ECON 1030 Sydney The Gender Pay Gap in The Australian Population Data Analysis

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RMIT Classification: Trusted
ECON 1030 – BUSINESS STATISTICS 1
Assessment 3: Individual Assignment
Instructions:
This is an individual assignment with a total of 40 marks. The allocation of marks is as follows:
Statistical Analysis:
(including Excel)
Professional Report:
Total:
32
8
40
The response to the assignment must be provided in the form of a professional report with no
more than 10 pages (excluding cover page). The structure of your professional report must
include: 1] A Title, 2] An Executive Summary, 3] An Introduction, 4] Analysis, and 5]
Conclusions.
You must submit an electronic copy of your assignment in Canvas. See the attached
Template for your submission for more details.
This assignment requires the use of Microsoft Excel. If you have Windows, you will need to
use the Data Analysis Tool Pack. If you have a Mac with Excel 2011, you may need to use
StatPlus: MAC LE. The Excel workbook you submit needs to be clear and carefully organised.
It will be treated as an appendix to your report, i.e. not included in the page count. You will
need to take the relevant results from your Excel workbook and incorporate into your
report. Do not refer to the Excel workbook within the Professional report. The Report
needs to be standalone.
Presentation Instructions:
Your written professional report should comply with the following presentation standards:
1. Typed using a standard professional font type (e.g. Times Roman), 12-point font size.
2. 1.5-line spacing, numbered pages, and clear use of titles and section headings.
3. Delivered as a Word (.doc or .docx) or PDF (.pdf) file.
4. Checked for spelling, typographical and grammatical errors. Where relevant, round to 3
decimal places.
5. With all relevant tables and charts, the report should be no more than 10 pages long.
Problem Description:
This is a further analysis of the gender pay gap in the Australian population. According to a
recent report by KPMG Consulting, gender discrimination continues to be the single largest
factor contributing to the gender pay gap (KPMG, 2019). In order to estimate the extent of
discrimination in the job market where women with identical labour market characteristics as
their male counterparts receive different wages, you will estimate a set of linear regression
models.
Since this is an additional analysis on the gender pay gap, the content in the Introduction section
of your report may overlap with the one in the Group Assignment. However, you are
RMIT Classification: Trusted
encouraged to develop/source new background materials. You will use the same dataset as in
Assignment 2. The data are drawn from the 2017 Household, Income and Labour Dynamics in
Australia (HILDA) survey. The sample used for analysis comprises 824 full-time Australian
workers in the age group 20-74. The dataset contains the following information:
1. Worker’s earnings: weekly earnings in 1000 AU dollars of full-time workers. [note the
unit of measurement]
2. Gender: the dummy variable male = 1 if the individual is a male, and = 0 for a female.
3. Educational attainment: the dummy variable degree = 1 if the individual has a bachelor
degree or higher qualification, and = 0 for lower than degree qualifications.
4. Skill level: the dummy variable skill = 1 if the individual is highly skilled, and = 0 if
not highly skilled.
5. Experience: number of years of work experience.
[Marks distribution: 5 + 6 + 9 + 2 + 5 + 2 + 3 = 32 marks; professional report = 8 marks]
Locate the data file (IndividualBusStats.xls) on CANVAS.
