AGS DATA ANALYSIS THE GENDER WAGE GAP 2013 AN ANALYSIS OF THE AUSTRALIAN GRADUATE LABOUR MARKET EDWINA LINDSAY, GCA MEDIA 2 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. 3 WOMEN DEMONSTRATING OUTSIDE MELBOURNE’S TRADES HALL IN SUPPORT OF EQUAL PAY IN 1969. • Equal Pay Case, 1969 4 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 5 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 employment and field of education (males more likely to be found in higher paying occupations) (Jewell, 2008) 6 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. 7 - Li and Miller, 2012: - GDS data (1999 – 2009). - Blinder- Oaxaca decomposition– a gender wage gap of 5 per cent. THE STUDY 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. 8 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: 9 - Australian bachelor degree graduates - Aged less than 25 - In first full-time employment in Australia - Indicated gender - No missing data on key variables DATA • Dependent variable – annual starting salary - Outliers excluded (below $20,000 and above $112,500) • Final analysis sample of 8,185 graduates - 10 3,103 males and 5,082 females DATA Figure 1: Distribution of full-time starting salaries for male and female graduates, 2013 11 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. 12 METHODOLOGY Dummy variables • Female • Field of education (22) • Personal and enrolment (4) • State of employment (14) • Other employment characteristics (6) • Occupation (7) 13 METHODOLOGY Explanatory Variables Variable of interest Female Omitted: Male Field of education Accounting Agricultural Science Architecture & Building Art & Design Biological Sciences Computer Sciences Dentistry Earth Sciences Economics & Business Education Engineering Law Mathematics Medicine Optometry Paramedical Studies Pharmacy Physical Sciences Psychology Social Sciences Social Work Veterinary Science Omitted: Humanities 14 Personal characteristics Disability Omitted: No disability Non-English speaking background Omitted: English speaking background Enrolment characteristics Honours bachelor Omitted: pass bachelor Double degree Omitted: single degree State of employment NSW Capital NSW Regional VIC Capital VIC Regional QLD Capital QLD Regional SA Capital WA Capital WA Regional TAS Capital TAS Regional NT Capital NT Regional ACT Omitted: Regional South Australia Employment characteristics ¤ Weekly working hours Other employment characteristics Small and medium enterprise Omitted: large enterprise Public/government sector Omitted: private/not for profit sector Short-term contract Omitted: permanent or open-ended contract Field of study of limited importance Omitted: field of study important/formal requirement In full-time work in final year of study Omitted: not in full-time work in final year of study Occupation Managers Professionals Technicians and Trades workers Clerical and administrative workers Sales workers Machinery operators and drivers Labourers Omitted: Community and personal service workers FINDINGS Model 1: Model 1 Female • -0.094 (0.006)** 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. 15 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. 16 FINDINGS Model 2: Graduates average annual starting salaries: controlling for gender and enrolment. Model 1 Model 2 -0.094 (0.006)** -0.047 (0.006)** Sex Model 2 Field of education (cont.) Female Medicine Field of education (a) Optometry 0.070 (0.014)** 0.069 (0.029)* 0.061 (0.019)** -0.121 (0.020)** -0.002 (0.017) 0.125 (0.019)** 0.446 (0.052)** 0.285 (0.033)** 0.059 (0.011)** 0.177 (0.013)** 0.306 (0.013)** 0.152 (0.019)** 0.134 (0.038)** Accounting Agricultural Science Architecture & Building Art & Design Biological Sciences Computer Sciences Dentistry Earth Sciences Economics & Business Education Engineering Law Mathematics Adjusted R2 17 Paramedical Studies Pharmacy Physical Sciences Psychology Social Sciences Social Work Veterinary Science 0.238 (0.021)** 0.529 (0.060)** 0.155 (0.012)** -0.110 (0.020)** 0.101 (0.034)** 0.026 (0.020) 0.023 (0.029) 0.028 (0.032) 0.024 (0.048) Personal characteristics Disability Non-English speaking background 0.023 (0.016) -0.003 (0.008) Enrolment characteristics Honours bachelor Double degree 0.114 (0.010)** 0.107 (0.008)** .026 .203 Adjusted R2 .203 F-statistic 221.85 78.03 F-statistic 78.03 Sample size 8,185 8,185 Sample size 8,185 FINDINGS What can explain the 9.4 per cent gap? • Traditional gender patterns • More males in higher paying fields. • Engineering vs. Humanities 18 FINDINGS Model 2 : Graduates average annual starting salaries: controlling