Case Study on Gender equality in Australia

Running Header: Case Study on Gender equality in Australia 1
Case Study on Gender equality in Australia
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Case Study on Gender equality in Australia 2
Section 1: Introduction
a. Introduction
The gender gap is the difference between the salary of men and that of women. The gender gap
is attributed to not only discrimination in hiring but also the different industries which women
and men work among others. Gender equality has been a major case of discussion by many
people across different fields globally. According to Schwab (2017), the gender biases been
experienced across the different field in the economy are keeping the mass from closing the
gender gap thereby causing an overwhelming of the economy.
The following research aims at finding the relationship between the gender gap and the GDP.
Thus the arising research question:
 What is the a relationship between gender gap and the GDP
The research is necessitated by the fact that closing the gender gap is vital for policymaking and
development. According to Revenga and Shetty (2012), gender equality is vital for enhancing
economic productivity, improving the outcomes of development for future generations, and
making institutional and policies more representative. Momsen (2009), states that progress is a
course which expands freedom similarly for all the people both female and male. Thus, closing
gender equality improves economic productivity and improves other outcomes of development.
The net impact of gender inequality on growth is quite ambiguous. In some way, gender
inequality is attributed to hindering growth or support growth circumstantially. Income and
wages rapidly affect and bring about changes in aggregate demand. In the long-run, benefits of
gender-equal opportunities in labor, education, and health are more efficient than the pervasive

Case Study on Gender equality in Australia 3
gender inequality seeing today. Thus, conversion of gender equality creates opportunities for
equal outcomes.
Therefore, the question that arises is whether differences in wages and income affect economic
growth or not? The following research will, therefore, endeavor to determine whether gender
inequality has an economic impact. Thus, this provides a guide for the researcher to determine if
indeed there is a relationship between gender gap and the GDP.
b. Dataset 1 description
Dataset 1 is a dataset specifically assigned to the undersigned researcher. The dataset entails an
individual sample file from 2013 to 2014 that was obtained from the Australian Taxation Office
(ATO). Thus, the dataset can be described as secondary in nature.
The dataset entails four variables; gender, occ_code, Sw_amt, and Gift_amt. The characteristics
of the variables are as shown in the table below:
Table 1: Variable description
Variable Description Values Type
Gender Gender (sex) Female or Male Dichotomous
Occ_code Salary/wage
occupation code
0 = Occupation not listed/Occupation
not specified
1 = Managers
2 = Professionals
3 = Technicians and Trades Workers
4 = Community and Personal Service
Workers
5 = Clerical and Administrative
Workers
6 = Sales worker
7 = Machinery operators and drivers
Dichotomous

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8 = Laborers
9 = Consultants, apprentices and type
not specified or not listed
Sw_amt Salary/wage amount All numeric Continuous
Gift_amt Gifts or donation
deductions
All numeric Continuous
The first 5 cases of dataset 1 are as shown below:
Table 2: first 5 cases of dataset 1
Gende
r Occ_code
Sw_am
t Gift_amt
Male 3 143179 0
Female 5 28801 0
Female 5 27675 168
Female 5 77297 0
Male 0 0 0
c. Dataset 2 description
Dataset 2 was collected from online sources, which is the Organization for Economic Co-
operation and Development (OECD). The sample collected cannot be termed as biased since it
was obtained from a verified source. However, the use of online data source meant that the data
being searched had various disadvantages. For instance, the data collected had limited time
frame as it only captured data from 1975 till 2016. Moreover, there was missing data as there
was no recorded wage gap index for 1996. Collection of the data from the OECD implies that
the data is secondary in nature.
The variables used in dataset 2 are wage gap and GDP. The two variables are all numerical, thus
they are continuous in nature.

