The Glass Ceiling: A Study on Annual Salaries

1. The Glass Ceiling: A Study on Annual Salaries Group 4 Julie Shan, Brian Abe, Yu-Ting Cheng, Kathinka Tysnes, Huan Zhang, Andrew Booth

2. Agenda • Introduction • Exploratory Analysis • Linear Regression & Analysis • Conclusion • Further Analysis

3. Introduction • What? • A sample of 1980’s managers salaries • Why? • To determine factors that affect the salary • How? • Linear regression

4. Introduction • Data Set Analyzed • A subsample of a large data set (from the early 1980s) from a study investigating potential gender bias in determination of professional salary differentials. The individuals come from several large corporations. • Data was organized by • Management Level • Gender • Education Level • Years in Job • Salary

5. Exploratory Analysis

6. Exploratory Analysis • Affects of the independent variables on the dependent variable SALARY. • Independent Variables: • Years in job • Management level • Education level • Gender

7. Exploratory Analysis • Positive Relationship Between Years in Job and Salary

8. Exploratory Analysis • Upper Management Earns More Than Lower Management

9. Exploratory Analysis • More Educated Managers Earn More • Outliers May Skew Regression Results

10. Exploratory Analysis • Female=0 if Male • Female=1 if Female • Note: Many More Males than Females in Data Set • Females Seem to have Cap, Lower Max Salary

11. Exploratory Analysis • New Variable: Female_management • 1 and 2 correspond to men and women in lower management respectively • 3 and 4 correspond to men and women in upper management respectively • Again, females earn less, have a cap on salary

12. Linear Regression & Analysis • A regression of salary vs. the other variables • Ed1-3 are dummy variables for education level • Ed1=high school • Ed2=bachelors • Ed3=graduate degree

13. Linear Regression & Analysis • All variables, except female, are significant at a 5% level. • R2 = 0.94, so it is a good fit • The Durbin-Watson is less than 2 but greater than 1.

14. Linear Regression & Analysis • Jarque-Bera statistic is greater than 0.05, indicating normality of the residuals

15. Linear Regression & Analysis • Updated regression excluding variable FEMALE.

16. Linear Regression & Analysis • R2 = 0.93: still a good fit. • The Durbin-Watson statistic is once again less than 2 but greater than 1

17. Linear Regression & Analysis • Jarque-Bera statistic is greater than 0.05, indicating normality of the residuals

18. Linear Regression & Analysis • Wald Test for equivalency of intercepts for various education levels Ho : ED2=ED3 Ho : ED1=ED2

19. Linear Regression & Analysis • Final Model: SALARY = 615.0378*YEARS + 7509.9807*MANAGEMENT + 7352.3861*ED1 + 10907.4441*ED23

20. Linear Regression & Analysis

21. Conclusion • The variable FEMALE was not statistically significant. • No gender bias at a 5% significance level. • There is gender bias at a 10% significance level. • Other variables played important role in determining salary: • The number of years worked in a job add to salary level. • The higher one’s education level the higher the salary level. • Upper management has higher salaries than lower management.

22. Further Analysis • Newer, Larger Data Set • Allows Removal of Outliers • Additional Independent Variables: • Company Size • Industry • Age of Company • More in Depth Analysis of Potential for Gender Bias (At 10% it was Significant)

23. Fin • Any Questions?