1. Before estimating the regression equation, conduct a preliminary analysis of the
relationship between workers’ earnings and 1) gender; 2) educational attainment; 3) skill
level; and 4) experience. Use tables and/or appropriate graphs for the categorical variables
(male, degree, skill) and the continuous variable (experience). Interpret your findings by
answering the following questions: how much more/less does a male worker earn compared
to a female worker? how much more/less does a degree holder earn versus a non degree
holder? How much more/less does a highly skilled worker earn versus a worker who is not
highly skilled? What kind of relationship do you observe between workers’ earnings and
experience? (5 marks)
2. Use a simple linear regression to estimate the relationship between workers’ earnings (Y)
and gender (X) (Model A). You may use the Data Analysis Tool Pack. Based on the Excel
regression output, first write down the estimated regression equation and interpret the slope
coefficient. Carry out any relevant two-tailed hypothesis test of the slope coefficient using
the critical value approach, at the 5% significance level, showing the step by step
workings/diagram in your report. Interpret your hypothesis test results. (6 marks)
3. Now use a multiple regression model to explore the relationship of workers’ earnings (Y)
with, gender (X1), educational attainment (X2), skill level (X3) and work experience (X4)
(Model B). You may use Data Analysis Tool Pack for this. Based on the Excel regression
output, first write down the estimated regression equation and interpret the slope
coefficients. Carry out any relevant two-tailed hypothesis tests for each individual slope
coefficient using the p-value approach, at the 5% significance level. Carry out an overall
significance test using the p-value approach. Carefully interpret your hypothesis test
results. (9 marks)
4. Interpret the R-squared in Model A and adjusted R-squared in Model B. Which one is a
better model? Why? (2 marks)
5. Compare the coefficient of gender in Model A and Model B. Explain carefully why the
results are different, relating your discussion to gender discrimination. (5 marks)
RMIT Classification: Trusted
6. Predict the earnings of a male worker who has a university degree, is highly skilled and has
10 years of work experience. Next, predict the earnings of a female worker with the same
characteristics. (2 marks)
7. If you could request additional data to study the factors that influence workers’ earnings,
what extra variables would you request? Discuss two such variables, explaining why you
choose them and how each of your proposed variables could be measured in the regression
model. [You could draw evidence from journal articles, newspapers, etc] (3 marks)
References:
KPMG Consulting. (2016). She’s price(d)less: The economics of the gender pay gap
RMIT Classification: Trusted
#
Data overview:
The dataset consists of a random sample of 824 full-time Australian workers in the age group 20-74, in 2017.
