Feminist economics. Wage determinants, wage differences and wage discrimination: Results for Austria. Christine Zulehner. Linz, November 3, 2011

Transcription

1 Feminist economics Wage determinants, wage differences and wage discrimination: Results for Austria Christine Zulehner JKU Linz, Wifo Linz, November 3, 2011 Feminist Economics: Wage determinants 1/24

2 Outline Wage determinants Methods and data Results for Austria (extra slides) Feminist Economics: Wage determinants 2/24

3 Questions Income and wage differentials impact of education, experience, employment history effect of occupational choice, i.e. horizontal segregation effect of differences in promotion and firm specific variables like firm size how large is the wage differential which is not explained by these variables, i.e. unexplained residual or discrimination? homogenous sample: full-time salaried employees in private and public sector in private sector Feminist Economics: Wage determinants 3/24

4 Questions Differences over the wage distribution are gender specific wage differences in low-wage jobs different to differences in high-wage jobs? is there a sticky floor or a glass ceiling? two definitions of the glass ceiling lower probability of promotion (McDowell et al. 1999) larger differences in high-wage jobs (Arulampalam et al. 2007) Development in recent years gross wage rates per hour: 2002 and 2007 net wage rates per hour: 1983, 1997 and 2007 decomposition of wage differentials Feminist Economics: Wage determinants 4/24

5 Data construction of a new data set merged data from the income report 2008 with data from the social security micro-census 2007, tax records 2007, social security data since 1972 variables micro-census: hours worked, education, experience, occupation, industry, hierarchy, household tax records: gross and net yearly income social security data: detailed experience in the labor market, tenure, firm information Feminist Economics: Wage determinants 5/24

6 Definitions Construction of the variables hourly wage rate: annual income/(days employed number of hours) experience, tenure, employment history number of job changes, number of changes in employment status Forms of employment at least 270 days employed in 2007 part-time: 1h and < 35h full-time employees: 35h maternity leave, military and civilian service employees vs. self-employed persons private and public sector Feminist Economics: Wage determinants 6/24

7 Description of the sample women men Alle labor force more than one hour % 83% 79% employees more than one hour % 70% 67% more than one hour, at least 270 days % 64% 60% more than 35 hours, at least 270 days % 61% 47% private sector more than one hour, at least 270 days % 51% 43% more than 35 hours, at least 270 days % 49% 35% Number of year olds Feminist Economics: Wage determinants 7/24

8 Hourly wages of employees per Average wage (in Euro) women men Δ month in % public + private sector 1h and < 35h 13,71 17,53 3,82 687,6 21,1 35h 13,95 17,19 3,24 583,2 18,8 private sector 1h and < 35h 12,94 16,11 3,17 570,6 19,7 35h 12,75 16,69 3,94 709,2 23,6 hourly wage rate: annual income/(days employed number of hours) wages in the private sector are lower than wages in the public sector wages of women are lower than wages of men part-time wages of women are lower than full-time wages of women Differential per month with 40h and 4.5 weeks Basis: Hourly wage rates of men Feminist Economics: Wage determinants 8/24

9 Kernel density estimations log wages kdensity lwage_hg x kdensity lwage_hg kdensity lwage_hg distributions have a similar shape question: are corrected wage differentials also similar? Feminist Economics: Wage determinants 9/24

10 Education 1h 35h Variable women men women men Education (Percentage) Compulsory school 18,6 14,0 19,0 13,7 Apprenticeship 31,3 49,0 27,3 48,2 BMS, nurse s training school 20,7 8,0 20,1 8,2 Highschool (AHS, BHS) 16,6 13,8 18,2 14,1 Work master craftsman s certificate 0,6 5,7 0,4 5,8 University of applied science, academy 4,8 2,1 6,0 2,1 University 6,5 6,1 7,7 6,3 University (Second degree) 0,9 1,3 1,2 1,5 more women than men have only a compulsory school certificate women in full-time jobs are better educated than women in part-time jobs women are better educated than men (university, university of applied science, highschool :: master craftsman s certificate, apprenticeship) Feminist Economics: Wage determinants 10 / 24

