Evaluation of the gender wage gap in Austria

Transcription

1 Evaluation of the gender wage gap in Austria René Böheim 1,2 Marian Fink 2 Silvia Rocha-Akis 2 Christine Zulehner 3,2 1 Vienna University of Economics and Business, JKU Linz 2 Austrian Institute of Economic Research 3 University of Vienna, Telekom ParisTech Workshop Arbeitsmarktökonomie 2017 IHS Vienna, November 2017 Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 1 / 24

2 Gender wage gap in Austria we estimate and decompose the gender wage gap in Austria using EU-SILC data from and standard techniques data set consistent over time mean decomposition: Blinder/Oaxaca, Juhn/Murphy/Pierce quantile decomposition: Chernozhukov/Fernandez-Val/Melly how much has changed and why? Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 2 / 24

3 Comparison across Europe - unadjusted wage gap Source: Eurostat Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 3 / 24

4 Evolution of the gender wage gap in Austria Raw Unexplained Year wage gap wage gap Data, sample and method a MZ, private sector, net wages, full-time and part-time workers, white collar workers only, male based decomposition b MZ, private sector, net wages, full-time workers, male based decomposition c VESTE, private sector, gross wages, full-time and part-time workers, firms with more than 10 employees, female dummy b MZ, private sector, net wages, full-time workers, male based decomposition c VESTE, private sector, gross wages, full-time and part-time workers, firms with more than 10 employees, female dummy f MZ, tax records and ASSD, private sector, gross wages, full-time and part-time workers, male based decomposition d EU-SILC , private sector, gross wages, full-time workers, female dummy e VESTE, private sector, gross wages, firms with more than 10 employees, Reimers (1983) decomposition f MZ, tax records and ASSD 2007, private sector, gross wages, full-time and part-time workers, male based decomposition g EU-SILC, private + public sector, gross wages, full-time and part-time workers, male based decomposition h PIAAC, private + public sector, gross wages, full-time and part-time workers, male based (quantile) decomposition a Zweimüller and Winter-Ebmer 1994; b Böheim et al. 2007; c Pointner and Stiglbauer 2010; d Grünberger and Zulehner 2009; e Frauenbericht 2010; f Böheim et al. 2013a; g Grandner and Gstach 2015; h Christl and Köppl-Turyna Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 4 / 24

5 Trend of raw and the unexplained wage gap in Austria Wage gap in log points Wage gap in log points Year Year Raw gap Adjusted gap Linear prediction (raw) Linear prediction (adj.) Raw gap Adjusted gap Linear prediction (raw) Linear prediction (adj.) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 5 / 24

6 Decomposition over time by Juhn, Murphy and Pierce 1992 wages for a worker i in period t is given by the following equation: Y it = X it β t +σ tθ it then, the average male-female wage gap for period t is given by: D t Y mt Y ft = (X mt X ft )β t +σ t(θ mt θ ft ) = X tβ t +σ t θ t the change in the wage gaps between two periods t and s can then be decomposed as follows: D t D s = ( X t X s)β s + X s(β t β s)+( X t X s)(β t β s) +( θ t θ s)σ s + θ s(σ t σ s)+( θ t θ s)(σ t σ s) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 6 / 24

7 Data and sample Austrian part of the European Union Statistics on Income and Living Conditions (EU-SILC) for the years 2005 until 2015 sample surveys private households and their current members each year and collects data on income, poverty, social exclusion, housing, labor, education, and health on the household and individual level on average 6,010 households and 13,929 persons are surveyed per year persons between 20 and 60 years old private and public sector we calculate the hourly gross wage by dividing the usual monthly earnings (including overtime and bonuses) by the number of usual hours worked, 2014 CPI adjusted Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 7 / 24

8 Average wages, usual hours and some explanatory variables, Year Men Average wages Usual hours worked Number of observations Women Average wages Usual hours worked Number of observations education share of only compulsory schooling decreased: (males) and (females) share of academic degrees increased from about 10 (11)% to 14 (17)% for males (females) difference in experience decreased from 4.5 years to 3.95 years difference in having a leading position increased from 5 to 8.6 pp difference in being in a large firm decreased from 15 to 9 pp technical professionals: (males) and (females) manufacturing: (males) and (females) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 8 / 24

9 Evolution of the gender wage gap Wage gap in log points Year Raw gap Adjusted gap Linear prediction (raw) Linear prediction (adj.) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 9 / 24

10 Evolution of adjusted and unexplained gender wage gap Wage gap in log points Year Adjusted gap Unexplained gap Linear prediction (adj.) Linear prediction (unexp.) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 10 / 24

