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AGS DATA ANALYSIS THE GENDER WAGE GAP 2013 AN ANALYSIS OF THE AUSTRALIAN GRADUATE LABOUR MARKET EDWINA LINDSAY, GCA.

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Presentation on theme: "AGS DATA ANALYSIS THE GENDER WAGE GAP 2013 AN ANALYSIS OF THE AUSTRALIAN GRADUATE LABOUR MARKET EDWINA LINDSAY, GCA."— Presentation transcript:

1 AGS DATA ANALYSIS THE GENDER WAGE GAP 2013 AN ANALYSIS OF THE AUSTRALIAN GRADUATE LABOUR MARKET EDWINA LINDSAY, GCA

2 2 MEDIA

3 3 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. AUSTRALIAN POLITICAL FRAMEWORK

4 4 Equal Pay Case, 1969 WOMEN DEMONSTRATING OUTSIDE MELBOURNE’S TRADES HALL IN SUPPORT OF EQUAL PAY IN 1969.

5 5 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 KEY CONTRIBUTORS

6 6 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 employment and field of education (males more likely to be found in higher paying occupations) (Jewell, 2008) LITERATURE - INTERNATIONAL

7 7 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. LITERATURE - AUSTRALIAN

8 8 1.Investigates whether a gender wage gap exists within the graduate population 2.The extent of the gender wage gap when the personal, enrolment and employment characteristics of graduates are held constant. THE STUDY

9 9 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 DATA

10 10 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 DATA

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

12 12 OLS Regression lnS i = β 0 + β F i + β X i + ε i lnSi = annual starting salary of graduate i expressed in logarithmic form β 0 = constant Fi = variable indicating that graduate i is female Xi = vector containing the various characteristics of graduate i (including personal, enrolment and occupational characteristics) ε i = an error term. METHODOLOGY

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

14 14 METHODOLOGY Explanatory Variables Variable of interest Personal characteristicsEmployment characteristics Female Disability¤ Weekly working hours Omitted: Male Omitted: No disability Non-English speaking backgroundOther employment characteristics Field of education Omitted: English speaking backgroundSmall and medium enterprise Accounting Omitted: large enterprise Agricultural Science Enrolment characteristicsPublic/government sector Architecture & Building Honours bachelorOmitted: private/not for profit sector Art & Design Omitted: pass bachelorShort-term contract Biological Sciences Double degreeOmitted: permanent or open-ended contract Computer Sciences Omitted: single degreeField of study of limited importance Dentistry Omitted: field of study important/formal requirement Earth Sciences State of employmentIn full-time work in final year of study Economics & Business NSW CapitalOmitted: not in full-time work in final year of study Education NSW Regional Engineering VIC CapitalOccupation Law VIC RegionalManagers Mathematics QLD CapitalProfessionals Medicine QLD RegionalTechnicians and Trades workers Optometry SA CapitalClerical and administrative workers Paramedical Studies WA CapitalSales workers Pharmacy WA RegionalMachinery operators and drivers Physical Sciences TAS CapitalLabourers Psychology TAS RegionalOmitted: Community and personal service workers Social Sciences NT Capital Social Work NT Regional Veterinary Science ACT Omitted: Humanities Omitted: Regional South Australia

15 15 Model 1: FINDINGS 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. Model 1 Female -0.094 (0.006)**

16 16 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. FINDINGS

17 17 Model 2: Graduates average annual starting salaries: controlling for gender and enrolment. FINDINGS Model 1Model 2 Sex Field of education (cont.) Female -0.094 (0.006)** -0.047 (0.006)** Medicine 0.238 (0.021)** Field of education (a) Optometry 0.529 (0.060)** Accounting 0.070 (0.014)** Paramedical Studies 0.155 (0.012)** Agricultural Science 0.069 (0.029)* Pharmacy -0.110 (0.020)** Architecture & Building 0.061 (0.019)** Physical Sciences 0.101 (0.034)** Art & Design -0.121 (0.020)** Psychology 0.026 (0.020) Biological Sciences -0.002 (0.017) Social Sciences 0.023 (0.029) Computer Sciences 0.125 (0.019)** Social Work 0.028 (0.032) Dentistry 0.446 (0.052)** Veterinary Science 0.024 (0.048) Earth Sciences 0.285 (0.033)**Personal characteristics Economics & Business 0.059 (0.011)** Disability 0.023 (0.016) Education 0.177 (0.013)** Non-English speaking background -0.003 (0.008) Engineering 0.306 (0.013)**Enrolment characteristics Law 0.152 (0.019)** Honours bachelor 0.114 (0.010)** Mathematics 0.134 (0.038)** Double degree 0.107 (0.008)** Adjusted R 2.026.203Adjusted R 2.203 F-statistic 221.8578.03F-statistic 78.03 Sample size 8,185 Sample size 8,185

