AGS data analysis: the gender wage gap and Q&A

AGS DATA ANALYSIS
THE GENDER WAGE GAP 2013
AN ANALYSIS OF THE AUSTRALIAN GRADUATE LABOUR MARKET
EDWINA LINDSAY, GCA
MEDIA
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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
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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)
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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.
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-
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.
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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:
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-
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
-
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3,103 males and 5,082 females
DATA
Figure 1: Distribution of full-time starting salaries for male and female graduates,
2013
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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.
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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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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
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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.
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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.
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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
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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
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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).
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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.
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-
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
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QUESTIONS?
An analysis of the gender wage gap in the Australian graduate
labour market, 2013
[email protected]
Thank you.
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