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Section 2: Descriptive Statistics
a. The relationship between the Gender variable and Occupation
The relationship between the gender variable and occupation can is as seen in the figure below:
0 1 2 3 4 5 6 7 8 9
46% 42%
52%
12%
64%
72%
65%
6%
30%
45%
54% 58%
48%
88%
36%
28%
35%
94%
70%
55%
% Female % Male
Figure 1: Gender distribution against the occupation
Figure 1 shows that most of the occupations including the ones not listed were highly dominated
by the male gender. However, occupation 4, 5, and 6 were dominated by the female gender with
a representation of 65%, 72% and 65% each. It can be noted that the male gender main
domination is in occupation 7 where they have a representation of 94% compared to the female

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gender who have a representation of 6%. The female gender has mainly dominated occupation 5
where they are represented by 75% while the male gender gets a meager representation of 25%.
b. The relationship between the Gender Variable and Salary or wage amount
The following dot plot was constructed with the aim of coming up with a graphical presentation
to show the relationship between the gender variable and the salary or wage amount.
Figure 2: Salary/wage amount against gender
Figure 2 shows that most of the female genders earn less than $200,000 except for one incidence
(outlier) who earns more than $200,000. On the other hand, the more of the male gender earn
more than $200,000 when compared to the female gender. Additionally, the incidence (outliers)

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of those who earn a great amount of salary or wages in the male gender is two with one matching
the maximum of the female gender while the other earning more than $800,000.
c. The relationship between the variables Gender and Salary or wage amount (numerical
summary)
The table below shows the numerical statistics which shows the relationship between gender and
salary or wage amount.
Table 3: Gender vs. salary or wage amount
Row
Labels
Average of
Sw_amt
StdDev of
Sw_amt
Min of
Sw_amt
Max of
Sw_amt
Count of
Sw_amt
Female 35,461.83 40,188.86 - 308,183.00 461.00
Male 55,679.90 68,244.44 - 839,840.00 539.00
The mean of female gender with regards to salary or wage amount is $35,461.83 with a standard
deviation of $40,188.86. On the other hand, the male gender had a salary or wage amount that
averaged $55,679.90 with a standard deviation of $68.244.44. From this, it is evident that the
male gender earned a high salary or wage amount compared with the female gender. Conversely,
the male gender had a high variation ($68,244.44 standard deviation) compared to the female
gender ($40,188.86 standard deviation).
d. The relationship between the Salary or wage amount and gifts or donation deductions

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0
100000
200000
300000
400000
500000
600000
700000
800000
900000
0
20000
40000
60000
80000
100000
120000
140000
160000
Salary/wage amount Vs. Gifts or donation
deductions
Gift_amt
Linear (Gift_amt)
Sw_amt
Gft_amt
Figure 3: Salary/wage amount Vs. Gifts or donation deductions
From figure 3, it can be seen that is almost impossible to tell if salary or wage amount has a
relationship with gifts or donations deductions. However, incorporation of a linear trend line
shows that there is a relationship. Thus, salary or wage amount has a relationship with gifts or
donation deductions.
Section 3: Inferential Statistics
Use Dataset 1
a. Top 4 occupations based on median salary and proportion of the gender
The following table displays the ranks of the occupations based on the median salary. The ranks
highlighted in green represent the top 4 occupations which is of interest.
Table 4: Rank of Occupations

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Rank Occupation Median
Proportion of
Female
Proportion of
Male
1 2 70427 0.52 0.48
2 1 59606 0.42 0.58
3 7 59316 0.06 0.94
4 3 56628 0.12 0.88
5 5 41304 0.72 0.28
6 8 39776 0.30 0.70
7 9 33785 0.45 0.55
8 4 27334 0.64 0.36
9 6 26255 0.65 0.35
10 0 0 0.46 0.54
From the above, it is evident that the top four occupations are 2, 1, 7 and 3 with a respective
median of 79427, 59606, 59316, and 56628. Consequently, it can also be deduced that the top
four occupations are highly dominated by the male gender with exemption to occupation 2. The
subsequent 3 occupations in the top 4 see the gap increase where 1 has a difference of 0.16, 7 has
a difference of 0.88 and 7 has a difference of 0.76 in the gender proportions.
b. Significance of proportion of male machinery operators and drivers is more than 80%
Null hypothesis > 0.8
Alternate hypothesis < 0.8
Significance level is 0.05
Solution:
σ = sqrt [ P * (1 – P) / n ]
= 0.062