11 Decomposition results Prediction males females Difference unadjusted adjusted Difference explained unexplained # of obs all males females Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 11 / 24

12 Results for decomposition over time Quantity Price Interaction Wage gap effect effect effect Change from Explained gap Unexplained gap Change from Explained gap Unexplained gap Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 12 / 24

13 Decomposition of explained gap over time Quantity Price Interaction Wage gap effect effect effect Explained gap Origin Urban Eduction Experience Occupation Industry Leading position Married Status Firm size Part-time Lambda Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 13 / 24

14 Gender wage gap and the business cycle Wage gap in log points GDP growth rate Wage gap in log points Unemployment rate Raw gap Unexp. gap Linear prediction (raw) Linear prediction (unexp.) Raw gap Unexp. gap Linear prediction (raw) Linear prediction (unexp.) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 14 / 24

15 Quantile Decomposition q-th conditional quantile of the logarithmic wage distribution as a linear function of characteristics: where q (0,1) and E[ǫ iq X i ] = 0 lny iq = β iq X i +ǫ iq, i = M,W, (1) for each quantile q, we estimate one equation for men, M, and for women, W and estimate counterfactual distributions following Melly 2006 Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 15 / 24

16 Raw gap quantiles Wage gap in log points Year Raw gap mean Raw gap Q50 Raw gap Q25 Raw gap Q75 Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 16 / 24

17 Unexplained gap quantiles Wage gap in log points Year GDP growth rate Unexplained gap q50 Unexplained gap q25 Unexplained gap q75 Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 17 / 24

18 Quantiles and the business cycle Wage gap in log points Growth rate Wage gap in log points Unemployment rate Unexp. gap q25 Unexp. gap q75 Lin. prediction (q25) Lin. prediction (q75) Unexp. gap q25 Unexp. gap q75 Lin. prediction (q25) Lin. prediction (q75) Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 18 / 24

19 Estimated correlation Adjusted gap Q25 Adjusted gap Q75 Constant *** *** *** Growth rate ** Unemployment rate ** R-squared R-squared adjusted # obs Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 19 / 24

20 Conclusions using EU-SILC data from and standard decomposition techniques, we estimate and decompose the gender wage gap in Austria results show that the gender wage gap is decreasing over time reasons are the following explained part decreases as differences in observables as education and occupation became narrower unexplained part is decreasing as well unexplained part at the 75th percentile is highly correlated with the business cycle gives direction for potential policy measures Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 20 / 24

21 Summary statistics I male female male female male female Age Married Children aged Children aged Children aged Children aged Austria EU High urbanization Medium urbanization Low urbanization Experience Compulsory schooling Apprenticeship, Craftsmen diploma Intermediate vocational education Upper secondary (academic) Upper secondary (techn. and voc.) Academic degree Managerial authority Leading position Firm with more than 10 employees Part-time N Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 21 / 24

22 Summary statistics II male female male female male female Blue-collar worker White-collar worker Civil servant Managerial Professional Technical and ass. professional Clerical support Service and sales Skilled agriculture Skilled trades Plant/machine operatives Elementary Agriculture, forestry, mining Manufacturing Energy, water, waste Construction Trade Transport, information, communication Accommodation, food services Finance, insurance, real Estate Professional services Public services Other services N Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 22 / 24

23 Estimated coefficients I male female male female male female Constant *** *** *** *** *** *** Austria *** ** *** *** *** * EU * * *** Medium urbanisation Low urbanisation * *** Apprenticeship, Craftsmen diploma *** * Intermediate voc. education ** *** *** *** ** Upper secondary (academic) *** *** *** *** Upper secondary (techn. and voc.) *** *** *** *** *** * Academic degree *** *** *** *** *** *** Cohabiting partner *** *** Experience *** *** *** *** *** *** Experience sq * ** * *** Managerial position *** *** *** *** *** *** Highly skilled/senior employees *** *** *** *** *** Firm size > *** *** *** *** *** *** Part-time *** lambda # obs censored # obs Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 23 / 24

24 Estimated coefficients II male female male female male female Blue-collar worker * * *** *** ** Civil servant ** * ** * * Managerial *** ** ** *** *** *** Professional *** *** ** *** *** *** Technical & ass. professional *** *** * *** ** *** Clerical support *** * *** *** Skilled agricult * *** Skilled trades * Plant, machine operatives * Elementary *** *** * Agriculture, forestry, mining Manufacturing ** * *** *** Energy * * Construction * * ** Trade ** ** Transport, information, communication Accommodation, food services *** * *** *** ** ** Finance, insurance, real Estate ** * * *** *** *** Public services Other services * * * Böheim, Fink, Rocha-Akis and Zulehner: Gender Wage Gap in Austria 24 / 24