AGS DATA ANALYSIS the gender wage gap 2013 an analysis of the Australian graduate labour market

1. AGS DATA ANALYSIS the gender wage gap 2013an analysis of the Australian graduate labour market Edwina lindsay, gca

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3. Australian POLITICAL framework • Prior to the ‘60s, males wages higher than female wages due to familial obligations. • National Wage Case, 1967 • Equal Pay Case, 1969 • 1984 Sex Discrimination Act, 2006 Work Choices, 2009 Fair Work, 2012 Workplace Gender Equality legislation.

4. women demonstrating outside Melbourne’s Trades Hall in support of equal pay in 1969. • Equal Pay Case, 1969

5. KEY CONTRIBUTORS • Gender wage gap increases as age increases • Disparities in labour market experience • Career breaks • Hours worked • Differences in level and field of education • Occupational choices and Industry • Region of employment

6. LITERATURE - International Graduate labour market • Key contributors were ‘observed’ factors such as: • Hours worked and field of education (females over-represented in lower-earning fields of education) (Finnie and Wannell, 2004) • Industry of employmentand field of education (males more likely to be found in higher paying occupations)(Jewell, 2008)

7. LITERATURE - AUSTRALIAN • Broad labour market • Borland, 1999 – 15 per cent • ABS, 2014 – 17.1 per cent • Graduate labour market • Birch, Li and Miller, 2009: • 2003 GDS data. Field of education, occupation type, and industry – a gender wage gap of 3 per cent. • Li and Miller, 2012: • GDS data (1999 – 2009). • Blinder- Oaxaca decomposition– a gender wage gap of 5 per cent.

8. The study • Investigates whether a gender wage gap exists within the graduate population • The extent of the gender wage gap when the personal, enrolment and employment characteristics of graduates are held constant.

9. DATA • Graduate Destinations Survey (2013) • 109,304 responses; a response rate of 60.0 per cent • Reliability of GDS data (Guthrie and Johnson 1997) • Sample restricted to: • Australian bachelor degree graduates • Aged less than 25 • In first full-time employment in Australia • Indicated gender • No missing data on key variables

10. DATA • Dependent variable – annual starting salary • Outliers excluded (below $20,000 and above $112,500) • Final analysis sample of 8,185 graduates • 3,103 males and 5,082 females

11. DATA Figure 1: Distribution of full-time starting salaries for male and female graduates, 2013

12. METHODOLOGY OLS Regression lnSi = β0 + βFi + βXi + εi • lnSi = annual starting salary of graduate i expressed in logarithmic form • β0= constant • Fi = variable indicating that graduate i is female • Xi = vector containing the various characteristics of graduate i (including personal, enrolment and occupational characteristics) • εi = an error term.

13. METHODOLOGY Dummy variables • Female • Field of education (22) • Personal and enrolment (4) • State of employment (14) • Other employment characteristics (6) • Occupation (7)

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15. findings • Model 1: • Controlling for no other factor, female graduates earn, on average, 9.4 per cent less than male graduates. • Aggregate 9.4 per cent gap is due to varying enrolment patterns of males and females, and occupational pathways resulting from these patterns.

16. FINDINGS Model 2: • Builds on Model 1 by controlling for field of education, personal and enrolment characteristics. • Female coefficient halved from -0.094 to -0.047. • Field of education has considerable explanatory power on the starting salaries of graduates.

17. findings • Model 2: Graduates average annual starting salaries: controlling for gender and enrolment.

18. FINDINGS What can explain the 9.4 per cent gap? • Traditional gender patterns • More males in higher paying fields. • Engineering vs. Humanities

19. findings Model 2 : Graduates average annual starting salaries: controlling for gender and enrolment.

20. Sample descriptives Table 1: Graduates’ field of education enrolment patterns, by gender, 2013 (%)

21. FINDINGS Model 2: • But – not all female-dominated fields are associated with lower starting salaries. • E.g. Education and Paramedical Studies.

22. findings Model 2 : Graduates average annual starting salaries: controlling for gender and enrolment.

23. findings Table 1: Graduates’ field of education enrolment patterns, by gender, 2013 (%)

24. FINDINGS Model 3: • Builds on Models 1 and 2, by adding occupation and employment characteristics. • The addition of the various employment variables in Model 3 only changed the female coefficient marginally, from -0.047 to -0.044. • Adjusted R2 of .344 • 4.4 per cent figure is similar to previous findings: 3 per cent by Birch, Li and Miller (2009) and 5 per cent by Li and Miller (2012).

25. conclusions 1. Field of education characteristics of graduates assert considerable explanatory power • Differences in male and female enrolment patterns • Field of education controls halved female coefficient 2. After controlling for all explanatory variables, gender wage gap of 4.4 per cent remainedunexplained by our data. • Differences not captured in our data/models. • Differences in negotiating behaviour? • Discriminative practices within the workplace? • Need for social reform? • Female participation in STEM subjects? • Need for further research – perhaps using a matching technique and analysing longitudinal data (BGS).

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27. Questions? • An analysis of the gender wage gap in the Australian graduate labour market, 2013 • edwina.lindsay@graduatecareers.edu.au • Thank you.