BUS708 - Gender Inequality and GDP: A Statistical Analysis Report

This report investigates the relationship between the gender wage gap and Australia's GDP using statistical analysis. It utilizes two datasets: one from the Australian Taxation Office (ATO) providing individual salary and occupation data, and another from the OECD containing wage gap and GDP figures. The analysis includes descriptive statistics to compare gender distribution across occupations and salary levels, inferential statistics to test hypotheses about gender proportions and salary differences, and regression analysis to model the impact of the wage gap on GDP. The findings indicate a statistically significant, negative relationship, suggesting that a wider gender wage gap is associated with a reduction in GDP. The report concludes that addressing gender inequality is crucial for economic growth and provides insights relevant for policymaking and development. Desklib offers a range of similar solved assignments and resources for students.

Running Header: Gender equality in Australia 1
Gender equality in Australia
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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 (Bekhouche ey al.,
2013). 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 (Grown et al., 2005). 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 (World Bank, 2012). 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 (Hausmann, 2009).
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 (Galor and Moav, 2004). 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 gender
inequality seeing today (Booth and Bennett, 2002). 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 0 = Occupation not listed/Occupation not Dichotomous

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code 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
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
Gender
Occ_cod
e
Sw_am
t
Gift_am
t
Male 0 17360 0
Male 3 2861 120
Male 0 0 0
Female 9 6661 0
Female 5 29881 25
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 (Boakes et al., 2010). However, the use of online data source meant that the data being
searched had various disadvantages (Louise Hunter, 2012). 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.
Section 2: Descriptive Statistics

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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%
Gender Distribution against Occupation
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 64%, 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 gender who have a
representation of 6%. The female gender has mainly dominated occupation 5 where they are
represented by 72% while the male gender gets a meager representation of 28%.
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)
of those who earn a great amount of salary

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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 35,462 40,189 0 308,183 461
Male 55,680 68,244 0 839,840 539
Grand Total 46,359 57,910 0 839,840 1,000
The mean of female gender with regards to salary or wage amount is $35462 with a standard deviation
of $40,189. On the other hand, the male gender had a salary or wage amount that averaged $55,680
with a standard deviation of $68,244. 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 standard deviation) compared to the female gender ($40,189 standard deviation).
d. The relationship between the Salary or wage amount and gifts or donation deductions
0 50000 100000 150000 200000 250000 300000 350000
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Gift_amt
Gift_amt
Linear (Gift_amt)
Sw_amt
Gft_amt
Figure 3: Salary/wage amount Vs. Gifts or donation deductions

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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
From the above, it is evident that the top
four occupations are 2, 3,1 and 7 with a
respective median of 60,970, 57,141, 56,076,
and 52,940 each. Consequently, it can also
be deduced that the top four occupations are
highly dominated by the male gender with
exception to 2. However, the topmost
occupation, 2, has a small gap between the
male gender and female gender since the
proportions of males is 0.48 while the
proportions of males are 0.48.
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
Z = (p – P) / σ
Rank Occupation Median Female Male
1 2 60970.5 0.52 0.48
2 3 57141 0.12 0.88
3 1 56076 0.42 0.58
4 7 52940.5 0.06 0.94
5 5 38526.5 0.72 0.28
6 4 35661.5 0.64 0.36
7 0 33030.5 0.46 0.54
8 9 32981 0.45 0.55
9 8 27662 0.30 0.70
10 6 26185 0.65 0.35

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= (0.93 – 0.8) / 0.062
= 4.17
Using the normal distribution calculator, the p-value of 2.1 z statistics is:
P (z < 4.17) = 1.5E-05
Since the p value is < 0.05 we choose to not reject the null hypothesis. Thus, the proportion of male
machinery operators and drivers is more 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)
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.

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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
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 1,881,778,478,970.03 1,881,778,478,970.03 27.66 0.00
Residual 39 2,653,070,419,185.07 68,027,446,645.77
Total 40 4,534,848,898,155.11
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 1907575.45 269020.97 7.09 0.00
WAGEGAP -84526.45 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 $84,521.77. Consequently, the wage
gap coefficient is statistically significant since p < 0.005.
Section 4: Discussion & Conclusion
a. Discussion and Conclusions of findings
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

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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.
References;
Bekhouche, Y., Hausmann, R., Tyson, L.D. and Zahidi, S., 2013. The global gender gap report 2013.
Geneva Switzerland World Economic Forum 2013.
Boakes, E.H., McGowan, P.J., Fuller, R.A., Chang-qing, D., Clark, N.E., O'Connor, K. and Mace, G.M., 2010.
Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS biology, 8(6),
p.e1000385.
Booth, C. and Bennett, C., 2002. Gender mainstreaming in the European Union: towards a new
conception and practice of equal opportunities?. European Journal of Women's Studies, 9(4), pp.430-
446.
Galor, O. and Moav, O., 2004. From physical to human capital accumulation: Inequality and the process
of development. The Review of Economic Studies, 71(4), pp.1001-1026.
Grown, C., Gupta, G.R., Kes, A. and Projet Objectifs du millénaire, 2005. Taking action: achieving gender
equality and empowering women. London: Earthscan.
Hausmann, R., 2009. The global gender gap report 2009. World Economic Forum.
Higgins, J.P., Thompson, S.G., Deeks, J.J. and Altman, D.G., 2003. Measuring inconsistency in meta-
analyses. BMJ: British Medical Journal, 327(7414), p.557.
Louise Hunter, M.A., 2012. Challenging the reported disadvantages of e-questionnaires and addressing
methodological issues of online data collection. Nurse Researcher (through 2013), 20(1), p.11.
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.

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World Bank’s 2012 World Development Report: Gender Equality and Development.