11 Employment career 1h 35h Variable women men women men Age in years 38,7 38,6 37,6 38,6 Living in partnership (in percent) 64,2 67,0 52,3 67,2 Experience in years 15,3 18,8 15,9 18,9 Tenure 7,9 10,0 9,3 10,3 Work interruptions (in years) 1,4 0,6 1,0 0,6 Number of job changes 6,8 7,8 6,3 7,5 Number of employment status changes 17,3 17,1 15,4 16,9 Leading positions (in percent) 3,1 7,6 4,4 7,9 women in full-time jobs are younger and live less often in a partnership than female part-time employees women (part- and full-time) have less experience and shorter tenure than men women in part-time jobs have less tenure, longer work interruptions and more job and employment status changes than women in full-time jobs proportion of women in leading positions is smaller than that of men Feminist Economics: Wage determinants 11 / 24

12 Horizontal segregation 1h 35h Variable (in percentage) women men women men Public sector 35,9 20,9 36,4 19,2 Office workers 23,2 9,1 25,1 8,8 Service occupations 23,7 8,4 17,8 8,3 Trade and related professions 2,3 26,2 2,8 25,9 Plant and machine operators, assemblers 1,9 12,3 2,6 11,5 Non-skilled workers 14,3 9,4 11,1 9,4 Commercial goods production 12,2 29,2 15,7 31,5 Energy- and water supply Communications and information transmission 4,3 10,4 4,8 7,7 Trade, reparation (cars, commodities) 20,9 13,9 17,4 14,4 women work more often in the public sector, as office workers, in service occupations than men women work less often in the manufacturing sector, in the energy sector than men Feminist Economics: Wage determinants 12 / 24

13 Firm specific variables 1h 35h Variable women men women men Size of firm ,2 18,2 20,4 15, ,1 12,4 14,2 12, ,0 19,1 20,5 19, ,5 36,7 33,8 38, ,1 13,4 11,1 14,6 Firm female wage/male wage in % 78,3 77,8 80,9 76,7 ratio women to men in % 65,3 30,0 58,6 31,5 turnover 17,9 4,7 27,3 4,8 women work for smaller firms than men, women in part-time jobs more often women tend to work in less segregated firms than men part-time employed women work more often in more segregated firms than full-time employed women womenworkinfirmswithahigherturnoverthanmen Feminist Economics: Wage determinants 13 / 24

14 Estimated coefficients from separate estimates Variable women men Δ in PP # Observations Adjusted R-square 0,638 0,619 Constant 1.625* 1.848* -0,223* Education (reference: compulsory school) Apprenticeship 0,180* 0,230* -0,050* BMS, nurse s training school 0,256* 0,284* -0,028* Highschool (AHS, BHS), university course 0,371* 0,431* -0,060* Work master craftsman s certificate 0,281* 0,295* -0,014 University of applied science, academy 0,415* 0,451* -0,036 University 0,538* 0,612* -0,074* University (Second degree) 0,616* 0,666* -0,050 differences in coefficients reflect differences in choice of school, profession and programme of study sample: full-time employees, priv. + pub. sector, specification 1 (see paper), significant at the 95% level Feminist Economics: Wage determinants 14 / 24

15 Estimated coefficients from separate estimates Variable women men Δ in PP Professional experience 0,045* 0,049* -0,004 squared 100-0,086* -0,096* 0,010 Duration of employment 0,008* 0,008* 0,000 squared 100-0,002 0,010* -0,012 Partnership 0,006 0,058* -0,052* Leading position 0,117* 0,092* -0,025 Firm Ratio of women to men -0,164* -0,221* 0,055* Wage of women/wage of men 0,024-0,179* 0,203* no differences in payment for experience and tenure between women and men married men earn 5% more than unmarried men, this does not apply to women a higher proportion of women in the firm leads to lower wages for both women and men sample: full-time employees, priv. + pub. sector, specification 1 (see paper), significant at the 95% level Feminist Economics: Wage determinants 15 / 24

16 Estimated coefficients from separate estimates Variable women men Δ in PP unemployment * 0,022* out of labor force (OLF) 0,013* 0,013* 0,000 length of parential leave -0, ,070 military * 0,134 time sick -0,062* -0,040* -0,022 times of OLF and in military service have a positive effect on wage times of parential leave and unemployment have a negative effect on wage sample: full-time employees, priv. + pub. sector, significant at the 95% level Feminist Economics: Wage determinants 16 / 24

