Executive Summary
The gender pay gap refers to the mean variation in the wages of men and women in an economy. Discrimination based on gender has been eradicated and prohibited in many nations worldwide with the focus of achieving equity among men and women. However, in the labor market of Australia, differences in wages between women and men are worrying. The study aimed to analyze factors that affect the revenues of full-time workers in the Australian population. Specifically, it sought to reflect how the wage pay differences are associated with different attributes of male and female workers. The research used secondary data with wages as the dependent variable and gender, degree, skills, and years of experience as independent variables. Simple regression analysis was used to establish the relationship between gender and earnings and multiple regression to evaluate the link with all the independent variables. The research shows that female workers earn less in average weekly wages as compared to their male counterparts. Moreover, a few numbers of females are engaged in the labor market in Australia. The unskilled and lower degree qualification individuals are many as compared to skilled and higher degree holders. The variables can only explain the 17.39 percent variation in the weekly earnings of the workers. The percentage is less than 50 percent implying that other variables can explain the variation.
Introduction
The gender pay gap is the mean variation in the wages of women and men in an economy. It is expressed as the ratio of wages of men to women. Alternatively, the gender pay gap is computed as the actual value of the difference between the median or mean of salaries of full-time workers of the same job group in a country (Bennedsen et al., 2019). Over decades past, initiatives at the international level have been implemented to curb poverty among women. Discrimination based on gender has been eradicated and prohibited in many nations worldwide with the focus of achieving equity among men and women. However, in the labor market of Australia, differences in earnings between men and women are worrying (Yanadori et al., 2018). The difference of wages between women and men of the same professional rank persists.
The Australian pay gap between men and women bears a perplexing record against policies implemented to eliminate gender discrimination. By 2017, women’s earnings rated 84 percent of men’s income, indicating a 16 percent deviation of men’s salaries higher than women (Yanadori et al., 2018). Inequality in payment between men and women stems from career interruptions following family affairs, which demands the attention of female workers (Meara et al., 2020). Women are faced with hurdles in participating fully in the employment field within a nation. As a result, the female personnel gets discriminated against at workplaces with poor arrangements for baby care services they offer naturally (Pennington, 2021). Thus, many women are limited to low-quality jobs which do not demand technical skills are full-time engagement, making them remain poor as their male counterparts get better economically.
Issues Under Analysis
The gender pay gap varies significantly with the orientation of sexuality of an individual. Despite being lesbian, bisexual, or heterosexual, women generally receive low wages in return for their labor services compared to men (Cassells et al., 2017). According to the report by Miller and Vagins (2018), feminine individuals experience a decline in their wages return compared to masculine. Individuals who transitioned from male to female gender following interest to be gays had their salary drops (Miller & Vagins, 2018). It implies that discrimination based on gender influences earnings. Consequently, it determines the gender pay gap reflected in the workforce of many nations worldwide.
Human capital is one of the determinants of economic growth in a nation. Human resources in the workforce are evaluated on the professional qualifications, skills, and levels of experience in the assigned duties. A strong relationship exists between academic qualifications, experience, and wages (Alsulami, 2018). Education demonstrates skills and understanding in a given discipline, while experience is expertise in job duties. People are promoted annually as years of their experience, and academic profiles increase. Thus, the earnings of a rise in person with higher levels of education and more years of practice in a given field (Alsulami, 2018). However, people exhibit variation in the qualifications leading to discrimination and inequality based on wages given to people with different categories of experience and professionalism.
Aim and Methodology
The study aimed at analyzing factors that affect the earnings of full-time workers in the Australian population. Specifically, it sought to reflect how the gender pay gap is associated with different attributes of male and female workers. It employed both quantitative and qualitative analysis while establishing how earnings associate with gender, skills, degree, and years of experience at work. The dependent variable was the earnings, while independent variables were degree, skills, and gender as categorical variables and years of experiences and a quantitative variable. Descriptive statistics were used to summarize the data after sorting each independent variable with earnings. Multiple regression analysis was used to evaluate how the independent variables relate to employees’ salaries (Kennedy et al., 2017). Moreover, simple regression analysis was used to establish the link between gender and earnings of employees in Australia.
