An Analysis of Gender Bias and Wage Inequality in the Workplace

This report investigates gender bias and wage inequality in the Australian workplace using a dataset of 1000 employees from the Australian Taxation Office (ATO). The study examines the distribution of males and females across various occupations, revealing disparities in representation. Statistical analyses, including correlation tests and independent paired sample t-tests, are employed to assess the relationship between gender and salary, as well as to determine significant differences in earnings. The research also analyzes the proportion of females in different professions, highlighting potential biases in hiring practices and salary levels. The findings indicate variations in gender representation across occupations and suggest a significant difference in salaries between males and females. The report concludes by discussing the implications of these findings and emphasizing the need for further investigation into the root causes of gender bias in the workplace.

Research 1
Name
Tutor
Institution
Date

Research 2
1.0 Introduction
Equality among human being is a very important aspect of human dignity. As such each every person
should be treated with equal in every environment regardless of their color, gender or any other
characteristic (McKinsey, 2010). Gender bias is a practice that should not still exist in this century.
However, because of corrupt personal values, backward perceptions and patriarchal behavior, men and
women still suffer. In the topic of equality, gender bias has always been at the center stage with much
weight leaning on women discrimination. Studies have shown that women have been the main victims
of discrimination at work places. The same studies have indicated that gender biasness in work places
have come with dire consequences. It has come with social and financial consequences. Organizations
with this kind of practice or lack gender diversity in their top management have been seen to suffer
because of being less innovative, successful and profitable (McKinsey, 2010). On the contrary,
organizations with females in their top management such as in board have been seen to perform better
than organizations whose top management is purely made of males alone.
According to a research done by Stanford University Institute for gender research revealed that female
workers suffered criticism from their male counterparts when it came to decision making (Major &
McFarlin, 2012). The same research found that when women succeed at work places, other members
usually do not believe that it is their individual effort. They therefore normally want to attribute the
success to such factors as luck. Employers or hiring managers have been seen to show favoritism to
males when it comes to employment. They believe that certain tasks can only be handled by men.
Example of these tasks is technical jobs (Roxana, 2013).
Due to so many unanswered questions and gaps in gender discrimination topic, this research seeks to
use data sample of 1000 employees sourced from Australian taxation Office (ATO) to find answers to the
question. The data contains employees by gender, their earnings and profession. Another primary data
was collected to support the research so as to answer the research question adequately. This data was
collected by use of questionnaires which had its limitations. The major limitation of this method is that
there were cases of non-responses.

Research 3
2.0 Summary statistics
a) Occupation and gender table
Occupation Gender
Male Female Grand
Total
Clerical & Administrative worker 16 80 96
Community & Personal service workers 27 54 81
Consultants & Apprentices 36 45 81
Laborers 51 24 75
Machinery operators & Drivers 50 2 52
Managers 55 33 88
Not specified 94 83 177
Professionals 74 116 190
Sales workers 16 45 61
Technicians & Trade workers 85 14 99
Grand Total 504 496 1000
Table 1
Occupation and gender graph
Clerical & Administrative worker
Community & Personal service workers
Consultants & Apprentices
Labourers
Machinery operators & Drivers
Managers
Not specified
Professionals
Sales workers
Tecnicians & Tradeworkers
0
40
80
120
80
54 45
24
2
33
83
116
45
14
gender and occupation graph
Male
Female
Figure 1
The graph above is a pictorial representation of how males and females are distributed across different
occupations in Australian context. A general overview shows that there are more females than males in
5 occupations and there are also more males than females in 5 occupations. The occupations where

Research 4
females are more than males are in clerical and administrative, community and personal service,
consultancy and apprentice, professionals and lastly sales workers. The professions where the number
of males is higher than that of females include laborers, machinery operators and drivers, managers and
technicians. In clerical and administrative jobs there were 80 females and only 16 males. In community
and personal service, the females were 54 and while the males were 27. In consultants and apprentice,
the females were 45 while the males were 36. In professionals the females were 116 while the males
were 74. In sales the number of females was 45 while the number of males was 16. In occupations
where the males were more than females, this is how the numbers were distributed. Among laborers
there were 51 males and 24 females. Among machinery and operators, there are 50 males and 2
females only. Among technicians and trade workers the number of males was 85 while that of females
was 14.
b) Graph of Salary/wage and gender.
0.8 1 1.2 1.4 1.6 1.8 2 2.2
0
50000
100000
150000
200000
250000
300000
350000
f(x) = 21149.6140552995 x + 13836.7004608295
R² = 0.05029249767553
Salary and gender amount
Gender
Salary-wage amount
Figure 2
c. Test for correlation
Table of results
gender Salary/
wage
gender 1
Salary/wage 0.22425988
9
1
Table 2
Correlation values usually spans from -1 to 1. A value of 1 normally suggests a perfect correlation
between any two variables being tested. The positive or negative sign gives the direction of the
correlation. A zero value means there is no correlation between the variables. Now we can gauge the
extent of correlation when the value is 0.22 as in the table above. Comparing this to 1, it can be said that
there is a weak correlation between the two variables (salary and gender).