Sources: 2017 Household, Income and Labour Dynamics in Australia(HILDA) Survey
The worksheet ‘data’ contains the dataset.
Variable Definition:
weekly earnings of full-time Australian workers in 1000 AU$
earnings
male
male=1, female=0
degree
degree or higher qualification=1, lower than degree qualifications=0
skill
highly skilled=1, not highly skilled=0
experience
number of years of job experience
roup 20-74, in 2017.
RMIT Classification: Trusted
#
RMIT Classification: Trusted
earnings (‘000 AU$) male
1.841
1.5
0.75
1.618
0.909
2.685
1.5
0.825
1.128
1.841
1
0.75
1.315
2.489
2.167
1.406
1.4
1.25
0.85
1.726
1.197
1.431
1.65
0.73
1.208
0.72
0.797
1.634
1.491
1.745
1
1.435
3.15
1
1.15
0.931
0.9
0.914
1.25
1.2
1.093
1.5
2.535
1.611
1.1
1.297
1.25
1.764
1.05
degree
0
0
0
0
0
0
0
0
0
0
0
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0
0
0
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0
0
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0
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0
0
0
0
0
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0
0
0
0
0
0
0
0
0
0
0
0
skill
1
1
1
0
0
1
1
0
0
1
1
0
1
1
1
1
1
1
1
0
1
1
1
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
1
0
1
1
1
0
1
1
1
0
1
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
0
0
1
0
0
1
1
0
1
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
1
1
0
1
0
0
experience
10
4
2
31
6
6
6
11
30
10
25
10
6
16
24
16
16
3
14
20
4
2
30
20
1
2
5
10
4
25
2
10
6
2
10
1
5
36
7
1
19
28
4
1
1
5
2
19
10
#
RMIT Classification: Trusted
1.45
4.987
2.647
1.247
1.189
7
6.45
2.493
1.32
0.86
1.021
0.7
1.72
0.78
1.216
1.151
0.813
1.36
1.127
1.732
0.85
1.094
1.093
2.25
1.14
0.42
2.65
1.563
1.5
1.726
1.6
1.568
1.241
1.105
0.95
1.247
2.301
0.84
1.072
0.693
0.9
0.875
0.9
2.05
1.755
0.997
1.904
0.75
1.6
0.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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1
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1
1
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
1
0
1
1
1
1
0
0
1
1
0
0
1
0
1
0
1
1
0
1
0
1
0
0
1
0
1
1
1
0
1
1
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
1
0
1
1
1
1
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
17
3
10
6
2
18
23
24
30
1
13
22
1
7
25
9
16
25
5
2
2
3
2
3
2
16
15
5
3
2
6
10
38
11
20
5
24
8
9
2
3
6
10
30
2
13
12
11
10
12
#
RMIT Classification: Trusted
1.205
0.953
1.75
1.228
1.1
1.6
0.908
0.881
1.5
1.2
3.164
2.091
0.78
1.519
2.3
0.786
1.25
0.9
1.596
1.35
2.877
1.1
1.381
1.2
0.97
1
2.2
1.196
1.132
1
1.4
0.94
1.227
0.91
2.1
1.295
0.95
3.567
1.44
0.962
1.1
1.4
2.887
1.779
1.423
1.75
1.241
5.5
1.118
1.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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1
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1
1
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1
1
0
1
0
1
0
0
0
1
1
0
0
0
1
0
0
1
0
0
0
1
1
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
1
1
0
0
0
1
1
0
0
1
0
0
0
25
2
6
1
4
5
14
1
20
11
17
12
3
21
25
15
8
8
15
18
2
15
5
19
3
6
3
15
5
7
10
2
10
21
6
8
4
14
4
5
16
23
20
4
25
3
1
6
1
10
#
RMIT Classification: Trusted
