0.0003)

% of females with tertiary education 0.163*** 0.124* 0.138** 0.126* 0.161*** 0.177*** 0.079(0.0498) (0.0801) (0.0818) (0.0816) (0.0522) (0.0522) (0.0920)

Mean age of active females 0.004*** 0.002 0.002 0.002 0.004*** 0.004*** 0.002(0.0013) (0.0020) (0.0020) (0.0020) (0.0013) (0.0013) (0.0020)

% of HH with children under 5 -0.115*** -0.004 -0.002 -0.002 -0.119*** -0.127*** -0.048(0.0159) (0.0354) (0.0354) (0.0353) (0.0163) (0.0164) (0.0389)

EBRD - large scale privatization -0.007 -0.014(0.0077) (0.0098)

EBRD - small scale privatization -0.002 -0.005(0.0064) (0.0100)

EBRD - governance and enterprise restructuring 0.003 0.008(0.0083) (0.0102)

CIRI - womens’ economic rights -0.010 0.009(0.0070) (0.0109)

CIRI - womens’ social rights -0.010*** -0.010(0.0040) (0.0068)

Constant 0.622*** 0.480*** 0.450*** 0.445*** 0.630*** 0.613*** 0.448***(0.0916) (0.1531) (0.1509) (0.1519) (0.0966) (0.0961) (0.1616)

Observations 628 207 207 207 589 586 199R-squared 0.898 0.804 0.803 0.803 0.899 0.901 0.813





Conclusions & Plans

Great international variation in the extent of genderdifferences on the labor market

Most of the variation seems to be caused by the country fixedeffects

Higher FLFP seem to be related with the lower adjusted wagegaps - The Goodwill Effect!

Plans for the future

Improving data on the RHS and LHS!





Thank you for your attention


Female access to the labor market and wages over transition

Economy & Finance

Transcript of Female access to the labor market and wages over transition