for gender and enrolment. Model 1 Model 2 -0.094 (0.006)** -0.047 (0.006)** Sex Model 2 Field of education (cont.) Female Medicine Field of education (a) Optometry 0.070 (0.014)** 0.069 (0.029)* 0.061 (0.019)** -0.121 (0.020)** -0.002 (0.017) 0.125 (0.019)** 0.446 (0.052)** 0.285 (0.033)** 0.059 (0.011)** 0.177 (0.013)** 0.306 (0.013)** 0.152 (0.019)** 0.134 (0.038)** Accounting Agricultural Science Architecture & Building Art & Design Biological Sciences Computer Sciences Dentistry Earth Sciences Economics & Business Education Engineering Law Mathematics Adjusted R2 19 Paramedical Studies Pharmacy Physical Sciences Psychology Social Sciences Social Work Veterinary Science 0.238 (0.021)** 0.529 (0.060)** 0.155 (0.012)** -0.110 (0.020)** 0.101 (0.034)** 0.026 (0.020) 0.023 (0.029) 0.028 (0.032) 0.024 (0.048) Personal characteristics Disability Non-English speaking background 0.023 (0.016) -0.003 (0.008) Enrolment characteristics Honours bachelor Double degree 0.114 (0.010)** 0.107 (0.008)** .026 .203 Adjusted R2 .203 F-statistic 221.85 78.03 F-statistic 78.03 Sample size 8,185 8,185 Sample size 8,185 SAMPLE DESCRIPTIVES Table 1: Graduates’ field of education enrolment patterns, by gender, 2013 (%) Gender Male Female Total 38.0 62.0 100.0 Field of education 20 Male Female Total Field of education (continued) Humanities 5.7 11.6 9.3 Accounting 9.4 6.6 7.7 Law 2.4 3.4 3.0 Agricultural Science 1.1 0.9 1.0 Mathematics 1.0 0.3 0.6 Architecture & Building 4.0 2.1 2.8 Medicine 2.3 2.0 2.1 Art & Design 2.0 2.9 2.5 Optometry 0.2 0.2 0.2 Biological Sciences 3.1 4.4 3.9 Paramedical Studies 6.3 21.0 15.4 Computer Sciences 6.0 0.8 2.8 Pharmacy 2.2 3.0 2.7 Dentistry 0.2 0.4 0.3 Physical Sciences 1.2 0.4 0.7 Earth Sciences 1.4 0.4 0.8 Pyschology 1.1 3.3 2.4 Economics & Business 21.6 18.8 19.8 Social Sciences 0.6 1.3 1.1 Education 3.5 10.9 8.1 Social Work 0.2 1.3 0.8 Engineering 24.6 3.7 11.6 Veterinary Science 0.0 0.6 0.4 FINDINGS Model 2: • But – not all female-dominated fields are associated with lower starting salaries. • E.g. Education and Paramedical Studies. 21 FINDINGS Model 2 : Graduates average annual starting salaries: controlling for gender and enrolment. Model 1 Model 2 -0.094 (0.006)** -0.047 (0.006)** Sex Model 2 Field of education (cont.) Female Medicine Field of education (a) Optometry 0.070 (0.014)** 0.069 (0.029)* 0.061 (0.019)** -0.121 (0.020)** -0.002 (0.017) 0.125 (0.019)** 0.446 (0.052)** 0.285 (0.033)** 0.059 (0.011)** 0.177 (0.013)** 0.306 (0.013)** 0.152 (0.019)** 0.134 (0.038)** Accounting Agricultural Science Architecture & Building Art & Design Biological Sciences Computer Sciences Dentistry Earth Sciences Economics & Business Education Engineering Law Mathematics Adjusted R2 22 Paramedical Studies Pharmacy Physical Sciences Psychology Social Sciences Social Work Veterinary Science 0.238 (0.021)** 0.529 (0.060)** 0.155 (0.012)** -0.110 (0.020)** 0.101 (0.034)** 0.026 (0.020) 0.023 (0.029) 0.028 (0.032) 0.024 (0.048) Personal characteristics Disability Non-English speaking background 0.023 (0.016) -0.003 (0.008) Enrolment characteristics Honours bachelor Double degree 0.114 (0.010)** 0.107 (0.008)** .026 .203 Adjusted R2 .203 F-statistic 221.85 78.03 F-statistic 78.03 Sample size 8,185 8,185 Sample size 8,185 FINDINGS Table 1: Graduates’ field of education enrolment patterns, by gender, 2013 (%) Gender Male Female Total 38.0 62.0 100.0 Field of education 23 Male Female Total Field of education (continued) Humanities 5.7 11.6 9.3 Accounting 9.4 6.6 7.7 Law 2.4 3.4 3.0 Agricultural Science 1.1 0.9 1.0 Mathematics 1.0 0.3 0.6 Architecture & Building 4.0 2.1 2.8 Medicine 2.3 2.0 2.1 Art & Design 2.0 2.9 2.5 Optometry 0.2 0.2 0.2 Biological Sciences 3.1 4.4 3.9 Paramedical Studies 6.3 21.0 15.4 Computer Sciences 6.0 0.8 2.8 Pharmacy 2.2 3.0 2.7 Dentistry 0.2 0.4 0.3 Physical Sciences 1.2 0.4 0.7 Earth Sciences 1.4 0.4 0.8 Pyschology 1.1 3.3 2.4 Economics & Business 21.6 18.8 19.8 Social Sciences 0.6 1.3 1.1 Education 3.5 10.9 8.1 Social Work 0.2 1.3 0.8 Engineering 24.6 3.7 11.6 Veterinary Science 0.0 0.6 0.4 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. Female Model 1 -0.094 (0.006)** Model 2 -0.047 (0.006)** Model 3 -0.044 (0.006)** • 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). 24 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 remained unexplained by our data. 25 - 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). MEDIA 26 QUESTIONS? An analysis of the gender wage gap in the Australian graduate labour market, 2013 edwina.lindsay@graduatecareers.edu.au Thank you. 27