1.532
2.071
0.95
1.885
0.282
1.635
1.25
1.167
1.25
1.25
1.764
0.759
0.865
3.356
1.681
0.479
1.225
1
1.5
1.73
1.73
1.5
1.681
1.918
1.15
0.9
0.9
0.72
1.496
1.035
0.9
0.7
1.25
1.381
0.981
1.54
1.381
2.353
3.195
1.5
1.35
1.327
1.638
1.093
1.726
1.189
1.25
1.5
0.94
1.15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
1
1
1
0
1
1
1
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
0
1
1
1
1
1
1
1
0
0
1
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
1
1
0
1
1
0
1
1
0
1
0
0
1
1
0
0
0
0
0
1
0
0
1
0
0
0
1
1
0
1
0
0
1
0
0
0
0
1
0
0
16
15
3
12
20
7
3
4
10
7
7
6
2
12
8
3
1
4
11
2
3
3
30
6
9
30
28
5
1
8
3
9
5
7
9
8
12
16
10
2
1
5
14
10
14
5
4
1
32
15
#
RMIT Classification: Trusted
1.35
1.464
1.15
1.74
0.95
1.544
1.25
1.6
1.25
0.95
0.7
2
1.75
1.75
1.264
0.418
1.25
1.49
1.799
1.7
1.288
1.675
1.198
0.9
3.337
1.915
1.018
1.4
1.4
3.267
0.674
1.84
1.726
1.359
1
2.685
1.841
2.8
3.406
0.652
0.75
1.26
3.486
0.996
0.84
1.1
1.646
1.25
1.65
2.189
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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0
0
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0
0
0
0
0
0
0
0
0
1
1
1
1
0
1
0
0
1
0
0
1
1
1
0
0
0
0
1
0
0
0
0
0
1
1
1
1
1
1
0
1
0
1
0
1
1
1
1
0
0
0
1
1
0
0
0
0
1
1
1
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
1
0
1
0
0
1
1
1
0
0
0
1
0
1
0
1
1
1
1
0
1
0
0
0
1
0
0
0
1
0
0
0
20
2
5
42
40
16
3
35
3
21
17
7
15
30
1
14
2
1
31
10
6
4
1
3
5
4
5
2
3
30
2
13
20
2
1
15
2
3
10
9
2
1
11
1
30
14
10
5
6
44
#
RMIT Classification: Trusted
1.918
1.796
0.846
1.99
1.05
1.726
1.85
1.012
1.001
0.659
0.9
1.473
4.2
1.25
0.904
4.833
0.844
1.85
0.907
1.15
1.908
0.875
1.25
0.9
0.92
1.138
0.95
0.92
2.004
1.285
1.15
2.596
0.15
0.6
1.55
0.85
0.8
0.95
0.896
0.697
0.521
0.75
1.128
0.98
1.215
0.94
1.5
4.1
1.7
0.863
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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0
0
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0
0
0
0
0
1
1
0
1
0
1
1
1
0
0
0
1
1
0
0
1
0
1
0
1
0
0
1
0
0
0
0
0
0
1
1
0
0
0
1
0
0
0
0
0
0
1
0
0
0
1
1
1
1
1
0
1
0
0
0
1
0
1
1
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
0
0
35
2
10
5
8
45
10
2
12
20
30
2
10
3
1
12
6
11
2
2
4
26
1
3
15
6
8
7
11
5
5
12
2
1
26
7
13
35
1
3
12
14
5
9
1
1
11
15
8
6
#
RMIT Classification: Trusted
1.542
1.85
1.002
1.519
0.9
1.147
1.1
1.1
1.1
0.75
0.5
1.1
0.966
1
1.315
1.16
1.15
0.673
1.151
0.85
1.4
1.45
4.219
2.135
1.381
3.682
0.98
1.841
1.745
0.633
1.413
1.534
0.735
0.786
3.35
2.11
0.72
1.05
1.5
2.762
0.941
2.493
1.46
2
7.75
1.726
1.4
1.1
1.3
1.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
1
1
0
1
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
1
1
0
0
1
1
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
1
1
0
1
1
0
0
1
0
0
0
0
1
0
0
0
1
1
1
1
0
1
0
6
3
3
7
2
4
8
1
45
30
23
9
1
10
5
7
12
11
24
7
7
33
30
12
11
6
2
9
34
12
13
3
2
12
25
12
1
10
20
9
15
3
10
40
1
10
15
30
12
6
#
RMIT Classification: Trusted
1.35
0.65
0.945
4.833
2.653
2.973
1.237
0.955
1.12
1.918
1.4
1.1
0.75
2.1
0.817
6
1.88
3.452
1.73
1.8
3
1.5
1.15
0.83
1.576
1.687
3.002
2.5
