The Gender Pay Gap seems to be a topic that can easily make your family dinners awkward and turn your Friday night parties into very heated and alcohol fuelled debates. Why is that the case? The data clearly shows that there is a gap in the average annual earning between males and females. However, I believe (yes this is going to be an opinion-based piece), that many individuals are unaware of how the data is calculated and what variables are used in order to come to an overall figure. With that being said, let’s jump into it.

As of February 2019, the Workplace Gender Equality Agency (WGEA) reported that Australia’s full-time gender pay gap was 14.1%, where on average, women’s earnings were $1455.80 per week, while males were earning $1695.60[1]. As mentioned, there are many factors that influence how the agency comes up with these figures, one of these influences being the fact that there is still discrimination and biases when employing individuals. Now, I found this very interesting considering that back in 1969, the Commonwealth Conciliation introduced the equal pay for work of equal value principle[2], which based on the name, it’s pretty self-explanatory. However, the WGEA still states that discriminative pay is still a major factor contributing to the gender pay gap. Even though this factor can be eventually sorted (hopefully), there are still several factors including unpaid domestic work and the pay differences within different industries which contribute to the data.

WGEA Fact Sheet[1]

Let’s first discuss how unpaid domestic work affects the gender pay gap calculations. The Australian Bureau of Statistics (ABS) considers unpaid domestic work that of unpaid childcare, unpaid care or assistance, domestic activities and voluntary work. Moreover, according to the 2016 Census, women were responsible for the majority of the unpaid domestic work[3]. Now, it’s understandable that women need more time at home post-pregnancy to recover. However, that in itself decreases the overall average that females make collectively per year, as they are out of the workforce for a considerably longer period compared to the male partner. Additionally, Fair Work states that the primary carer of the new child will receive 18 weeks paid parental leave at the national minimal wage, while the working dads and partners (including same-sex partners) get 2 weeks leave paid at the national minimum wage working [3].

Evidently, the primary carer of the child/children will in most cases be the mother (as child birth is a thing that happens), and thus, will receive the 18 weeks parental leave compared to the 2 weeks given to the partner. Hence, we already see the gender pay gap forming, even if wage and salary nor the industry have been considered as a variable. Simply having females out of the workforce for longer periods of time will skew the data in such a way that, on average, it is perceived that the males are earning more. It may just be the case that women are participating less on average due to the parental leave, and thus further bringing down that weekly average dollar amount that was mentioned earlier. 

Next, we should mention the distribution of male to female workers in particular industries and how this affects the gender pay gap. It is noted by the WGEA that male dominated industries include jobs within the fields of construction and mining. While jobs within the industries of health care, social assistance and education are female dominated [4]. In addition to these findings, the WEGA reported that the industries in which men dominated, the wages were higher than those industries where women dominate[1].

The majority of individuals will infer from this that the males, who are paid more, are working collectively in the same industry and thus results in the wages in male dominated industries being higher. However, for this is not the case.

For a second, let’s forget which gender dominates which industry and let’s focus on the jobs themselves. The jobs that fall under mining and construction are considered significantly riskier than those jobs that fall under social work and education. It is due to this major differentiation that the pay is higher under mining and construction jobs. The increase in pay is compensation for the increase in danger the individuals are faced with. Given the dangerous activities that need to be under taken, if the pay was not high enough, no one would be willing to take on the role. This here is a simple example of finding where the individuals marginal costs equal their marginal benefit (MC = MB). 

It is from this observation that when we consider which industries are dominated by which genders, it can affect the overall figures which form the gender pay gap. Moreover, if the males are dominating an industry which is deemed dangerous in comparison to those industries deemed not as dangerous which are dominated by women, then overall, we will see a pay gap favouring the men. This is due to the choice in jobs and not due to the gender completing the job.

It is with this, that I would like to state that the gender pay gap is not as simple as the factors I have stated. There are numerous interpretations of the data and methods it is calculated which is dependent on the institution conducting the research. Furthermore, even though the equal pay for work of equal value principle exists within Australia, businesses and companies still choose the violate this, and thus, contribute to the gender pay gap and its reputation.

Overall, there are many underlying factors that contribute to the gender pay gap figures that get thrown around in the media. It is very rare that those reporting on the issue discuss how the figures are calculated and what is taken into consideration. Therefore, it is with this that I believe that statistics shouldn’t be considered at face value. With a greater understanding of where the statistics come from, we can further appreciate the data itself. The gender pay gap is only one of many forms of data, that I believe, becomes more valuable when deconstructed down to its roots.

-Jade Woodall



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