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Z = (p – P) / σ
= (0.93 – 0.8) / 0.062
= 2.10
Using the normal distribution calculator, the p-value of 2.1 z statistics is:
P (z < 2.10) = 0.018
Since the p value is < 0.05 we choose to reject the null hypothesis. Thus, the proportions of male
machinery operators and drivers is less than 80%.
c. Hypothesis test to determine whether there is a difference in salary amount between genders.
Proportion of male gender: 0.539
Proportion of female gender: 0.461
Significance level = 0.05
Solution
Null hypothesis: p1 <= p2
Alternate hypothesis: p1 > p2
p = (p1 * n1 + p2 * n2) / (n1 + n2)
p = (0.539 * 539 + 0.461 * 461 ) / (1000)

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p = 0.503
SE = sqrt { p * (1 – p) * [(1/n1) + (1/n2)]}
SE = sqrt (0.503 * 0.407 * [(1/539) + (1/461)]
SE = 0.0287
z = (p1 – p2) / SE = (0.539 -0.461) / 0.0287 = 2.72
Using the normal distribution calculator, the p-value of 2.72 z statistics is:
P (z < 2.72) = 0.003
Since the p value is < 0.05 we choose to reject the null hypothesis (Higgins et al., 2003). Thus,
the proportion of the male gender is more than that of the female gender.
d. Regression analysis (using dataset 2).
To answer the research question that is, is there a relationship between gender gap and the GDP,
a regression analysis was carried out. The tables below show the regression results.
Table 5: Model summary
Regression Statistics
Multiple R 0.64
R Square 0.41
Adjusted R Square 0.40

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Standard Error 260820.72
Observations 41
The regression model has an adjusted R square of 0.4. Thus, the variables explain 40% of the
variability in the model while 60% is explained by variables, not in the model. Consequently, the
regression model does represent a good fit.
Table 6: ANOVA
df SS MS F Significance F
Regression 1 1.88178E+12 1.88178E+12 27.66204777 0.00
Residual 39 2.65307E+12 68027446646
Total 40 4.53485E+12
Table 6 shows that the regression is statistically significant since the p < 0.05 level of
significance. Therefore, there is a relationship between gender gap and GDP per capita.
Table 7: Coefficients
Coefficients Standard Error t Stat P-value
Intercept 1,907,575.45 269020.97 7.09 0.00
WAGEGAP -84,526.45 16071.28 -5.26 0.00
From table 7, it can be seen that there is a negative relationship between GDP per capita and
wage gap. Thus, a unit increase in wage gap reduces the GDP per capita by $84,5226.45.
Consequently, the wage gap coefficient is statistically significant since p < 0.005.
Section 4: Discussion & Conclusion
a. Discussion and Conclusions of findings

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From the regression model, it can be deduced that the research question has been sufficiently
answered. It was established that there was a relationship between GDP per capita and gender
gap. Moreover, the relationship is also statistically significant. It was also found out that gender
gap has a negative impact on GDP. As the gender gap increases in an economy, the amount of
GDP per capita is bound to reduce greatly. Thus, the findings support Revenga and Shetty (2012)
claim. Therefore gender equality is important in enhancing economic productivity, improving the
outcomes of development for future generations, and making institutional and policies more
representative.
b. Suggestions for further research
The findings obtained from the statistical analysis carried out can be further improved by
carrying out further research in the future. The statistical analysis was a case study done for
Australia. Thus, future researchers can opt to do research on other economies in the world either
on a country basis or regionally.

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References;
Momsen, J., 2009. Gender and development. Routledge.
Revenga, A. and Shetty, S., 2012. Empowering Women Is Smart Economics-Closing gender
gaps benefits countries as a whole, not just women and girls. Finance and Development-
English Edition, 49(1), p.40.
Schwab, K., 2017. The fourth industrial revolution. Crown Business.