1.375
0.937
1.273
1.606
1.4
2
2.1
1.563
1.5
2.016
1.034
2.852
1.019
1.65
0.8
1.918
1.125
0.4
0.9
1.4
1.277
1.208
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
1
0
1
0
1
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
1
1
1
0
1
1
1
0
0
0
0
1
1
1
0
0
0
0
0
15
2
4
20
8
3
2
3
8
2
11
13
4
5
7
3
5
6
9
20
4
18
3
3
7
4
28
10
28
7
10
2
5
17
35
18
23
17
10
18
2
5
13
32
2
25
26
4
8
6
#
RMIT Classification: Trusted
1.15
5
0.8
2.5
2.25
0.767
0.9
1.1
1.271
0.743
3.28
1.25
1.487
1.45
2.493
1.555
1
0.85
2.4
1.04
2.85
3.913
1.208
1.329
1.196
1.65
1.1
1.05
2.362
2.6
0.8
0.75
1.44
1.8
1.6
2.635
1.55
0.7
1.15
0.8
1.347
2.762
1.2
1.335
1.243
1.25
1.754
0.95
1.725
3.797
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
1
0
0
0
0
1
1
0
0
0
1
1
0
0
1
0
0
1
0
0
0
1
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
1
32
5
4
3
16
35
7
20
10
6
7
14
4
37
3
10
11
1
15
11
2
17
9
46
35
25
48
2
2
30
3
5
2
8
7
7
30
10
1
11
1
43
2
4
10
20
40
5
21
16
#
RMIT Classification: Trusted
0.98
1.458
1.55
0.65
1.4
1.423
4.143
3.069
1
0.874
1.2
1.5
1.3
0.69
1.7
1.019
5.063
0.986
0.675
1.3
3.2
1.215
1.3
0.99
4.521
2.359
2.05
3.107
1.5
1.068
1.519
2
1.841
2.713
1.1
0.875
1.5
2.071
3.6
1.9
1.125
1.266
11.873
0.715
2.493
1.304
5.524
1.026
1.2
3.184
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
1
0
1
0
0
1
0
1
0
0
0
0
0
1
1
0
0
1
0
0
0
1
1
0
1
1
0
1
1
0
0
1
1
1
0
0
1
0
1
0
0
1
0
0
1
0
0
1
0
1
0
1
1
1
0
1
0
0
0
0
0
0
1
1
1
0
1
1
0
0
0
1
1
0
1
7
10
3
6
3
2
28
10
7
6
5
2
6
5
23
5
16
10
12
2
12
40
5
5
4
21
14
11
17
12
2
11
6
17
35
3
9
2
6
22
4
1
13
24
40
36
10
1
35
6
#
RMIT Classification: Trusted
1.275
1
1.13
2.5
1.35
2.417
1.6
0.75
1.05
1.55
2.5
1.381
1.714
1.189
2.474
2.762
3.5
1.2
2.05
0.875
0.935
1.73
2.301
1.381
1.35
1.784
1
0.449
3
2.25
1.175
1.32
1.822
0.959
1.749
0.881
1.312
1.625
2.193
1.032
1.34
1.1
1.75
1.1
1.1
1
3.386
1.384
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
0
1
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
1
1
0
0
0
0
0
1
1
0
0
1
0
0
1
0
0
1
1
1
0
0
0
0
0
0
1
0
0
1
0
0
1
0
1
1
0
0
0
0
0
0
0
1
1
1
0
1
0
0
1
1
1
1
0
0
1
1
0
0
0
1
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
0
0
13
25
5
8
8
12
3
2
9
9
9
9
7
14
14
35
16
11
6
30
6
2
15
1
13
8
8
5
17
2
5
11
15
4
2
2
30
6
17
1
15
6
35
8
4
15
17
38
10
4
#
RMIT Classification: Trusted
0.975
1.266
2.436
2.019
1.65
2.397
1.8
1.093
3.682
0.223
6
1.75
0.8
3.25
2.162
2.75
2.15
7
0.6
2
1.35
1.718
3.498
0.92
2.02
2.045
3.222
1.6
2.888
1.05
0.7
1.122
2.512
1.5
0.9
3.721
2.762
1.54
0.9
1.62
1.6
1.05
1.25
2.084
1.534
2.193
1.6
1.6
2.378
1.241
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
0
1
1
1
0
1
1
0
1
0
0
0
0
1
0
0
1
1
0
1
0
1
0
0
0
0
0
0
0
0
1
1
1
0
1
0
0
0
1
0
1
0
0
1
0
0
0
1
1
1
0
0
0
1
0
1
0
0
1
1
0
0
0
0
1
0
1
1
0
0
0
1
0
0
0
0
0
1
0
0
1
1
1
0
1
1
0
1
1
1
1
0
0
1
0
35
19
10
5
8
27
4
1
2
3
25
6
3
2
7
34
11
30
8
12
4
15
6
2
9
28
20
2
7
9
2
35
1
31
4
2
4
17
22
6
11
10
10
4
10
20
25
15
16
3
#
RMIT Classification: Trusted
1.576
1.381
2.5
1.196
1.515
1.764
1.296
2.45
1.85
1.63
1.112
3.498
1.88
2
1.045
2.877
2.35
1.104
1.4
0.9
0.871
2
2.2
