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 Introduction Exploratory Analysis Exploratory Analysis Linear Regression & Analysis Linear Regression & Analysis Conclusion Conclusion Further Analysis Further Analysis

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

4. Introduction Data Set Analyzed 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. 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 Data was organized by Management Level Management Level Gender Gender Education Level Education Level Years in Job Years in Job Salary Salary

5. Exploratory Analysis

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

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

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

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

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

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

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

13. Linear Regression & Analysis All variables, except female, are significant at a 5% level. All variables, except female, are significant at a 5% level. R 2 = 0.94, so it is a good fit R 2 = 0.94, so it is a good fit The Durbin-Watson is less than 2 but greater than 1. 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 Jarque-Bera statistic is greater than 0.05, indicating normality of the residuals

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

16. Linear Regression & Analysis R 2 = 0.93: still a good fit. R 2 = 0.93: still a good fit. The Durbin-Watson statistic is once again less than 2 but greater than 1 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 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 Wald Test for equivalency of intercepts for various education levels H o : ED2=ED3 H o : ED1=ED2

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

20. Linear Regression & Analysis

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

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

23. Fin Any Questions? Any Questions?