17 Decomposition of wage differentials Difference in coefficients (1) (2) (3) (4) Estimated value (Reimers 1983) 0,191 0,162 0,157 0,117 in % 18,1 15,0 14,5 11,0 in % of the mean wage differential 100,0 84,8 82,2 61,3 education, experience, tenure, interruptions marital status, citizenship, region x x x status, occupation, industry x x firm size, other firm characteristics, hierarchy x sample: full-time employees basis: hourly wage rates of men Feminist Economics: Wage determinants 17 / 24

18 Wage differentials over the wage distribution Log wage effects Quantile Total differential Different returns (betas) Different characteristics (X) unexplained part increases with the wage distribution (public and private sector) minimum wages reduce unexplained wage differentials higher level of heterogeneity in more complex occupations a glass ceiling exists in most European countries (Arulampalam et al. 2007) Feminist Economics: Wage determinants 18 / 24

19 Development over the last 30 years comparison with MZ 1983 and 1997 (Böheim, Hofer, Zulehner 2007) net wages, private sector Education: university in % women 1,5 3,9 5,0 men 1,9 5,7 5,1 Experience in years women 13,7 16,5 14,8 men 19,9 20,8 18,4 Wage differential (net wage) in % uncorrected 22,5 20,8 18,8 corrected (male based) 13,3 10,1 11,1 Feminist Economics: Wage determinants 19 / 24

20 Reasons in general: country-wide wage differentials have decreased, corrected differentials only marginally (Weichselbaumer, Winter-Ebmer 2005, 2007) development between 1983 and 2007 due to progressive taxation net wage differentials are lower than gross wage differentials women have caught up in terms of education and experience uncorrected wage differentials are declining more sharply than corrected wage differentials (an increased participation rate of women can raise wage differentials) Feminist Economics: Wage determinants 20 / 24

21 Development between 2002 and 2007 Böheim, Hofer, Zulehner 2011 gross wages, public and private sector priv.+öffentl. priv Education: university in % women 7,7 8,9 3,8 5,1 men 7,0 7,8 3,9 5,0 Experience in years womem 15,4 15,9 15,0 14,8 men 18,5 18,9 18,6 18,4 Wage difference in % uncorrected 21,7 18,1 25,9 23,4 corrected (Reimers 1983) 16,9 11,0 17,7 12,6 Feminist Economics: Wage determinants 21 / 24

22 Reasons development between 2002 and 2007 women have increased their abilities concerning tertiary education and the demand for tertiary education has increased as well but: women with compulsory schooling only lose (return to apprenticeship in 2002: (men) (women); in 2007: ) unexplained wage differential, i.e. discrimination or differences in unobserved characteristics, strongly decreased Feminist Economics: Wage determinants 22 / 24

23 Discussion: unexplained wage differentials Why should firms discriminate? statistical discrimination (Aigner und Cain 1985) discrimination by employers (Becker 1957) Empirical evidence of discrimination globalization, competition reduces wage differentials (Ashenfelter, Hannan 1986; Black, Strahan 2001; Black, Brainerd 2004; Weichselbaumer, Winter-Ebmer 2005) correlation of profit and pay gap (Hellerstein et al. 2003) selection of firms (Weber, Zulehner 2009, 2010) Unobservable characteristics salary negotiations (Riley, Babcock, McGinn 2005) behavior in competitions (Gneezy et al. 2003, Lavy 2008) selection in competitions (Niederle, Vesterlund 2007) efficiency of policy measures (Balafoutas, Sutter 2010) Feminist Economics: Wage determinants 23 / 24

24 Summary Wage differentials 18,1% 14,6% 11,0% (private + public sector) Determinants choice of school, profession and programme of study, employment career, profession, industry, promotion prospects differences in firms discrimination: substantial Austria s position in the EU is not very good traditional division of tasks, very small child care rate of 0-3 year olds, consequently a high part-time rate of women horizontal and vertical segregation Feminist Economics: Wage determinants 24 / 24