The response to the assignment must be provided in the form of a professional report with no more than 10 pages (excluding cover page). The structure of your professional report must include: 1] A Title, 2] An Executive Summary, 3] An Introduction, 4] Analysis, and 5] Conclusions.
You must submit an electronic copy of your assignment in Canvas. See the attached Report Template for more details.
This assignment requires the use of Microsoft Excel. If you have Windows, you will need to use the Data Analysis Tool Pack. If you have a Mac with Excel 2011, you may need to use StatPlus: MAC LE. The Excel workbook you submit needs to be clear and carefully organised. It will be treated as an appendix to your report, i.e. not included in the page count. You will need to take the relevant results from your Excel workbook and incorporate into your report. Do not refer to the Excel workbook within the Professional report. The Report needs to be standalone.
Presentation Instructions:
Your written professional report should comply with the following presentation standards:
1. Typed using a standard professional font type (e.g. Times Roman), 12-point font size.
2. 1.5-line spacing, numbered pages, and clear use of titles and section headings.
3. Delivered as a Word (.doc or .docx) or PDF (.pdf) file.
4. Checked for spelling, typographical and grammatical errors. Where relevant, round to 3 decimal places.
5. With all relevant tables and charts, the report should be no more than 10 pages long.
Problem Description:
This is a further analysis of the gender pay gap in the Australian population. According to a recent report by KPMG Consulting, gender discrimination continues to be the single largest factor contributing to the gender pay gap (KPMG, 2019). In order to estimate the extent of discrimination in the job market where women with identical labour market characteristics as their male counterparts receive different wages, you will estimate a set of linear regression models.
Since this is an additional analysis on the gender pay gap, the content in the Introduction section of your report may overlap with the one in the Group Assignment. However, you are encouraged to develop/source new background materials. You will use the same dataset as in Assignment 2. The data are drawn from the 2019 Household, Income and Labour Dynamics in Australia (HILDA) survey. The sample used for analysis comprises 1099 full-time Australian workers in the age group 20-74. The dataset contains the following information:
1. Worker’s earnings: weekly earnings in 1000 AU dollars of full-time workers. [note the unit of measurement]
2. Gender: the dummy variable male = 1 if the individual is a male, and = 0 for a female.
3. Educational attainment: the dummy variable degree = 1 if the individual has a bachelor degree or higher qualification, and = 0 for lower than degree qualifications.
4. Skill level: the dummy variable skill = 1 if the individual is highly skilled, and = 0 if not highly skilled.
5. Experience: number of years of work experience.
[Marks distribution: 5 + 6 + 9 + 2 + 5 + 2 + 3 = 32 marks; professional report = 8 marks]
Locate the data file (IndividualBusStats.xls) on CANVAS.
1. Before estimating the regression equation, conduct a preliminary analysis of the relationship between workers’ earnings and 1) gender; 2) educational attainment; 3) skill level; and 4) experience. Use tables and/or appropriate graphs for the categorical variables (male, degree, skill) and the numerical variable (experience). Interpret your findings by answering the following questions: how much more/less does a male worker earn compared to a female worker? how much more/less does a degree holder earn versus a non degree holder? How much more/less does a highly skilled worker earn versus a worker who is not highly skilled? What kind of relationship do you observe between workers’ earnings and experience? (5 marks)
2. Use a simple linear regression to estimate the relationship between workers’ earnings and gender (Model A). You may use the Data Analysis Tool Pack. Based on the Excel regression output, first write down the estimated regression equation and interpret the slope coefficient. Carry out any relevant two-tailed hypothesis test of the slope coefficient using the critical value approach, at the 5% significance level, showing the step by step workings/diagram in your report. Interpret your hypothesis test results. (6 marks)
3. Now use a multiple regression model to explore the relationship of workers’ earnings with gender, educational attainment, skill level, and experience (Model B). You may use Data Analysis Tool Pack for this. Based on the Excel regression output, first write down the estimated regression equation and interpret carefully the slope coefficients. Carry out any relevant two-tailed hypothesis tests for each individual slope coefficient using the p-value approach, at the 5% significance level. Carry out an overall significance test using the p-value approach. Carefully interpret your hypothesis test results. (9 marks)
4. Interpret the R-squared in Model A and adjusted R-squared in Model B. Which one is a better model? Explain why, relating your answer to the interpretations. (2 marks)
5. Compare the coefficients of male in Model A and Model B. Explain carefully why the results are different, relating your discussion to gender discrimination. (5 marks)
6. Predict the earnings of a male worker who has a university degree, is highly skilled and has 15 years of work experience. Next, predict the earnings of a female worker with the same characteristics. (2 marks)
7. If you could request additional data to study the factors that influence workers’ earnings, what extra variables would you request? Discuss two such variables, explaining why you choose them and how each of your proposed variables could be measured in the regression model. [You could draw evidence from journal articles, newspapers, etc] (3 marks)
References:
KPMG Consulting. (2016). She’s price(d)less: The economics of the gender pay gap https://home.kpmg/content/dam/kpmg/au/pdf/2019/gender-pay-gap-economics-full-report-2019.pdf