Question One
Descriptive Statistics for the Variables.
- Earnings and female.
- Earnings for male respondents.
- Interpretations
A total number of 824 respondents were selected for the study, out of which 504 were male while 320 were female workers. The average weekly earnings for males were AU$1,774.865, while that of females was AU$1,449.775. The statistics indicate that the number of males selected for the study exceeded that of females. Moreover, the male workers were paid AU$325.09 (AU$1,774.865 – AU$1,449.775) more than female workers.
- Descriptive Statistics for Unskilled Workers
- Descriptive Statistics for Skilled Workers
- Interpretation
A total number of 824 respondents were selected for the study, out of which 543 were unskilled workers while 281 were skilled workers. The average weekly earnings for unskilled full-time workers were AU$1439.23, while skilled workers were AU$2053.23. The statistics indicate that unskilled workers outnumbered the skilled workers in the labor market. Moreover, the skilled workers were paid AU$614 (AU$2053.23 – AU$1439.23) more than unskilled full-time workers.
- Descriptive Statistics for Lower Degree Holders
- Descriptive Statistics for Higher qualification Persons
- Interpretation
A total number of 824 respondents were selected for the study, out of which 526 had lower degree qualifications while 298 had higher degree qualifications. The average weekly earnings for lower degree qualification persons were AU$1,391.69while. That of higher degree holders was a worker was AU$2,102.12. The statistics indicate that there are more workers with lower degree qualifications than those of higher degree holders. Moreover, the higher degree holders earn AU$710.43 (AU$2,102.12 – AU$1,391.69) more than workers with a lower degree qualification.
Relationship Between Earnings and Experience
From the scatter plot, the reflection of earnings indicates that years of experience have little impact on the wages earned by full-time workers in Australia. More individuals with an understanding between 0 to 4 years make a lot of money compared to those with 6 to 12 years of experience.
Question Two
Since weekly earnings are in 1000 AU$, the weekly earning coefficient will be multiplied by 1000.
Model A
Y = 1,449.775 + 325.090079X1
(58.4445) (74.73)
Where, Y = Earnings X = Male variable
Interpretation
Since the P-Value for the male variable coefficient is less than 0.05, we reject the null hypothesis and conclude that the male variable has a significant effect on the weekly earnings of full-time workers. Male workers earn AU$325.090079 more weekly payments as compared to female workers holding other factors constant. The mean weekly wages for female workers is AU$1,449.775 keeping all other factors stable.
Hypothesis Test
- H0: There is no variation in weekly earnings across male and female workers
- HA: There is variation in weekly earnings across gender.
Since the P-Value (9.9255E-115) is less than 0.05, reject the null hypothesis and conclude that there is variation in the weekly earnings of full-time workers across gender.
Question Three
Model B
Y = 919.936 + 376.841X1 + 640.694X2 + 376.501X3 + 12.182X4
(73.29) (69.793) (75.437) (75.881) (3.419)
Interpretation
Since the P-Value of all the coefficients is less than 0.05, then all the four variables are statistically significant to the weekly earnings of the full-time workers. Moreover, using the thumb rule that states that the at-statistics coefficient above 2.96 is considered statistically significant in explaining the dependent variable, all the variables are statistically significant in explaining the weekly earning since their t statistic coefficient is more critical than 2.96.
When all the variables are zero, the total earnings of the full-time workers are AU$919.936.
When all the factors are held constant, male workers earn AU$376.841 more than female full-time workers in Australia.
The weekly earnings for higher degree qualified workers exceed those with lower qualifications, with AU$640.694 units holding other factors constant.
