Gender Equality in Australia: Relationship between Gender Gap and GDP

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

Gender equality in Australia 3
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