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

19 19 Model 2 : Graduates average annual starting salaries: controlling for gender and enrolment. FINDINGS Model 1Model 2 Sex Field of education (cont.) Female -0.094 (0.006)** -0.047 (0.006)** Medicine 0.238 (0.021)** Field of education (a) Optometry 0.529 (0.060)** Accounting 0.070 (0.014)** Paramedical Studies 0.155 (0.012)** Agricultural Science 0.069 (0.029)* Pharmacy -0.110 (0.020)** Architecture & Building 0.061 (0.019)** Physical Sciences 0.101 (0.034)** Art & Design -0.121 (0.020)** Psychology 0.026 (0.020) Biological Sciences -0.002 (0.017) Social Sciences 0.023 (0.029) Computer Sciences 0.125 (0.019)** Social Work 0.028 (0.032) Dentistry 0.446 (0.052)** Veterinary Science 0.024 (0.048) Earth Sciences 0.285 (0.033)**Personal characteristics Economics & Business 0.059 (0.011)** Disability 0.023 (0.016) Education 0.177 (0.013)** Non-English speaking background -0.003 (0.008) Engineering 0.306 (0.013)**Enrolment characteristics Law 0.152 (0.019)** Honours bachelor 0.114 (0.010)** Mathematics 0.134 (0.038)** Double degree 0.107 (0.008)** Adjusted R 2.026.203Adjusted R 2.203 F-statistic 221.8578.03F-statistic 78.03 Sample size 8,185 Sample size 8,185

20 20 Table 1: Graduates’ field of education enrolment patterns, by gender, 2013 (%) SAMPLE DESCRIPTIVES MaleFemaleTotal MaleFemaleTotal Gender38.062.0100.0Field of education (continued) Field of education Humanities5.711.69.3 Accounting9.46.67.7Law2.43.43.0 Agricultural Science1.10.91.0Mathematics1.00.30.6 Architecture & Building4.02.12.8Medicine2.32.02.1 Art & Design2.02.92.5Optometry0.2 Biological Sciences3.14.43.9Paramedical Studies6.321.015.4 Computer Sciences6.00.82.8Pharmacy2.23.02.7 Dentistry0.20.40.3Physical Sciences1.20.40.7 Earth Sciences1.40.40.8Pyschology1.13.32.4 Economics & Business21.618.819.8Social Sciences0.61.31.1 Education3.510.98.1Social Work0.21.30.8 Engineering24.63.711.6Veterinary Science0.00.60.4

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

22 22 FINDINGS Model 1Model 2 Sex Field of education (cont.) Female -0.094 (0.006)** -0.047 (0.006)** Medicine 0.238 (0.021)** Field of education (a) Optometry 0.529 (0.060)** Accounting 0.070 (0.014)** Paramedical Studies 0.155 (0.012)** Agricultural Science 0.069 (0.029)* Pharmacy -0.110 (0.020)** Architecture & Building 0.061 (0.019)** Physical Sciences 0.101 (0.034)** Art & Design -0.121 (0.020)** Psychology 0.026 (0.020) Biological Sciences -0.002 (0.017) Social Sciences 0.023 (0.029) Computer Sciences 0.125 (0.019)** Social Work 0.028 (0.032) Dentistry 0.446 (0.052)** Veterinary Science 0.024 (0.048) Earth Sciences 0.285 (0.033)**Personal characteristics Economics & Business 0.059 (0.011)** Disability 0.023 (0.016) Education 0.177 (0.013)** Non-English speaking background -0.003 (0.008) Engineering 0.306 (0.013)**Enrolment characteristics Law 0.152 (0.019)** Honours bachelor 0.114 (0.010)** Mathematics 0.134 (0.038)** Double degree 0.107 (0.008)** Adjusted R 2.026.203Adjusted R 2.203 F-statistic 221.8578.03F-statistic 78.03 Sample size 8,185 Sample size 8,185 Model 2 : Graduates average annual starting salaries: controlling for gender and enrolment.

23 23 Table 1: Graduates’ field of education enrolment patterns, by gender, 2013 (%) FINDINGS MaleFemaleTotal MaleFemaleTotal Gender38.062.0100.0Field of education (continued) Field of education Humanities5.711.69.3 Accounting9.46.67.7Law2.43.43.0 Agricultural Science1.10.91.0Mathematics1.00.30.6 Architecture & Building4.02.12.8Medicine2.32.02.1 Art & Design2.02.92.5Optometry0.2 Biological Sciences3.14.43.9Paramedical Studies6.321.015.4 Computer Sciences6.00.82.8Pharmacy2.23.02.7 Dentistry0.20.40.3Physical Sciences1.20.40.7 Earth Sciences1.40.40.8Pyschology1.13.32.4 Economics & Business21.618.819.8Social Sciences0.61.31.1 Education3.510.98.1Social Work0.21.30.8 Engineering24.63.711.6Veterinary Science0.00.60.4

24 24 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 R 2 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). FINDINGS Model 1Model 2Model 3 Female -0.094 (0.006)** -0.047 (0.006)** -0.044 (0.006)**

25 25 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 remained unexplained 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). CONCLUSIONS

26 26 MEDIA

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

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