The weekly earnings for the skilled employees in Australia exceed unskilled workers by AU$376.501 units holding other factors constant.
One unit increase in the years of experience results in an AU$12.182 increase in the weekly earning of the Australian full-time workers holding other factors constant.
Question Four
The R square for Model A is 0.022503818 (2.25%). The R square implies that the male variable can explain a 2.25 percent variation in the weekly earnings of the workers.
The R square for Model B is 0.178926(17.89%). The independent variables that are gender, education attainment, skill level, and work experience can explain a 17.89 percent variation in the weekly earnings of the full-time workers.
Model B is better than Model A since it explains a more significant variation in the dependent variable (17.89%) than A, which only explains 2.25 percent variation in weekly earnings of the full-time workers.
Question Five
The coefficient of gender in Model A is AU$325.090079, while in Model B, it is AU$376.841. Simple regression establishes how a dependent variable is affected by one independent variable. However, multiple regression determines how one dependent variable is affected by more than one independent variable. Model A is a simple regression, while Model B is a multiple regression since it has more than one independent variable (X1, X2, X3, and X4). Incorporating other independent variables, including educational attainment, skills, and years of experience where more males are at an advantage affects the coefficient of the gender variable. Due to the positive impact of other variables on the male workers, the coefficient will rise.
Question Six
Use Model B to predict the values.
(Male – 1, University Degree – 1, Highly Skilled – 1, 10 Years of experience)
Substituting the values in model B
Y = 919.936 + 376.841X1 + 640.694X2 + 376.501X3 + 12.182X4
Y = 919.936 + 376.841(1) + 640.694(1) + 376.501(1) + 12.182(10)
Y = AU$2,435.792
(Female – 0, University Degree – 1, Highly Skilled – 1, 10 Years of experience)
Y = 919.936 + 376.841(0) + 640.694(1) + 376.501(1) + 12.182(10)
Y = AU$2,058.951
Question Seven
Considering other variables that affect individuals’ earnings in an organization, the job group of full-time employees based on the government scheme of service would be a consideration. Job group is an index of seniority in employment, and that affects wages allocation in the remuneration plan. I would measure it as a categorical variable by using the Likert scale. Another variable would be the duration of working which affects the pay rate for the daily activities. The working duration would be measured in hours spent while on duty. Nature of work is another variable to consider. Some working environments require hardship or risk-taking, which gets the employer compensated by giving allowances. Thus, earnings will increase as per the demands of the working environment. The variable would be measured as a dummy variable with references to risky or not.
References
Alsulami, H. (2018). The effect of education and experience on wages: The case study of Saudi Arabia. American Journal of Industrial and Business Management, 8, 129-142.
Bennedsen, M., Simintzi, E., Tsoutsoura, M., &Wolfenzon, D. (2019). Do firms respond to gender pay gap transparency? National Bureau of Economic Research, (25435), 2-70.
Miller, K., &Vagins, D. J. (2018). The simple truth about the gender pay gap. American Association of University Women.
Pennington, A. (2021). Women’s casual job surge widens the gender pay gap—the Australia Institute: Centre for Future Work.
Yanadori, Y., Gould, J. A., & Kulik, C. T. (2018). Does a fair go? The gender pay gap among corporate executives in Australian firms. The International Journal of Human Resource Management, 29(9), 1636-1660.
Cassells, R., Duncan, A. S., & Ong, R. (2017). Gender equity insights 2017: Inside Australia’s gender pay gap (No. GE02). Bankwest Curtin Economics Centre (BCEC), Curtin Business School.
Meara, K., Pastore, F., & Webster, A. (2020). The gender pay gap in the USA: A matching study. Journal of Population Economics, 33(1), 271-305.
Kennedy, T., Rae, M., Sheridan, A., &Valadkhani, A. (2017). Reducing gender wage inequality increases economic prosperity for all: Insights from Australia. Economic Analysis and Policy, 55, 14-24.