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IMBA 6050

Business Analytics

Workshop 3

Multiple Regression: Winner Plc

Salary Discrimination?

At the last board meeting of Winner Plc., the headquarters of a large multinational company, the issue was raised that women were being discriminated in the company, in the sense that the salaries were not the same for male and female executives. A quick analysis of a sample of 50 employees (of which 24 men and 26 women) revealed that the average salary for men was about £8,400 higher than for women. This seemed like a considerable difference, so it was decided that a further analysis of the company salaries was warranted. In the table on the next page, the salaries of the 50 executives in the sample are given.

Assignment

This is Part II of the Winner Plc case. In Workshop 2, you conducted the basic analysis to see if the salaries between male and female managers are significantly different. In Part II, you will conduct further analysis to examine whether the difference is due to gender or some other factors.

Download the Excel file Winner.xls. The Excel file contains all data for the sample of (the same) 50 executives in the company. Conduct analysis as asked below.

Can you conclude that there is a significant difference between the salaries of the male and female executives?

Note that you can perform different types of analyses. Check whether all the following analyses lead to the same conclusion (see attached Excel Summary Sheet for tips on implementing the analyses in Excel):

• Regression (gender can be formulated as a “dummy” independent variable: gender = 0 if male, gender = 1 if female)

2. Relationship Experience – Gender?

At the board meeting, someone raised the issue that there was indeed a substantial difference between male and female salaries, but that this was attributable to other reasons such as differences in experience. A questionnaire sent out to the 50 executives in the sample reveals that the average experience of the men is approximately 21 years, whereas the women only have about 7 years experience on average (see table below, same data as in Winner.xls).

Based on this evidence, can you conclude that there is a significant difference between the experience of the male and female executives? Perform similar analyses as in Section 1. Does your conclusion validate or endanger your conclusion about the difference in male and female salaries?

3. Relationship Salary – Experience?

Someone at the meeting argues that clearly, a more thorough analysis of the relationship between salary and experience is required before any conclusion can be drawn about whether there is any gender-based salary discrimination in the company.

Analyse the relationship between salary and experience. What can you conclude?

4. Relationship Salary – Gender - Experience?

Extend your analysis of Section 3 to investigate whether there is salary discrimination based on gender or not.

Again at the meeting, someone was looking at the results of the analysis of salary versus experience, and said that it was to be expected that an employee with more experience than another employee would have a higher salary. However, he argued that the difference in salary between two people with 2 and 4 years experience would be higher than the difference in salary between two people with 26 and 28 years experience!

Can you find any indication in the results reported in section 4 that this is the case? If yes, how can you modify your analysis to take this into account?

Hint: Consider the possibility that the relationship between salary and experience is non- linear. See Lecture Slide 7 for more information.

6. Additional Thoughts

Do you have any additional thoughts on whether there might be a gender disadvantage in the firm or not?

Excel Summary Sheet

Excel also has built-in tools useful for summarising and analysing data quickly (if you cannot find the menu option Data\Data Analysis, select File\Options\Add-Ins\Excel Add-Ins\Go\Analysis Toolpak):

– Data\Data Analysis\Regression allows for a regression analysis. In Input Y Range, specify the dependent variable. In Input X Range, specify the range with the explanatory variable(s) — all the explanatory variables should be in columns next to each other. In Labels, indicate whether you have included labels in the input range or not. In Output Range, specify a cell where the output can be written. Also highlight all the options in Residuals. When you carry out a regression analysis, write down the obtained equation, interpret it (“What does it mean?”) and evaluate it by checking the model statistics (R2, t-values, p-values) and by inspecting the Line Fit and Residual plots.