Research 5
d. Graph of relationship involving salary and gift amount
0 50000 100000 150000 200000 250000 300000 350000
0
20000
40000
60000
80000
100000
120000
140000
160000
f(x) = − 0.000230000027042235 x + 282.720516834362
R² = 5.16161091790312E-06
Salary versus Gift amount
Gift amount
Salary wage
Figure 3
In order to establish the relationship between salary and gift amount, the research employed the use of
a scatterplot. In this case the gift amount became the independent variable while salary was the
dependent variable. It can be seen from the chart above that the regression line is almost horizontal
indicating that there is very insignificant effect of the independent variable (gift amount) on the
dependent variable (salary). The value of R-square is 0.0002. This means that gift amount explains only
0.02% of the variation that occurs in salary.
3.0 Inferential statistics
a) List occupations with highest median salaries
managers
Mean 83416.784
1
Standard
Error
5971.9279
3
female 33
Median 72401.5 total 88
Mode 0 proportio
n
0.375
Table 3
Technicians & Trade workers
female 14
Mean 69624.4040 total 99

Research 6
4
Standard
Error
4447.82987
4
proportio
n
0.14
Median 64886
Mode #N/A
Table 4
Professional
Mean 69771.03158
Standard Error 3843.825377 female 116
Median 62108 total 190
Mode 308183 proportio
n
0.61
Table 5
Clerical & Administrative
worker
Mean 46762.51
Standard Error 4163.464 female 80
Median 41605 total 96
Mode #N/A proportio
n
0.83
Table 6
The research also sought to find out the best paying professions. Therefore in order to find a more valid
value, the measure of central tendency, median was used. This was used instead of the mean since it is
always not affected by extreme values. The top 4 paying professions according to median salaries are as
shown from table3 to table 6. The proportion of females in those four top professions was also
established. It can be observed that the proportion of females in clerical and administrative jobs was
0.83. In profession, the proportion of female was 0.61 while among the technicians and trade workers,
the proportion was 0.14.
b) Test for sample proportion

Research 7
Table 7
Test hypothesis
Null hypothesis: ῥ = 0.8
Versus
Alternative hypothesis: ῥ > 0.8
Z−value= ῥ− p
√ p .q
n
Z−value= 0.8−0.6
√ 0.2× 0.8
127
=1.69
From the calculations above, the value of Z is 1.69. In order to make a decision, the Z-value computed is
compared with the Z-tabulated. For 95% confidence level, the Z-value tabulated is 1.65. This means that
the z-tabulated is less than the z-computed. This means that the null hypothesis is not rejected. Hence,
the population of females who are machinery operators and drivers is 0.8.
c) Testing whether salaries for males and females differ significantly
Independent paired sample t-test is used in this test because the variables are two. Hypothesis
H0: There is no difference in the salary amount between males and females.
Versus
H1: There is a significant difference in the salary amount between males and females.
Results
Machinery operators & Drivers
Mean 45955.07087
Standard Error 3387.913542 female 26
Median 38470 total 127
Mode 0 proportion 0.2

Research 8
Table 8
From the calculations above, the p-value is 0.00. In order to make a decision, the p-value computed is
compared with the alpha value. For 95% confidence level, the alpha value is 0.00. This means that the
null hypothesis is rejected. Hence, there is a significant difference in the salary amount between males
and females.
d) In another analysis, the data that I collected which also sought to give answers to the research topic,
indicated that the proportion of female workers was lower than the proportion of male workers. This
scenario is explained by the fact that many hiring managers are largely patriarchal and believe that some
jobs or that generally males better than females at work. The results of the analysis are as shown in
table 9 below.
Current salary level ($) Gender
Male Female Grand
Total
25,000 ≥ 10 2 12
26,000 – 36,000 2 2
37,000 – 47,000 2 2
48,000 – 58,000 2 2
48,000-58,000 1 1
58,000 ≤ 1 1
Grand Total 14 6 20
Table 9
female male
Mean 34986.31452 56135.92857
Variance 1113052376 3102585978
Observations 496 504
Hypothesized Mean Difference 0
df 825
t Stat -7.297318046
P(T<=t) one-tail 3.45144E-13
t Critical one-tail 1.646702708
P(T<=t) two-tail 6.90288E-13
t Critical two-tail 1.962843616

Research 9
25,000 ≥ 26,000 –
36,000 37,000 –
47,000 48,000 –
58,000 48,000-
58,000 58,000 ≤
0
2
4
6
8
10
12
Male
Female
Figure 4
The number of males enjoyed preference from employers thus explaining why the number of males in
many of professions is the majority. The second data results shown in figure four are a manifestation of
this. The females are 30% while the males constitute to 70% of the total workers.
It can be observed from the table and figure above that the percentage of females in the work place is
lower than the percentage of males. The percentage of females is 30% while that of males is 70%.
4.0 Discussion and conclusion
From the analysis conducted in the previous section, the research has been able study the results and
come up with various conclusions which verge to answer the main question of the research. The findings
indicated that the proportion of the males and females in the professions in this research varied in each.
On the same context, it was also found that the males did not dominate all the professions in terms of
numbers. There are jobs where the number of males were more than the females and jobs where the
females were more than the males. The professions where the number of males is higher than that of
females include laborers, machinery operators and drivers, managers and technicians. In clerical and
administrative jobs there were 80 females and only 16 males. In community and personal service, the
females were 54 and while the males were 27. In consultants and apprentice, the females were 45 while
the males were 36. In professionals the females were 116 while the males were 74. In sales the number
of females was 45 while the number of males was 16. In occupations where the males were more than
females, this is how the numbers were distributed. Among laborers there were 51 males and 24
females. Among machinery and operators, there are 50 males and 2 females only. Among technicians
and trade workers the number of males was 85 while that of females was 14. The research also found
out that there were differences in salaries of males and females but this was insignificant.

Research
10
References
Major, B., & McFarlin, D. B. (2012). Overworked and underpaid: On the Nature of Gender Differences in
Personal Entitlement. Journal of Personality and Social Psychology, 47(6), 44-56.
McKinsey, C. (2010). Women Matter:Gender Diversity; A Corporate Performance Driver.
Roxana , B. (2013). Women in the workplace: A research round up.