1.7
5.15
0.8
1
0.6
1
0.925
2.206
1.35
4.833
1.496
1.7
2.75
1.529
4
0.9
2.633
0.88
1.2
2.5
1.34
1.45
2.301
1.33
0.691
0.95
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
0
0
0
0
1
0
0
0
1
1
0
0
1
0
1
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
1
0
1
0
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
1
1
1
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
1
1
0
1
0
0
1
0
0
0
0
0
0
0
16
12
10
32
17
18
10
21
5
23
30
43
10
28
2
11
24
7
8
3
2
7
9
18
12
15
5
3
4
12
2
14
2
26
3
8
19
20
35
7
9
24
2
7
5
1
20
1
8
16
#
RMIT Classification: Trusted
1.5
2.839
2.493
2.25
1.346
1.09
1.496
1
1.4
0.65
5
1.918
0.659
1.6
1.351
1.75
3.3
1.9
2.5
1.6
2.301
1
1.76
2.45
1.2
0.73
1.26
1.3
1.343
2
1.965
3.8
1.05
1.208
0.88
1.5
1.459
1.438
2
1
1.85
1.1
1.25
2.2
0.984
1.77
1.7
0.985
2.071
0.6
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
0
0
0
1
1
0
1
0
0
0
0
1
1
1
1
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
0
1
0
0
0
0
0
0
0
2
17
15
35
11
3
11
18
5
19
18
10
3
27
7
12
11
32
3
9
15
9
7
5
30
1
18
12
16
6
2
7
9
8
9
2
16
15
3
12
16
5
7
15
17
8
10
2
35
12
#
RMIT Classification: Trusted
3.061
0.9
2.206
1.704
1
1.1
1.112
2.2
2.877
2.57
0.775
1.5
3.7
1.7
0.85
2.569
1.067
0.97
0.3
1.346
0.84
3.559
1.1
1.283
1.266
1.1
1.7
1.343
0.909
2.014
2.175
1.6
1.7
1.825
1.52
1.35
1
2.28
1.2
1.3
1.841
0.7
2.7
0.937
1.115
1.365
2.9
1.57
1.5
1.08
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
0
0
0
1
1
0
1
1
0
0
1
0
1
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
0
0
0
0
0
1
0
1
1
0
0
0
0
1
0
0
1
1
0
0
1
0
1
1
1
0
1
1
0
1
0
1
0
0
0
1
0
0
1
0
0
0
0
0
0
1
0
0
0
1
0
0
1
0
0
2
7
3
5
2
10
12
3
7
10
8
10
42
25
13
1
10
5
21
2
5
9
2
10
15
15
10
10
7
6
3
10
11
3
2
19
6
33
1
5
10
1
8
3
1
3
3
17
20
9
#
RMIT Classification: Trusted
1
1.6
1.12
1.3
1.17
1.703
1.15
1.15
2.004
1.247
1.538
2.7
1.69
0.628
0.262
2.071
1.019
1.1
1.803
1.5
2
2.3
1.4
0.5
11.873
1.233
4.39
2.1
2.905
1.612
1
1.841
1.6
1.63
2.4
0.269
1.498
1.074
1
1.4
1.982
2.263
2.433
3.4
1.6
1.5
2.11
1.3
0.84
0.9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
0
1
0
0
0
0
1
0
0
0
0
1
0
0
1
1
0
1
0
1
1
1
0
1
1
1
1
1
0
0
0
0
1
0
1
0
1
1
0
1
0
0
0
0
0
0
0
0
1
0
0
1
1
0
0
0
0
0
0
1
0
1
0
0
1
1
0
1
0
1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
1
0
0
0
0
1
0
0
1
0
0
0
6
2
1
10
15
10
14
2
7
11
1
3
30
11
11
6
6
9
23
3
36
3
7
4
10
2
20
21
30
22
8
34
19
5
6
2
13
4
6
20
30
8
12
10
3
38
7
5
10
8
#
RMIT Classification: Trusted
1.122
1.6
3.075
3.682
1.167
0.895
1.15
1.7
1.41
1.1
2.2
1.036
3.338
1.189
2.301
3.069
0.921
1.1
1.5
0.797
2.061
1.937
2.992
1.112
0.882
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
1
1
0
0
0
0
0
0
1
1
0
1
0
1
0
0
0
0
0
1
0
0
1
1
1
1
1
1
1
0
0
1
0
1
0
0
1
0
0
1
0
0
0
0
1
0
0
7
6
4
10
4
5
25
5
12
7
29
4
1
17
37
15
1
12
31
3
3
20
14
10
5
#
IndividualBusStats-2.xlsx

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Explanation & Answer:
2000 words

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gender pay gap

Australian Population

Income and Labour Dynamics

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